Journal of Experimental Botany

Tooke, Fiona;Battey, Nicholas H.

doi: 10.1093/jxb/erq165pmid: 20576790

Abstract Individuals, families, networks, and botanic gardens have made records of flowering times of a wide range of plant species over many years. These data can highlight year to year changes in seasonal events (phenology) and those datasets covering long periods draw interest for their perspective on plant responses to climate change. Temperate flowering phenology is complex, using environmental cues such as temperature and photoperiod to attune flowering to appropriate seasonal conditions. Here we give an overview of flowering phenological recording, outline different patterns of flowering, and look at the interpretation of datasets in relation to seasonal and climatic change. Climate, flowering, life cycles, plant phenology Introduction Recording when plants flower is a pastime that has appealed to people for centuries. Observations on flowering appear in datasets alongside the first croak of the common frog, the arrival of swallows, or the unfurling of oak leaves. Capturing the timing of these seasonal events is the study of phenology. Well-known recorders include Gilbert White who noted events in 18th century Selborne, England and Henry David Thoreau who recorded in Concord, USA in the 19th century. Once, phenological records were perhaps simply considered interesting but of little other value. Now, long-term datasets of seasonal events are widely sought-after for their potential to reveal how the natural world responds to climate change. Historical records have been the subject of recent analysis and several phenological recording networks have been established or revitalized in response to this resurgence of interest. For the most part, flowering phenological data focus on first flowering dates (FFD) and records cover a wide range of species with diverse life-forms. How flowering responses may be altered by climate change is difficult to predict but of great significance with far-reaching impacts on the functioning of ecosystems, for example, through the potential de-coupling of flowering time and pollinator life cycles (Peňuelas and Filella, 2001; Memmott et al., 2007). In this paper, a brief overview of some flowering phenological records is provided and flowering life cycles and what is recorded about them are considered. Finally, we look at potential differential flowering responses to climate change. An overview of the history of recording flowering phenology Noting flowering times is part of a long tradition of recording seasonal events. With little need for specialist equipment or knowledge, this is an activity that has, at times, attracted large numbers of people, who have often made records as a pastime or ‘amateur’ interest (Whitfield, 2001). Its popularity as an activity has probably been greatest in the 19th century and now, at the start of the 21st century when it has become the focus of several ‘citizen science’ projects. Long-term datasets useful to research are generally considered to be those spanning 20 years or more, although Amano et al. (2010) have recently used hierarchical models and short-term records from multiple sites to estimate a 250-year index of first flowering. A few examples of long-term datasets are outlined below and some that have been used in recent research are given in Table 1. Table 1. Examples of long-term datasets which include flowering observations and which have been used in recent research Recorder  Dataset dates  Location  Reference  Robert Marsham and family  1736–1958  Stratton Strawless, Norfolk, UK  Sparks and Lines, 2008  Aarne Juhonsalo and family  1952–  Oulainen-Ohineva, Finland  Lappalainen et al., 2008  Henry David Thoreau  1851–1858 followed by Alfred Hosmer 1888–1902 and Primack and Miller-Rushing, 2003–2007  Concord, Massachusetts, USA  Willis et al., 2008  Richard Fitter  1954–1989  Chinnor, Oxfordshire, UK  Fitter and Fitter, 2002  Fred Last and family  1977–2008  East Lothian, Scotland  Last, 2001  James McNab  1850–1878 followed by John Sadler 1878–1882 and Robert Lindsay up to 1895  Royal Botanic Gardens, Edinburgh, Scotland  Harper et al., 2006  Charles Robertson  1884–1916  W Illinois, USA  Memmott et al., 2007  Mary Manning  1965–  UK  Sparks and Manning, 2000  Aldo Leopold  1936–1947 followed by Nina Leopold Bradley 1976–1998  Fairfield Township, Sauk County, Wisconsin  Bradley et al. 1999  Recorder  Dataset dates  Location  Reference  Robert Marsham and family  1736–1958  Stratton Strawless, Norfolk, UK  Sparks and Lines, 2008  Aarne Juhonsalo and family  1952–  Oulainen-Ohineva, Finland  Lappalainen et al., 2008  Henry David Thoreau  1851–1858 followed by Alfred Hosmer 1888–1902 and Primack and Miller-Rushing, 2003–2007  Concord, Massachusetts, USA  Willis et al., 2008  Richard Fitter  1954–1989  Chinnor, Oxfordshire, UK  Fitter and Fitter, 2002  Fred Last and family  1977–2008  East Lothian, Scotland  Last, 2001  James McNab  1850–1878 followed by John Sadler 1878–1882 and Robert Lindsay up to 1895  Royal Botanic Gardens, Edinburgh, Scotland  Harper et al., 2006  Charles Robertson  1884–1916  W Illinois, USA  Memmott et al., 2007  Mary Manning  1965–  UK  Sparks and Manning, 2000  Aldo Leopold  1936–1947 followed by Nina Leopold Bradley 1976–1998  Fairfield Township, Sauk County, Wisconsin  Bradley et al. 1999  View Large Individuals Robert Marsham is often seen as the founding father of British phenological recording. A tree-lover and the owner of an estate in the east of England, Marsham began his recordings of ‘Indications of Spring’ in 1736. The ‘Indications’ continued to be observed by generations of the Marsham family for 211 years, ending in 1958 when the family was advised that the data were no longer useful (Sparks and Lines, 2008). Amongst the recordings, the Marshams noted the first flowering dates of snowdrop, wood anemone, hawthorn, and turnip (Sparks and Carey, 1995). More recent examples of individuals who have kept long-term records are Richard Fitter, Mary Manning, Fred Last, and Nigel Hepper who have made records spanning 20 years or more of flowering in British gardens or countryside in Oxfordshire, Norfolk, East Lothian, and Leeds/Richmond, respectively (Last, 2001; Bisgrove and Hadley, 2002; Hepper, 2003; Sparks and Manning, 2000). Networks Networks of recorders have been established in a number of countries. In the UK, the Royal Meteorological Society set up a Phenological Network in 1875. Annual reports from this network were compiled by reporters, but the group was disbanded in 1947 and a final report produced in 1948. Records were sent in from 11 meteorological districts of Britain from an average of 200 observers a year but up to around 600 at the network's peak (Jeffree, 1960). Mean flowering times of the 11 species recorded through the 58 year period are shown in Table 2 (Sparks et al., 2000). In a similar manner, the German Meteorological Service has run a phenological network since 1951. During the 1970s this network had up to 4000 recorders and currently there are around 1550 who collect data on cultivated and native plants (Estrella et al., 2007). Many other European phenological networks exist and have recently been summarized in a report by Nekovar et al. (2008). Table 2. Mean dates, standard deviations, and earliest and latest dates (as differences from mean dates) of flowering reported by Jeffree (1960), from a 58-year time series for species at the top of the table and 20-year time series for those in the second section (from Sparks et al., 2000). Species    Mean date  SD (d)  Earliest mean  Latest mean  Hazel  Corylus avellana  Feb 10  9.6  –15  +34  Coltsfoot  Tussilago farfara  Mar 3  8.7  –18  +26  Wood anemone  Anemone nemorosa  Mar 29  7.3  –15  +20  Garlic mustard  Alliaria petiolata  Apr 24  6.9  –14  +14  Horse chestnut  Aesculus hippocastanum  May 8  7.6  –20  +16  Hawthorn  Crataegus monogyna  May 13  8.4  –19  +16  Ox-eye daisy  Leucanthemum vulgare  May 30  4.8  –16  +13  Dog rose  Rosa canina  June 8  6.2  –21  +12  Greater bindweed  Convolvulus sepium  July 10  7.2  –21  +15  Harebell  Campanula rotundiflora  July 10  4.6  –19  +9  Ivy  Hedera helix  Sep 28  5.4  –17  +13  Christmas rose  Helleborus niger  Dec 21  5.0  –7  +12  Winter aconite  Eranthis hiemalis  Jan 21  9.2  –12  +21  Snowdrop  Galanthus nivalis  Jan 25  10.7  –13  +32  Yellow crocus  Crocus aureus  Feb 10  11.1  –11  +28  Lesser celandine  Ranunculus ficaria  Feb 28  11.5  –16  +27  Almond  Amygdalus communis  Mar 20  11.6  –17  +28  Horse chestnut (leafing)  Aesculus hippocastanum  Apr 10  6.6  –10  +15  Redcurrant  Ribes rubrum  Apr 12  7.1  –13  +12  Bird cherry  Prunus padus  May 2  9.2  –19  +13  Purple lilac  Syringa vulgaris  May 6  9.1  –17  +16  Laburnum  Cytisus laburnum  May 13  8.4  –19  +16  Elder  Sambucus nigra  June 4  6.7  –13  +12  Madonna Lily  Lilium candidum  July 18  4.8  –11  +6  Autumn crocus  Colchicum autumnale  Sep 6  3.6  –11  +6  Species    Mean date  SD (d)  Earliest mean  Latest mean  Hazel  Corylus avellana  Feb 10  9.6  –15  +34  Coltsfoot  Tussilago farfara  Mar 3  8.7  –18  +26  Wood anemone  Anemone nemorosa  Mar 29  7.3  –15  +20  Garlic mustard  Alliaria petiolata  Apr 24  6.9  –14  +14  Horse chestnut  Aesculus hippocastanum  May 8  7.6  –20  +16  Hawthorn  Crataegus monogyna  May 13  8.4  –19  +16  Ox-eye daisy  Leucanthemum vulgare  May 30  4.8  –16  +13  Dog rose  Rosa canina  June 8  6.2  –21  +12  Greater bindweed  Convolvulus sepium  July 10  7.2  –21  +15  Harebell  Campanula rotundiflora  July 10  4.6  –19  +9  Ivy  Hedera helix  Sep 28  5.4  –17  +13  Christmas rose  Helleborus niger  Dec 21  5.0  –7  +12  Winter aconite  Eranthis hiemalis  Jan 21  9.2  –12  +21  Snowdrop  Galanthus nivalis  Jan 25  10.7  –13  +32  Yellow crocus  Crocus aureus  Feb 10  11.1  –11  +28  Lesser celandine  Ranunculus ficaria  Feb 28  11.5  –16  +27  Almond  Amygdalus communis  Mar 20  11.6  –17  +28  Horse chestnut (leafing)  Aesculus hippocastanum  Apr 10  6.6  –10  +15  Redcurrant  Ribes rubrum  Apr 12  7.1  –13  +12  Bird cherry  Prunus padus  May 2  9.2  –19  +13  Purple lilac  Syringa vulgaris  May 6  9.1  –17  +16  Laburnum  Cytisus laburnum  May 13  8.4  –19  +16  Elder  Sambucus nigra  June 4  6.7  –13  +12  Madonna Lily  Lilium candidum  July 18  4.8  –11  +6  Autumn crocus  Colchicum autumnale  Sep 6  3.6  –11  +6  View Large ‘Citizen science’ initiatives have recently been successful at publicizing phenology and encouraging public participation in recording. Examples of this are Plantwatch Canada (http://www.naturewatch.ca/english/plantwatch/), Project Budburst run by the USA National Phenology Network (Donaldson, 2009; http://www.windows.ucar.edu/citizen_science/budburst/), and the UK Phenology Network's ‘Nature's Calendar’ launched by the Centre for Ecology and Hydrology in 1998 with involvement of the Woodland Trust from 2000 (Whitfield, 2001; http://www.naturescalendar.org.uk/). Botanic gardens Botanic gardens are significant contributors to flowering phenology records. They are considered well-placed to carry out this research as they contain living collections (often long-lived), herbaria, and detailed plant records as well as public access and education provision (Harper et al., 2006; Donaldson, 2009; Primack and Miller-Rushing, 2009). At the Royal Botanic Garden Edinburgh, 250–300 accessions are monitored daily and recordings made of beginning and end dates of flowering (Harper et al., 2006). The Kew 100 programme, which began in 2000, records flowering data on 100 trees, shrubs, and bulbs at the Royal Botanic Gardens, Kew (http://data.kew.org/wild/phenology/). In both locations earlier datasets exist (Hepper, 2003; Harper et al., 2006). Many botanic gardens in Europe are also International Phenological Gardens, a garden network initiated in 1957 and co-ordinated by Humboldt University, Berlin. There are around 50 sites and these gardens grow clones of specific plants thus allowing direct comparison of flowering across a range of locations (Chmielewski and Rötzer, 2001; Primack and Miller-Rushing, 2009). Flowering and time Phenology is concerned with the timing of events. In plants, the timing of flowering is regulated by mechanisms which act to ensure that flower emergence occurs in suitable conditions. In temperate climates, the flowering process is attuned to seasons through environmental cues, particularly photo-period and temperature. These cues for flower induction can also be involved in signalling times of dormancy to the plant. Before considering phenological records further, a brief summary of patterns of flowering in some different groups of plants is given. Grainger (1939) observed that, ‘any attempt to elucidate the action of various factors upon the time of flowering would, moreover, seem to demand a relation to the time when the flower bud was first formed, and not only to the time when it emerged. It is known that many plants make their flower buds a considerable time before the emergence of bloom.’ His classification of flowering types reflects this, in particular in the categories of ‘direct’ and ‘indirect’ flowering, designations which recognize whether the development from initiation to emergence is uninterrupted or integrates a period of rest (Fig. 1; Grainger, 1939). Fig. 1. View largeDownload slide ‘Direct’ and ‘indirect’ flowering, according to Grainger (1939), who dissected over 100 plants from the Huddersfield (UK) area 1937–1939. He classified coltsfoot as ‘direct’ flowering and bilberry as ‘indirect’ flowering. Top: Coltsfoot, Tussilago farfara. (A) Plan view of the inflorescence initial, 17 September 1938. (B) Plan view of inflorescence initial, 29 Oct. 1938: developing disk florets. (C) side view of inflorescence 3 December 1938. The total length of the inflorescence, shown as 10 mm on 3 December, had increased to an average of 17 mm on 1 January 1939, when the florets also appeared yellow. Below: Bilberry, Vaccinium myrtillus. Dissection of flower initial from terminal bud of the adjoining shoot, 17 July 1938– fully-formed at this time. (Flower emergence was the following May.) Reproduced with permission from Wiley-Blackwell. Fig. 1. View largeDownload slide ‘Direct’ and ‘indirect’ flowering, according to Grainger (1939), who dissected over 100 plants from the Huddersfield (UK) area 1937–1939. He classified coltsfoot as ‘direct’ flowering and bilberry as ‘indirect’ flowering. Top: Coltsfoot, Tussilago farfara. (A) Plan view of the inflorescence initial, 17 September 1938. (B) Plan view of inflorescence initial, 29 Oct. 1938: developing disk florets. (C) side view of inflorescence 3 December 1938. The total length of the inflorescence, shown as 10 mm on 3 December, had increased to an average of 17 mm on 1 January 1939, when the florets also appeared yellow. Below: Bilberry, Vaccinium myrtillus. Dissection of flower initial from terminal bud of the adjoining shoot, 17 July 1938– fully-formed at this time. (Flower emergence was the following May.) Reproduced with permission from Wiley-Blackwell. In annuals, flowering appears to be a straightforward, direct, and all-consuming process in which all meristems flower and the life cycle is completed within one year. Perennials, however, are more complex, living for years and flowering repeatedly. Although there are exceptions, such as some bamboos and monocarpic biennials including carrot and foxglove, perennials are typically polycarpic (as opposed to annuals, which are monocarpic). The polycarpic strategy allows them to retain vegetative meristems and thus the capacity for continued growth after flowering (Battey, 2000). Within this group, there are diverse growth habits and cases of Grainger's ‘indirect flowering’. In particular, there may be very long periods between flower initiation and emergence in trees and bulbs; it takes around 9–10 months for the flowering process to be completed in Malus (Sedgley and Griffin, 1989), whilst over a year elapses between flower initiation and emergence in Nerine sarniensis (Le Nard and De Hertogh, 1993; Rees, 1966). These examples may be at the more extreme end of the spectrum but, in temperate fruit trees, flowers are generally initiated in the year prior to their spring emergence and bulbs may initiate the next year's flowers shortly after those of the current year have died. Trees Dormancy in temperate tree species is described as being composed of endodormancy, regulated by physiological factors including chilling; and ecodormancy, which reflects the fact that conditions (for example, of nutrient or water availability or temperature) are unsuitable for growth. A further term, paradormancy, describes situations in which the control of dormancy in meristematic tissue is signalled or perceived at first by another part of the plant (e.g. leaves, bud scales) (Lang, 1987). The onset of dormancy is often controlled by photoperiod (short days) (Rohde and Bhalerao, 2007), but this appears not to be the case in many Rosaceous/temperate fruit trees. Low temperatures (<12 °C) have been found to play a role in promoting dormancy of apples and pears (Heide and Prestrud, 2005). Breaking dormancy requires the completion of a period of chilling, which can be measured in hours below a certain temperature; for example, Bartlett pear and Delicious apple require around 1500 h of chilling (Sedgley and Griffin, 1989). For many cultivars, the chilling requirement falls between 1000 h and 1200 h (Barden and Neilson, 2003). Sub-optimal chilling can make budbreak protracted. Sunley et al. (2006) report that increasing chilling of blackcurrants and raspberries leads to more synchronous flowering. The point at which endodormancy is completed is rarely recorded (Legave et al., 2008) since, whilst buds may now have the ability to grow, unless conditions are conducive to this, the plant moves seamlessly to ecodormancy. Experimental intervention can, however, show when buds become capable of breaking dormancy (e.g. Mahmood et al., 2000). Budburst, at the end of ecodormancy, occurs once a heat requirement of accumulated degree days has been met (Sedgley and Griffin, 1989). Whilst endo- and eco-dormancy are represented here as discrete phases with different temperature requirements, there is a relationship between the two (see discussion in Battey, 2000). Longer durations of chilling have been found to decrease the thermal time required for budburst (Murray et al., 1989; Heide and Prestrud, 2005; Welling and Palva, 2006). In temperate fruit trees (e.g. apples, pears, cherries, plums) dormancy through the winter interrupts the flowering process such that flowers are initiated in the summer, yet trees do not blossom until the following spring (Grainger, 1939). Bulbs In general, flower initiation in bulbs is not responsive to photoperiod and is controlled by temperature and the size of the storage organ and its available food reserves (Rees, 1992). Flower initiation times vary; in Galanthus and Narcissus flowers are initiated after the current year's flowering is over in the spring or early summer; emergence occurs the following spring. Similarly, flower initiation in tulip takes place in mid-summer but emergence is in spring of the next year; but in Lilium and Gladiolus, flowers are initiated after shoot emergence from the storage organ, with flower emergence occurring in the summer (Rees, 1992; Le Nard and De Hertogh, 1993). Summer and winter dormancy is a feature of these geophytes, which typically survive the summer underground and complete a cold requirement before above-ground growth (Rees, 1992). Grasses Temperate annual, and some perennial grasses require only long days (LD) to flower, but for most temperate perennial grasses flowering requires low temperatures or short-days (SD), followed by transition to LD for flower initiation (Heide, 1994). The primary induction by SD/low temperatures is met by autumn or winter conditions and is a vernalization or winter requirement during which there are not generally morphological changes in terms of flower initiation. Initiation typically happens during the secondary induction in the LDs of spring and summer (Heide, 1994). In perennial grasses of Mediterranean origin, flowering in late spring is followed by summer dormancy (endodormancy), triggered by longer days and warmer temperatures. On release from dormancy by higher temperatures towards the end of summer, buds begin re-growth as the temperatures decrease in autumn (Volaire and Norton, 2006). Recording flowering Flowering phenological datasets are often composed of first flowering dates (FFD) and are, therefore, mainly records of flower emergence times. There is, however, a need to define what constitutes flower emergence, particularly when there are a large number of different recorders. In some instances observers state clearly their definition of flowering, for example, ‘visible anthers’ (Fitter et al., 1995) or ‘an open flower is a flower in which stamens or stigmas could be seen without the observer pushing petals aside’ (Last, 2001). Another approach is to use a universal scale, such as the BBCH scale which is named using the initial letters of the institutions involved in its development (Biologische Bundesanstalt, Bundessortenamt, Chemische Industrie). This scale was originally drawn up to allow uniform descriptions of growth stages of agricultural crops (Lancashire et al., 1991) and has been used by Menzel et al. (2006) to group data from various sources across Europe. FFD constitutes the most straightforward recording activity. Other, more demanding observations relate to the duration of flowering. Elzinga et al. (2007) outlined how flowering phenology can be recorded at different levels; population, individual plants within a population, or even flowers within an individual. So, whilst FFD might be recorded as the first flower seen (perhaps in a garden), a ‘flowering season’, as defined by Elzinga et al. is the number of days between the first and last flowering of individuals in a population. Flowering ‘spread’ or duration is recorded in some long-term datasets for perennial fruit crops, perhaps because it is seen as a better indicator of chill than a single flowering date (Sunley et al., 2006). Records for raspberry and blackcurrant cultivars over >40 year periods are held at East Malling, UK and cover first flower, full bloom, and end of flowering (Sunley et al., 2006). Blossom records of first flower (10% open), full flower (80% open), and petal fall (90% over) have been kept since 1960 for 12 apple varieties from the UK National Fruit Collections at Brogdale (M Jeger, personal communication). There are significant challenges in recording data other than FFD, for example, in gauging the percentage of flowers open, taking into account flowers dying as well as opening and monitoring populations rather than individuals. Miller-Rushing et al. (2008) favour observations of flowering distribution, or peak or mean flowering dates, as first flowering dates represent one extreme of flowering distribution. Increases and decreases in population size can lead to earlier or later first flowering dates, respectively (see Fig. 2 for a representation). To detect changes in flowering dates, sampling needs to be fairly frequent, especially if the flowering duration is short. In one of the authors' assessments it was found that a flowering date change of 1 d °C−1 would have a 97% chance of being detected as a significant trend over a 10-year period if sampling was carried out every 2 d, but only a 54% chance of detection if sampling was every 7 d (Miller-Rushing et al., 2008). Fig. 2. View largeDownload slide The theoretical effect of changes in population size on changes in first flowering date (Miller-Rushing et al., 2008). For the solid curves for (a) increased or (b) decreased population size, mean flowering dates are earlier than for the distribution of flowering dates for a population in a year in the past (dashed curve). Arrows indicate changes in peak and first flowering dates over time. Reproduced with permission from Wiley-Blackwell. Fig. 2. View largeDownload slide The theoretical effect of changes in population size on changes in first flowering date (Miller-Rushing et al., 2008). For the solid curves for (a) increased or (b) decreased population size, mean flowering dates are earlier than for the distribution of flowering dates for a population in a year in the past (dashed curve). Arrows indicate changes in peak and first flowering dates over time. Reproduced with permission from Wiley-Blackwell. Alternative sources of data To assess trends of flowering phenology against climate change, long-term datasets are required. In the quest to uncover historical data, alternatives to numerical records have been found. Flowering herbarium specimens, photographs, and even diaries or adverts for Japanese cherry blossom festivals have been used as the basis for phenological datasets (Aono and Kazui, 2008; Miller-Rushing et al., 2006). Where there is no history of phenological recording these alternatives could prove useful and can, as in the case of Japanese cherry flowering, give long-term datasets. Aono and Kazui (2008) were able to compile a dataset covering 60.7% of the years from 801–2005 using dates from old diaries, chronicles, adverts, and poetry. The accuracy of their data may be aided by the fact that, in Japan, cherry blossom viewing parties are usually held at full-flowering, which covers only a 2–4 d period. However, compiling data sets from alternative sources has limitations which need careful consideration. Herbarium specimens are often not from just one location. In their study of flowering phenology of coltsfoot (Tussilago farfara L.), Lavoie and Lachance (2006) needed to introduce a correction procedure to take account of different climatic conditions (snowmelt dates and urban heat island effects) across the sampling locations. Dated photographs can provide dramatic and indisputable evidence of plant developmental states at given times, and are likely to be a quite widespread resource over the last 100 years. Miller-Rushing et al. (2006) used photographs of cultivated plants at Arnold Arboretum and wild plants taken by photographer Herbert Wendell Gleason between 1900–1921 in their research. They point out, however, that these photographs were taken on one day in a flowering period and exactly when in that period is unknown. Cases in which photography is likely to be particularly useful are when repeat photographs of the same subject are taken over a period of time (Fig. 3; Willis, 1944; Sparks et al., 2006), or when the species photographed has a narrow flowering window, for example, wood anemone (Sparks, 2007). In all cases details regarding the flowering status of the plant need to be stated. Fig. 3. View largeDownload slide ‘How seasons vary’: photographic series from ‘Weatherwise’ by John Willis (1944) showing the same snowdrop clump each 1st January from 1913 to 1942. Willis described the start of 1913 as follows:‘The year 1913, the starting-point of our pilgrim's progress through thirty years of weather entered in a memorable fashion; for, as the photographic records of my station will show, no subsequent January has seen vegetation so amazingly advanced, not a New Year's Day so carnivalled with flowers. Only one touch of winter swept through the year's mild opening month, to streak the ground on the 11th with six inches of snow; while following a mild, showery January a drier and sunnier February lured vegetation to such further eager advance that the earliest spring flowers were already on the wane by the middle of the month, and daffodils were awakening in the woods weeks ahead of their time by its close’. Fig. 3. View largeDownload slide ‘How seasons vary’: photographic series from ‘Weatherwise’ by John Willis (1944) showing the same snowdrop clump each 1st January from 1913 to 1942. Willis described the start of 1913 as follows:‘The year 1913, the starting-point of our pilgrim's progress through thirty years of weather entered in a memorable fashion; for, as the photographic records of my station will show, no subsequent January has seen vegetation so amazingly advanced, not a New Year's Day so carnivalled with flowers. Only one touch of winter swept through the year's mild opening month, to streak the ground on the 11th with six inches of snow; while following a mild, showery January a drier and sunnier February lured vegetation to such further eager advance that the earliest spring flowers were already on the wane by the middle of the month, and daffodils were awakening in the woods weeks ahead of their time by its close’. Flowering and climate change Flowering phenological data alone, or within datasets of wider categories of phenological events, have been analysed over time and often in relation to long-term temperature data. The main finding of these studies is that spring is advancing. Based on 30 years of data from the International Phenological Gardens, Menzel and Fabian (1999) found spring phases to be earlier by 6 d on average, whilst analysis of further datasets gave a mean advance of spring and summer of 2.5 d per decade from 1971–2000 (Menzel et al., 2006). Data from across Europe, from 1951–1998 have led Ahas et al. (2002) to suggest that timing of spring phases has altered over this time period, advancing by four weeks in Western and Central Europe, but being delayed by up to two weeks later in Eastern Europe. A study of 385 British plant species found that FFD was an average of 4.5 d earlier than in the previous four decades, which had shown little variation in flowering time (Fitter and Fitter, 2002). The resurgence of interest in phenological data has been partly fuelled by the finding that there is a strong correlation between earlier flowering and warmer spring temperatures (Fitter et al., 1995; Sparks and Carey, 1995; Sparks et al., 2000). Using temperature datasets, there are opportunities to calculate trends of number of days advance (or otherwise) of flowering time per °C rise in temperature and thus patterns of flowering in relation to climate change; conversely, flowering data can be used to reconstruct past climates (Aono and Kazui, 2008). Examples of the former include the 250-year index of first flowering dates produced by Amano et al. (2010) which correlates closely with mean Central England Temperature data for February to April. Based on this study, flowering is occurring five days earlier for each 1 °C rise in temperature, while analysis of 58 years of data from the British phenological network of the Royal Meteorological Society, alongside temperature data for Central England, suggests a flowering response of 2–10 d earlier per °C (Sparks et al., 2000). The impact of a possible future climate scenario for SE England by 2100, termed the IS92a scenario, of a 3.5 °C rise in winter temperature, 3 °C rise in spring, summer, and, autumn temperature, and a 10% increase in rainfall has been forecast using the Marsham family dataset and Central England temperature data as the basis for predictions. Flowering of the Marsham-recorded species (snowdrop, wood anemone, hawthorn, and turnip) is predicted to be 20–25 d earlier under these conditions (Sparks and Carey, 1995). Taken together with data on range boundaries, phenology contributes to the definition of a global ‘fingerprint’ to sum up predicted shifts in time and space of events, species ranges and abundance in response to climate change (Paramesan and Yohe, 2003; Root et al., 2003). One element of this fingerprint is a temperature-related shift of 2.3 d per decade advance in timing of spring events (Parmesan and Yohe, 2003). Flowering phenological data contributed to this global picture. Behind general trends, however, there are intriguing anomalies and nuances. Unsurprisingly, perhaps, given the complexity of flowering physiology and its relation to the environment, not all species, or all reproductive phases respond to temperature in a uniform manner. Grouping responses In one approach to defining a global fingerprint, Root et al. (2003) analysed spring phenological events by major taxa, classed as invertebrates, amphibians, birds, trees, and ‘other plants’, and singled out trees as statistically different from the rest with a mean shift of only 3 d earlier per decade as opposed to 5 d for the other groups. Thackeray et al. (2010) assessed phenological change for 726 terrestrial, freshwater, and marine taxa in the UK and found that the most rapid rate of change in the study period of 1976–2005, was in leafing, flowering, and fruiting dates of terrestrial plants, with flowering dates advancing particularly rapidly. Flowering phenological datasets too, reveal that some life-forms are more responsive than others to warming temperatures. Annuals have been found to flower an average of 10 d earlier than perennials and have more variable FFDs (Fitter et al., 1995; Fitter and Fitter, 2002). As groups, ‘trees’ and ‘perennials’ overlap and have a common characteristic of ‘indirect flowering’ responding to temperature over a prolonged period, such that warmer winters might be expected to reduce chilling effect and delay breaking of endodormancy. Whilst warmer springs could hasten flowering, the net effect is less than for species responding only to warmer spring temperatures. Flowering of Golden Delicious apple in France has advanced 7–8 d since the late 1980s (Legave et al., 2008). Modelling of flowering and temperature data implies a slower mean rate of achievement of the chilling requirement (by 3–5 d since the 1980s) and a more rapid rate of completion of the heat requirement (by 10–13 d since the 1980s) (Fig. 4). A correlation between higher mean temperatures in February to April and earlier dates of full bloom has been detected since 1977 in the selection of apple varieties recorded at the UK National Fruit Collections, Brogdale (M Jeger, personal communication). There is a correlation between higher mean temperatures in February to April and earlier dates of full-bloom. A declining winter chill has been reported by Sunley et al. (2006) in their work on chill unit models, using raspberry and blackcurrant data from East Malling. They have discussed preliminary data suggesting the high chilling requirements (around 2500 h at <7.2 °C) of some blackcurrant cultivars are near the limits of being met by the UK climate. Fig. 4. View largeDownload slide Representation of time sequence of chilling and heat effects for ‘Golden Delicious’ apple trees in different locations and sub-periods (from Legave et al., 2008). Chilling accumulated from 22nd October of the year before (n–1) the flowering year (n). F1 date is the date of flowering stage when approximately 10% of flowers are open. Durations and chilling onset date estimated from chilling model simulation detailed in Legave et al. (2008). Reproduced from Legave et al. (2008) with permission from the Editor, Journal of Horticultural Science & Biotechnology. Fig. 4. View largeDownload slide Representation of time sequence of chilling and heat effects for ‘Golden Delicious’ apple trees in different locations and sub-periods (from Legave et al., 2008). Chilling accumulated from 22nd October of the year before (n–1) the flowering year (n). F1 date is the date of flowering stage when approximately 10% of flowers are open. Durations and chilling onset date estimated from chilling model simulation detailed in Legave et al. (2008). Reproduced from Legave et al. (2008) with permission from the Editor, Journal of Horticultural Science & Biotechnology. It is apparent that species can differ markedly in their response to temperature. Research suggests, for example, that warmer summers would have opposing effects on Daboecia cantabrica and Galanthus nivalis (Harper and Morris, 2007) and that a general 1 °C warming predicts a delay of up to 6 weeks in flowering of Petasites hybridus but more than a 5 week advance in flowering of Geranium robertianum (Fitter et al., 1995). In addition, Fitter et al. (1995) found 24 species in their dataset which showed no significant relationship between flowering-time and temperature. Bradley et al. (1999) noted ‘responders’ and ‘non-responders’ in their data, classed by increases or lack of increases in earliness over 61 years of recording. These cases serve as a reminder that temperature is but one environmental parameter in flowering, and photoperiodic responses are also involved. There are also other potential non-climate related drivers of phenological change, such as population age structure or aquatic nutrient enrichment (see Thackeray et al., 2010). Harper and Morris (2007) suggest that ‘functional groups’ of plants with shared characteristics could be identified and expected to react to climate change in a similar way. They used a provisional description of a ‘Mediterranean-type’ grouping as an example. (Plants included are not necessarily native to the region but appear to have adaptations to its climatic features). Characteristics of these plants are a relatively highly synchronized end to the reproductive phase in late spring/early summer, even though first flowering dates are highly variable across different species. It is proposed that the start of the reproductive phase in this functional group, which includes Ulex europaeus, Viburnum tinus, Forsythia×intermedia, and Cyclamen, will be more responsive to climate change than the end of this phase (Harper and Morris, 2007). The approach advocated is to draw up groupings based on physiological properties. Correlation and regression analysis offers a way to identify such characteristics by discovering temperature-sensitive phases of the flowering process in different species (Harper et al., 2009). Life cycles and temperature In a theoretical approach to how temperature might affect the reproductive phase of plant development, it might be envisaged that specific events (e.g. FFD, seed set) could happen earlier or later, while life cycle phases (e.g. duration of flowering) might be shortened or prolonged to give an attenuated or extended aggregate life cycle. Alternatively the life cycle may retain its pattern and duration but shift temporally (Post et al., 2008; Steltzer and Post, 2009). Post et al. (2008) found evidence of some of these alterations in response to warming. In Cerastium alpinum, despite a shorter duration of the emergence phase, the aggregate life cycle retained its length. In Salix glauca and Betula nana, some or all phenological events were earlier and aggregate life cycles, shorter. Significantly altered durations of the reproductive phases of budding, flowering, and fruiting in response to experimental warming were found in winter annuals and perennials studied by Sherry et al. (2007). Of five winter annuals in the study, three showed shorter life cycles on warming (and two were unchanged) and of six perennials under the same conditions, three life cycles were longer, one shorter, and two unchanged. In some cases, even though the phenology of bud appearance was unaltered, the bud stage was prolonged, delaying fruiting and flowering (Sherry et al., 2007). It is widely reported that temperature in the months prior to flowering correlates with flowering time (Sparks et al., 2000; Fitter and Fitter, 2002). Fitter et al. (1995) found that nearly 60% of those species in their study which flowered in January to April were affected by temperature two months before flowering and for summer flowering species, temperatures up to 4 months previously were important. Whilst the general trends are negative correlations, that is, warmer temperatures giving rise to earlier flowering, positive correlations with high temperatures in the previous autumn have also been found in both annuals and herbaceous and woody perennials (Fitter et al., 1995). Conclusion Flowering phenological datasets cover a wide range of species and locations. The existence of datasets spanning many years is testament to the commitment of recorders to capture flowering events. Flowering phenology is interdisciplinary, combining history, meteorology, seasonality, and plant physiology. Increasingly, it appears to focus on identifying trends that might help to provide a glimpse of the future in terms of the effects of climate change on plants. To date, those trends are largely quite generalized and from a plant physiologist's perspective, it is often the anomalies that are most intriguing, offering potential insights into environmental response paths in flowering and inviting discussion as to why certain groups of plants may be responding differently to others. To allow a better understanding of the effects of changing climate, flowering phenological datasets need to include more flowering stages and records of flowering duration. In addition, recording and interpretation of climate data need to take full account of the developmental cycle underlying flower production. FT is grateful to The University of Reading for a visiting research fellowship. 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Harrison, C. Jill;Alvey, Elizabeth;Henderson, Ian R.

doi: 10.1093/jxb/erq191pmid: 20576791

Abstract Sexual eukaryotes generate gametes using a specialized cell division called meiosis that serves both to halve the number of chromosomes and to reshuffle genetic variation present in the parent. The nature and mechanism of the meiotic cell division in plants and its effect on genetic variation are reviewed here. As flowers are the site of meiosis and fertilization in angiosperms, meiotic control will be considered within this developmental context. Finally, we review what is known about the control of meiosis in green algae and non-flowering land plants and discuss evolutionary transitions relating to meiosis that have occurred in the lineages giving rise to the angiosperms. Meiosis, organogenesis, recombination, sex, sporangia Introduction Man has selected from natural genetic variation to breed crop species useful for agriculture. Considerable genetic diversity remains to be fully exploited, including variation capable of increasing yield and broadening crop range. Sustainable increases in agricultural yield in the range of 50% will be required to feed the 9 billion people estimated to exist by 2050 (The Royal Society, 2009). To increase the efficiency of crop breeding, it is important to understand the mechanism by which variation is generated and transmitted. Variation is generated in a specialized, reductive cell division termed meiosis during which recombination between homologous chromosomes, termed crossover (CO), occurs. CO frequency varies within and between species and can be limiting such that useful variation is not accessible for breeding. Although the majority of crops are angiosperms in which meiosis occurs within floral organs, non-flowering plants have provided novel insights into the control of plant meiosis. The nature of the meiotic cell division in plants, the genetic mechanisms that promote variation, and the developmental context in which meiosis occurs in land plants and their closest sister groups are reviewed here. Meiotic cell divisions Meiosis is a mode of cell division specific to eukaryotic organisms whereby four haploid daughter cells are produced from a single diploid parent cell (Villeneuve and Hillers, 2001; Hamant et al., 2006; Mezard et al., 2007). This reductive division is achieved by a single round of DNA replication followed by two rounds of chromosome segregation and cell division (meiosis-I and meiosis-II) (Fig. 1). Meiosis appears to be an ancestral trait within eukaryotes and is speculated to have arisen close to the group's origin (Villeneuve and Hillers, 2001; Cavalier-Smith, 2002). This idea is strengthened by observations that many unicellular eukaryotes, once presumed to be asexual, have since been found to possess conserved meiosis-specific genes (Ramesh et al., 2005; Malik et al., 2007). The first meiotic division differs dramatically from mitosis as homologous chromosomes pair before segregation. During the second division sister chromatids segregate to opposite poles in the same manner as during mitosis. While homologues are paired during meiosis-I they are tightly associated via the synaptonemal complex that forms along their length (Page and Hawley, 2004). Meiosis-specific expression of an endonuclease, SPO11, causes a large number of double strand breaks (DSBs) along the paired chromosomes (Villeneuve and Hillers, 2001). A subset of these break sites are repaired through recombination pathways that lead to physical exchange between the paired chromosomes (CO) (Villeneuve and Hillers, 2001; Hamant et al., 2006; Mezard et al., 2007). As paired chromosomes segregate during meiosis-I, CO sites can be visualized cytologically as chiasmata (Armstrong et al., 2009). Independent segregation of maternal and paternal chromosome sets during meiosis in combination with CO between chromosomes means that gametes are likely to possess novel combinations of genetic variation (Fig. 1). Fig. 1. View largeDownload slide Chromosome inheritance during meiosis. (A) Maternally (white) and paternally (black) inherited copies of two chromosome pairs are shown in a diploid parental cell. (B) During S-phase of meiosis-I each chromosome is replicated. (C) Maternally and paternally inherited pairs of homologous chromosomes physically associate during prophase of meiosis-I. Coincident with pairing, a large number of DNA double-strand breaks (shown as short vertical lines) are induced by the SPO11 endonuclease. (D) At the end of meiosis-I spindle microtubules attach to the centromeres of paired bivalents and recombination events are completed. (E) Members of homologous chromosome pairs segregate to opposite cell poles. (F) Meiosis-I is completed with equal numbers of replicated, recombined chromosomes in daughter cells. (G) During meiosis-II cohesion is lost between replicated chromatids, which segregate to opposite cell poles. (H) Four haploid daughter cells are produced with novel combinations of maternally and paternally inherited genetic information. Fig. 1. View largeDownload slide Chromosome inheritance during meiosis. (A) Maternally (white) and paternally (black) inherited copies of two chromosome pairs are shown in a diploid parental cell. (B) During S-phase of meiosis-I each chromosome is replicated. (C) Maternally and paternally inherited pairs of homologous chromosomes physically associate during prophase of meiosis-I. Coincident with pairing, a large number of DNA double-strand breaks (shown as short vertical lines) are induced by the SPO11 endonuclease. (D) At the end of meiosis-I spindle microtubules attach to the centromeres of paired bivalents and recombination events are completed. (E) Members of homologous chromosome pairs segregate to opposite cell poles. (F) Meiosis-I is completed with equal numbers of replicated, recombined chromosomes in daughter cells. (G) During meiosis-II cohesion is lost between replicated chromatids, which segregate to opposite cell poles. (H) Four haploid daughter cells are produced with novel combinations of maternally and paternally inherited genetic information. Programmed DNA breakage and repair An essential first step in achieving CO is the generation of DSBs throughout the genome by SPO11. SPO11 is related to the A subunit of archaebacterial topioisomerase IV, which acts to relieve torsion in DNA by generating transient DSBs (Villeneuve and Hillers, 2001; Cavalier-Smith, 2002). Hence, components of the meiotic recombination machinery may have been recruited from prokaryotic DNA repair mechanisms. Two related orthologues SPO11-1 and SPO11-2 are non-redundantly required for meiotic DSBs in Arabidopsis thaliana (Grelon et al., 2001; Stacey et al., 2006; Hartung et al., 2007). The spo11-1 and spo11-2 mutants lack meiotic DSBs and show an absence of homologue pairing and synapsis, meaning that univalent chromosomes segregate at meiosis-I (Grelon et al., 2001; Stacey et al., 2006; Hartung et al., 2007). As univalent chromosomes segregate independently from their homologous partner, spo11 mutants show a high incidence of chromosomally unbalanced gametes (Grelon et al., 2001; Stacey et al., 2006; Hartung et al., 2007). The PUTATIVE RECOMBINATION INITIATION DEFECTS1 (PRD1), PRD2 and PRD3 genes are also necessary for SPO11-dependent DSB formation and cause univalent segregation when mutated (De Muyt et al., 2007, 2009). PRD1 and PRD3 share sequence similarity with known meiotic proteins, mammalian Mei1 and rice PAIR1, respectively, but perform unknown functions during DSB formation (Libby et al., 2002; Nonomura et al., 2004; De Muyt et al., 2007, 2009). A second functional class of genes, including MND1, AHP2, RAD50, RAD51C, XRCC3, MRE11, and COM1/SAE2, share a mutant phenotype of meiotic chromosome fragmentation without synapsis, which is suppressed when combined with spo11 or prd3. (Schommer et al., 2003; Bleuyard et al., 2004; Bleuyard and White, 2004; Puizina et al., 2004; Li et al., 2004; Kerzendorfer et al., 2006; Panoli et al., 2006; Uanschou et al., 2007; Vignard et al., 2007). This indicates that these proteins act to process DSBs during meiotic recombination, and unrepaired DSBs cause chromosome fragmentation. Meiotic DSB processing proceeds via break site resection to form 3' single-stranded DNA, which is used to invade the intact duplex of a paired homologous chromosome (Bhatt et al., 2001; Villeneuve and Hillers, 2001; Hamant et al., 2006; Mezard et al., 2007). Invasion of DNA duplexes by ssDNA requires the DMC1 and RAD51 recombinases, which are related to prokaryotic RecA DNA strand exchange proteins (Couteau et al., 1999; Masson and West, 2001; Villeneuve and Hillers, 2001; Cavalier-Smith, 2002; Li et al., 2004). Though RAD51 and DMC1 are both thought to act at the step of ssDNA invasion, rad51 mutants show SPO11-dependent chromosome fragmentation, whereas dmc1 mutants show univalent segregation (Couteau et al., 1999; Li et al., 2004). One explanation for this difference may be that DSBs are repaired using sister chromatids in dmc1, but not in rad51 (Couteau et al., 1999; Li et al., 2004). DSBs lead to CO via formation of a double Holliday junction (dHJ) between paired homologous DNA duplexes (Villeneuve and Hillers, 2001). Subsequent to strand invasion, a series of events are required for dHJ formation, including second end capture and DNA synthesis and ligation (Villeneuve and Hillers, 2001). These junctions are ultimately resolved into CO events via an unknown dHJ resolvase in plants, although the structure-specific endonucleases GEN1/Yen1 and SLX4 serve this function in animals (Svendsen and Harper, 2010). An excess of DSBs are generated by SPO11 relative to the number of CO events ultimately observed. The remaining DSBs are repaired via a gene conversion (also known as a non-crossover, NCO) pathway, which does not involve the exchange of flanking genetic markers (Villeneuve and Hillers, 2001). The synthesis-dependent strand annealing pathway appears to be a major mechanism for NCO formation in Saccharomyces cerevisiae, which acts independently of dHJ formation (McMahill et al., 2007). In S.cerevisiae the decision to repair DSB sites as CO or NCO events is made early in prophase-I, prior to dHJ formation, so it is unlikely that the CO/NCO choice represents alternative processing of dHJs (Allers and Lichten, 2001; Borner et al., 2004). Control of meiotic crossover frequency The position of COs can be influenced by other COs on the same chromosome via a phenomenon known as interference. In interference one event inhibits the formation of adjacent events in a distance-dependent manner meaning that they are more widely distributed than expected at random (Sturtevant, 1915; Muller, 1916; Copenhaver et al., 2002). However, non-interfering COs that have a random distribution also occur (Copenhaver et al., 2002). In A. thaliana class I interfering COs are the majority (∼75–85%) and class II non-interfering COs the minority (∼15–25%) (Copenhaver et al., 2002; Higgins et al., 2004; Mercier et al., 2005; Berchowitz et al., 2007; Higgins et al., 2008,a). Class I COs in A. thaliana require a set of genes including MSH4, MSH5, MLH3, MER3/ROCK-N-ROLLERS, PARTING DANCERS, ZIP4/SPO22, RPA1a and SHOC1 (Higgins et al., 2004, 2008b; Chen et al., 2005; Mercier et al., 2005; Jackson et al., 2006; Wijeratne et al., 2006; Chelysheva et al., 2007; Macaisne et al., 2008; Osman et al., 2009). Knockout of these genes results in a dramatic reduction in CO frequency and the events that remain are randomly distributed. The ancestry of meiotic proteins in prokaryotic DNA repair is again evident as MSH4 and MSH5 proteins are related to bacterial MutS mismatch repair proteins (Villeneuve and Hillers, 2001; Cavalier-Smith, 2002). Class II non-interfering COs require the MUS81 gene, which encodes a protein similar to structure-specific endonucleases (Berchowitz et al., 2007; Higgins et al., 2008a). Paired chromosomes generally show at least one CO event, the obligate CO, that is required for proper patterns of chromosome segregation in A. thaliana (Grelon et al., 2001). Each A. thaliana chromosome shows approximately 1.8 CO per meiosis and very few chromosomes show no CO (Copenhaver et al., 1998, 2002; Higgins et al., 2004; Drouaud et al., 2006, 2007). CO position is highly variable and hot- and cold-spots of CO frequency exist along the chromosomes (Copenhaver et al., 1998, 1999; Drouaud et al., 2006, 2007). Pronounced increases in CO frequency and gene density are observed towards the telomeres of wheat and maize chromosomes (Liu et al., 2009; Saintenac et al., 2009; Schnable et al., 2009). A recombination hotspot at the maize Bronze (Bz) locus lies in a gene-rich region close to the telomere. This region shows 40–80-fold higher CO rates than the genome average and is flanked by large stretches of nested retrotransposon insertions that infrequently crossover (Dooner and Martinez-Ferez, 1997; Fu et al., 2001, 2002). Repetitive regions flanking centromeres are also suppressed for CO, and centromere-proximal events associate with chromosome mis-segregation (Koehler et al., 1996; Lamb et al., 1997; Deng and Lin, 2002; Rockmill et al., 2006). Differences in crossover frequency may be accounted for by epigenetic information. For example in mouse and S. cerevisiae H3 K4 trimethylation marks DSB hotspots, and disruption of this modification reduces DSBs (Borde et al., 2009; Buard et al., 2009). Conversely, in A. thaliana the CO-cold repetitive sequences flanking the centromere are transcriptionally silenced using epigenetic information including DNA cytosine methylation and targeted DNA methylation in Ascobolous immersus is sufficient to repress CO frequency several hundred fold (Maloisel and Rossignol, 1998; Zhang et al., 2006; Zilberman et al., 2007). Repetitive insertions and inversions can also locally suppress CO, and play an important functional role in genome organization (Dooner, 1986; Nacry et al., 1998). This is illustrated by CO suppression at sex chromosomes, mating-type loci and self-incompatibility loci, where it is important to maintain linkage between genes required for opposite mating/incompatibility types (Ferris and Goodenough, 1994; Casselman et al., 2000; Ming and Moore, 2007; Bergero and Charlesworth, 2009). Hence, CO frequency is likely to be determined by a combination of local DNA sequence, trans-factors, and epigenetic information. Control of meiotic cell cycle progression Meiosis requires the modification of mitotic cell cycle control, such that a single S-phase is followed by two sequential rounds of chromosome segregation. Progression through the cell cycle is controlled by cyclins that interact with and activate cyclin-dependent kinases (CDKs) which mediate stepwise transitions through the cycle via phosphorylation (Huntley and Murray, 1999). Several genes implicated in the regulation of meiotic progression have been identified in A. thaliana. A novel, cyclin-like gene SOLO DANCERS (SDS) is specifically expressed during meiosis and sds mutants show defects in chromosome pairing, segregation, and CO (Azumi et al., 2002). Recently, sds mutants have been observed to form DSBs but repair them efficiently, most likely via RAD51-mediated inter-sister repair (De Muyt et al., 2009). The novel gene OMISSION OF SECOND DIVISION1 (OSD1) is required for meiosis-II and osd1 produces diploid dyad products of meiosis instead of haploid tetrads (d'Erfurth et al., 2009). A temperature-sensitive substitution allele of CYCLINA1;2, termed tardy asynchronous meiosis1 (tam1) causes a delay in meiotic progression, which also leads to dyad formation (Magnard et al., 2001; Wang et al., 2004). Regulation of protein stability is critical for cell cycle control and SKP1-like F-box proteins act to promote ubiquitination and destruction of target proteins, including cyclins (Bai et al., 1996). Consistently, mutations in the SKP1-related gene ASK1 show defects in meiotic chromosome segregation (Yang et al., 1999a; Zhao et al., 2006). In animals, cell cycle checkpoints cause later events to depend upon the completion of earlier events (Murakami and Nurse, 1999). Meiotic checkpoint mechanisms have not been genetically defined in A. thaliana, although msh4 and asy1 show a significant delay in meiosis, suggesting feedback on the regulation of meiotic progression (Higgins et al., 2004; Sanchez-Moran et al., 2007). Together these results demonstrate that defects in cell cycle progression can disrupt meiosis. Key early events in meiosis are the identification of homologous chromosome partners, pairing, and formation of the synaptonemal complex (SC) (Page and Hawley, 2004). Homologous partner identification occurs by unknown mechanisms during prophase-I. In polyploid species pairing is complex as homologues must also avoid pairing with related homeologues (Martinez-Perez et al., 1999). For instance, in hexaploid wheat Ph1 affects the stringency of homologue/homeologue discrimination by influencing chromatin remodelling associated with pairing and localization of the SC component ASY1 (Martinez-Perez et al., 1999; Prieto et al., 2004, 2005; Boden et al., 2009). Ph1 maps to a repetitive locus containing cell cycle-dependent kinase genes, which causes down-regulation of unlinked CDK genes (Griffiths et al., 2006; Al-Kaff et al., 2008). As related CDK genes in mammals influence meiotic progression, trans-silencing of CDKs by Ph1 could lead to changes in homologue pairing (Ortega et al., 2003). Pairing may depend on DSB formation as in A. thaliana or may occur via achiasmate mechanisms as in female Drosophila melanogaster (Grelon et al., 2001; Page and Hawley, 2004). SWITCH1/DYAD encodes a novel protein expressed during prophase-I, necessary for chromosome pairing, synapsis, and recombination and in swi1/dyad mutants univalents segregate at meiosis-I. Studies of a maize homologue, AMEIOTIC1 indicate that these functions are conserved within angiosperms (Golubovskaya et al., 1993; Mercier et al., 2001, 2003; Agashe et al., 2002; Ravi et al., 2008; Pawlowski et al., 2009). POOR HOMOLOGOUS SYNAPSIS1 encodes a second novel protein required for chromosome pairing and synapsis, which causes high levels of non-homologous pairing when mutated (Pawlowski et al., 2004; Ronceret et al., 2009). Co-incident with pairing, the SC forms between homologous chromosomes (Page and Hawley, 2004) and SC components identified in A. thaliana include ASYNAPTIC1 (ASY1), ZYP1A, ZYP1B, SCC3, and REC8/DIF1/SYN1 (Bai et al., 1999; Bhatt et al., 1999; Caryl et al., 2000; Armstrong et al., 2002; Cai et al., 2003; Chelysheva et al., 2005; Higgins et al., 2005). ASY1 and ZYP1 show distant identity with the animal HOP1 and ZIP1 SC proteins, respectively (Caryl et al., 2000; Armstrong et al., 2002; Higgins et al., 2005). Loss of SC proteins causes failures in synapsis and CO formation, and can lead to univalent segregation and chromosome fragmentation (Bai et al., 1999; Bhatt et al., 1999; Caryl et al., 2000; Armstrong et al., 2002; Cai et al., 2003; Chelysheva et al., 2005; Higgins et al., 2005). Interestingly, increases in CO frequency are associated with increases in total SC length via an unknown mechanism (Lynn et al., 2002; Drouaud et al., 2007). These genetic mechanisms provide novel insights into the interrelated processes of homologue recognition, pairing, and synapsis in plants. Correct patterns of chromosome segregation are required to generate balanced gametes and depend on regulation of the SC and chromosome cohesion. During mitosis the cohesin complex holds sister chromatids together until the SCC1 subunit is cleaved by SEPERASE1 at anaphase, allowing chromosome segregation (Uhlmann et al., 1999). REC8/DIF1/SYN1 is a meiosis-specific orthologue of SCC1 and the rec8/dif1/syn1 mutant disrupts normal SC localization of SCC3 (a cohesin subunit shared with mitosis) (Bhatt et al., 1999; Cai et al., 2003; Chelysheva et al., 2005). As in animal systems, A. thaliana REC8 is cleaved by the cysteine protease SEPERASE1 (ESP1) (Liu and Makaroff, 2006). Cohesion in the chromosome arms is released by ESP1 at anaphase-I, but maintained at the centromeres until anaphase-II (Liu and Makaroff, 2006). In maize, centromeric REC8/AFD1 is protected from ESP1 destruction by the conserved Shugoshin protein (SGO1) during anaphase-I (Hamant et al., 2005; Watanabe, 2005). SGO1 is then removed and REC8 is destroyed at the centromere during anaphase-II to allow chromatid segregation. Step-wise formation and removal of connections between homologues is thus required for correct patterns of meiotic chromosome segregation and recombination. The developmental context for meiosis Successful completion of meiosis and sexual life cycles depends on activation of the meiotic cell cycle at the correct developmental time and place. In many animals separation of a dedicated germ cell lineage from somatic cell types occurs during embryogenesis, a distinction not observed in some early diverging animal lineages and plants (Gilbert, 1994; Dickinson and Grant-Downton, 2009). In the algal sister groups to land plants single-celled zygotes undergo meiosis immediately following fertilization (Fig. 2A). By contrast, in all land plants mitotic divisions intercede fertilization and meiosis, and meiosis occurs after a period of diploid development in specialized structures termed sporangia that produce numerous spores (Bower, 1935; Becker and Marin, 2009). The initiation of sporangium development pathways often follows a switch in meristem identity from a vegetative to a reproductive fate (Steeves and Sussex, 1989). The location and structure of sporangia vary by plant group and are associated with their secondary functions, which are spore dispersal and nutrition. Heteromorphic sporangia and spores have evolved convergently in vascular plants and associate with specialized functions (Fig. 2). Female megasporangia have fewer, larger spores (megaspores) that may be retained within the parent plant after fertilization, whereas male microsporangia develop numerous small spores (microspores) that have a dispersal function (Bower, 1935). Gender can also influence patterns of CO frequency (Drouaud et al., 2007). Thus the initiation and progression of meiosis depend on the developmental identity of the tissue in which it is activated, discussed by plant group below. Fig. 2. View largeDownload slide Phylogenetic distribution of characters associated with meiosis in plants. (A) Phylogenetic relationships between major plant groups showing synapomorphies associated with meiosis. Heterospory has a polyphyletic origin in lycophytes, monilophytes, and spermatophytes, indicated by asterisks. (B) Reproductive structures of representatives of clades illustrated in (A), and extinct protracheophyte fossil forms. 1. Electron micrograph of Chlamydomonas monoica zygospores (photograph courtesy of Professor Karen P Van Winkle-Swift and by kind permission of John Wiley and Sons: see VanWinkle-Swift KP, Rickoll WL. 1997. The zygospore wall of Chlamydomonas monoica (Chlorophyceae): Morphogenesis and evidence for the presence of sporopollenin1. Journal of Phycology 33, 655–665.) 2. Light micrograph of the haploid plant and oogonium (inset) in the charophyte alga Chara sp. 3. The creeping haploid thallus and a diploid erect and determinate sporophyte of Pellia epiphylla (photographs of bryophytes courtesy of Li Zhang). 4. The haploid leafy gametophyte of Atrichum angustatum bearing diploid unbranched determinate sporophytes. 5. The haploid creeping thallus of Folioceros sp. with upright indeterminate sporophytes. 6. A fossil sporophtye of Cooksonia sp. showing branching with terminal sporangia (photo courtesy of Jenny Morris and Dianne Edwards). 7. A fossil sporophyte of Zosterophyllum showing lateral sporangia (photo courtesy of Jenny Morris and Dianne Edwards). 8. A Selaginella kraussiana sporophyte showing vegetative branching habit, an unbranched reproductive strobilus, and a mega- and microsporangium (inset). 9. Sporangia formed on the stem of Psilotum nudum. 10. A frond of Adiantum mairisi showing marginal sori that contain sporangia. 11. The terminal cones of Cupressus sp. that contain the mega- and microsporangia. 12. The stamens and carpels of a Magnolia sp. flower that contain the micro- and megasporangia, respectively. Fig. 2. View largeDownload slide Phylogenetic distribution of characters associated with meiosis in plants. (A) Phylogenetic relationships between major plant groups showing synapomorphies associated with meiosis. Heterospory has a polyphyletic origin in lycophytes, monilophytes, and spermatophytes, indicated by asterisks. (B) Reproductive structures of representatives of clades illustrated in (A), and extinct protracheophyte fossil forms. 1. Electron micrograph of Chlamydomonas monoica zygospores (photograph courtesy of Professor Karen P Van Winkle-Swift and by kind permission of John Wiley and Sons: see VanWinkle-Swift KP, Rickoll WL. 1997. The zygospore wall of Chlamydomonas monoica (Chlorophyceae): Morphogenesis and evidence for the presence of sporopollenin1. Journal of Phycology 33, 655–665.) 2. Light micrograph of the haploid plant and oogonium (inset) in the charophyte alga Chara sp. 3. The creeping haploid thallus and a diploid erect and determinate sporophyte of Pellia epiphylla (photographs of bryophytes courtesy of Li Zhang). 4. The haploid leafy gametophyte of Atrichum angustatum bearing diploid unbranched determinate sporophytes. 5. The haploid creeping thallus of Folioceros sp. with upright indeterminate sporophytes. 6. A fossil sporophtye of Cooksonia sp. showing branching with terminal sporangia (photo courtesy of Jenny Morris and Dianne Edwards). 7. A fossil sporophyte of Zosterophyllum showing lateral sporangia (photo courtesy of Jenny Morris and Dianne Edwards). 8. A Selaginella kraussiana sporophyte showing vegetative branching habit, an unbranched reproductive strobilus, and a mega- and microsporangium (inset). 9. Sporangia formed on the stem of Psilotum nudum. 10. A frond of Adiantum mairisi showing marginal sori that contain sporangia. 11. The terminal cones of Cupressus sp. that contain the mega- and microsporangia. 12. The stamens and carpels of a Magnolia sp. flower that contain the micro- and megasporangia, respectively. Meiosis in chlorophyte and charophyte algae In both chlorophyte and charophyte algal sister groups to the land plants meiosis occurs immediately following fertilization (Becker and Marin, 2009). In the single-celled chlorophyte alga, Chlamydomonas reinhardtii two haploid mating types, plus and minus, differentiate into gametes which fuse during fertilization to form a single-celled zygote (Fig. 2B) (Lee et al., 2008). Plus and minus gamete identity is specified from the MATING-TYPE locus and requires cytoplasmic accumulation of BEL [GAMETE-SPECIFIC PLUS1 (GSP1)] and KNOX [GAMETE-SPECIFIC MINUS1 (GSM1)] class homeodomain proteins, respectively (Ferris and Goodenough, 1994; Lee et al., 2008). Following fertilization GSP1 and GSM1 heterodimerize, translocate to the nucleus, and initiate zygotic gene expression patterns (Lee et al., 2008). Constitutive expression of either GSP1 or GSM1 in the opposite gamete type is sufficient to trigger zygote development in the absence of fertilization (Zhao et al., 2001; Lee et al., 2008). Stable C. reinhardtii diploids that do not initiate meiosis can also be generated, and constitutive expression of GSP1/GSM1 together in these cells is sufficient to induce meiosis with normal patterns of recombination (Lee et al., 2008). This indicates that GSP1/GSM1 homeodomain proteins are potential triggers of meiosis in a chlorophyte alga. In contrast to chlorophytes, charophytes have a multicellular haploid body that generates free-swimming sperm in antheridia and egg cells that are retained within an oogonium on the parent plant (Fig. 2B). Egg retention (oogamy) is an innovation shared with land plants thought to have been a key adaptation in their evolution (McCourt et al., 2004). As there are currently no charophyte genetic models, potential roles for KNOX/BEL genes are unexplored, and the initiation of meiosis in charophytes is via an unknown mechanism. Sporophyte development and meiosis in bryophytes In contrast to their algal sisters, all land plants have a period of multicellular diploid growth, the extent of which varies by plant group (Lewis and McCourt, 2004; McCourt et al., 2004; Becker and Marin, 2009). The bryophyte sister groups to the vascular plants exhibit limited post-embryonic development with no indeterminate apical growth (Mishler and Churchill, 1985; Shaw and Renzaglia, 2004; Donoghue, 2005). Sporophytes comprise a small single stem with a terminal sporangium that represents the simplest basal land plant body plan (Kenrick, 2002; Donoghue, 2005; Qiu et al., 2006; Boyce, 2008) (Fig. 2). In liverworts and mosses sporangium development arrests diploid growth, whereas hornwort sporophytes contribute to their own nutrition and have sporangia that grow indeterminately from a basal meristem (Boyce, 2008; Kato and Akiyama, 2005). A sub-epidermal archesporial cell layer is specified during sporangium development and divides either by meiosis to generate spores (mosses) or spore mother cells and interspersed elater cells that perform nutritive or dispersal functions (liverworts and hornworts). The tissues surrounding the archesporial cell layer perform dispersal functions specific to each bryophyte group (Bower, 1935). The genetic and developmental mechanisms that regulate bryophyte sporophyte development are currently poorly understood, but interest has recently accelerated due to the establishment of moss (Physcomitrella patens) and liverwort (Marchantia polymorpha) models (Ishizaki et al., 2008; Rensing, 2008). Two gene classes that affect sporangium development in P. patens are homologues of A. thaliana LEAFY (LFY) and KNOX genes (Champagne and Ashton, 2001; Tanahashi et al., 2005; Sakakibara et al., 2008). A pair of LFY homologues redundantly control the first zygotic division in P. patens and double mutants exhibit arrested zygotic development (Tanahashi et al., 2005). In mutants that do not arrest, sporangium number, initiation, and development are perturbed. These defects may arise as a consequence of abnormal sporophytic development, although spore number and germination are also highly variable in the mutants, suggesting meiotic defects (Tanahashi et al., 2005). Similarly Class I KNOX mutants in P. patens have abnormal sporangia and reduced spore numbers (Sano et al., 2005; Sakakibara et al., 2008). Interestingly KNOX expression is sporophyte specific in P. patens (Champagne and Ashton, 2001; Sakakibara et al., 2008), and a potential role of KNOX and BEL genes in diploid development conserved between algae and bryophytes remains to be explored. Protracheophytes and seed plant sister groups Key features that distinguish vascular plants from bryophytes are the elaboration of an indeterminately growing and branching diploid body (Mishler and Churchill, 1985; Donoghue, 2005; Langdale and Harrison, 2008). Fossil plants whose form is not represented in living plants, such as Cooksonia, have low orders of branching and may have amplified spore numbers by increasing numbers of terminal sporangia (Fig. 2B) (Edwards and Feehan, 1980; Graham et al., 2000; Donoghue, 2005; Gerrienne et al., 2006). These fossils raise interesting questions about the developmental nature of the association between axis development, sporangium development, and branching and, intriguingly, rare bryophyte branching mutants strikingly resemble Cooksonia sporophytes (Fig. 2B). Alternative lateral sporangial placements appear independent of branching and may have served a similar purpose in other fossil groups (Fig. 2B). This arrangement is exhibited in modern lycophytes, and sporangia arise either at the base of leaves or from the stem via one or two sub-epidermal archesporial cell layers. These archesporial cells give rise to sporogenous tissue (Lycopodium) or sporogenous and tapetal tissues (Selaginella, Isoetes) (Bower, 1935). Monilophyte sporangia are diverse in terms of their size, the number of spores produced per sporangium, and their number and position on the plant (Fig. 2B). The eusporangiate basal monilophyte grade comprising marattioid ferns, horsetails, ophioglossoid ferns, and whisk ferns possess sporangia that develop from several cells and produce thousands of spores (Bower, 1935; Wagner, 1977; Parkinson, 1987; Pryer et al., 2004). By contrast, the leptosporangiate ferns develop numerous, small sporangia from single cells, which typically contain tens of spores (Bower, 1935; Pryer et al., 2004). Sporangia may show terminal, adaxial, abaxial, or marginal locations on leaves (Fig. 2B). With the exception of the leptosporangiate and whisk ferns, nutritive tapetal tissues arise from non-sporogenous tissue (Bower, 1935; Parkinson, 1987). As in bryophytes, the genetic basis of diploid development is poorly characterized in lycophytes and monilophytes. Notably sporophytic KNOX expression is conserved, and meristematic expression domains suggest likely roles in indeterminate growth (Bharathan et al., 2002; Harrison et al., 2005; Sano et al., 2005), although reproductive roles have not yet been explored. Thus the structure and dispersal functions of sporangia vary broadly across the land plants and the developmental context for the initiation of meiosis is lineage specific. An evolutionary trend towards the amplification of spore numbers by alterations in body plan, sporangium size, and the number of sporangia is apparent (Bower, 1935). Sporophyte development in seed plants In seed plants (gymnosperms and angiosperms) a prolonged period of vegetative growth is followed by the reproductive transition. This transition involves a change in meristem identity and leads to the development of cones or flowers (Steeves and Sussex, 1989). Seeds develop in the context of the ovule following fertilization of the female egg cell by a male sperm cell transferred in pollen, thus dispersal functions are provided both by haploid pollen and diploid seed. Ovules are the site of megasporangium (nucellus) development, which precedes meiosis. Whilst in gymnosperms one to several nucellar cells enter meiosis, in angiosperms a single megaspore mother cell undergoes meiosis to form a tetrad, three members of which degenerate to form a single functional megaspore, which divides mitotically to form the embryo sac (Campbell, 1940; Colombo et al., 2008). Pollen sac (microsporangium) development occurs from a microsporophyll or in the anther in gymnosperms and angiosperms respectively. In both, sub-epidermal cells are specified as archesporial cells that divide periclinally to form a layer of parietal cells surrounding the sporogenous cells (Campbell, 1940; Feng and Dickinson, 2007). Sporogenous cells may then either directly enter meiosis or continue to proliferate. Parietal cells divide further to form a variable number of concentrically arranged cell layers, the innermost of which differentiates into the nutritive tapetum (Campbell, 1940; Feng and Dickinson, 2007). During meiosis sporogenous cells become encased in an impermeable callose ((1-3)-β-D-glucan) wall, which later breaks down when members of the microspore tetrads are released (Gifford and Foster, 1988). Callose appears to play an important role in sporogenesis, as tapetal expression of callase causes male sterility in tobacco (Worrall et al., 1992). Following meiosis, the resulting haploid microspores undergo mitosis and differentiate into pollen grains. The genetic control of vegetative development, the reproductive transition, and sporangium formation are well studied in the angiosperm A. thaliana. Activity of the class I KNOX gene SHOOTMERISTEMLESS (STM) is necessary for the establishment of an indeterminate meristem (Long et al., 1996), and STM and BREVIPEDICELLUS (BP) act redundantly to maintain indeterminacy and repress determinate leaf development (Byrne et al., 2002). Class I KNOX proteins promote indeterminacy by dimerizing with BEL transcription factors and triple bellringer, poundfoolish, Arabidopsis thaliana homeobox1 BEL mutants phenocopy stm mutants (Rutjens et al., 2009). Thus, in A. thaliana KNOX and BEL genes play key roles in elaboration of the diploid body, providing the context for later reproductive development and meiosis. Flower development follows conversion of indeterminate, vegetative shoot meristems to reproductive fates. This switch is controlled by a large network of genes that ensure reproduction is co-ordinated with environmental and developmental conditions (Baurle and Dean, 2006). These signalling pathways converge on a key set of transcription factors required for floral meristem identity, including LEAFY and APETALA1 (Baurle and Dean, 2006). Floral organ identity genes encode three functional classes of MADS-box transcription factors (A, B, and C) that are expressed in overlapping domains to specify the four floral organ types (Coen and Meyerowitz, 1991). Stamen identity requires overlapping expression of the B and C class MADS genes PISTILLATA and APETALA3, and AGAMOUS (AG) (Yanofsky et al., 1990; Jack et al., 1992; Goto and Meyerowitz, 1994). Carpel identity requires activity of the C class gene AG, which acts in conjunction with three additional MADS proteins, SEEDSTICK, SHATTERPROOF1, and SHATTERPROOF2 to specify ovule identity (Yanofsky et al., 1990; Pinyopich et al., 2003). The KNOX genes STM and KNAT2, and BEL1 genes also play roles in the specification of carpel and ovule identity (Modrusan et al., 1994; Pautot et al., 2001; Scofield et al., 2007). Thus genes involved in meristem identity also play roles in the specification of reproductive fate. In both micro- and megasporangia archesporial cell specification precedes sporangium formation (Gifford and Foster, 1988). The mechanisms of archesporial cell specification are poorly understood, but one gene, SPOROCYTELESS/NOZZLE (SPL/NZZ), functions directly downstream of the C class organ identity gene AG to promote sporogenesis (Schiefthaler et al., 1999; Yang et al., 1999b; Ito et al., 2004). SPL is a nuclear protein with distant homology to MADS box transcription factors that performs an unknown function (Schiefthaler et al., 1999; Yang et al., 1999,b). spl mutants differentiate microsporangial archesporial cells that divide once, but fail to form microsporocytes or tapetal cells, causing sterility (Schiefthaler et al., 1999; Yang et al., 1999b). The spl/nzz mutants are also female sterile due to nucellar defects, meaning that the archesporial cells do not differentiate and meiosis fails to initiate (Schiefthaler et al., 1999; Yang et al., 1999b). Ectopic activation of SPL in agamous mutants is sufficient to induce staminoid development and pollen formation (Ito et al., 2004). Together this indicates that SPL/NZZ performs an upstream meiotic function in both male and female development. Male sporocyte identity in A. thaliana is also regulated by the leucine-rich repeat (LRR) receptor kinase EXTRA SPOROGENOUS CELLS/EXCESS MICROSPOROCYTES1 (EXS/EMS1) in conjunction with its small protein ligand TAPETUM DETERMINANT1 (TPD1) (Yang et al., 1999,b, 2003; Canales et al., 2002; Zhao et al., 2002). The first archesporial cell division normally separates reproductive sporocyte fate from non-reproductive wall and tapetal fates. Microsporangial development is altered in exs/ems1 and tpd1 mutants such that sporogenous cells develop at the expense of tapetal cells (Yang et al., 1999,b, 2003; Canales et al., 2002; Zhao et al., 2002). This implies that EXS/EMS1 kinase signalling is important either to promote tapetal or to repress sporogenous cell identity. Although exs/ems1 mutations in A. thaliana show normal megasporangial development, mutations in the rice homologue MULTIPLE SPOROCYTES1 (MSP1) show supernumerary sporocytes in both the anther and ovule, as does the multiple archesporial cells1 (mac1) mutant in maize (Sheridan et al., 1996; Nonomura et al., 2003). Interestingly, additional LRR receptor kinases have also been implicated in proper differentiation of the anther cell layers (Albrecht et al., 2005; Colcombet et al., 2005; Mizuno et al., 2007; Hord et al., 2008). How these signalling processes are organized between the cell types within the developing anther is not yet clear. Future perspectives Developmental genetic studies in A. thaliana have significantly advanced our understanding of the context in which meoisis arises in plants. Future goals will be to tie together our understanding of the context, initiation, and progress of meiosis in diverse plant groups so that potential variation can be released to breeding. During plant diversification, genes that may have originally been involved in reproductive development have been co-opted to vegetative development pathways. Whilst KNOX/BEL proteins may trigger meiosis in a chlorophyte alga, their role is unknown in charophytes and most bryophytes and thus the point of functional diversification remains to be identified. The mechanisms for archesporial cell development are not yet fully characterized in flowering plants and are unknown in non-flowering plants. The derivation of nutritive tapetal tissues in different plant groups may or may not be independent of archesporial lineages. It will be interesting to test homology between archesporial and tapetal cell types by testing the function of SPL, EXS, and TPD1 homologues in different lineages. A detailed picture is emerging of the mechanisms that control plant meiotic chromosome pairing, synapsis, recombination, and segregation in A. thaliana. Understanding how these mechanisms integrate during progression of the meiotic cell cycle will be a major challenge. Equally, the pattern of CO hot- and cold-spots is complex and the mechanisms that determine plant CO frequency remain to be determined. Knowledge of these mechanisms may allow CO to be targeted during crop breeding and facilitate the generation of novel high-yielding agricultural strains. We thank the Royal Society and the Gatsby Charitable Foundation for funding, Karen Van Winkle-Swift, Vasily Kantsler, Li Zhang, Jenny Morris, and Dianne Edwards for photographs, and two anonymous reviewers for helpful comments on the manuscript. 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van Heerden, Philippus D. R.;Donaldson, Robin A.;Watt, Derek A.;Singels, Abraham

doi: 10.1093/jxb/erq144pmid: 20547566

Abstract Constant radiation use efficiency throughout the entire sugarcane crop cycle is often assumed for crop yield forecasting and management purposes. However, several examples are known where the linear relationship between cumulative intercepted radiation and biomass accumulation becomes uncoupled at some stage, with the latter declining by 21% in one reported case. This slowdown in growth is commonly referred to as the reduced growth phenomenon (RGP). In certain instances, this phenomenon appears to be related to the timing of crop initiation and harvesting. Summer-initiated sugarcane crops do not always resume expected growth rates after the transition from winter to spring, despite conditions being favourable for vigorous growth. Possible factors underlying the failure of sugarcane crops to realize full yield potential are reported and interrogated in this review. The potential involvement of lodging, flowering, and tiller mortality have been reviewed and the data suggest that, while such factors may contribute, they are unlikely to be the major causes of sugarcane RGPs. Similarly, reports indicate that temperature cannot account for reduced growth, as rates remain low despite the onset of favourable conditions in spring. In contrast, a decline in specific leaf nitrogen, potential initiation of sugar-mediated source–sink feedback inhibition of photosynthesis, and increased rates of maintenance respiration that occur during sugarcane development and maturation appear to be likely factors contributing to RGPs. An evaluation of areas of sugarcane biology and agronomy that would benefit from further research towards overcoming yield restriction imposed by reduced growth phenomena is provided. Feedback inhibition, flowering, lodging, photosynthesis, radiation use efficiency, respiration, specific leaf nitrogen, stalk death, sucrose accumulation Introduction Second-generation bioethanol production through the hydrolysis of lignocelluloses is highly desirable and the focus of much research and evaluation of suitable energy crop candidates (Waclawovsky et al., 2010). For the purpose of bioenergy production, a crop should be fast growing and high yielding, and crop energy output must exceed fossil fuel energy input. In terms of satisfying the criteria above, sugarcane is currently the most promising energy crop (Waclawovsky et al., 2010). However, despite its high yielding nature, the experimental maximum yield (212 t ha−1) for sugarcane (Waclawovsky et al., 2010) remains lower than those calculated from crop models (Monteith, 1977; Zhu et al., 2008). Under high input conditions, where water and nutrient availability for sugarcane (Saccharum spp hybrids) growth remains sufficient throughout the duration of the crop cycle, biomass accumulation is primarily driven by the amount of solar radiation intercepted by the green leaf canopy and the photosynthetic efficiency of conversion of intercepted radiation to dry matter (Robertson et al., 1996). The radiation use efficiency (RUE) of a crop can therefore be defined as the ratio of biomass accumulated to intercepted radiation (Monteith, 1977). Crop species differ in RUE (Sinclair and Muchow, 1999) but, despite these differences, many species sustain very consistent RUE values throughout the duration of the cropping season (Park et al., 2005). Crop models therefore often use constant values of potential RUE when simulating plant growth during the season. In sugarcane, constant RUE throughout the crop cycle is also often assumed. However, there are several examples where constant RUE in sugarcane crops is not achieved throughout the full crop cycle. In affected crops there is a distinct uncoupling of the linear relationship between cumulative intercepted radiation and biomass accumulation, with the latter declining well before the harvest date (Rostron, 1972; Sweet and Patel, 1985; Muchow et al., 1994; Robertson et al., 1996; Wood et al., 1996; Park et al., 2005; Donaldson et al., 2008). This slowdown in growth is commonly referred to as the reduced growth phenomenon (RGP). However, Lonsdale and Gosnell (1975), Sweet and Patel (1985), and Donaldson et al. (2008) reported another phenomenon in annual sugarcane crops started/harvested in summer, where the slowdown of growth starts with the onset of low temperatures during winter, but where the reduced growth then persists during the following spring despite temperature and water being favourable for resumption of vigorous crop growth. To distinguish this phenomenon, which is specific to summer-harvested crops, from RGP in general, it will be called the reduced spring growth phenomenon (RSGP) in this review. For the purpose of this review, RGP therefore cover all forms of growth slowdown, excluding RSGP. The objectives of this paper are to: (i) review existing knowledge about the occurrence and factors that could be involved in RGP and RSGP; and (ii) identify priorities for research which must be addressed to enhance mechanistic understanding of these phenomena, with particular emphasis on RSGP. Radiation interception, RUE, and biomass accumulation Before reviewing existing knowledge on RGP and RSGP, it is necessary to have some understanding of how interception of solar radiation drives biomass accumulation in sugarcane. The efficiency of crop growth is determined by the amount of solar radiation intercepted and its conversion into dry matter. In order to increase commercial sugarcane biomass yield it would be necessary to increase inputs (water, fertilizer, etc.) or ensure better use of available resources by the crop (Park et al., 2005). In well-irrigated crops, supplied with adequate nutrients, however, the amount of solar radiation intercepted and its conversion to dry matter (i.e. RUE) (Evensen et al., 1997) becomes the main driver of biomass accumulation. RUE of sugarcane is strongly dependent on temperature (Donaldson et al., 2008). Over a range of crop species it was found that under good growing conditions ∼1.4 g of dry matter was accumulated for every mega joule (MJ) of solar radiation intercepted (Monteith, 1977). Leaf quantum efficiency (moles of photosynthetically active radiation required to fix 1 mole of CO2) and maximum leaf photosynthetic rate have a large impact on RUE values (Sinclair and Muchow, 1999). Because of the higher efficiency of the C4 photosynthetic pathway, it is not surprising that C4 species such as maize and sugarcane generally have higher RUE values than C3 species (Murata, 1981; Sinclair and Muchow, 1999). For example, in sugarcane, RUE values of between 1.7 g MJ−1 (Robertson et al., 1996) and 2 g MJ−1 (Muchow et al., 1997) are quoted in the literature, with the higher values from crops where all trash was recovered and included in the calculation of total above-ground biomass. Typically, for the calculation of maximum RUE, cumulative intercepted radiation (MJ m−2) over the duration of the cropping season is plotted against above-ground biomass (kg m−2) and a linear regression fitted to all the data points (e.g. Park et al., 2005). The slope of the regression is then taken as the maximum RUE for the particular crop. As mentioned previously, many plant species sustain a very consistent RUE value during the entire growing season. This is also the case in many sugarcane crops, as indicated by the example shown in Fig. 1. As a consequence, a constant value for RUE is often used in models of sugarcane growth such as APSIM (Keating et al., 2003) and Canegro v1 (Singels and Bezuidenhout, 2002). With the relatively high values of maximum RUE (Robertson et al., 1996; Muchow et al., 1997), sugarcane is regarded as one of the most productive crop species in terms of biomass accumulation. However, maximum yields can only be achieved if maximum RUE values are maintained throughout the growing season up to harvest. Fig. 1. View largeDownload slide The relationship between aerial biomass and cumulative radiation intercepted in sugarcane crops where maximum RUE is being maintained throughout the full cropping cycle. The maximum RUE value can be calculated from the linear regression fitted to the data points. Data points represent averages of seven cultivars (redrawn from data in Donaldson et al., 2008 with permission). Fig. 1. View largeDownload slide The relationship between aerial biomass and cumulative radiation intercepted in sugarcane crops where maximum RUE is being maintained throughout the full cropping cycle. The maximum RUE value can be calculated from the linear regression fitted to the data points. Data points represent averages of seven cultivars (redrawn from data in Donaldson et al., 2008 with permission). RGP in sugarcane There are several studies, however, that showed that RUE is not constant during the full cropping cycle in sugarcane (Muchow et al., 1994, 1997; Robertson et al., 1996; Wood et al., 1996; Evensen et al., 1997; Park et al., 2005; Donaldson et al., 2008). In these crops, growth slows down well before harvest even though the main growth factors such as soil water availability, nutrient status, and temperature are all regarded as favourable for vigorous crop growth. This slowdown in growth is known as RGP, which lowers biomass accumulation to levels well below the full potential of the particular crop. In crops grown on a 2-year cutting cycle, RGP is often observed during the second year of crop growth when the cane is already relatively mature (Evensen et al., 1997; Muchow et al., 1997; Park et al., 2005). The implication of this is that the observed high rate of biomass accumulation in some crops during the first year of growth is not necessarily continued into the second year. However, in addition to crops on a 2-year cutting cycle, there are several examples of annual crops where RGPs were also observed (Rostron, 1972; Robertson et al., 1996; Wood et al., 1996; Park et al., 2005). This implies that RGP is not only restricted to mature crops but can also occur when the cane is still immature, causing these annual crops not to realize their full yield potential. The distinct uncoupling of the linear relationship between cumulative intercepted radiation and biomass accumulation that occurs in these crops is illustrated in Fig. 2 using idealized data. Fig. 2. View largeDownload slide Idealized (theoretical) data showing the relationship between aerial biomass and cumulative radiation intercepted in sugarcane crops experiencing RGP or RSGP. The slowdown in biomass accumulation (deviation from linear regression) from the maximum (circles) with the onset of RGP or RSGP is indicated by the square symbols. Fig. 2. View largeDownload slide Idealized (theoretical) data showing the relationship between aerial biomass and cumulative radiation intercepted in sugarcane crops experiencing RGP or RSGP. The slowdown in biomass accumulation (deviation from linear regression) from the maximum (circles) with the onset of RGP or RSGP is indicated by the square symbols. In a comprehensive study by Park et al. (2005) the extent of RGP within the Australian sugar industry, and its possible impact on biomass yields in affected crops, was investigated. Those authors analysed crop growth data obtained from 34 data sets on five different sugarcane cultivars over a wide range of locations and with different crop start and harvest dates. The conclusion was that RGP was observed in ∼50% of the crops studied, suggesting that the phenomenon is frequently experienced in Australia. The impact of RGP on yield was also estimated and it was found that in crops that experienced RGP, final yields were on average 21% lower than potential yields estimated by assuming constant RUE. In affected crops, RUE remained constant (i.e. a linear relationship between aerial biomass and cumulative radiation intercepted) during the initial period of rapid growth that lasted anything from 223 d to 665 d after planting/harvest. During this rapid growth phase, RUE reached values as high as 1.81 g MJ−1, but a slowdown in growth occurred thereafter and RUE dropped to values not exceeding 0.68 g MJ−1 in any of the affected crops (Park et al., 2005). In South Africa, slowdown of growth in certain crops of the cultivar NCo376 was already reported as early as 1972 (Rostron, 1972). RSGP in sugarcane Donaldson et al. (2008) recently showed in a number of different cultivars grown in South Africa that biomass yields in annual crops started in December (summer) were reduced by between 12% and 62% compared with crops started in July (winter). The average relationship between aerial biomass and cumulative radiation intercepted for seven of these cultivars for both start dates showed that, in the crops started in June, the linear relationship between aerial biomass and cumulative radiation intercepted was maintained for the full 12-month cropping cycle (Fig. 3). In the crops started in December, however, the relationship was initially also linear but then a distinct uncoupling of the relationship, indicative of the onset of RGP, occurred. Further analysis of the data revealed that in those crops vigorous growth was not resumed in spring following the normal growth slowdown that occurred during the winter, despite the fact that these crops continued to intercept high levels of solar radiation. This particular phenomenon, which occurs in annually harvested crops started in summer, is defined here as RSGP instead of RGP. Interestingly, as mentioned above, the severity of RSGP differed between cultivars, with some experiencing mild or intermediate biomass yield reductions while others were more severely affected. Examples of these different responses are shown in Fig. 4. Other researchers in South Africa also observed RSGP in sugarcane crops started in the summer (Rostron, 1972; Lonsdale and Gosnell, 1975; Sweet and Patel, 1985). These summer crops consistently had lower yields than crops started at other times of the year. In Australia it was also shown that crops with a November/December (summer) start date had significantly lower cane yields than those with a start date at all other start times (McDonald, 2006). Fig. 3. View largeDownload slide A comparison of the relationship between aerial biomass and cumulative radiation intercepted in annually harvested sugarcane crops started during June (winter, filled circles) or December (summer, open circles). Data points represent averages of seven cultivars. In crops started in June, maximum RUE is maintained as indicated by the linear regression, but, in crops started in December, the slowdown in biomass accumulation is clearly evident (redrawn from data in Donaldson et al., 2008 with permission). Fig. 3. View largeDownload slide A comparison of the relationship between aerial biomass and cumulative radiation intercepted in annually harvested sugarcane crops started during June (winter, filled circles) or December (summer, open circles). Data points represent averages of seven cultivars. In crops started in June, maximum RUE is maintained as indicated by the linear regression, but, in crops started in December, the slowdown in biomass accumulation is clearly evident (redrawn from data in Donaldson et al., 2008 with permission). Fig. 4. View largeDownload slide A comparison of the relationship between aerial biomass and cumulative radiation intercepted in three different annually harvested sugarcane cultivars (N12, N14, and N21) started during June (winter, filled circles) or December (summer, open circles). In crops started in June, maximum RUE is maintained as indicated by the linear regression, but, in crops started in December, the slowdown in biomass accumulation, the extent of which depends on variety, is clearly evident (redrawn from data in Donaldson et al., 2008 with permission). Fig. 4. View largeDownload slide A comparison of the relationship between aerial biomass and cumulative radiation intercepted in three different annually harvested sugarcane cultivars (N12, N14, and N21) started during June (winter, filled circles) or December (summer, open circles). In crops started in June, maximum RUE is maintained as indicated by the linear regression, but, in crops started in December, the slowdown in biomass accumulation, the extent of which depends on variety, is clearly evident (redrawn from data in Donaldson et al., 2008 with permission). Based on available evidence it would therefore appear that the growth of well-irrigated sugarcane crops supplied with adequate nutrition can be reduced by factors that affect both young and older high-yielding crops. In addition there appear to be factors that in particular affect crops younger than 365 d started in summer. In the following sections the possible factors that could be contributing to RGP and RSGP are discussed. Possible factors contributing to RGP and RSGP in sugarcane Lodging Loss of crop erectness due to lodging has serious consequences for sugarcane crop quality and yield (Berding and Hurney, 2005). By maintaining an erect crop using bamboo scaffolding, Singh et al. (2002) achieved up to 15% higher cane yields compared with treatments where the crop was free to lodge. Lodging causes smothering and breakage of cane stalks, which enables entry of microflora into the stalks. These factors often result in stalk death, which will reduce biomass accumulation leading up to harvest. It is quite possible, therefore, that lodging could be a factor involved in RGP. Robertson et al. (1996) observed the appearance of substantial dead millable stalks at ∼60 d after lodging. Loss of live millable stalks as a result of stalk death caused by lodging will result in poor use of intercepted radiation. In the comprehensive study conducted by Park et al. (2005), the onset of lodging in relation to the commencement of RGP was determined. Those authors found that lodging and RGP co-occurred in 16 of the 34 trials used in their analysis. On average, lodging occurred 81 d before the onset of RGP. However, in three of these 16 trials, lodging only occurred after the onset of RGP. In the data set of 34 trials there were also nine trials where no RGP occurred despite the occurrence of lodging, as well as one trial where RGP occurred without the occurrence of lodging. Examples of some of the scenarios given above are shown in Table 1. For example, the first row in Table 1 is an example of where lodging preceded the onset of RGP by 63 d and where actual biomass at harvest was 87% of the potential biomass that could have been achieved had RGP not occurred. However, the third row in Table 1 presents an example of a crop where actual biomass at harvest was 77% of the potential biomass, but in this case lodging followed the onset of RGP by 42 d. The inconsistency between the onset or presence of lodging and the onset or presence of RGP makes it difficult to determine how much of the reduction in growth that occurred after the onset of RGP can be ascribed to lodging per se. Table 1. Crop age at onset of RGP and lodging (LOD) and actual biomass obtained at harvest expressed as a percentage (in parenthesis) of potential biomass (data from Park et al., 2005) Occurrence of lodging/RGP  Crop age at onset of RGP (d)  Crop age at onset of lodging (d)  Time of lodging relative to onset of RGP (d)  Potential biomass at harvest (g m−2) (actual biomass at harvest as % of potential)  + LOD/+ RGP  245  182  –63  6720 (87)  + LOD/+ RGP  304  270  –34  8375 (81)  + LOD/+ RGP  223  265  +42  4779 (77)  + LOD/+ RGP  223  251  +28  4468 (90)  + LOD/– RGP  a  222  a  8809 (100)  + LOD/– RGP  a  220  a  5824 (100)  Occurrence of lodging/RGP  Crop age at onset of RGP (d)  Crop age at onset of lodging (d)  Time of lodging relative to onset of RGP (d)  Potential biomass at harvest (g m−2) (actual biomass at harvest as % of potential)  + LOD/+ RGP  245  182  –63  6720 (87)  + LOD/+ RGP  304  270  –34  8375 (81)  + LOD/+ RGP  223  265  +42  4779 (77)  + LOD/+ RGP  223  251  +28  4468 (90)  + LOD/– RGP  a  222  a  8809 (100)  + LOD/– RGP  a  220  a  5824 (100)  In the first column +/– symbols, respectively, denote the presence or absence of lodging or RGP. The time of lodging relative to the onset of RGP is also provided, with positive and negative values indicating the number of days that lodging followed or preceded RGP, respectively. a Examples where RGP did not occur. View Large In the analysis conducted by Donaldson et al. (2008), only data sets where no lodging occurred were used, and the presence of RSGP in December-start crops (see Figs 3, 4) can therefore not be ascribed to lodging. In other crops, lodging was generally light and occurred both early and late in the season and could therefore also not explain the poor yield performance due to RSGP that occurred in crops started in summer (Sweet and Patel, 1985). Rostron (1972), however, found that the poor yield performance in crops with a December start was caused by lodging that occurred during September when the crop was 40 weeks old. Hence, depending on the extent and duration of lodging, it is certainly a factor that could induce RGP and RSGP in certain sugarcane crops. Flowering Flowering in sugarcane is an important factor that negatively affects cane yield and quality (Berding and Hurney, 2005). Flowering places an upper limit on crop growth because it terminates the production of phytomeres at the apex of the main axis (culm), causing poor use of intercepted radiation in terms of biomass accumulation. Thus, it is potentially feasible that flowering could be involved in RGP and RSGP. However, Sweet and Patel (1985) did not find a good correlation between the intensity of flowering and the occurrence of RSGP. For example, RSGP also occurred during years when flowering was light. Rostron (1972) also noted the occurrence of RGP under good growing conditions in the absence of flowering. In several of the crops where Donaldson et al. (2008) noted the occurrence of RSGP, flowering did not occur. Park et al. (2005) documented RGP in numerous trials but in none of these did flowering occur to any great extent (<0.1% of flower stalks). The widespread involvement of flowering in the occurrence of RGP and RSGP therefore remains questionable. Stalk death In both plant and ratoon crops, a clear pattern of stalk population over the growing season is observed. In sugarcane crops, stalk numbers typically increase during the earlier part of growth up to a certain peak population followed by a steady decline over the remainder of the growing season (Inman-Bamber, 1994; Robertson et al., 1996; Bell and Garside, 2005). Robertson et al. (1996) observed that the slowdown in biomass accumulation during RGP was caused by a decline in the number of live millable stalks due to accelerated rates of stalk death, which cancelled out biomass accumulation through the increase in the mass of the remaining live millable stalks. Those authors concluded that RGP would not have occurred if there had been no stalk death in the period close to harvesting. In other experiments conducted in Australia and Hawaii, the involvement of stalk death in the cessation of biomass accumulation was also demonstrated (Muchow et al., 1994, 1996). The causes of stalk death were related to intra- and interplant competition, smothering or mechanical damage caused by lodging, as well as damage caused by pests. As discussed above, lodging often preceded the onset of RGP (e.g. Park et al., 2005), and at least also in December-started crops in the RSGP observations made by Rostron (1972). In the experiments conducted by Robertson et al. (1996), the appearance of substantial numbers of dead millable stalks was noted ∼60 d after lodging occurred, suggesting that lodging triggered the onset of RGP in these experiments through acceleration of millable stalk death. Muchow et al. (1997) also concluded that the final yield of crops grown for longer than 12 months depended to a large extent on the degree of stalk death, which was triggered by lodging. However, Park et al. (2005) noted the onset of RGP in crops where live millable stalk numbers remained constant. This finding, and the absence of lodging in several experiments where RGP and RSGP were noted, suggests that stalk death due to lodging or some other factors cannot explain the occurrence of these phenomena in all cases. Temperature effects The slowing down of biomass accumulation rates in sugarcane with the onset of cooler winter temperatures is well known in sugarcane (Rostron, 1971; Singels et al., 2005; Donaldson et al., 2008). This forms an important component of the natural ripening process in sugarcane stalks, because at lower temperatures more sucrose is stored in the stalk due to a reduction in sink strength related to structural growth (see also below). However, upon resumption of warmer temperatures in spring, vigorous crop growth should again resume, but in crops affected by RSGP this is delayed (Donaldson et al., 2008), indicating that low temperatures per se are not the cause of RSGP. Robertson et al. (1996) also found that cessation of biomass accumulation during RGP already commenced before mean daily temperature became limiting for crop growth and that biomass accumulation did not increase following the onset of warmer temperatures. By careful analysis of extensive data sets, Park et al. (2005) demonstrated that in only four of 17 field trials displaying RGP did temperature appear to be involved in the phenomenon. Those authors concluded that temperature appeared not to have played a substantial role in the reduction in RUE during RGP or that its role might have been masked by some other factors. Specific leaf nitrogen (SLN) When soil water is not a limiting factor, SLN content (g N m−2) is an important determinant of leaf photosynthetic rate and, therefore, of RUE (Sinclair and Horie, 1989). Previous studies have shown that most of the N taken up by sugarcane occurs during the first 6 months of crop growth (Haslam and Allison, 1985; Thompson, 1988b). The young crop is capable of storing considerable amounts of N for use during subsequent growth. As a consequence, there is typically a progressive decrease in SLN with sugarcane crop age (Hartt and Burr, 1965; Allison et al., 1997; Park et al., 2005). Although it is commonly believed that low plant N close to harvest is necessary to increase sucrose content, it is entirely possible that SLN below a certain threshold may limit CO2 assimilation capacity and therefore biomass accumulation. Hartt and Burr (1965) demonstrated a striking correlation between leaf N content and CO2 assimilation rate in sugarcane. They observed large decreases in leaf N and CO2 assimilation rates in sugarcane crops between 6 and 12 months of age. In another study it was shown that a linear relationship between SLN and CO2 assimilation rate existed in sugarcane within the range of 1–1.7 g N m−2, and model simulations indicated further that this restriction of photosynthesis could reach sizable proportions later during the crop life cycle (Allison et al., 1997). An interesting observation in sugarcane is that, as the crop ages, the photosynthetic capacity of successive youngest fully expanded leaves becomes progressively lower (Hartt and Burr, 1965) (Fig. 5). Since the decrease in SLN with crop age appears to be a phenomenon in all leaf ranks within the green leaf canopy (Allison et al., 1997), the progressive decline in photosynthetic capacity of young fully expanded leaves could, at least in part (see below), be caused by low SLN. Sinclair and Horie (1989) showed that when SLN decreased to below a certain threshold, RUE became very sensitive to changes in N due to the depression of photosynthetic capacity. In maize, the threshold for SLN below which RUE is reduced is regarded as 1.2 g N m−2 (Sinclair and Horie, 1989). In sugarcane, Park et al. (2005) assumed the same threshold and found that the mean SLN at the onset of RGP was 1.2 g N m−2 and that the mean values of SLN across all experiments were significantly greater before than after the onset of RGP. The reduction in SLN after the onset of RGP was also not compensated for by an increase in leaf area index, and those authors concluded that the decline in SLN with crop age is associated with the occurrence of RGP. As mentioned previously, Allison et al. (1997) demonstrated a linear relationship between SLN and CO2 assimilation rate in sugarcane within the range of 1–1.7 g N m−2 and found that SLN varied between 0.8 g N m−2 and 1.2 g N m−2 from the bottom to the top of the green leaf canopy at a crop age of only 45 weeks. These values are at or below the threshold of 1.2 g N m−2 regarded as limiting RUE in maize (Sinclair and Horie, 1989). Hence, SLN must be regarded as an important factor that could contribute to the occurrence of RGP and RSGP. Fig. 5. View largeDownload slide Changes in photosynthetic capacity of the youngest fully expanded (top visible dewlap) leaves of sugarcane with crop age. Light-saturated rates of photosynthesis (Asat) are expressed as a percentage of the photosynthetic rate measured in leaves of 2-month-old plants (100%). Data points represent values recalculated from data shown in Hartt and Burr (1965). Fig. 5. View largeDownload slide Changes in photosynthetic capacity of the youngest fully expanded (top visible dewlap) leaves of sugarcane with crop age. Light-saturated rates of photosynthesis (Asat) are expressed as a percentage of the photosynthetic rate measured in leaves of 2-month-old plants (100%). Data points represent values recalculated from data shown in Hartt and Burr (1965). An interesting possibility arises when the irrigated December and June crops (Fig. 3) in Donaldson et al. (2008) are compared in terms of crop age at the onset of warmer temperatures in spring, when fast rates of biomass accumulation would normally resume. In crops started in December, the age of the plants at the onset of the next spring was ∼8 months compared with June crops that were only 2 months old at the same time. In crops which are 8 months old, light-saturated rates of photosynthesis of young fully expanded leaves could already have been more than 40% lower than the same leaves in 2-month-old crops (Fig. 5). Therefore, the principle source leaves of crops started in December should have a much lower photosynthetic capacity, limiting exploitation of the favourable spring growth conditions, predisposing these crops to RSGP. Interestingly, based on available evidence, it appears that the decrease in SLN and photosynthetic capacity with crop age cannot be prevented by higher N application rates and that the reduction in biomass accumulation because of RGP and RSGP is not due to insufficient rates of N fertilizer applied (Allison et al., 1997; Park et al., 2005). Feedback inhibition of photosynthesis by high sugar content Natural ripening in irrigated sugarcane during winter is primarily driven by low temperature, which causes a reduction in sink strength related to structural growth (Singels et al., 2005; Donaldson, 2009). Under these conditions, a larger proportion of the sucrose formed during photosynthesis is stored in existing stalk tissue. With the onset of warmer spring temperatures, however, vigorous crop growth normally resumes, causing a decline in stalk sucrose content on a fresh mass basis. Besides this well-known variation in stalk sucrose content on an annual cycle (Lonsdale and Gosnell, 1976; Sweet and Patel, 1985), stalk sucrose content generally also increases with crop age (Rostron, 1971; Donaldson, 2009). The possibility of high foliar sugar content in sugarcane being involved in the feedback inhibition of photosynthesis has been recognised for many years already (Hartt and Burr, 1965). Those authors suggested that high sucrose content in a leaf blade will lower the photosynthetic capacity of that blade and that translocation of sucrose out of leaves could be of great importance in maintaining high photosynthetic rates. Interestingly, it has been shown that Saccharum spontaneum L., a low sucrose accumulator, has a 30% higher photosynthetic rate than higher sucrose accumulating commercial Saccharum spp. hybrids (Irvine, 1975). Feedback regulation of sucrose metabolism through accumulation of photoassimilates in source leaves has been demonstrated in several other plant species including spinach, tobacco, and potato (Krapp et al., 1993; Krapp and Stitt, 1995). High sucrose content in the stalk may result in changes in the rate of phloem loading (Lalonde et al., 2003) leading to altered sugar levels in the leaves. Besides the lowering of photosynthetic rates, it is also known that high sucrose levels in leaves can, in principle, induce premature leaf senescence (van Doorn, 2008, and references therein). Recent work on sugarcane suggested that it is not foliar sucrose content per se that exerts feedback regulatory control over photosynthesis, but rather foliar hexose content (McCormick et al., 2006). Those authors demonstrated a strong negative correlation between foliar hexose content and apparent carboxylation efficiency and CO2-saturated rates of photosynthesis, and suggested that changes in the foliar glucose pool could act as a signal. By employing a cold-girdling technique it was demonstrated that accumulation of sucrose and hexoses occurred in the leaves and that this was followed by a decline in photosynthetic capacity in affected leaves (McCormick et al., 2008a). Those authors also demonstrated a negative correlation between hexokinase expression and photosynthetic rates in sugarcane leaves (McCormick et al., 2008b), lending further support to the idea that glucose may constitute a signal that regulates photosynthetic activity in sugarcane as in some other plant species (Krapp et al., 1991; Kilb et al., 1995; Roitsch et al., 1995). In the cases where RGP was observed during the second year of crop growth in already relatively mature crops (Evensen et al., 1997; Muchow et al., 1997; Park et al., 2005) it is therefore possible that high stalk sucrose content could have resulted in feedback inhibition of photosynthesis, which in turn could have contributed towards the characteristic decline in biomass accumulation. Also in annual crops that experienced RSGP (Rostron, 1972; Lonsdale and Gosnell, 1975; Sweet and Patel, 1985; Donaldson et al., 2008), where reduced growth was observed following the winter, high stalk sucrose content due to natural winter ripening could have been an important factor involved in the suppression of biomass accumulation through down-regulation of photosynthesis. At this point it would again be useful to compare the irrigated December and June crops (Fig. 3) in Donaldson et al. (2008). In the crops started in December, the developmental stage of the plants at the onset of winter would have been such that substantial stalk tissue would have already been formed to allow for winter ripening. In the crops started in June, however, no stalks would have been formed, which would have prevented winter ripening. In December crops suffering from RSGP, the inability to resume fast rates of biomass accumulation during spring could therefore be caused through the negative feedback regulation of photosynthesis associated with high sugar accumulation during the previous winter. Respiration In higher plants, substantial amounts of photoassimilates produced each day are consumed by respiration during the same time period (van der Werf et al., 1992). A distinction can be made between respiration required for plant growth (Rg) and that required for maintenance (Rm). Respiration within all living cells involves an Rm component, whereas Rg is specific to cells undergoing division during the process of biomass production (Hesketh et al., 1971). Glover (1972) showed that the respiration rate of sugarcane depends on both biomass attained and ambient temperature, with larger crops having a larger Rm component (required to maintain the higher biomass). Thompson (1988a, b) showed that a first ratoon crop of cultivar N14 initially accumulated above-ground biomass quicker than a plant crop. However, this advantage diminished with crop age so that both crops yielded similarly at harvest. The effects of higher respiration losses in the ratoon crop were proposed as at least a partial explanation for this phenomenon (Thompson, 1988a). As biomass increases with crop age, the proportion of respiring to photosynthesizing tissue increases, causing a gradual decline in net productivity (Rostron, 1972). When the irrigated December and June crops (Fig. 3) in Donaldson et al. (2008) are compared in terms of biomass attained at the onset of warmer temperatures in spring, the following possibility arises. In December crops, where a much higher biomass would have been attained compared with June crops, a higher demand for photoassimilates towards Rm would exist at the onset of spring, while the warmer ambient temperatures per se would further favour higher respiration rates. High respiratory demand could thus exacerbate low carbon availability for allocation towards new structural growth in crops suffering from RSGP. In cases where RSGP might involve negative feedback regulation of photosynthesis associated with high sugar accumulation during the previous winter, high sugar content could also favour higher respiration rates, since positive correlations between carbohydrate content and respiration rate have been demonstrated in several other plant species (Fondy and Geiger, 1982; Stitt et al., 1990; Averril and ap Rees, 1995). Perspectives for further research on RGP and RSGP With its high values of maximum RUE, sugarcane is a promising candidate as a sustainable bio-ethanol source. For this purpose, above-ground biomass production will become increasingly important for the generation of an energy cane (Waclawovsky et al., 2010). However, this paper reviewed available evidence which shows that RUE is not constant during the full cropping cycle in sugarcane and that in many crops biomass accumulation slows down well before harvest due to phenomena that have been defined as RGP and RSGP. Park et al. (2005) and Donaldson et al. (2008) estimated substantial losses in biomass production in sugarcane crops affected by RGP and RSGP. As such a better understanding of the factors that could be involved in RGP and RSGP is crucial. In this review several factors that could be involved in the induction of RGP and RSGP were considered. The four main factors that emerged were lodging with or without associated stalk death, low SLN, feedback inhibition of photosynthesis by high sugar content in mature or winter-ripened sugarcane, and high respiratory demand. There are numerous examples where it could be shown that lodging and lodging-induced stalk death occurred before the onset of especially RGP. It therefore follows that research towards better crop erectness could go a long way towards limiting RGP in high-yielding sugarcane crops. Berding and Hurney (2005) proposed that the solution to economic loss from lodging will most probably come from breeding efforts coupled to appropriate cultivation practices. An exciting possibility could also be the use of plant growth regulators, such as Moddus (Trinexapac-ethyl), to modify plant growth to lower the risk of lodging. Plant growth regulators have been used effectively in cereals to reduce the incidence of lodging (Rajala et al., 2002; Ramburan and Greenfield, 2007). In Brazil a substantial amount of work has indicated that sugarcane ratoons treated with Moddus have a larger and more extensive root system than untreated controls (Resende et al., 2000), emphasizing the need for detailed research on the effects of Moddus and other plant growth regulators on lodging in sugarcane. However, there are also several examples where RGP and RSGP occurred in the absence of lodging or where lodging only occurred after the onset of reduced biomass accumulation. In these cases low SLN, feedback inhibition of photosynthesis by high sugar content, and high maintenance respiration rates could be key factors involved. Our understanding of the exact mechanisms involved in negative feedback between source and sink in sugarcane is still very limited, and the possibility for uncoupling the signalling pathways involved through transgenic approaches, which may lead to sustained higher photosynthetic rates even at high sugar levels, should be investigated further (McCormick et al., 2009). An even higher level of complexity needs to be considered in the form of the intimate link that exists between carbon and nitrogen metabolism in leaves. For example, it has been shown that low N status sensitizes leaves to high glucose concentrations, causing, for example, accelerated senescence rates (Wingler et al., 2004). A scenario that needs to be investigated is the possibility that SLN below a certain threshold might increase the negative feedback regulation that high sugar levels potentially exert on photosynthesis. High respiration rates under conditions of elevated sugar concentrations could further lower carbon availability towards new structural growth. The possibility of sugarcane cultivars with greater ability to take up N later during the cropping cycle, or with higher N allocation towards the green leaf canopy, which could help to maintain SLN at higher levels in more mature crops, could be explored. Donaldson et al. (2008) reported substantial varietal differences in the extent of RSGP (Fig. 4), indicating that considerable scope could exist to reduce the impacts of RGP and RSGP once our understanding of the mechanisms involved are more advanced. The way in which these mechanisms (low SLN, feedback inhibition of photosynthesis, and high respiration rates) could potentially interact in manifesting reduced growth phenomena is depicted in a hypothetical scheme (Fig. 6). Crop simulation modelling could be helpful in investigating the plausibility of these mechanisms. These mechanisms can be represented by reasonably simple algorithms, which could be incorporated into existing process-based sugarcane crop models. Algorithms can then be activated or deactivated, individually or simultaneously, and simulations compared with growth data from experiments where RGPs were observed or not. This should shed more light on the plausibility of these mechanisms and the way in which they interact. The Canegro model (Inman-Bamber, 1991; Singels and Bezuidenhout, 2002) is a good candidate for such an exercise. It simulates two of the key processes involved in RGP, namely net photosynthesis (as a function of radiation, temperature, and water status; Singels et al., 2005) and maintenance respiration (as a function of total biomass and temperature; Singels et al., 2005). It also simulates the processes of biomass partitioning and sucrose accumulation, and can therefore be used to investigate the plausibility of negative feedback of high stalk sucrose content on photosynthesis. The mechanism of low SLN causing RGP through reduced photosynthesis could possibly be tested with an algorithm that uses an empirical equation that estimates leaf N level from crop age, hence circumventing the need for a full N submodel. In this review use was made of a growing body of evidence, all based on biomass accumulation and RUE measurements, which supports the existence of RGP and RSGP under field conditions. Yet, to the best of our knowledge, no analogous studies focusing on the physiological processes, and the mechanistic basis underpinning varietal differences in RGP and RSGP, are available. This review provides direction for such physiological studies, which will be important in attempts to overcome sugarcane yield restrictions due to reduced growth phenomena. Fig. 6. View largeDownload slide A simple Forrester diagram (Forrester, 1961) to illustrate the interconnections between driving factors, rate processes, and plant component states that could possibly explain the reduced growth phenomenon in sugarcane. Mass flow: the process of photosynthesis produces sugar mass that is consumed by the processes of maintenance respiration and structural growth. The latter builds plant structural mass. Information flow: the driving factors solar radiation, air temperature, and leaf nitrogen affect processes (positive effect—increased level of driving factor results in increased rate). Sugar and plant structural mass states affect process rates negatively (negative feedback on photosynthesis—increased level results in reduced photosynthetic rates) or positively (positive feedback on maintenance respiration). The driving factor leaf nitrogen also affects the feedback signal from sugar mass to photosynthesis rate positively (i.e. increased negative feedback on photosynthesis) if leaf nitrogen falls below a certain threshold. Fig. 6. View largeDownload slide A simple Forrester diagram (Forrester, 1961) to illustrate the interconnections between driving factors, rate processes, and plant component states that could possibly explain the reduced growth phenomenon in sugarcane. Mass flow: the process of photosynthesis produces sugar mass that is consumed by the processes of maintenance respiration and structural growth. The latter builds plant structural mass. Information flow: the driving factors solar radiation, air temperature, and leaf nitrogen affect processes (positive effect—increased level of driving factor results in increased rate). Sugar and plant structural mass states affect process rates negatively (negative feedback on photosynthesis—increased level results in reduced photosynthetic rates) or positively (positive feedback on maintenance respiration). The driving factor leaf nitrogen also affects the feedback signal from sugar mass to photosynthesis rate positively (i.e. increased negative feedback on photosynthesis) if leaf nitrogen falls below a certain threshold. Abbreviations Abbreviations APSIM agricultural production systems simulator RGP reduced growth phenomenon RSGP reduced spring growth phenomenon RUE radiation use efficiency SLN specific leaf nitrogen The authors acknowledge Dr Geoff Inman-Bamber (CSIRO Sustainable Ecosystems, Davies Laboratory, Townsville, Australia) for his critical reading of the manuscript. 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Stamm, Petra;Kumar, Prakash P.

doi: 10.1093/jxb/erq147pmid: 20501746

Abstract In contrast to animals, plants maintain highly plastic growth and development throughout their life, which enables them to adapt to environmental fluctuations. Phytohormones coordinately regulate these adaptations by integrating environmental inputs into a complex signalling network. In this review, the focus is on the rapid elongation that occurs in response to canopy shading or submergence, and current knowledge and recent advances in deciphering the network of phytohormone signalling that regulates this response are explored. The review concentrates on the involvement of the phytohormones auxins, gibberellins, cytokinins, and ethylene. Despite the occurrence of considerable gaps in current understanding of the underlying molecular mechanisms, it was possible to identify a network of phytohormone signalling intermediates at multiple levels that regulates elongation growth in response to canopy shade or submergence. Based on the observations that there are spatial and temporal differences in the interactions of phytohormones, the importance of more integrative approaches for future studies is highlighted. Auxins, cytokinins, ethylene, gibberellins, phytochromes, phytohormone cross-talk, phytohormone signalling network, shade avoidance, stem elongation, submergence Introduction Plants retain a high level of growth plasticity throughout their life in order to adapt to and survive unfavourable and unexpected fluctuations in their environment. This adaptability is achieved by integrating various complex environmental as well as developmental signals. Plant hormones, or phytohormones, which are a collection of structurally unrelated small molecules, serve as integrators of those exogenous (environmental) and endogenous (developmental) cues. The main classes of phytohormones are auxins, cytokinins, gibberellins, ethylene, (+)-abscisic acid, brassinosteroids, and jasmonates, with more substances being added to the list periodically. Phytohormones regulate every aspect of plant growth and development—from the determination of the stem cell niches during embryogenesis (reviewed in Wolters and Jürgens, 2009) to organogenesis and growth during post-embryonic development. During the past two decades various genes encoding key players for biosynthesis, perception, and signalling of phytohormones have been identified for most classical phytohormones (e.g. Teale et al., 2006; Hirayama and Shinozaki, 2007; To and Kieber, 2008; Schwechheimer and Willige, 2009; Yoo et al., 2009). Also, interesting similarities between the signalling cascades of different phytohormones have been identified (McSteen and Zhao, 2008). The ubiquitin–proteasome pathway, in particular, seems to play a key role in many phytohormone signalling pathways (reviewed in Santner and Estelle, 2009). Furthermore, several signalling intermediates have been identified as common to the signalling pathways of at least two different phytohormones, which has fuelled considerable research interest in phytohormone interactions (e.g. Benková and Hejátko, 2009; Santner and Estelle, 2009; Wolters and Jürgens, 2009; Yoo et al., 2009). The notion that phytohormones interact at some level in order to integrate the vast number of external as well as internal inputs into a growth response is not new. Phytohormones, in contrast to the animal hormones, are known to have pleiotropic effects, which is mirrored by the fact that many mutations affecting the level or signalling of phytohormones produce overlapping phenotypes. In fact, many phytohormone signalling mutants show a defect in the response to at least one other phytohormone (a list for auxin mutants is given in Swarup et al., 2002). Comparing changes in the transcriptome of Arabidopsis seedlings upon treatment with various phytohormones, Nemhauser et al. (2006) identified a surprisingly small number of genes that appear to be coordinately regulated by more than one phytohormone. This led the authors to reject the idea of the occurrence of a common set of genes acting as a central plant growth module. The phytohormones tested in this study, namely abscisic acid, gibberellin, auxin, ethylene, cytokinin, brassinosteroid, and jasmonate, thus appear to regulate similar processes through the activation of different sets of genes. This corresponds to observations of certain phytohormone responses being independent of other phytohormones, and the fact that a total deficiency in one phytohormone cannot be fully rescued by the application of any other phytohormone. In the attempt also to identify robust target genes of each phytohormone tested, Nemhauser et al. (2006) were able to assemble lists of high confidence, unique targets for each phytohormone. Interestingly, this was not the case for gibberellins; no such robust targets could be identified. This allows the hypothesis that this class of phytohormones might predominantly act through the interaction with other phytohormone pathways. Furthermore, the authors also showed that every phytohormone regulates metabolic genes of at least one other phytohormone. Nevertheless, it has to be taken into consideration that the data set of Nemhauser et al. (2006) was derived from phytohormone-treated whole seedlings. Spatial and temporal specificities of phytohormone action and interaction were thus not factored into this study. Furthermore, target genes of phytohormones at different stages of development, for example during seed germination or flower development, were not captured. Taken together, it appears that for every phytohormone there are responses which are dependent on, mediated by, as well as independent of other phytohormones, resulting in a complex signalling network, which allows response dynamics and patterns to be fine-tuned in a highly sophisticated way. This also reinforces the hypothesis that the balance of several phytohormones rather than the actual amount of one phytohormone determines each response. Here, the current understanding of the interaction between phytohormones in relation to enhanced growth as a response to canopy shading is reviewed. In addition, changes in stress-related enhanced growth in response to submergence, which is akin to canopy shading in some ways, are discussed. The recent advances in understanding how the phytohormone balance is altered upon receiving such an environmental stimulus is then examined, with a focus on auxins, gibberellins, cytokinins, and ethylene. The shade avoidance response Plants sense changes in light quality due to shading by competing vegetation, using the ratio of red to far-red light (R:FR) with the help of phytochromes. Shade-avoiding species then react by elongating dramatically, altering the whole plant architecture, in order to emerge from the blockage. This elongation response is often linked with reduced leaf development, enhanced apical dominance, and a re-allocation of resources from storage favouring shoot elongation to reproductive growth; a similar elongation response to that which can be observed if a plant is temporarily submerged. If the plant is unsuccessful in outgrowing its competing vegetation, flowering is accelerated (Halliday et al., 1994), a reaction that would be detrimental for the yield of most crop species. The phytochrome family of photoreceptors in the Arabidopsis genome consists of five members, phyA–phyE. By sensing and reacting to R and FR light, they regulate a variety of developmental processes in response to light (reviewed in Franklin and Quail, 2010). PhyB has been shown to be the key player in shade avoidance (Reed et al., 1993), with additional redundant activities of phyD and phyE (Aukerman et al., 1997; Devlin et al., 1998, 1999; Franklin et al., 2003). On the other hand, phyA, which is the major factor regulating de-etiolation in seedlings, appears to moderate the shade avoidance response in light-grown seedlings (Johnson et al., 1994). Phytochromes directly bind to PHYTOCHROME-INTERACTING FACTORS (PIFs), which are transcription factors involved in light-regulated responses. The model of phytochrome function suggests that the active (FR-absorbing) form of phytochromes (Pfr) interacts with PIFs, leading to their degradation via the ubiquitin–proteasome pathway. Through the absorption of FR light they convert into the red-light absorbing inactive form (Pr), which is unable to interact with PIFs, thus allowing the transcription of light-regulated genes (Smith and Whitelam, 1997; Lorrain et al., 2008; Fig. 2). PIF4 and PIF5 have recently been shown to be positive regulators of shade avoidance responses, participating in the regulation of some key players in these responses, such as ATHB2, a homeodomain leucine-zipper (HD-Zip) protein with a positive role, LONG HYPOCOTYL IN FAR RED1 (HFR1)/SLENDER IN CANOPY SHADE1 (SICS1), a basic helix–loop–helix (bHLH) transcription factor with a major negative regulatory role in shade avoidance, and PIF3-LIKE1 (PIL1), another bHLH transcription factor positively regulating shade avoidance (Salter et al., 2003; Lorrain et al., 2008) (Table 1). Phytochrome-mediated regulation of gene expression thus appears to be one of the main mechanisms of growth regulation in response to light signals (Franklin and Quail, 2010). Furthermore, the shade avoidance response appears to be coupled to the circadian clock, since the rapid elongation response upon a low R:FR signal is strongest at dusk, and requires both PIL1 and TIMING OF CAB EXPRESSION 1 (TOC1), a known circadian clock protein (Salter et al., 2003). Table 1. List of genes involved in the regulation of the shade avoidance and submergence response Mediator of response  Genes involved  Nature of action of the protein    Remarks  Reference(s)      Positive  Negative      Phytochromes    phyA      Johnson et al. (1994)    phyB        Reed et al. (1993); Franklin et al. (2003)    PhyD        Aukerman et al. (1997); Devlin et al. (1999); Franklin et al. (2003)    phyE        Devlin et al. (1998); Franklin et al. (2003)  Phytochrome-interacting factors (PIFs)  PIF4    Transcription factor    Lorrain et al. (2008)    PIF5    Transcription factor    Lorrain et al. (2008)  Key transcription factors    PIL1  Transcription factor  bHLH type  Lorrain et al. (2008); Salter et al. (2003)    ATHB2    Transcription factor  HD-Zip type  Lorrain et al. (2008)    ATHB4  ATHB4  Transcription factor  HD-Zip type  Sorin et al., 2009      HFR1/ SICS1  Transcription factor  bHLH type  Lorrain et al. (2008)  Phytohormones            Auxins  IAA29    Signalling intermediate    Sessa et al. (2005)    PIN3    Transport of hormone    Devlin et al. (2003)    PIN7    Transport of hormone    Devlin et al. (2003)    TAA1    Biosynthesis of hormone    Tao et al. (2008)    IAA19    Signalling intermediate    Pierik et al. (2009)  Gibberellins  GA20ox    Biosynthesis of hormone    Devlin et al. (2003)      GAI  Signalling repressor  DELLA protein  Sessa et al. (2005)      RGA  Signalling repressor  DELLA protein  Djakovic-Petrovic et al. (2007); Pierik et al. (2007)      RGL1  Signalling repressor  DELLA protein  Djakovic-Petrovic et al. (2007); Pierik et al. (2007)      RGL2  Signalling repressor  DELLA protein  Djakovic-Petrovic et al. (2007); Pierik et al. (2007)  Cytokinins  CKX5    Metabolism of hormone    Sessa et al. (2005)    CKX6    Metabolism of hormone    Carabelli et al. (2007)  Ethylene  ACS8    Biosynthesis of hormone    Sessa et al. (2005)    SK1    Signalling intermediate  Submergence induced  Hattori et al. (2009)    SK2    Signalling intermediate  Submergence induced  Hattori et al. (2009)  Brassinosteroids  BRI1    Hormone receptor    Devlin et al. (2003)  Effectors  RpEXPA1    Cell wall-losening enzyme  From Rumex palustris model  Vreeburg et al. (2005)  Mediator of response  Genes involved  Nature of action of the protein    Remarks  Reference(s)      Positive  Negative      Phytochromes    phyA      Johnson et al. (1994)    phyB        Reed et al. (1993); Franklin et al. (2003)    PhyD        Aukerman et al. (1997); Devlin et al. (1999); Franklin et al. (2003)    phyE        Devlin et al. (1998); Franklin et al. (2003)  Phytochrome-interacting factors (PIFs)  PIF4    Transcription factor    Lorrain et al. (2008)    PIF5    Transcription factor    Lorrain et al. (2008)  Key transcription factors    PIL1  Transcription factor  bHLH type  Lorrain et al. (2008); Salter et al. (2003)    ATHB2    Transcription factor  HD-Zip type  Lorrain et al. (2008)    ATHB4  ATHB4  Transcription factor  HD-Zip type  Sorin et al., 2009      HFR1/ SICS1  Transcription factor  bHLH type  Lorrain et al. (2008)  Phytohormones            Auxins  IAA29    Signalling intermediate    Sessa et al. (2005)    PIN3    Transport of hormone    Devlin et al. (2003)    PIN7    Transport of hormone    Devlin et al. (2003)    TAA1    Biosynthesis of hormone    Tao et al. (2008)    IAA19    Signalling intermediate    Pierik et al. (2009)  Gibberellins  GA20ox    Biosynthesis of hormone    Devlin et al. (2003)      GAI  Signalling repressor  DELLA protein  Sessa et al. (2005)      RGA  Signalling repressor  DELLA protein  Djakovic-Petrovic et al. (2007); Pierik et al. (2007)      RGL1  Signalling repressor  DELLA protein  Djakovic-Petrovic et al. (2007); Pierik et al. (2007)      RGL2  Signalling repressor  DELLA protein  Djakovic-Petrovic et al. (2007); Pierik et al. (2007)  Cytokinins  CKX5    Metabolism of hormone    Sessa et al. (2005)    CKX6    Metabolism of hormone    Carabelli et al. (2007)  Ethylene  ACS8    Biosynthesis of hormone    Sessa et al. (2005)    SK1    Signalling intermediate  Submergence induced  Hattori et al. (2009)    SK2    Signalling intermediate  Submergence induced  Hattori et al. (2009)  Brassinosteroids  BRI1    Hormone receptor    Devlin et al. (2003)  Effectors  RpEXPA1    Cell wall-losening enzyme  From Rumex palustris model  Vreeburg et al. (2005)  View Large The involvement of phytohormones in the shade avoidance response has been shown early and frequently. However, many of the exact molecular mechanisms linking shade-initiated transcriptional changes with phytohormone-related responses are only beginning to be revealed. It has been shown that the phytohormones brassinosteroids, auxins, ethylene, and gibberellins are involved in the shade avoidance response, because mutations in genes involved in their metabolism or signalling lead to a reduced FR light-induced elongation response, and suppression of the constitutive shade-avoiding phyB phenotype, respectively (e.g. Kim et al., 1998; Kanyuka et al., 2003; Pierik et al., 2004; Hisamatsu et al., 2005). In a microarray analysis to identify early and late gene expression changes in FR-enriched light, Devlin et al. (2003) identified several genes involved in phytohormone metabolism and signalling. It was not until Sessa et al. (2005) discovered that HFR1/SICS1 is a major negative regulator of shade avoidance that the first link between perception of canopy shading and phytohormone responses was identified. The bHLH-type transcription factor HFR1 down-regulates ATHB2 (Carabelli et al., 1993; Schena et al., 1993), amongst others. It is thought to possess a fundamental role in plant acclimation by preventing an exaggerated elongation growth, in cases of unsuccessful avoidance of shading. The molecular mechanism of this negative regulation was recently elucidated by Hornitschek et al. (2009). The authors were able to show that PIF4 and PIF5 induce the expression of shade avoidance-related genes by directly binding to G-boxes in their promoters; in prolonged shade HFR1 will accumulate and bind to both PIF4 and PIF5, forming non-DNA-binding heterodimers, thus preventing PIF4- and PIF5-mediated gene expression. In the hfr1 loss-of-function background, several phytohormone-related genes are up-regulated, for example GAI, IAA29, ACS8, or CKX5, linking shading responses with the signalling or metabolism of gibberellins, auxins, ethylene, and cytokinins, respectively (Sessa et al., 2005). This observation is corroborated by the resemblance of the shading-induced elongation phenotype to those phenotypes that are caused by increased levels of auxins, ethylene, or gibberellin, and decreased levels of cytokinin, respectively, further substantiating the involvement of those four phytohormones in the shade avoidance response. Furthermore, Kurepin et al. (2007) identified significant changes in the content of gibberellins and the auxin indole 3-acetic acid (IAA) in sunflower seedlings in response to changes in light quality. Subsequently, Roig-Villanova et al. (2007) showed that the sensitivity to phytohormones is rapidly changed in response to canopy shading. A second HD-Zip transcription factor, ATHB4, was identified recently as another key player in shade avoidance responses (Sorin et al., 2009). The study revealed that ATHB4 specifically alters the sensitivity of hypocotyls to auxins, brassinosteroids, and gibberellins in FR-enriched light. The authors also showed that it effectively down-regulates subsets of auxin- and/or brassinosteroid-regulated genes. However, it appears to be difficult to categorize ATHB4 as being either a positive or a negative regulator, since both gain and loss of function diminished the hypocotyl elongation response to shading (Sorin et al., 2009). It thus appears that the perception of canopy shade leads to extensive changes not only in phytohormone content through the regulation of metabolic enzymes, but also in the sensitivity to phytohormones, which might be achieved by the up- or down-regulation of the expression of receptor genes. This in fact was shown to be the case for the brassinosteroid receptor BRI1, whose expression is rapidly up-regulated in FR-supplemented light (Devlin et al., 2003) (Table 1). Loss of function of BRI1 leads to a severely dwarfed stature due to a strong reduction in cell expansion, and dark-grown bri1 seedlings appear de-etiolated (Noguchi et al., 1999), further supporting a key role for BRI1 (and thus brassinosteroids) in elongation growth in response to light signals. At the same time, intermediates of phytohormone signal transduction pathways appear to be regulated during the shade avoidance response as well. It was therefore suggested by Sorin et al. (2009) that transcriptional networks regulated by light quality changes (low R:FR) intersect with phytohormone-related transcriptional and signalling networks controlling cell proliferation and expansion, as was suggested by Nemhauser (2008) for photomorphogenesis. Auxin-mediated growth is essential for shade avoidance Auxins are involved in controlling virtually all aspects of growth and development in plants, including, but not limited to, directional growth responses to external cues (phototropism and gravitropism), de novo organogenesis of leaves, flowers, floral organs, and lateral roots (Benková et al., 2003), the formation of vascular tissue in leaves (Mattsson et al., 2003; Scarpella et al., 2006), and the maintenance of meristem identity in shoot apical meristem (SAM) and root apical mersitem (RAM) (Sabatini et al., 1999; Friml et al., 2003). Auxin responses are mediated by a number of transcriptional regulators named AUXIN RESPONSE FACTORS (ARFs) and corresponding AUXIN/INDOLE-3-ACETIC ACID (Aux/IAA) proteins that inhibit ARFs. This inhibition is relieved by the auxin signal triggering the ubiquitination and subsequent degradation of Aux/IAAs (reviewed in Vanneste and Friml, 2009). It thus seems that auxin regulates a wide range of developmental responses by one common mechanism. Vanneste and Friml (2009) therefore suggested that auxins merely act as a trigger for a change that is pre-programmed in the target cell or tissue. In that case, the auxin signal only selects time and space for the change of the developmental programme. Such a genetic framework for auxins in the regulation of lateral root initiation was recently shown by De Smet et al. (2010). The authors elegantly showed that auxins control the initiation of lateral roots through the activation of at least two successive modules, namely the SOLITARY ROOT (SLR)/IAA13-ARF7-ARF19-mediated pericycle cell division, followed by the BODENLOS (BDL)/IAA12-MONOPTEROS/ARF5-mediated lateral root patterning. Interestingly, neither of these modules is able to initiate the formation of lateral roots on its own. The authors speculate, furthermore, that such coordinated activation of multiple modules could be the general mode of action of auxins, providing a possible explanation for the wide variety of responses to the phytohormone. Interestingly, in the case of auxins, not only the phytohormone itself can serve as a signal, but the actual level of auxins in a specific cell or tissue is able to determine the response. This is reflected in the highly dynamic pattern of differential auxin distribution between cells of a plant tissue, resulting in auxin maxima and gradients. Those gradients are established mostly through local biosynthesis (Cheng et al., 2007; Stepanova et al., 2008; Tao et al., 2008) and polar auxin transport (reviewed in Tanaka et al., 2006). It is well established that both polar auxin transport and the resulting auxin gradient are necessary for plant growth and morphological patterning (Feraru and Friml, 2008). Both auxin biosynthesis and transport are in turn controlled by diverse environmental signals as well as other phytohormones. Interestingly, polar auxin transport appears to be directly influenced by membrane lipid content, since the auxin transport protein PIN1 is redistributed in response to a decrease in sitosterol and an increase in cholesterol (Willemsen et al., 2003) as well as a reduction in very-long-chain fatty acids (Roudier et al., 2010). This reinforces the tight link that phytohormones, in this case auxins, form between exogenous cues and development, since membrane lipid content in turn is subject to environmental control (e.g. Guy et al., 2008; Narise et al., 2010). Studies have shown that the inhibition of auxin transport alone is sufficient to abolish the hypocotyl elongation response to FR-enriched light (Steindler et al., 1999), and the auxin efflux carriers PIN3 and PIN7 have been identified amongst genes regulated under this condition (Devlin et al., 2003). This led Morelli and Ruberti (2000) to hypothesize that the auxin transport stream is re-directed in response to canopy shading, either by redistribution of specific auxin efflux carrier or activation of regulatory proteins that control those efflux carriers, or both. Furthermore, an enzyme involved in the IAA biosynthetic pathway from L-tryptophan (L-Trp), TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1 (TAA1), has been identified recently (Tao et al., 2008). Interestingly, the authors showed that TAA1 is directly involved in de novo auxin biosynthesis in leaves in response to FR-enriched light, and that it is required for hypocotyl and petiole elongation as well as the leaf hyponasty response. These findings suggest that auxin transport to the sites of elongation (the hypocotyl) is required for those responses to occur, a notion that is corroborated by the above-mentioned studies which indicate an important role for auxin transport in shade avoidance responses (Steindler et al., 1999; Devlin et al., 2003). The actual level of bioactive auxins, however, is equally important for the growth responses to occur, which is represented by the rapid induction of several auxin-responsive genes (IAA genes) upon the perception of FR-enriched light (Devlin et al., 2003). The transcripts of one such auxin-induced gene, IAA19, has been shown to be localized in petioles and hypocotyls (Pierik et al., 2009), further substantiating the need for auxin transport to occur in the shade avoidance response. Despite findings of auxins affecting the stability of DELLA proteins to induce growth in roots (see below; Achard et al., 2003; Fu and Harberd, 2003), Pierik et al. (2009) concluded that the auxin-mediated shade avoidance appears to be independent of the gibberellin signalling pathway. Application of an auxin transport inhibitor abolishes hypocotyl elongation induced by FR-enriched light not only in the wild type, but also in a mutant with increased gibberellin signalling (in a mutant with four out of five DELLA genes knocked out; see below). The auxin pathway thus probably represents an alternative route in the shade avoidance response, functionally parallel to, but not intersecting with the gibberellin route (Pierik et al., 2009). On the other hand, the intersection of the shade-induced auxin pathway with the gibberellin route might also represent a tissue-specific interaction that is necessary for the root elongation growth, but of lesser importance for shade-induced elongation growth. Gibberellin-induced elongation is necessary, but not sufficient, for the shade avoidance response According to the classical view, gibberellins are another growth-promoting class of phytohormones, regulating a wide range of growth and developmental processes throughout the life cycle of a plant, including seed germination, leaf expansion, induction of flowering, as well as flower and seed development (Sun and Gubler, 2004; Yamaguchi, 2008). It is therefore not surprising that they frequently appear to work synergistically with auxins. For example, stem elongation has been shown to be coordinately regulated by auxins and gibberellins (Ross et al., 2001). The effects of auxins and gibberellins overlap with respect to cell expansion as well as tissue differentiation. Gibberellin-induced root elongation was also shown to require auxins (Fu and Harberd, 2003). The level of active gibberellins is controlled by several factors, both external, for example light and temperature (Yamaguchi and Kamiya, 2000; García-Martínez and Gil, 2001), and internal, specifically auxins (Ross et al., 2000, 2001; Wolbang and Ross, 2001; Goda et al., 2004; Frigerio et al., 2006), which were shown to induce the gibberellin biosynthetic genes GA20ox and GA3ox (O'Neill and Ross, 2002; Frigerio et al., 2006; Nemhauser et al., 2006). This effect of auxins on gibberellin biosynthesis is mediated by the auxin receptor TRANSPORT INHIBITOR RESPONSE1 (TIR1) (Frigerio et al., 2006), and occurs via the degradation of Aux/IAA proteins and the resulting activation of AUXIN RESPONSE FACTOR7 (ARF7) (reviewed in Teale et al., 2006; Fig. 1). Furthermore, TIR3, identified as a protein involved in auxin transport, appears to influence the C20 oxidation of gibberellins, since tir3 mutants display an abnormal stem elongation response to C20-gibberellin (Sponsel et al., 1997). Fig. 1. View largeDownload slide Interplay of auxins (AUX) and gibberellins (GA) in gene regulation. The regulation of auxin- and gibberellin-induced genes occurs in a similar manner; in the presence of the phytohormone, a phytohormone–receptor–repressor complex (auxin–SCFTIR1–Aux/IAA and gibberellin–GID1–DELLA, respectively) is formed. This leads to the degradation of the repressors via the ubiquitin–proteasome pathway, thus releasing transcription factors (ARF7 and ‘TF’, respectively) from inactive complexes with the repressors. Auxin appears to work upstream of the gibberellin signalling pathway, since gibberellin accumulation occurs as a result of auxin-induced transcription of the gibberellin biosynthetic genes GA20ox and GA3ox. Furthermore, auxin induces the expression of CKX6, a gene encoding a cytokinin-degrading enzyme. This results in a reduced cytokinin level, leading to the repression of cytokinin-induced, ARR-mediated accumulation of Aux/IAA proteins, amongst others, which provide a positive feedback regulation of auxin-induced transcription. Both auxin and cytokinin affect the degradation of DELLA proteins (dotted lines), either by stabilizing DELLAs (cytokinin) or by being required for their degradation (auxin) in an as yet unknown fashion. Arrows indicate activation, and blocked arrows indicate repression of either protein function or accumulation. Fig. 1. View largeDownload slide Interplay of auxins (AUX) and gibberellins (GA) in gene regulation. The regulation of auxin- and gibberellin-induced genes occurs in a similar manner; in the presence of the phytohormone, a phytohormone–receptor–repressor complex (auxin–SCFTIR1–Aux/IAA and gibberellin–GID1–DELLA, respectively) is formed. This leads to the degradation of the repressors via the ubiquitin–proteasome pathway, thus releasing transcription factors (ARF7 and ‘TF’, respectively) from inactive complexes with the repressors. Auxin appears to work upstream of the gibberellin signalling pathway, since gibberellin accumulation occurs as a result of auxin-induced transcription of the gibberellin biosynthetic genes GA20ox and GA3ox. Furthermore, auxin induces the expression of CKX6, a gene encoding a cytokinin-degrading enzyme. This results in a reduced cytokinin level, leading to the repression of cytokinin-induced, ARR-mediated accumulation of Aux/IAA proteins, amongst others, which provide a positive feedback regulation of auxin-induced transcription. Both auxin and cytokinin affect the degradation of DELLA proteins (dotted lines), either by stabilizing DELLAs (cytokinin) or by being required for their degradation (auxin) in an as yet unknown fashion. Arrows indicate activation, and blocked arrows indicate repression of either protein function or accumulation. Fig. 2. View largeDownload slide PIF4-mediated shade-induced gene regulation. In white light (high R:FR), PIF4-mediated transcriptional activation is inhibited. Part of the PIF4 protein pool is sequestered in inactive complexes with DELLA proteins, whereas another part is bound by the FR-absorbing active phyB (PfrB), which leads to the degradation of PIF4 via the ubiquitin–proteasome pathway. In FR light-enriched environments, e.g. due to canopy shade, PfrB is converted to its R-absorbing inactive form (PrB), which abolishes its ability to bind PIF4. Thus, PIF4 can accumulate, allowing transcription of shade-induced genes. In addition, gibberellin levels increase in response to shade, which leads to the degradation of DELLA proteins, releasing an additional pool of PIF4 protein. In prolonged shade, HFR1 accumulates and forms heterodimers with PIF4 that do not bind to DNA any more, thus preventing an exaggerated response. Fig. 2. View largeDownload slide PIF4-mediated shade-induced gene regulation. In white light (high R:FR), PIF4-mediated transcriptional activation is inhibited. Part of the PIF4 protein pool is sequestered in inactive complexes with DELLA proteins, whereas another part is bound by the FR-absorbing active phyB (PfrB), which leads to the degradation of PIF4 via the ubiquitin–proteasome pathway. In FR light-enriched environments, e.g. due to canopy shade, PfrB is converted to its R-absorbing inactive form (PrB), which abolishes its ability to bind PIF4. Thus, PIF4 can accumulate, allowing transcription of shade-induced genes. In addition, gibberellin levels increase in response to shade, which leads to the degradation of DELLA proteins, releasing an additional pool of PIF4 protein. In prolonged shade, HFR1 accumulates and forms heterodimers with PIF4 that do not bind to DNA any more, thus preventing an exaggerated response. For the case of one of the gibberellin biosynthetic genes, GA20ox, Desgagné-Penix and Sponsel (2008) propose four different levels of regulation following a strict hierarchy of spatial, developmental, metabolic, and auxin-mediated regulation. The authors suggest that the spatial regulation of GA20ox expression (i.e. high expression in cotyledons and leaves, and low expression in roots) over-rides all other levels of regulation. DELLA proteins are the key negative regulators of gibberellin signalling; they repress gibberellin-induced growth responses, which is relieved mostly, but not exclusively, by their degradation via the ubiquitin–proteasome pathway (reviewed in Schwechheimer and Willige, 2009). Arabidopsis contains five DELLA proteins (GAI, RGA, RGL1, RGL2, and RGL3) with both overlapping and unique functions (Lee et al., 2002; Cheng et al., 2004; Cao et al., 2005). The current model of gibberellin action suggests that DELLAs sequester transcription factors in inactive complexes. Through the recognition of gibberellins, the receptor GID1 changes its conformation and binds to DELLA proteins (Murase et al., 2008; Shimada et al., 2008). This complex also recruits the SCFSLY E3 ubiquitin ligase, leading to the degradation of DELLA proteins, and the relief of the transcription factors. This transcriptional regulation by binding to transcription factors might well represent the main mode of DELLA function, since to date there are no reports of DELLAs directly associating with DNA, and chromatin immunoprecipitation studies with DELLA proteins only lead to subtle promoter enrichment (Zentella et al., 2007). Interestingly, all DELLA-regulated genes identified in this study were shown to be gibberellin repressed and DELLA induced. This indicates that DELLAs might not only repress transcription by sequestering transcription factors, but also activate transcription by forming either active complexes with transcription factors or inactive complexes with transcriptional repressors. DELLAs have been proposed to integrate several other phytohormone pathways on several occasions. For example, in the case of gibberellin-induced root elongation, auxin was shown to be necessary for DELLA degradation to occur (Fu and Harberd, 2003). The endodermis seems to represent the main tissue governing this elongation response, since gibberellin signalling in other root tissues was shown not to affect the overall growth rate (Ubeda-Tomás et al., 2008). On the other hand, both ethylene and abscisic acid seem to stabilize DELLA proteins during root growth (Achard et al., 2003, 2006). In the SAM, stem cell maintenance requires high cytokinin and low gibberellin levels (Sakamoto et al., 2001; Jasinski et al., 2005; Yanai et al., 2005). This is achieved by cytokinins, inducing DELLA protein expression (Brenner et al., 2005). Cytokinins were also shown to control gibberellin levels by inhibiting gibberellin biosynthesis and inducing gibberellin catabolism (Brenner et al., 2005; Jasinski et al., 2005). This negative regulation is reflected in the generally antagonistic action of cytokinins and gibberellins, for example in the regulation of shoot and root elongation, cell differentiation, shoot regeneration in culture, or meristem activity (Greenboim-Wainberg et al., 2005; Jasinski et al., 2005). However, Greenboim-Wainberg et al. (2005) showed that cytokinins do not affect gibberellin biosynthesis or signalling. These seemingly contradictory results might simply be due to the different approaches used. Whereas Brenner et al. (2005) performed a genome-wide expression analysis of Arabidopsis seedlings at several time points after cytokinin treatment, Greenboim-Wainberg et al. (2005) based their conclusion on phenotypic analyses (germination in the presence of the gibberellin biosynthesis inhibitor paclobutrazol as well as the gibberellin effect on flowering time) and the expression level of one gibberellin-regulated gene. In line with the hierarchy of regulation proposed by Desgagné-Penix and Sponsel (2008), feedback mechanisms might regulate the misexpression of genes on a developmental level following a single exogenous phytohormone application; the actual change in expression levels could therefore remain without effect on the overall growth and development. Despite the emerging evidence of DELLAs acting as key integrators for several hormonal as well as environmental pathways, growth control is not entirely DELLA dependent. Another negative regulator of gibberellin signalling, SPINDLY (SPY), was shown to suppress cytokinin responses by inhibiting the induction of a subset of the cytokinin signalling intermediates ARABIDOPSIS RESPONSE REGULATORs (ARRs) (Greenboim-Wainberg et al., 2005). Swain et al. (2001) showed that SPY can act in those two different pathways through the interaction with different proteins. Although evidence has yet to be reported, it is proposed that SPY might distinguish between different branches of cytokinin signalling by specifically targeting only a subset of ARRs (Greenboim-Wainberg et al., 2005). The requirement for gibberellins in the elongation response upon a monochromatic FR stimulus was shown by Martínez-García et al. (2000), and the rapid induction of one of the gibberellin biosynthetic genes, GA20ox, in FR-enriched light (Devlin et al., 2003) reinforces this view. The involvement of DELLA proteins in the shading-induced elongation response has also been determined on many occasions. Their exact role, however, was only recently shown by Djakovic-Petrovic et al. (2007) and Pierik et al. (2007). According to these studies, the elongation response to canopy shade in dense stands is mediated by the breakdown of DELLA proteins due to an increase in gibberellin action. However, they also provided evidence that this degradation of DELLA proteins is necessary, but not sufficient, to induce the hypocotyl elongation response upon shading; quadruple DELLA knockout mutants (gai rga rgl1 rgl2) do not exhibit a constitutive shade-avoiding phenotype, and react to shading with an induced elongation similar to that of wild-type plants (Djakovic-Petrovic et al., 2007; Pierik et al., 2007). On the other hand, Feng et al. (2008) observed that a mutant lacking all five DELLAs (gai rga rgl1 rgl2 rgl3) shows a slightly altered response to red light. This leaves room to speculate that RGL3 could be the DELLA protein with the main role in the shade-induced elongation growth, or that all five DELLA proteins are required to regulate this response in a cumulative way. Nonetheless, DELLA proteins are accorded an important role in the shade avoidance response. They were shown to bind to the light-responsive transcription factors PIF3 and PIF4, sequestering them in inactive complexes, preventing transcriptional activation of downstream genes (de Lucas et al., 2008; Feng et al., 2008). PIF4 is thought to be involved in shade avoidance, regulating genes associated with cell elongation. Therefore, the degradation of DELLA proteins upon shading-induced gibberellin biosynthesis would lead to the release of PIF4, allowing transcription of target genes. DELLA stability, as discussed earlier, is regulated not only by gibberellins, but also by auxins and ethylene (Achard et al., 2003; Fu and Harberd, 2003). This further reinforces the view that DELLA proteins might be acting as key integrators of different environmental as well as endogenous (hormonal) cues. Furthermore, based on the hypothesis of Franklin and Quail (2010) that phytochrome-mediated regulation of gene expression could be the main mechanism of growth regulation in response to light signals, it is tempting to speculate that DELLA proteins could be key players in this gene expression control, integrating hormonal control of growth with light signals. Cytokinins antagonistically regulate shade-induced morphological changes Cytokinins were discovered based on their ability to induce cell division (Miller et al., 1955), but are now known also to have roles in the regulation of germination, shoot and root development, leaf senescence, interaction with pathogens, as well as circadian rhythms (To and Kieber, 2008). In the induction of cell proliferation, cytokinins were shown to up-regulate CycD3 transcripts in shoots (Nogue et al., 2000). However, the G1 transition in the cell cycle can only occur if both cyclin-dependent kinase and the corresponding cyclin are present. Intriguingly, auxins can induce cell proliferation by up-regulating the Cdc2 class of cyclin-dependent kinases (John et al., 1993), resulting in a situation where both phytohormones need to be present in order to induce cell proliferation. In roots, on the other hand, auxins and cytokinins appear to regulate Cyc2 expression and thus cell division antagonistically (John et al., 1993). An opposing effect of cytokinins and auxins seems to be more frequent, and is reflected in the regulation of each other's abundance. Auxin, if overproduced in tobacco plants, leads to reduced levels of cytokinins, and vice versa (Eklöf et al., 1997, 2000). It was suggested that auxins can reduce the pool of active cytokinins by directly regulating the activity of cytokinin oxidase (Zhang et al., 1995). A similar contrast in the cytokinin–auxin interaction between shoot and root can be observed in the determination and maintenance of meristematic tissue. Auxin accumulation and response is required for the specification of the embryonic root meristem founder cell (hypophysis), which will give rise to the quiescent centre and the neighbouring stem cells of the root meristem in the adult plant (Friml et al., 2003; Aida et al., 2004; Weijers et al., 2006). Cytokinins have also been shown to be required for the formation of the root stem cell niche during embryogenesis (Müller and Sheen, 2008). The authors show, however, that high endogenous auxin content directly induces the cytokinin signalling repressors ARR7 and ARR15. Furthermore, phytohormone levels visualized by green fluorescent protein (GFP) expression under phytohormone-inducible promoter control indicate adjacent, rather than overlapping, functions of cytokinins and auxins in the hypophysis-derived cells. In the adult root, cytokinins are implicated to act at the border between the proliferation and differentiation zone (transition zone) and inhibit auxin responses via ARR1-mediated induction of SHY2 (IAA3), a negative regulator of auxin signalling (Dello Ioio et al., 2007). Auxin, on the other hand, retains its maximum concentration in the quiescent centre of the root during post-embryonic development, where it functions in stem cell maintenance, and inhibits cytokinin biosynthesis and signalling. Interestingly, Miyawaki et al. (2004) have shown that auxin treatment can induce some of the cytokinin biosynthetic ISOPENTENYLTRANSFERASE (IPT) genes in Arabidopsis. Although it has to be taken into consideration that exogenously applied auxin does not mirror physiological conditions with respect to the actual amount of phytohormone or the normal site of its action, this induction of cytokinin biosynthesis might represent a dose-dependent effect of auxins. A dose-dependent activation of inward and outward K+ channels in stomata by auxins has been shown earlier (Blatt and Thiel, 1994), and could also be the case in roots, where an obvious auxin gradient is present. In shoots, cytokinins accumulate in the meristematic tissue, whereas auxin levels are generally low due to the polar (basipetal) transport. The homeobox transcription factors SHOOT MERISTEMLESS (STM) and WUSCHEL (WUS) play a key role in the establishment and maintenance of the SAM, and their functions appear to be tightly linked with cytokinins (Wolters and Jürgens, 2009). STM belongs to the KNOTTED-like homeobox proteins, and was shown to induce the cytokinin biosynthetic gene IPT7 (Jasinski et al., 2005; Yanai et al., 2005), while WUS down-regulates the cytokinin signalling repressors ARR5 and ARR7 (Leibfried et al., 2005). Auxins, on the other hand, only locally accumulate in the periphery of the shoot meristem, where they trigger the initiation of organ primordia (Reinhardt et al., 2003). At those sites auxins also inhibit cytokinin biosynthesis (Nordström et al., 2004), and down-regulate STM expression (Furutani et al., 2004; Heisler et al., 2005). It thus appears that auxins play a role in limiting both CK and STM action in maintaining the stem cell identity in the SAM. The role of cytokinin breakdown in the inhibition of leaf development during shade avoidance was substantiated when Carabelli et al. (2007) showed that CKX6, a cytokinin oxidase, is induced in simulated shade and promotes cytokinin breakdown specifically in pre-procambial cells of developing leaf primordia. This, in turn, appears to be sufficient to arrest leaf primordial growth, which was shown to be due to a reduction in cell proliferation rather than elongation. They showed furthermore that this CKX6 induction is an auxin response, and is mediated by the auxin receptor TIR1, which does not seem to be involved in the hypocotyl elongation response to shading. It is therefore tempting to conclude that multiple receptors of one phytohormone might be involved in non-redundant responses, either in different tissues, in different developmental stages, or upon different environmental cues. In fact, this hypothesis was corroborated recently, when Vidal et al. (2010) showed that microRNA 393 (miR393) targets transcripts of a bHLH transcription factor as well as the auxin receptors TIR1, AFB1, AFB2, and AFB3. However, in response to nitrate, miR393 specifically down-regulates only AFB3 in Arabidopsis roots to induce changes in root architecture. Similar regulation by other microRNAs in shoot development may occur in response to phytohormones and/or environmental signals, adding an additional level of complexity to the growth-regulating network. Another role for cytokinins in perception and response to shading was elucidated when Pons et al. (2001) showed that the transpiration rate of leaves correlates with the vertical light gradient in tobacco plants, and that this correlation is potentiated in dense, more shaded stands. This reduced transpiration rate in shaded leaves leads to a reduced import of compounds from the transpiration stream, of which cytokinin was shown to be responsible for the induced re-allocation of resources as well as the adaptation of photosynthesis, which is reflected by a reduced chlorophyll a/b ratio (Boonman and Pons, 2007; Boonman et al., 2007, 2009). Furthermore, this photosynthetic adaptation to canopy density appears to be redundantly regulated by cytokinin and phyD, since it is reduced in both phyD and cytokinin signalling mutants (Boonman et al., 2009). Hence it is clear that cytokinins are involved in regulating the response to shading along with the other major phytohormones, such as gibberellins and auxins. The complex signalling cross-talk between cytokinins and auxins as well as cytokinins and gibberellins described earlier demonstrates the intricate regulatory network in the process. Ethylene appears to be the initiator of stress-induced growth The phytohormone ethylene is involved in several growth responses, including seed germination, seedling growth, development of leaves, root, stem, and flowers, fruit ripening, senescence, and abscission. Its biosynthesis is greatly enhanced by various stresses as well as a range of other phytohormones. It is therefore thought to have a major role in the integration and coordination of environmental and endogenous cues. Interestingly, Finlayson et al. (1999) observed a circadian rhythm for ethylene production in Sorghum bicolor. They also showed that the amplitude of the circadian rhythm is increased dramatically in the phyB-1 mutant which exhibits a constitutive shade-avoiding phenotype, providing an interesting link between circadian rhythm, phytochromes, and the phytohormone ethylene. Ethylene has been shown to be involved in the shade-induced stem and petiole elongation in tobacco (Pierik et al., 2004), and recently this growth stimulation of ethylene was shown to occur through the auxin pathway, since auxin signalling mutants lose the ability to respond to exogenous ethylene, but ethylene signalling mutants retain their responsiveness to auxins (Pierik et al., 2009). The authors therefore infer that auxins might be a downstream regulator of ethylene-induced hypocotyl elongation. This in fact has been shown to be the case in root elongation, where ethylene stimulates both auxin biosynthesis and transport (Ruzicka et al., 2007; Stepanova et al., 2007; Swarup et al., 2007). On the other hand, Pierik et al. (2009) determined that the shade-induced hypocotyl elongation mediated by ethylene can occur independently of gibberellins. Despite the observation that DELLA proteins accumulate in response to ethylene, the full gibberellin-deficient ga1-3 mutant shows only a slight reduction in light quality-triggered hypocotyl elongation. On the other hand, evidence is emerging linking ethylene to gibberellin signalling in the morphologically similar response to flooding. Temporary submergence triggers a similar response, and involves similar key players Temporary flooding leads to a highly increased internode elongation in some rice cultivars as well as other semi-aquatic species in the so-called deepwater response. It results in morphological changes similar to the shade avoidance response, most importantly a rapid elongation of the stem at the expense of leaf development and size. Under water, the diffusion of oxygen and carbon dioxide (CO2) is reduced 10 000-fold compared with air. Thus, if a plant is submerged, hypoxic and anoxic conditions affect the respiratory metabolism, and the limited CO2 availability slows down the photosynthetic rate. To date, both the actual signal(s) and the corresponding receptor(s) that translate flooding into an elongation response remain elusive. It has been proposed, however, that changes in reactive oxygen species (ROS), pH changes, metabolic changes, and/or changes in the availability of nutrients could serve as signals (Dat et al., 2004). As in canopy shade, the light intensity is reduced under water; however, the spectral composition differs. Light that reaches submerged plants is enriched in R, resulting in a higher R:FR ratio, whereas the shade avoidance response is triggered by a lower R:FR ratio. Thus, although the triggers for inducing the rapid stem elongation differ in shading and flooding, the molecular mechanisms involved in these responses show close parallels. In fact, the downstream mechanisms regulating the submergence-induced elongation response are believed to be so similar that it was proposed to use the shade avoidance response as a tool for flooding research (Pierik et al., 2005). It was shown very early that ethylene is involved in this response (Métraux and Kende, 1983), and that gibberellins are required for the ethylene-mediated elongation response of rice (Raskin and Kende, 1984). It was furthermore shown that both submergence and ethylene treatment result in the degradation of abscisic acid and an increased sensitivity to gibberellins in rice (Hoffmann-Benning and Kende, 1992), leading the authors to conclude that the ratio of growth-promoting (gibberellins) to growth-inhibiting (abscisic acid) phytohormones determines the elongation response. It thus appears that mainly ethylene, gibberellins, and abscisic acid control this elongation response. The exact mechanism of their interaction, however, has yet to be determined. Recently, however, two genes involved in this elongation response have been identified and named SNORKEL1 (SK1) and SK2 (Hattori et al., 2009). These genes were identified to be novel ETHYLENE RESPONSE FACTORS (ERFs), and were shown to be connected to gibberellin signalling, since gibberellins appear to be required for this elongation response. The authors speculated that SK1 and SK2 may also stimulate the biosynthesis of gibberellins (Hattori et al., 2009). Interestingly, the authors showed a strong induction of SK1 and SK2 transcripts upon ethylene treatment after 1 h. An induction of SK1 and SK2 transcripts, even though it is lower and occurs after 3 h only, can be observed upon treatment with gibberellin, the auxin IAA, and also cytokinins. Despite the authors’ conclusion that SK1 and SK2 are responsive to ethylene only, this slight induction could be interpreted as a contribution of these other phytohormones to the expression of SK1 and SK2, either through independent pathways following submergence, or as a result of feedback regulation. The aim of this phytohormone interaction network in response to either shade or flooding is cell elongation in order to emerge from the blockage. Interestingly, Vreeburg et al. (2005) showed that ethylene can quickly induce both acidification of the apoplast and the expression of the cell wall-loosening expansin A1 (RpEXPA1) in Rumex palustris. This process of acidification is similar to the effect of auxins on cell expansion (Rayle and Cleland, 1970, 1992; Cleland, 1973; Rayle et al., 1977), suggesting that the two types of phytohormones might be mediating this response in concert. Alternatively, the ethylene-induced acidification of the apoplast could be mediated by the auxin pathway, as was shown to be the case for shade-induced stem and petiole elongation (Pierik et al., 2009). Conclusions and future perspectives Auxins can be attributed a major role in the enhanced elongation response upon shading or flooding, since the inhibition of both biosynthesis and transport abolishes submergence- as well as shade-induced growth. On the other hand, gibberellins were shown to be required (even though not sufficient) for the shade avoidance response. Several lines of evidence show that these two phytohormones not only act synergistically, but also positively regulate each others’ abundance in a normal growth context (Fig. 1). In the shade avoidance response, however, pathways of auxin and gibberellin appear to be parallel rather than connected, which could be achieved by the activation of different sets of genes in response to shading as compared with a normal growth context. Future studies should thus be aimed at understanding to what extent the auxin and gibberellin pathways intersect or depend on each other. The sequestering by DELLA proteins of PIF4, a major transcription factor positively regulating the transcription of genes associated with cell elongation (de Lucas et al., 2008; Feng et al., 2008), reinforces the importance of gibberellin signalling in shade-induced growth. DELLA proteins are, furthermore, thought to integrate several hormonal signals (Achard et al., 2003; Fu and Harberd, 2003), making them good candidates for key regulators of growth in response to environmental as well as hormonal cues. However, this hypothesis is difficult to reconcile with the observation that quadruple DELLA knockout mutants elongate in response to shading similarly to wild-type plants (Djakovic-Petrovic et al., 2007; Pierik et al., 2007) and pentuple DELLA knockout mutants showing only slightly altered elongation in red light (Feng et al., 2008). Thus, elongation growth mediated by the release of PIF4 through shade-induced DELLA degradation very probably represents a redundant mechanism. It is imaginable that, under white light, DELLAs sequester only a part of the available PIF4 protein, whereas another part is bound by the active phyB (PfrB) and subsequently degraded via the ubiquitin–proteasome pathway (Fig. 2A). Upon shading, both the conversion of PfrB into its inactive isoform (PrB), and the gibberellic acid-mediated DELLA degradation release PIF4, allowing the transcription of shade-induced genes to occur (Fig. 2B). Relatively little is known about the involvement of cytokinins in the shade avoidance response. It seems, however, that cytokinin breakdown mediated by the CKX family of proteins (Sessa et al., 2005; Carabelli et al., 2007) upon shading plays an important role in morphological changes other than stem elongation (e.g. arrest of leaf primordial growth). A reduction of cytokinin content also appears to occur passively, due to a decreased import through the transpiration stream into shaded leaves (Boonman and Pons, 2007; Boonman et al., 2007, 2009; Pons et al., 2001), and seems to play an important role in photosynthetic adaptations to shading. Nevertheless, it is clear that additional work needs to be done on the complex interactions between cytokinin signalling and gibberellin and auxin signalling. Accumulation of ethylene has been identified as one of the early responses to various stresses in plants. The findings that ethylene appears to act upstream of auxin signalling in shade-induced growth (Pierik et al., 2009), as well as upstream of the gibberellin signalling pathway in response to flooding (Raskin and Kende, 1984) substantiate this view. Other studies have shown that ethylene can induce the degradation of abscisic acid upon submergence (Hoffmann-Benning and Kende, 1992). It is therefore tempting to conclude that ethylene could act as the initiator of stress-related morphological changes, regulating both abundance and signalling of other phytohormones. However, the complete regulatory network of shade avoidance and/or flooding responses is likely to contain both intersecting and functionally parallel pathways. Furthermore, the strict hierarchy of regulation of phytohormone signalling described for GA20ox (Desgagné-Penix and Sponsel, 2008) is likely to be a mechanism common to most, if not all phytohormone signalling intermediates. If the spatial regulation thus over-rides all other regulatory mechanisms, specifically altering endogenous phytohormone levels in the target tissue instead of applying phytohormones exogenously will provide a much more powerful tool to determine the actual phytohormone responses. This is more likely to represent the actual phytohormone delivery and concentration in planta. As more interconnections of phytohormone pathways are unravelled, and more interactions of phytohormone biosynthesis and signalling pathways are being discovered, it is becoming more and more clear that no single phytohormone affects plant growth and development on its own. Rather it is a complicated network connecting external cues with phytohormone signalling, which are only beginning to be understood (Fig. 3). In many cases of phytohormone interactions described here it remains to be determined exactly how and when these specific interactions occur. Thus, in order to gain a holistic view of how phytohormones regulate plant growth it is crucial in future studies to examine not only the overall phytohormone context in the plant, but also possible spatial and/or temporal (developmental) differences in interactions. In this regard, it is worth noting that cDNA microarray studies can now be attempted with only a few cells as starting material, which will permit extremely fine resolution for analysing tissue-specific differences in gene expression. Fig. 3. View largeDownload slide Phytohormone signalling network regulating shade-induced elongation growth. Canopy shade leads to a series of reactions in a plant, some of the earliest including the phyB-mediated transcript accumulation of the transcription factors ATHB2, ATHB4, and HFR1/SICS, as well as the accumulation of ethylene. These fast responses are followed by a network of interactions of other phytohormones such as auxin, gibberellin, and cytokinin, whose signalling intermediates interact with and influence each other at multiple levels (A). The result is a complex change in plant architecture, the most obvious effect being a dramatic elongation compared with plants growing in white light. This is illustrated by Arabidopsis wild-type (Col-0) seedlings that were induced to germinate in white light for 4 d and either kept in white light (B) or incubated in simulated shade in a red-tinted phytatray II (Sigma-Aldrich®) (C) for another 4 d. Scale bar=3 mm. A plant's response to submergence includes similar phenotypic changes, the most obvious being a rapid stem elongation, and is likely to involve a similar, if not the same, network of phytohormone interactions. This is represented by submergence forming a second input into the same network, as illustrated by the increased levels of ethylene and gibberellin observed in submerged plants, and the induction of SNORKEL1 and SNORKEL2, which in turn increase gibberellin levels and enhance DELLA degradation. Each phytohormone and its respective signalling intermediates involved in this network is shaded in the same colour as follows: auxin, red; ethylene, blue; cytokinin, green; gibberellin, yellow. Arrows indicate positive effects (accumulation of transcript and/or protein and hormone level, respectively; activation through interaction, etc.), and blocked arrows indicate negative effects. Fig. 3. View largeDownload slide Phytohormone signalling network regulating shade-induced elongation growth. Canopy shade leads to a series of reactions in a plant, some of the earliest including the phyB-mediated transcript accumulation of the transcription factors ATHB2, ATHB4, and HFR1/SICS, as well as the accumulation of ethylene. These fast responses are followed by a network of interactions of other phytohormones such as auxin, gibberellin, and cytokinin, whose signalling intermediates interact with and influence each other at multiple levels (A). The result is a complex change in plant architecture, the most obvious effect being a dramatic elongation compared with plants growing in white light. This is illustrated by Arabidopsis wild-type (Col-0) seedlings that were induced to germinate in white light for 4 d and either kept in white light (B) or incubated in simulated shade in a red-tinted phytatray II (Sigma-Aldrich®) (C) for another 4 d. Scale bar=3 mm. A plant's response to submergence includes similar phenotypic changes, the most obvious being a rapid stem elongation, and is likely to involve a similar, if not the same, network of phytohormone interactions. This is represented by submergence forming a second input into the same network, as illustrated by the increased levels of ethylene and gibberellin observed in submerged plants, and the induction of SNORKEL1 and SNORKEL2, which in turn increase gibberellin levels and enhance DELLA degradation. Each phytohormone and its respective signalling intermediates involved in this network is shaded in the same colour as follows: auxin, red; ethylene, blue; cytokinin, green; gibberellin, yellow. Arrows indicate positive effects (accumulation of transcript and/or protein and hormone level, respectively; activation through interaction, etc.), and blocked arrows indicate negative effects. In the long term, detailed knowledge of the molecular mechanism of the shade avoidance response will not only contribute to our understanding of how plants modulate a network of internal and external factors to regulate growth, future crop improvement efforts will also benefit from such knowledge, since this will allow us to target and modulate specific plant responses in greater detail. 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Wagstaff, Carol; Bramke, Irene; Breeze, Emily; Thornber, Sarah; Harrison, Elizabeth; Thomas, Brian; Buchanan-Wollaston, Vicky; Stead, Tony; Rogers, Hilary

doi: 10.1093/jxb/erq113pmid: 20457576

Abstract Petal development and senescence entails a normally irreversible process. It starts with petal expansion and pigment production, and ends with nutrient remobilization and ultimately cell death. In many species this is accompanied by petal abscission. Post-harvest stress is an important factor in limiting petal longevity in cut flowers and accelerates some of the processes of senescence such as petal wilting and abscission. However, some of the effects of moderate stress in young flowers are reversible with appropriate treatments. Transcriptomic studies have shown that distinct gene sets are expressed during petal development and senescence. Despite this, the overlap in gene expression between developmental and stress-induced senescence in petals has not been fully investigated in any species. Here a custom-made cDNA microarray from Alstroemeria petals was used to investigate the overlap in gene expression between developmental changes (bud to first sign of senescence) and typical post-harvest stress treatments. Young flowers were stressed by cold or ambient temperatures without water followed by a recovery and rehydration period. Stressed flowers were still at the bud stage after stress treatments. Microarray analysis showed that ambient dehydration stress accelerates many of the changes in gene expression patterns that would normally occur during developmental senescence. However, a higher proportion of gene expression changes in response to cold stress were specific to this stimulus and not senescence related. The expression of 21 transcription factors was characterized, showing that overlapping sets of regulatory genes are activated during developmental senescence and by different stresses. Alstroemeria, cold stress, dehydration stress, flower, microarrays, petal, senescence, transcriptome Introduction Under optimal conditions floral lifespan is species specific and precisely controlled (Primack, 1985), senescence often being initiated following successful pollination. This is thought to be related to the resource investment of a flower, as well as other ecological factors (Rogers, 2006). While the flower is attached to the plant, environmental stresses such as temperature change can shorten floral lifespan. Once the flower is detached from the parent plant, one of the major factors affecting floral lifespan is supply of respiratory substrate. This is in part ameliorated by provision of a carbon source such as sucrose, or by removal of competing flowers on the same stem (Doi and Reid, 1995; Chanasut et al., 2003). Provision of sucrose can prevent or delay the up-regulation of senescence-associated genes (Hoeberichts et al., 2007), probably indirectly by delaying senescence. Petal senescence is typically characterized by a loss of fresh weight, protein content, and turgor, changes in pigmentation and, ultimately, in many species, programmed cell death and petal abscission (Wagstaff et al., 2003; van Doorn and Woltering, 2008). These changes are related to alterations in the patterns of gene expression which have been analysed in several species using microarrays (Channelière et al., 2002; van Doorn et al., 2003; Breeze et al., 2004; Price et al., 2008; Wagstaff et al., 2009). Many of the genes up-regulated during normal developmental petal senescence relate to remobilization of nutrients and include proteases, nucleases, lipases, and transporters (Hong et al., 2000; Wagstaff et al., 2002; Langston et al., 2005; Price et al., 2008). Genes encoding specific classes of transcription factors, NAC, MYB, MYC, MADS-box, WRKY, and zinc finger proteins, are also frequently up-regulated (Channelière et al., 2002; Breeze et al., 2004; Hoeberichts et al., 2007; Price et al., 2008; Wagstaff et al., 2009), indicating that they may play a regulatory role. Additionally, microarray screens and analyses of individual genes have revealed that a number of genes, generally considered to be stress related, are up-regulated during petal senescence. These include metallothioneins, abscisic acid (ABA)-responsive genes (Channelière et al., 2002; Breeze et al., 2004), and glutathione S-transferases (Meyer et al., 1991; Price et al., 2008). The effects of stress on gene expression have mainly been studied in whole plants or leaves, and there is a body of evidence showing that gene expression changes in leaves are rapid, specific, and highly stress responsive (Buchanan-Wollaston et al., 2005; Gombert et al., 2006). A number of studies have concentrated on different forms of stress such as dark-induced senescence (Roca et al., 2004), cold stress (Khodakovskaya et al., 2005), and dehydration stress (Munne-Bosch and Alegre, 2004). These have identified sets of genes regulated specifically by each of these processes, and several of which are changed by a multitude of stress elicitors. Thus there is support for a model in which there is cross-talk between different stress-induced processes (Nakashima et al., 2009). Abscisic acid (ABA) is involved in coordinating responses to both drought and cold stress, and another common signalling mechanism may be via reactive oxygen species (ROS) (Takahashi et al., 2004). Members of the MYB, MYC, and NAC transcription factor classes are involved in ABA-regulated signal transduction relating to cold and drought responses (Shinozaki and Yamaguchi-Shinozaki, 2003) and cross-talk between other stress responses (Fujita et al., 2006). Mitogen-activated protein (MAP) kinases are also heavily implicated in coordinating stress responses. For example Arabidopsis MEKK1 is transcriptionally up-regulated by multiple stresses including drought and cold (Mizoguchi et al., 1996). The interaction between stress and developmental senescence has been studied in leaves (Buchanan-Wollaston et al., 2003; Lim et al., 2007), and many of the physiological changes induced by stress in leaves such as chlorophyll degradation, and remobilization of nutrients parallel those seen during natural senescence. This suggests that in leaves many stresses induce an early onset of developmental senescence. However, the sharing of gene expression is not complete, indicating that the situation is more complex. In petals the overlap in gene expression between developmental senescence and stress-induced senescence in petals is poorly characterized in any species. Studies of the physiological effects of stress on cut flowers have focused on investigating ways to maximize floral longevity and determining ways to overcome the stresses experienced by flowers during their transport from grower to retailer (Ranwala and Miller, 1998; Andersen et al., 2004). Cold temperature storage at 4 °C is employed to delay flower development and causes little detrimental effect, in temperate species, if the stems are in solution, thereby retaining the maximum vase life. However, the majority of stems are transported without water, packed tightly in boxes. In these cases longevity may be reduced as a consequence of both drought and temperature stress (Rudnicki et al., 1991). In many species, excessive stress will induce temporary loss of petal turgor. This can be apparently reversed by a refreshment treatment; however, overall floral longevity is reduced. Alstroemeria cv. Rebecca forms part of the Liliales and has large, colourful, relatively long-lived (10–15 d) flowers under optimal conditions. The time frame of developmental senescence has been characterized in previous studies (Wagstaff et al., 2001, 2003; Leverentz et al., 2002; Breeze et al., 2004) and has been shown to be almost completely ethylene independent (Wagstaff et al., 2005). Alstroemeria is an economically important cut flower. The nature of the commercial handling chain means that flowers are subjected to periods of many hours or even days stored, or transported, without water at ambient temperatures and/or several days stored in the dark at ∼4 °C (Reid, 2004). Thus young flowers may be subjected to cold and/or dehydration stress during this period. In this study cDNA microarrays have been generated using RNA from petals of both young stress-treated flowers, following a recovery period, and naturally senescing petals. They have been used to investigate the overlap in gene expression between ambient dehydration stress, cold stress, and developmental senescence. Classes of genes specific to either or both stress treatments, specific to developmental senescence, or shared between stress and senescence have been identified. A significant finding is that the pattern of gene expression induced by ambient dehydration stress is similar to that seen during developmental senescence, whereas the pattern elicited by cold stress is different. Furthermore the patterns of gene expression of 21 transcription factors indicates a complex network of shared regulatory signals. Materials and methods Plant material Alstroemeria inflorescences were harvested and transported to the laboratory in water. For developmental senescence studies individual cymes were detached and placed in glass vials of distilled water throughout the experiment in a growth room (21 °C, 16 h photoperiod, 12–14 μM m2 s−1, 60% relative humidity). For ambient stress experiments the inflorescences were trimmed to 60 cm, placed horizontally within a flower box in the dark, and left at ambient temperature (21 °C) for 6 h or 48 h prior to a 3 h refreshment period in distilled water in the growth room as above. For cold stress treatments the inflorescences were placed horizontally within a flower box in the dark and left at 4 °C for 3 d before refreshment in water in the growth room for 3 h or 24 h. Petals were then detached, frozen in liquid nitrogen, and used for RNA extraction (method as in Breeze et al., 2004). To determine longevity of stress-treated flowers, cymes were isolated and placed in vials of distilled water as described above. cDNA libraries and EST analysis A total of five unsubtracted cDNA libraries were constructed in addition to the SSH libraries detailed in Breeze et al. (2004). The developmental libraries contained material from (i) stages S0 and S1; (ii) stages S2 and S4; and (iii) stages S5 and S7. Stress libraries were made from (i) both ambient dry and (ii) both cold dry treatments detailed above. Dynal beads were used to isolate mRNA from 75 μg of total RNA for each stage or treatment of tissue. Equal amounts of mRNA from each stage/treatment totalling 5 μg were then used to construct each unsubtracted library using Stratagene's λ-zap cDNA synthesis and Gigapack III Gold cloning kit. Primary libraries of 1×106 pfu (plaque-forming units) were obtained. A mass excision of each library was performed and ∼106 clones were plated using blue/white selective media. Over 9600 clones were picked from each library and stored as glycerol stocks. Inserts from selected clones were amplified by PCR using M13 forward and reverse primers either directly from glycerol stocks or from plasmid DNA isolated using a Qiaprep Miniprep kit (Qiagen, Crawley, UK). PCR products were purified using the Whatman vacuum manifold PCR cleanup system (Whatman, Maidstone, UK) or Qiaquick PCR cleanup kits (Qiagen). Sequencing was carried out using M13 forward and reverse primers with BigDye version 2 (Applied Biosystems, Foster City, CA, USA) and was analysed on an Applied Biosystems 3100 sequencer. All sequences determined are available on the NCBI expressed sequence tag (EST) database; accession numbers are detailed in Table S1 available at JXB online. Alignment of proteins and sequences was performed using BIOEDIT version 7.0.1 (Hall, 1999) and Seqman (Dnastar, Lasergene, Madison, WI, USA). Contig assembly was performed using Contig Express (Invitrogen, Paisley, UK). Sequences obtained were compared with database entries using the BLAST network service (NCBI). A match was assessed using a combination of low E value and the length of the homology in BLASTx. TAIR was used to provide extra details on the putative function of some of the genes. Microarray construction Each glycerol stock was used to inoculate a 1.5 ml LB culture containing 50 μg ml−1 ampicillin in a 96-well deep well culture plate. Cultures were grown overnight at 37 °C with shaking at 200 rpm. Plasmid DNA was extracted using the Whatman 96-well Plasmid Miniprep Kit, and ∼100 ng of DNA was used in a PCR to amplify the insert with M13 forward and reverse primers. PCRs were purified using the Whatman 96-well PCR Cleanup Kit protocol and the inserts were checked for size and for the presence of single clones on a 96-well gel (Amersham). Purified inserts were diluted by 50% with dimethylsulphoxide (DMSO) prior to printing onto Corning GAPS coated slides using a robotic Flexys array printer (Genomic Solutions, Ltd, Cambridgeshire, UK) with solid pins, having 120 μm tips delivering ∼340 pl. Slides were also spotted with Cy3/Cy5-labelled landmarks and DMSO blanks. The microarray contained: 960 probes derived from a cDNA library made from cold-stressed flowers, 960 probes derived from a cDNA library of ambient temperature dehydration-stressed flowers, 1269 ESTs from the subtractive libraries described in Breeze et al. (2004), a further 267 unsequenced probes from the same subtractive libraries, and 3053 probes picked randomly from the unsubtracted developmental libraries detailed above (S0–S1, S2, S4, and S5–S7). Each probe was printed in duplicate on each slide, giving a total of 13 018 probes per slide. Slide replication within the experiment gave a total of at least eight replicates per probe. Hybridization and washing Probes were made using 20 μg of total RNA from a TRI reagent (Sigma) extraction (see Breeze et al., 2004 for the method) that was subsequently purified using an Rneasy column (Qiagen) according to the manufacturer's instructions. Indirect post-labelling with Cy3 or Cy5 dye was performed with the Amersham Cyscribe kits according to the manufacturer's instructions. Unlabelled cDNA was purified by ethanol precipitation, and unincorporated Cy dye was removed at the final step using a Nucleospin column (Macheray Nagel). Samples were combined and dried down using a vacuum centrifuge and then resuspended in sterile distilled water. The final steps of probe manufacture and denaturing were performed as detailed in the Amersham Cyscribe handbook and included blocking poly(T)+ tails with an oligo(dA)80 primer with a final volume of 60 μl. All probes were replicated at least twice with reciprocal Cy dye labelling, and hybridization was performed using back to back manual hybridization with two array slides, so the minimum number of technical replicates for any sample was four. Slides were blocked in a solution of 5× SSC, 0.1% SDS, and 1% bovine serum albumin (BSA) for 45 min at 42 °C and then washed in four changes of milliQ water and twice in isopropanol. Hybridization was performed at the same temperature in a humid chamber. After hybridization for ∼16 h, slides were washed in 1× SSC/0.2% SDS for 10 min; 0.1× SSC/0.2% SDS for 2×10 min. Slides were scanned on an Affymetrix 428 scanner at a resolution of 10 μm per pixel. Statistical analysis Images were first assigned spot identity and quantified using Imagene (BioDiscovery). Genes that were absent in all samples of any experiment were filtered out using Excel (Microsoft) and the data were entered into Genespring (Silicon Genetics) for subsequent analysis. Where possible, two-colour analysis was performed; probes were normalized per chip and per gene using a Lowess algorithm with a 40% cut-off. Genes showing >2-fold changes between conditions and with a spread of <1.4 standard deviations from the mean were entered into a Welch t-test or Welch analysis of variance (ANOVA) as appropriate. In experiments such as the developmental study where four stages were compared, a circular design was employed and one-colour analysis was therefore used. Genes were corrected to a baseline of zero and then normalized on a per-chip and per-gene basis. A Welch ANOVA was subsequently used to identify genes showing significant changes between conditions (Supplementary Table S2 at JXB online). Clones containing genes of interest were grown and plasmid DNA extracted as described above prior to sequencing on an ABI 3700 capillary sequencing machine. Contingency χ2 test was performed using Minitab15 (Minitab Inc., PA, USA). Quantitative RT-PCR (qRT-PCR) RT-PCR was carried out using material from two different harvest times with similar results. Primers suitable for use in real-time PCR were designed using Primer 3 (http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi), checked for primer pair and primer self-complementarity using Operon's oligo toolkit (http://www.operon.com/oligos/toolkit.php?), and the secondary structure of the amplicon analysed using Mfold (http://bibiserv.techfak.uni-bielefeld.de/cgi-bin/mfold_submit) to ensure no significant folding at the primer binding sites. A list of the primer sequences is provided in Supplementary Table S3 at JXB online. Total RNA from Alstroemeria petals at the various stages of development or from the different treatments was treated with RNase-free DNase (Ambion TURBO DNase) for 60 min at 37 °C followed by inactivation. cDNA synthesis was performed on 2 μg of RNA using Euroscript reverse transcriptase (Eurogentec) with both oligo d(T) primer and random nonamers, according to the manufacturer's instructions. For the qRT-PCR 1 μl of cDNA was used in each reaction consisting of 12.5 μl of 2× qPCR mastermix for SYBR green No ROX (Eurogentec), 5 pmol each of forward and reverse primer, and 0.75 μl of SYBR green (1/2000 dilution) (Eurogentec) in a final volume of 25 μl. A standard curve was generated by 10-fold serially diluting a standard cDNA mix made up of equal aliquots of cDNA from all developmental stages in order to span the likely range of gene expression. All sample and standard reactions were performed in triplicate. The real-time PCR was carried out using the iCycler iQ Real Time Detection system (Bio-Rad). The reactions were incubated at 50 °C for 2 min for the uracil-N-glycosylase reaction, then heated to 95 °C for 10 min followed by 50 cycles of 15 s at 95 °C and 30 s at 60 °C. Melt curve analysis from 45 °C to 95 °C was performed on the end-products of the PCR to demonstrate that only a single product was being amplified. Each reaction was optimized so that the PCR efficiency was between 90% and 110%. The relative gene expression was calculated from the threshold cycle (Ct value) and was normalized using 18S rRNA expression as an internal control. Results Stress treatments induced early petal abscission Two stress treatments were employed in this study, chosen to mirror post-harvest conditions frequently experienced by cut flowers during the transport chain. First dehydration stress was imposed by storing flowers dry at ambient temperature for a short period (6 h) or a longer period (48 h) and then refreshing them in water for 3 h (Fig. 1A–C). The second stress was a cold treatment: in a separate experiment flowers were cold stressed dry at 4 °C for 72 h (Fig. 1D), and then refreshed in water for a short (3 h) or longer (24 h) period. Cold-stressed flowers were slightly wilted when removed from the cold. However, even a short period of refreshment in water appeared to restore turgidity such that inflorescences were indistinguishable from freshly harvested material. Dehydration-stressed flowers, especially when treated for 48 h, showed considerable wilting when first examined, but again a short refreshment period appeared to restore turgidity. The treatments were both started when the flowers were at stage S0 (freshly harvested tight buds, Fig. 1A), and cold treatment arrested any further development. However, the ambient dehydration treatment allowed development as far as stage S1 (loose bud) over the 48 h period (stages previously defined in Breeze et al., 2004) such that each bud was opening and showing colour (Fig. 1C). This is less than a flower would develop over the same time period if held in water. Other differences only became apparent several days later when the flowers were fully open; for example, anthocyanin pigment development was much reduced in the cold-stressed flowers (Fig. 1E). The longevity of ambient dehydration or cold dehydration-stressed flowers, once replaced in water, was measured as the time taken to tepal abscission (Fig. 2). Cold dehydration stress (72 h) reduced time to 50% abscission by nearly 2 d. Ambient dehydration stress (48 h) reduced the time to 50% abscission by 3 d, with the flowers only lasting 12 d following the refreshment treatment, compared with 15 d shown by the controls. In general, however, the appearance and development of flowers following stress treatment were similar to those of the controls. Therefore it seems that phenotypically stressed flowers followed a normal pattern of development over a more rapid time frame. Fig. 1. Open in new tabDownload slide Appearance of flowers (A) at harvest prior to any stress treatment (control), (B) after removal from 6 h dehydration stress at 21 °C, (C) after removal from 48 h dehydration stress at 21 °C, (D) after 72 h cold dry stress at 4 °C, and (E) opening of cold-stressed flower compared with control at stage S5, the latter showing much more intense pigmentation. Inflorescences showed some wilting following stress treatments, but this was completely ameliorated by a 3 h refreshment treatment in water at ambient temperature. Fig. 2. Open in new tabDownload slide Effect of stress treatments on rate of tepal abscission. Ambient dehydration stress (48 h followed by 3 h refreshment period) accelerated time to 50% tepal abscission by ∼2 d, whereas 3 d cold stress (followed by a 3 h refreshment period) accelerated time to 50% tepal abscission by ∼1 d. n=20 flowers, each of which had six tepals. ESTs from developmental and stress-treated petal cDNA libraries ESTs (1849) were derived from four sources. These were previously published subtracted libraries (Breeze et al., 2004) randomly picked from unsubtracted cDNA libraries representing stages from S0 to S7, from an ambient temperature dehydration-treated library, and from a cold-treated cDNA library. The sequences yielded 270 contigs and 553 singletons. The stress treatments were applied as described in the previous section and were followed by a refreshment period. Using database comparison, contigs and sequences were putatively assigned to 566 independent genes (Supplementary Table S1 at JXB online). As previously described in Breeze et al. (2004), sequences showing the closest homology to metallothioneins were heavily represented in both the developmental and stress-treated libraries. They comprised 358 sequences in 10 contigs and six singletons which represented 19% of the total sequences analysed (Table 1). A putative cell wall protein and a ubiquitin carrier-like protein as well as a genes related to anthocyanin and epicuticular wax biosynthesis were also well represented in the developmental libraries. The ESTs from the stress-treated libraries also included a CXE carboxyl esterase gene and a glutathione S-transferase. Table 1. Most abundant classes of ESTs from developmental senescence and stress-treated libraries Contig No. of clones Source library Putative function Nearest match e-value Developmental libraries Contig 175 102 S0S1 Metallothionein-like protein AY833008 7E-24 Contig 172 49 S0S1 Large subunit rRNA DQ008809 1E-73 Contig 180 40 S2S4 Metallothionein type II AF039003 9E-27 Contig 19 30 S0S1 Metallothionein type II AF039003 9E-27 Contig 239 19 S0S1 Metallothionein type II AF147786 4E-23 Contig 252 19 S2S4 Metallothionein type II AF039003 2E-28 Contig 35 19 S2S4 Metallothionein-like protein AF279655 3E-30 Contig 158 17 SSH0-2 Putative cell wall P8 protein AC146631 0.0009 Contig 203 16 S0S1 Cytochrome b6/f complex subunit VIII BAF64867 0.001 Contig 198 15 S0S1 Ubiquitin carrier-like protein DQ294271 1E-101 Contig 150 13 SSH0-2 GSDL-motif lipase AY491975 6E-62 Contig 242 13 S2S4 Metallothionein type I AF039002 1E-22 Contig 214 11 S0S1 40S ribosomal protein S17 (RPS17D) AT5G04800 3E-64 Contig 128 10 SSH2-45 Dihydroflavonol-4-reductase AY374471 1E-54 Contig 221 10 S2S4 Large subunit rRNA gene DQ008808 0 Contig 45 10 SSH2-0 Aldehyde decarbonylase (epicuticular wax associated) ATHCER1 2E-56 Stress libraries Contig 175 48 COLD Metallothionein-like protein AY833008 7E-24 Contig 172 22 COLD Large subunit rRNA DQ008809 1E-73 Contig 19 15 COLD Metallothionein type II AF039003 9E-27 Contig 242 12 COLD Metallothionein type I AF039002 1E-22 Contig 263 12 COLD Arabidopsis unknown protein/CXE carboxylesterase AT1G47480 0 Contig 180 9 COLD Metallothionein type II AF039003 9E-27 Contig 186 5 COLD No significant BLAST match Contig 187 4 COLD Elongation factor-1 alpha 2 Contig 239 4 COLD Metallothionein-like protein (ML2) AF147786 4E-23 Contig 252 4 COLD Metallothionein type II AF039003 2E-28 Contig 154 3 COLD Ubiquitin precursor-like EU249995 E-116 Contig 193 3 COLD Glutathione transferase AJ441055 4E-72 Contig 35 3 COLD Metallothionein-like protein AF279655 3E-30 Contig No. of clones Source library Putative function Nearest match e-value Developmental libraries Contig 175 102 S0S1 Metallothionein-like protein AY833008 7E-24 Contig 172 49 S0S1 Large subunit rRNA DQ008809 1E-73 Contig 180 40 S2S4 Metallothionein type II AF039003 9E-27 Contig 19 30 S0S1 Metallothionein type II AF039003 9E-27 Contig 239 19 S0S1 Metallothionein type II AF147786 4E-23 Contig 252 19 S2S4 Metallothionein type II AF039003 2E-28 Contig 35 19 S2S4 Metallothionein-like protein AF279655 3E-30 Contig 158 17 SSH0-2 Putative cell wall P8 protein AC146631 0.0009 Contig 203 16 S0S1 Cytochrome b6/f complex subunit VIII BAF64867 0.001 Contig 198 15 S0S1 Ubiquitin carrier-like protein DQ294271 1E-101 Contig 150 13 SSH0-2 GSDL-motif lipase AY491975 6E-62 Contig 242 13 S2S4 Metallothionein type I AF039002 1E-22 Contig 214 11 S0S1 40S ribosomal protein S17 (RPS17D) AT5G04800 3E-64 Contig 128 10 SSH2-45 Dihydroflavonol-4-reductase AY374471 1E-54 Contig 221 10 S2S4 Large subunit rRNA gene DQ008808 0 Contig 45 10 SSH2-0 Aldehyde decarbonylase (epicuticular wax associated) ATHCER1 2E-56 Stress libraries Contig 175 48 COLD Metallothionein-like protein AY833008 7E-24 Contig 172 22 COLD Large subunit rRNA DQ008809 1E-73 Contig 19 15 COLD Metallothionein type II AF039003 9E-27 Contig 242 12 COLD Metallothionein type I AF039002 1E-22 Contig 263 12 COLD Arabidopsis unknown protein/CXE carboxylesterase AT1G47480 0 Contig 180 9 COLD Metallothionein type II AF039003 9E-27 Contig 186 5 COLD No significant BLAST match Contig 187 4 COLD Elongation factor-1 alpha 2 Contig 239 4 COLD Metallothionein-like protein (ML2) AF147786 4E-23 Contig 252 4 COLD Metallothionein type II AF039003 2E-28 Contig 154 3 COLD Ubiquitin precursor-like EU249995 E-116 Contig 193 3 COLD Glutathione transferase AJ441055 4E-72 Contig 35 3 COLD Metallothionein-like protein AF279655 3E-30 Petal libraries: S0S1, S2S4 are non-subtracted libraries from combined stages S0 and S2, and S2 and S4 respectively; SSH0-2 and SSH2-45 are subtracted libraries of stage S2 from S0 and combined stages S4 and S5 from S2, respectively; COLD library combined a 3 d cold treatment followed by a 3 h or 24 h refreshment period (see Materials and methods for further details). Open in new tab Table 1. Most abundant classes of ESTs from developmental senescence and stress-treated libraries Contig No. of clones Source library Putative function Nearest match e-value Developmental libraries Contig 175 102 S0S1 Metallothionein-like protein AY833008 7E-24 Contig 172 49 S0S1 Large subunit rRNA DQ008809 1E-73 Contig 180 40 S2S4 Metallothionein type II AF039003 9E-27 Contig 19 30 S0S1 Metallothionein type II AF039003 9E-27 Contig 239 19 S0S1 Metallothionein type II AF147786 4E-23 Contig 252 19 S2S4 Metallothionein type II AF039003 2E-28 Contig 35 19 S2S4 Metallothionein-like protein AF279655 3E-30 Contig 158 17 SSH0-2 Putative cell wall P8 protein AC146631 0.0009 Contig 203 16 S0S1 Cytochrome b6/f complex subunit VIII BAF64867 0.001 Contig 198 15 S0S1 Ubiquitin carrier-like protein DQ294271 1E-101 Contig 150 13 SSH0-2 GSDL-motif lipase AY491975 6E-62 Contig 242 13 S2S4 Metallothionein type I AF039002 1E-22 Contig 214 11 S0S1 40S ribosomal protein S17 (RPS17D) AT5G04800 3E-64 Contig 128 10 SSH2-45 Dihydroflavonol-4-reductase AY374471 1E-54 Contig 221 10 S2S4 Large subunit rRNA gene DQ008808 0 Contig 45 10 SSH2-0 Aldehyde decarbonylase (epicuticular wax associated) ATHCER1 2E-56 Stress libraries Contig 175 48 COLD Metallothionein-like protein AY833008 7E-24 Contig 172 22 COLD Large subunit rRNA DQ008809 1E-73 Contig 19 15 COLD Metallothionein type II AF039003 9E-27 Contig 242 12 COLD Metallothionein type I AF039002 1E-22 Contig 263 12 COLD Arabidopsis unknown protein/CXE carboxylesterase AT1G47480 0 Contig 180 9 COLD Metallothionein type II AF039003 9E-27 Contig 186 5 COLD No significant BLAST match Contig 187 4 COLD Elongation factor-1 alpha 2 Contig 239 4 COLD Metallothionein-like protein (ML2) AF147786 4E-23 Contig 252 4 COLD Metallothionein type II AF039003 2E-28 Contig 154 3 COLD Ubiquitin precursor-like EU249995 E-116 Contig 193 3 COLD Glutathione transferase AJ441055 4E-72 Contig 35 3 COLD Metallothionein-like protein AF279655 3E-30 Contig No. of clones Source library Putative function Nearest match e-value Developmental libraries Contig 175 102 S0S1 Metallothionein-like protein AY833008 7E-24 Contig 172 49 S0S1 Large subunit rRNA DQ008809 1E-73 Contig 180 40 S2S4 Metallothionein type II AF039003 9E-27 Contig 19 30 S0S1 Metallothionein type II AF039003 9E-27 Contig 239 19 S0S1 Metallothionein type II AF147786 4E-23 Contig 252 19 S2S4 Metallothionein type II AF039003 2E-28 Contig 35 19 S2S4 Metallothionein-like protein AF279655 3E-30 Contig 158 17 SSH0-2 Putative cell wall P8 protein AC146631 0.0009 Contig 203 16 S0S1 Cytochrome b6/f complex subunit VIII BAF64867 0.001 Contig 198 15 S0S1 Ubiquitin carrier-like protein DQ294271 1E-101 Contig 150 13 SSH0-2 GSDL-motif lipase AY491975 6E-62 Contig 242 13 S2S4 Metallothionein type I AF039002 1E-22 Contig 214 11 S0S1 40S ribosomal protein S17 (RPS17D) AT5G04800 3E-64 Contig 128 10 SSH2-45 Dihydroflavonol-4-reductase AY374471 1E-54 Contig 221 10 S2S4 Large subunit rRNA gene DQ008808 0 Contig 45 10 SSH2-0 Aldehyde decarbonylase (epicuticular wax associated) ATHCER1 2E-56 Stress libraries Contig 175 48 COLD Metallothionein-like protein AY833008 7E-24 Contig 172 22 COLD Large subunit rRNA DQ008809 1E-73 Contig 19 15 COLD Metallothionein type II AF039003 9E-27 Contig 242 12 COLD Metallothionein type I AF039002 1E-22 Contig 263 12 COLD Arabidopsis unknown protein/CXE carboxylesterase AT1G47480 0 Contig 180 9 COLD Metallothionein type II AF039003 9E-27 Contig 186 5 COLD No significant BLAST match Contig 187 4 COLD Elongation factor-1 alpha 2 Contig 239 4 COLD Metallothionein-like protein (ML2) AF147786 4E-23 Contig 252 4 COLD Metallothionein type II AF039003 2E-28 Contig 154 3 COLD Ubiquitin precursor-like EU249995 E-116 Contig 193 3 COLD Glutathione transferase AJ441055 4E-72 Contig 35 3 COLD Metallothionein-like protein AF279655 3E-30 Petal libraries: S0S1, S2S4 are non-subtracted libraries from combined stages S0 and S2, and S2 and S4 respectively; SSH0-2 and SSH2-45 are subtracted libraries of stage S2 from S0 and combined stages S4 and S5 from S2, respectively; COLD library combined a 3 d cold treatment followed by a 3 h or 24 h refreshment period (see Materials and methods for further details). Open in new tab Of particular interest were ESTs from the stressed libraries. The 187 ESTs from the cold-stressed library and the 216 ESTs from the dehydration-stressed library were grouped according to function (Fig 3A, B). The distribution of functional classes differed significantly between the cold-treated and dehydration-treated libraries (analysed by contingency χ2). A higher proportion of genes from the cold-treated library were involved in biosynthesis, and genes related to redox processes were also overrepresented compared with the ambient dehydration stress library. Fig. 3. Open in new tabDownload slide Functional analysis of all ESTs derived from (A) dehydration-stressed (6 h or 48 h followed by 3 h refreshment) and (B) cold-stressed (3 d cold treatment followed by a 3 h or 24 h refreshment period) cDNA libraries. Gene expression during developmental senescence and following stress treatments Three genes were selected to compare gene expression following the stress treatments and during developmental senescence using qRT-PCR. These genes had putative functions as a metallothionein-like gene, an adenine-nucleotide translocator, and the α-subunit of the 20S proteasome, and were selected to represent different functional classes. Expression of all three genes was up-regulated >20-fold with either the cold (3 d followed by 3 h refreshment) or ambient dehydration (48 h followed by 3 h refreshment) treatments (Fig. 4A–C) applied as described in previous sections. Breeze et al. (2004) showed by northern analysis that metallothionen gene expression was first detectable at stage S3 and increased with petal age thereafter. The expression patterns for the other two selected genes followed a similar pattern (Fig. 4D, E). In the case of both the adenine nucleotide translocator and the 20S proteasome α subunit, expression was low in young petals, with a minor peak around the time of petal opening (stage S1) followed by a steady increase from stage S4 (early senescence) to stage S6 (mid to late senescence). In both cases, however, expression was reduced in the latest stage of senescence (stage S7). Fig. 4. Open in new tabDownload slide Real-time qPCR of three genes to analyse their expression following stress treatments (A, B, and C) and throughout development and senescence from stage S0 (closed bud) to stage S7 (petal abscision) (D and E). (A) Metallothionein, (B) and (D) adenine nucleotide translocator, (C) and (E) 20S proteasome α subunit. O is unstressed flowers at stage S0, C is 3 d cold treatment followed by a 3 h refreshment period, and WD is ambient dehydration stress of 48 h followed by a 3 h refreshment period. Each experiment was repeated on a second biological replicate with similar results. Maximum and minimum SDs for each experiment are shown. Dehydration stress and developmental senescence elicited gene expression changes in a common group of genes. To compare expression patterns across more genes, microarrays were constructed using the 1849 sequenced clones, and a further 4660 unsequenced clones from the same cDNA libraries. Changes in expression following the two stress treatments were compared with changes between stage S0 (young buds) and stage S5 (early senescence). These stages were chosen to represent genes whose expression changes with the onset of developmental senescence (Fig. 5, Supplementary Tables S2 and Supplementary Data at JXB online). The most striking feature was the greater sharing of gene expression between developmental senescence and ambient dehydration stress (281/1345) compared with developmental senescence and cold stress (68/1345). The similarity in gene expression patterns between ambient dehydration stress and developmental senescence was more marked in the up-regulated genes. Of the 1069 probes up-regulated between stages S0 and S5, 25% were also up-regulated following the ambient stress treatments while only 5% of them were up-regulated with the cold stress treatments. However, of the 276 probes down-regulated between stages S0 and S5, 7% were also down-regulated following the ambient stress treatments and 4% of them were down-regulated following cold treatments, thus the levels were more similar. Probes whose expression changed following dehydration stress and between stages S0 and S5 included several transcription factors as well as metallothioneins and an armadillo domain protein. Amongst those whose expression changed following cold stress and during developmental senescence, genes included a dihydroflavonol-4-reductase and metallothionein-related genes. Another feature of these results was the large number of probes whose expression pattern was unique to the specific treatment or developmental senescence. Expression of only 21 array probes was changed by both stress treatments and developmental senescence. All of the sequenced up-regulated probes were metallothionein related (or rRNA) whereas the single down-regulated probe was homologous to the 20S proteasome α subunit B1. Fig. 5. Open in new tabDownload slide Venn diagram and details of microarray probes whose signal changed following cold stress (3 d cold treatment followed by a 3 h or 24 h refreshment period) or ambient dehydration stress (6 h or 24 h followed by a 3 h refreshment period) compared with developmental changes between stage S0 and stage S5 (stage S0 petals were from closed buds and stage S5 represents the first visible signs of senescence). The two subtreatments associated with each stress were grouped together. Details of main gene functions associated with some of the Venn diagram intersections are listed below the diagram. Some gene expression changes were exclusive to stress, and were not shared with developmental changes Microarrays were used to assess to what extent changes in gene expression at the end of the stress treatment resembled changes seen in petals of the same chronological age held under optimal conditions. Probes whose expression changed post-stress were compared with probes showing little developmental change in non-stressed conditions between stages 0 and 2 (tight bud to open flower) (Fig. 6, Supplementary Tables S2 and Supplementary Data at JXB online). Similar numbers of probes changed in expression following either stress treatment but were stable in expression between stages S0 and S2. Of those whose expression changed following dehydration stress, putative functions included respiration, cellulose biosynthesis, and a signalling-related kinase. Those that changed in response to cold included ubiquitin-mediated proteolysis, terpene and anthocyanin biosynthesis, ribosomal proteins, and a BCL-2-associated athanogene. Only a small number of probes changed in expression following both treatments but were also stable in expression from stages S0 to S2. Only one probe, a metallothionein, was up-regulated by both treatments but not from stages S0 to S2, and the eight probes whose expression was down-regulated included a gibberellin (GA)-regulated protein, a defence response protein, and a S-adenosyl-L-methionine:jasmonic acid (SAM:JA) carboxyl methyltransferase. Fig. 6. Open in new tabDownload slide Venn diagram and details of microarray probes whose signal changed following cold stress (3 d cold treatment followed by a 3 h or 24 h refreshment period) or ambient dehydration stress (6 h or 24 h followed by 3 h refreshment period) compared with probes whose signal did not change during early unstressed floral development (from stage S0 to stage S2) The two subtreatments associated with each stress were grouped together. Details of the main gene functions associated with some of the Venn diagram intersections are listed below the diagram. The length of stress treatment and the rehydration period affected gene expression patterns Microarray analysis was used to investigate the effects of a longer (24 h) or shorter (3 h) refreshment period on gene expression (Fig. 7, Supplementary Tables S2 and Supplementary Data at JXB online). This experiment was designed to identify which genes show transitory changes in expression following the cold treatment and which show more stable changes in expression. Expression of less than half of the probes was changed following both refreshment periods, indicating that the length of the refreshment period has an important effect on gene expression profiles and that transcript half-life is relatively short. However, of those genes whose expression was down-regulated after the 3 h recovery period 37% were still down-regulated after 24 h recovery relative to unstressed controls. In contrast, only 19% of probes whose expression was up-regulated following 3 h of refreshment were still up-regulated after 24 h of refreshment. The expression of a number of transcription factors of various classes was differentially affected by the two refreshment periods, as well as genes related to fatty acid biosynthesis, a dihydroflavonol-4-reductase, and metallothionein-related genes. Fig. 7. Open in new tabDownload slide Venn diagram and details of microarray probes whose signal changed following 3 d cold stress followed by a period of 3 h or 24 h refreshment. Details of the main gene functions associated with the Venn diagram classes are listed below the diagram. Many more genes showed altered expression following 48 h of treatment than after just 6 h of treatment (Fig. 8, Supplementary Tables S2 and Supplementary Data at JXB online). However, changes in the expression of substantial numbers of probes were induced by either treatment. Again expression of several transcription factors was changed differentially between the two treatments, as well as a serine/threonine protein kinase, a cysteine protease, a GA-regulated protein, and an ABA-regulated protein. Fig. 8. Open in new tabDownload slide Venn diagram and details of microarray probes whose signal changed following ambient dehydration stress for 6 h or 48 h, followed by a 3 h refreshment. Details of the main gene functions associated with the Venn diagram classes are listed below the diagram. Stress treatments elicit changes in the expression of genes related to plant growth regulator (PGR) signalling EST sequencing identified 24 genes related to PGR signalling. Of these, a large number were altered in expression in response to the stress treatments (Table 2). Of the six auxin-related genes whose expression changed as a result of the stress treatments, four were down-regulated by the cold treatments, while expression of only two was up-regulated. In contrast, cold treatment appeared to up-regulate expression of four out of the six ABA-related genes. Expression of ethylene-, JA-, and GA-related genes was down-regulated by cold, whereas expression of the cytokinin-regulated gene was up-regulated. The dehydration treatments generally induced more down-regulation of PGR-related genes compared with up-regulation (Table 2). Expression of only two PGR-related genes was up-regulated: an auxin-regulated protein and an ABA-regulated protein. Table 2. Expression patterns of genes related to plant growth regulator signalling, in response to stress treatments PGR ID/contig Function Cold treatments Dehydration treatments Auxin 12.m15 ATAPP1 (aminopeptidase P1), auxin polar transport D Contig 94 Auxin binding/ubiquitin-protein ligase (AFB2) D 4.b13 Auxin response factor 4 U 15.a10 Auxin-induced protein D 14.c13 Auxin-induced protein D D Contig 229 Auxin-regulated protein U U ABA Contig 226 ABA ripening protein-like protein U 15.c18 ABA-responsive protein (GRAM domain) D Contig 1 ABA-responsive protein-related (GRAM domain) D D 10.b17 AtHVA22a protein, ABA- and stress-inducible U 11.l15 Supersensitive to ABA and drought D 1.j9 Transcription factor, ABA and drought responsive U D 8.p19 Abscisic stress ripening protein-like protein U U Ethylene 15.h15 EIL2, ethylene transcriptional factor D D JA Contig 57 SAM:JA carboxyl methyltransferase D D Cytokinin 10.j15 Proline dehydrogenase/oxygenase, cytokinin inducible U GA Contig 122 Gibberellin-regulated family protein D Contig 119 Gibberellin-regulated protein GASA2 precursor D D PGR ID/contig Function Cold treatments Dehydration treatments Auxin 12.m15 ATAPP1 (aminopeptidase P1), auxin polar transport D Contig 94 Auxin binding/ubiquitin-protein ligase (AFB2) D 4.b13 Auxin response factor 4 U 15.a10 Auxin-induced protein D 14.c13 Auxin-induced protein D D Contig 229 Auxin-regulated protein U U ABA Contig 226 ABA ripening protein-like protein U 15.c18 ABA-responsive protein (GRAM domain) D Contig 1 ABA-responsive protein-related (GRAM domain) D D 10.b17 AtHVA22a protein, ABA- and stress-inducible U 11.l15 Supersensitive to ABA and drought D 1.j9 Transcription factor, ABA and drought responsive U D 8.p19 Abscisic stress ripening protein-like protein U U Ethylene 15.h15 EIL2, ethylene transcriptional factor D D JA Contig 57 SAM:JA carboxyl methyltransferase D D Cytokinin 10.j15 Proline dehydrogenase/oxygenase, cytokinin inducible U GA Contig 122 Gibberellin-regulated family protein D Contig 119 Gibberellin-regulated protein GASA2 precursor D D Cold refers to either of the two cold treatments: 3 d cold followed by 3 h or 24h refreshment, and dehydration to either of the two ambient dehydration treatments: 6 h or 48 h followed by a 3 h refreshment. D, down-regulated; U, up-regulated. where no response is recorded, array values did not pass the statistical tests; for contigs, a change is noted if the signals from one or more of the probes from that contig on the arrays changed significantly and consistently. Open in new tab Table 2. Expression patterns of genes related to plant growth regulator signalling, in response to stress treatments PGR ID/contig Function Cold treatments Dehydration treatments Auxin 12.m15 ATAPP1 (aminopeptidase P1), auxin polar transport D Contig 94 Auxin binding/ubiquitin-protein ligase (AFB2) D 4.b13 Auxin response factor 4 U 15.a10 Auxin-induced protein D 14.c13 Auxin-induced protein D D Contig 229 Auxin-regulated protein U U ABA Contig 226 ABA ripening protein-like protein U 15.c18 ABA-responsive protein (GRAM domain) D Contig 1 ABA-responsive protein-related (GRAM domain) D D 10.b17 AtHVA22a protein, ABA- and stress-inducible U 11.l15 Supersensitive to ABA and drought D 1.j9 Transcription factor, ABA and drought responsive U D 8.p19 Abscisic stress ripening protein-like protein U U Ethylene 15.h15 EIL2, ethylene transcriptional factor D D JA Contig 57 SAM:JA carboxyl methyltransferase D D Cytokinin 10.j15 Proline dehydrogenase/oxygenase, cytokinin inducible U GA Contig 122 Gibberellin-regulated family protein D Contig 119 Gibberellin-regulated protein GASA2 precursor D D PGR ID/contig Function Cold treatments Dehydration treatments Auxin 12.m15 ATAPP1 (aminopeptidase P1), auxin polar transport D Contig 94 Auxin binding/ubiquitin-protein ligase (AFB2) D 4.b13 Auxin response factor 4 U 15.a10 Auxin-induced protein D 14.c13 Auxin-induced protein D D Contig 229 Auxin-regulated protein U U ABA Contig 226 ABA ripening protein-like protein U 15.c18 ABA-responsive protein (GRAM domain) D Contig 1 ABA-responsive protein-related (GRAM domain) D D 10.b17 AtHVA22a protein, ABA- and stress-inducible U 11.l15 Supersensitive to ABA and drought D 1.j9 Transcription factor, ABA and drought responsive U D 8.p19 Abscisic stress ripening protein-like protein U U Ethylene 15.h15 EIL2, ethylene transcriptional factor D D JA Contig 57 SAM:JA carboxyl methyltransferase D D Cytokinin 10.j15 Proline dehydrogenase/oxygenase, cytokinin inducible U GA Contig 122 Gibberellin-regulated family protein D Contig 119 Gibberellin-regulated protein GASA2 precursor D D Cold refers to either of the two cold treatments: 3 d cold followed by 3 h or 24h refreshment, and dehydration to either of the two ambient dehydration treatments: 6 h or 48 h followed by a 3 h refreshment. D, down-regulated; U, up-regulated. where no response is recorded, array values did not pass the statistical tests; for contigs, a change is noted if the signals from one or more of the probes from that contig on the arrays changed significantly and consistently. Open in new tab Transcription factor-like genes change in expression in response to stress treatments and developmental senescence, suggesting complex patterns of regulation Thirty-six EST sequences were identified as transcriptional regulators representing at least 24 different genes (Table 3). The largest group (eight genes) was represented by zinc finger proteins, but transcription factors of the Myb, Lim, Hap5B, and MADS box gene families were also present. Expression patterns for 21 different contigs and singletons could be analysed from the arrays (Table 3), revealing different expression patterns. Expression of only four of the transcriptional control genes changed during development from S0 to S5. Real time RT-PCR confirmed this result for two of the transcription factors: a MADS box and a C2H2-zinc finger-type transcription factor (Fig. 9), also revealing a more complex expression pattern during development. Transcript levels of the C2H2-zinc finger transcription factor peaked at stage S0 (tightly closed bud) and again at stage S6 (mid-senescent petals) (Fig. 9A), whereas the MADS box gene peaked at stage S1 (young bud) and stage S4 (open flower) (Fig. 9B). Expression of greater numbers of the transcriptional regulators was affected by the stress treatments. Fourteen changed in response to one or other of the two cold treatments, and 17 in response to one or other of the two ambient dehydration treatments. Expression of the same number of transcription factors was changed after a 3 h and 24 h refreshment period following the 3 d cold treatment, but expression of only six was changed at both time points, and expression of four was altered after a 3 h but not after a 24 h refreshment period. Cold treatment appeared to induce more down-regulation than up-regulation of transcription factor expression, and transcript levels of five regulators were reduced as a result of both cold treatments, whereas none was up-regulated. However, expression of only two transcriptional regulators was up- or down-regulated by both the ambient dehydration stress treatments. Each transcriptional control gene showed a unique pattern of expression on the array, indicating a complex network of transcriptional regulation. Table 3. Expression profiles of transcriptional regulators from microarray analysis For contigs, a change is noted if the signals from one or more of the probes from that contig on the arrays changed significantly. Treatments are detailed in text. dehydr., ambient dehydration treatment. Open in new tab Table 3. Expression profiles of transcriptional regulators from microarray analysis For contigs, a change is noted if the signals from one or more of the probes from that contig on the arrays changed significantly. Treatments are detailed in text. dehydr., ambient dehydration treatment. Open in new tab Fig. 9. Open in new tabDownload slide Real-time qPCR of two transcription factors to analyse their expression throughout development and senescence from stage S0 (closed bud) to stage S7 (petal abscission) (A) MADS box transcription factor and (B) zinc finger (C2H2 type) family protein. Each experiment was repeated on a second biological replicate with similar results. Maximum and minimum SDs for each experiment are shown. Discussion Both leaves and flowers show a remarkable recovery from moderate stress treatments (Hsiao, 1973) and, in the case of Alstroemeria flowers, their appearance following a recovery period was almost indistinguishable from that of untreated controls. However, the acceleration of leaf and flower abscission following dehydration stress is also well established (Hsiao, 1973). In Alstroemeria, following both cold and ambient dehydration stress, floral lifespan was substantially reduced from the 15 d achievable under optimal conditions (Chanasut et al., 2003). However, there were differences between the two stress treatments. Cold treatment arrested development which did not proceed appreciably during the 72 h of the treatment; however, during the ambient dehydration stress the flowers continued to develop, albeit more slowly than under optimal conditions. The effects of cold treatment are consistent with those seen in other flowers, for example rose (Faragher et al., 1986) and petunia (Ferrante et al., 2006), and in other lilies where prolonged cold storage also has an adverse effect on longevity (Ranwala and Miller, 2005). The differential physiological response of the flowers to the two kinds of stress treatment indicated that different sets of genes were being activated, as has been found in other systems (Nakashima et al., 2009) where complex networks of regulatory signals activate partially overlapping pathways. Over 800 new Alstroemeria petal ESTs were added to the EST collection in this work, almost doubling the number of ESTs reported in previously published work (Breeze et al., 2004) and providing additional ESTs from stressed material. ESTs derived from stressed material comprised a high proportion of metallothionein genes (>20% for both cold- and dehydration-stressed material) though the proportion was not as high as the 83% reported for Alstroemeria petal libraries enriched for genes expressed in later stages of senescence (Breeze et al., 2004). This abundance of metallothionein genes has been previously reported for libraries derived from senescent leaves (Gibbings et al., 2003) petals (Channèliere et al., 2002), and ripening fruit (Aharoni et al., 2000; Moyle et al., 2004). Likewise, in studies surveying the stressed transcriptome, metallothioneins are highly represented, for example in drought-stressed rice leaves (Reddy et al., 2002) and cold-stressed Arabidopsis leaves (Jung et al., 2003). The function of such high levels of metallothioneins is not clear; however, they may be involved in sequestering metal ions to avert increases in ROS levels during stress (Reddy et al., 2002). The EST collection also included genes relating to other important cellular processes that have been previously associated with leaf and petal senescence in other systems such as remobilization of nutrients. A comparison of the functional classes of the ESTs between those derived from the cold-stressed and ambient dehydration-stressed cDNA libraries indicates a difference in the representation of different biochemical processes. Genes related to biosynthetic processes were under-represented in the library derived from dehydration-stressed material compared with the cold-stressed material. Biosynthetic process-related genes were down-regulated during senescence in Alstroemeria (Breeze et al., 2004), suggesting a possible closer similarity between dehydration stress and senescence that between cold stress and senescence. Since stress treatments appear to accelerate processes associated with developmental senescence, it was predicted that genes whose expression was up-regulated by the stress treatments would also increase in expression during developmental senescence. This was confirmed in the case of three genes: a metallothionein and two genes related to remobilization and proteolysis, respectively. In the latter two genes, expression was reduced by the latest stage of senescence, suggesting that at this stage remobilization processes were being down-regulated. This indicates that some processes such as remobilization and ubiquitin-mediated proteolysis, associated in Alstroemeria and in other systems with floral senescence (e.g. Courtney et al., 1994), are being activated by the stress treatment. Gene expression was assessed here after a refreshment period which restored the appearance of the flowers to that of pre-senescent open flowers. Thus senescence-associated transcripts normally only evident after stage S4 are switched on, at least in some cells, in petals that have the appearance of stage S2–S3 flowers. This appears to indicate a change in the coordination of developmental senescence induced by the stress. Microarray experiments were used to investigate to what extent stress treatments simply accelerated normal developmental/senescence processes or to what extent genes specific to the stress were up- or down-regulated. In general the microarray results were confirmed by the RT-PCR and previously published northern analysis (Breeze et al., 2004). Metallothionein expression was up-regulated on the arrays by a mean of 21-fold (±3.37 SEM over 140 array probes) between stages S0 and S5, and this is supported by a strong up-regulation on the northern (Breeze et al., 2004). Likewise, metallothionein expression was up-regulated by 2.1-fold (±0.11 SEM over 150 array probes) following cold stress (3 d followed by 3 h refreshment) and by 3.6-fold (±0.28 SEM over 150 array probes) following ambient dehydration stress (48 h followed by 3 h refreshment), in agreement with the strong up-regulation seen with the real-time RT-PCR (Fig. 4A). For the zinc finger (C2H2 type) family transcription factor, the RT-PCR and array trends were again in general agreement. On the array, stage S5 expression was 0.4-fold of stage S0 expression (±0.19 SEM over three probes), while on the RT-PCR it was 0.6 fold (Fig 8B). In common with several other senescence-related experiments in other tissue types, far more genes were up-regulated by both development and stress than were down-regulated (Andersson et al., 2004; Lin and Wu, 2004). However, a key finding from the work presented here is that changes in gene expression induced by ambient dehydration stress were more similar to the pattern induced during developmental senescence than that induced by cold stress. Thus the data suggest that more senescence-related metabolic changes might be anticipated in flowers treated with dry ambient conditions than those treated with cold, supported by evidence that developmental events such as flower opening continue to some extent during dry ambient stress (Fig. 1). The fact that fewer genes are down-regulated compared with up-regulated may be due to fewer genes being expressed at stage S0 relative to later developmental stages. The comparison between the selected stages (stage S0 and S5) will also have missed genes whose expression patterns are more complex and only change in the intervening stages (stages S1–S4). Many of the individual probes up-regulated by stress and development encode metallothionein-like proteins (Breeze et al., 2004); however, other genes encoding other proteins were also up-regulated, including ribosomal proteins, regulators, and enzymes related to volatile production. The latter, characterized by a terpene synthase/cyclase, was unexpected given that this variety of Alstroemeria is not scented. However, several of its wild ancestors are scented (HJ Rogers et al. unpublished data) so part of the terpenoid biosynthesis pathway (Dudareva et al., 2003) may still be present in this species. A gene putatively encoding dihydroflavonol-4-reductase was down-regulated in response to cold treatment as well as during developmental senescence. This enzyme is the first step in anthocyanin biosynthesis (Holton and Cornish, 1995), and its down-regulation following cold treatment may be related to the reduced pigmentation seen in cold-treated flowers compared with controls. Expression of proteasome subunits was also noted in senescing Arabidopsis leaves (Guo et al., 2004), and some groups have reported an increase in proteasome and ubiquitin activity during senescence (Roberts et al., 2002). However, this is believed to be due to post-transcriptional events (Basset et al., 2002). Proteasome subunit genes were repressed during Alstroemeria petal senescence and in Alstroemeria petals that had undergone stress treatments. In Arabidopsis, expression of the majority of drought-responsive genes returned to control levels following 3 h of re-watering (Huang et al., 2008). Here results from the two refreshment periods after cold stress suggested that there was significant recovery. However, for a substantial number of genes, 3 h refreshment was insufficient to restore control transcriptional levels. This effect was more marked for down-regulated compared with up-regulated gene expression, suggesting that up-regulation of gene expression was more transient. The transient nature of gene expression induced by cold treatment may reflect the activation of transcriptional cascades known to play an important part in cold acclimation (Zhu et al., 2007). Of the 21 transcription factors examined here on the Alstroemeria microarrays, some expression was shared between the shorter or longer stress or refreshment periods. However, expression of some transcriptional regulators was only changed at one of the two time points. This suggests the possibility of transcriptional cascades also occurring during the refreshment period. Transcriptomic studies have investigated gene expression in Arabidopsis following recovery from cold stress (Oono et al., 2006) and found that expression of a substantial number of genes was up-regulated during cold acclimation but subsequently down-regulated during a 24 h recovery period, and vice versa. Likewise new sets of genes were induced during recovery from dehydration stress (Oono et al., 2003), indicating that different gene sets were required for acclimation and de-acclimation to stress treatments. Expression of a number of genes related to plant growth regulator signalling was altered in response to the cold stress treatments in Alstroemeria petals, as was found with cold stress in Arabidopsis seedlings (Lee et al., 2005). The greater number of auxin-related ESTs from Alstroemeria petals that were down-regulated by the cold treatment is in agreement with Lee et al. (2005). The down-regulation of the genes related to methyl-jasmonate biosynthesis and ethylene response in the Alstroemeria petals following both cold treatments is also in accordance with results from Arabidopsis (Lee et al., 2005). The majority of the ABA-related genes were up-regulated following cold treatment, again in agreement with data from Arabidopsis seedlings (Lee et al., 2005), although in rice, less cross-talk was found between ABA and cold signalling than between drought and ABA (Rabbani et al., 2003). Dehydration stress also appeared mainly to down-regulate expression of PGR-related genes, although expression of far fewer auxin-responsive genes was affected by dehydration stress compared with cold stress. Down-regulation of ABA-responsive genes was unexpected as dehydration is generally associated with up-regulation of ABA signalling (e.g. Rabbani et al., 2003). Note, however, that gene expression here was assessed after a refreshment period, so it may be that ABA signalling was only up-regulated during the period of stress, and that there is overcompensated down-regulation during refreshment. This seems likely since in other flowers such as rose (Le Page-Degivry et al., 1991) and daylily (Panavas et al., 1998) endogenous ABA levels rise in response to stress treatments when expression is measured immediately following the stress with no refreshment period. A number of different transcription factor types were identified from the ESTs, although no WRKY type transcription factors were found. This was unexpected as several WRKY transcription factors are important in leaf senescence in both dicotyledonous (Eulgem et al., 2000) and monocotyledonous plants (Ross et al., 2007). Different patterns of expression were revealed by the microarray analysis for 21 putative transcription factors, indicating some sharing of transcriptional regulation between the stress and developmental senescence (Table 3). This overlap is well documented in leaves both at the level of genes involved in effecting these processes (Weaver et al., 1998) and at the level of transcription factors (Singh et al., 2002). Less progress has been made in understanding the transcriptional control of floral senescence, although some of the characterized transcriptional regulators have also shown an overlap in expression between petal senescence and stress (van der Krol et al., 1999). Relatively few of the transcription factors identified from the Alstroemeria ESTs changed in expression during senescence (from stage S0 to S5) on the microarrays; however, analysis of a zinc finger (C2H2 type) family protein and a MADS box protein by RT-PCR reveals a bimodal expression pattern. Expression was high in buds at stage S0 and S1, respectively, and then again from stage S5 and S4, respectively; hence both these transcription factors may well be involved in senescence regulation. Transcription factors associated with petal senescence have been identified in a few systems such as rose (Channelière et al., 2002), petunia (van der Krol, 1999), and wallflower (Price et al., 2008). However the detailed expression during petal senescence of multiple transcription factors has not been studied in many species. MADS box transcription factors may well be generally important in petal senescence since overexpression of a MADS box transcription factor in Arabidopsis delayed petal senescence and abscission (Fang and Fernandez, 2002). Several transcription factors showed patterns of regulation in one or other of the stress treatments that mimicked developmental patterns, but none responded in the same way to all stresses and development. This may indicate the means by which specific responses are elicited to particular stresses. 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Interplay between cold-responsive gene regulation, metabolism and RNA processing during plant cold acclimation , Current Opinion in Plant Biology , 2007 , vol. 10 (pg. 290 - 295 ) Google Scholar Crossref Search ADS PubMed WorldCat Author notes † Present address: School of Food and Nutritional Sciences, University of Reading, PO Box 226, Whiteknights, Reading RG6 6AP, UK. © 2010 The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2010 The Author(s).

Wu, Xianshan; Wang, Zhenghang; Chang, Xiaoping; Jing, Ruilian

doi: 10.1093/jxb/erq117pmid: 20497970

Abstract Plant height (PH), a crucial trait related to yield potential in crop plants, is known to be typically quantitatively inherited. However, its full expression can be inhibited by a limited water supply. In this study, the genetic basis of the developmental behaviour of PH was assessed in a 150-line wheat (Triticum aestivum L.) doubled haploid population (Hanxuan 10×Lumai 14) grown in 10 environments (year×site×water regime combinations) by unconditional and conditional quantitative trait locus (QTL) analyses in a mixed linear model. Genes that were expressed selectively during ontogeny were identified. No single QTL was continually active in all periods of PH growth, and QTLs with additive effects (A-QTLs) expressed in the period S1|S0 (the period from the original point to the jointing stage) formed a foundation for PH development. Additive main effects (a effects), which were mostly expressed in S1|S0, were more important than epistatic main effects (aa effects) or QTL×environment interaction (QE) effects, suggesting that S1|S0 was the most significant development period affecting PH growth. A few QTLs, such as QPh.cgb-6B.7, showed high adaptability for water-limited environments. Many QTLs, including four A-QTLs (QPh.cgb-2D.1, QPh.cgb-4B.1, QPh.cgb-4D.1, and QPh.cgb-5A.7) coincident with previously identified reduced height (Rht) genes (Rht8, Rht1, Rht2, and Rht9), interacted with more than one other QTL, indicating that the genetic architecture underlying PH development is a network of genes with additive and epistatic effects. Therefore, based on multilocus combinations in S1|S0, superior genotypes were predicted for guiding improvements in breeding for PH. Development, drought stress, epistasis, plant height, quantitative trait loci, Triticum aestivum L Introduction Plant height (PH), an important trait related to plant architecture and yield potential, is controlled polygenically (Cadalen et al., 1998; Peng et al., 1999; Sakamoto and Matsuoka, 2004). Twenty-one major genes influencing PH have been designated as reduced height (Rht) genes in wheat (Pestsova and Roder, 2002). Polygenes with quantitative effects on PH have been mapped on all 21 chromosomes (Cadalen et al., 1998; Korzun et al., 1998; Börner et al., 2002; Eriksen et al., 2003; Peng et al., 2003; Schnurbusch et al., 2003; McCartney et al., 2005; Liu et al., 2006; Pestsova et al., 2006; Quarrie et al., 2006). Segregation patterns in progeny of wheat crosses indicate that PH is under major gene control. Genetic components estimated from generation means (parental, F1, F2, F3, and backcross) showed that additive gene effects were the major components of variation in the majority of crosses, but in some crosses epistasis was the primary source of genetic variation (Fick and Qualset, 1973). Quantitative trait locus (QTL)×environment interaction (QE) effects were also important in determining terminal PH (Cao et al., 2001,a; Yu et al., 2002; Li et al., 2003; Zhang et al., 2008). Recently, there has been an increased interest in developmental genetics (Gray, 2004; Gaudet et al., 2004; Brady et al., 2006; Levesque et al., 2006; Gross et al., 2008). Unconditional analysis is a traditional method for studying developmental behaviour (Zhu, 1995). Depending upon the phenotype at various stages of development, the method can be used to reveal the static genetic control of traits at different growth stages. Sequential data reflect cumulative effects from the original to time t, rather than the real effects of gene expression during ontogeny. Zhu (1995) developed a mixed model approach, conditional analysis for analysing the net genetic effects in the period from time (t–1) to time t on trait development. The genetic effects revealed by conditional analysis, independently of the causal genetic effects at time (t–1), were influenced by the developmental status and external cropping environment of crops (Wu et al., 2002), revealing new genetic variation arising in specific periods during ontogeny. Thus, conditional analysis should be a valid method for identifying dynamic gene expression during the development of quantitative traits (Cao et al., 2001,b). Conditional effects in early growth periods, as cumulative components, could affect later unconditional effects. The combination of unconditional and conditional analyses could more easily detect the expressional dynamics of PH QTLs, revealing the genetic bases for PH development, and perhaps uncovering a QTL expression pathway during plant growth. PH is easily measured throughout plant development, so it could serve as a suitable model trait for the study of developmental behaviour. The genetic control of PH development has been studied in rice, soybean, and mung bean by unconditional or conditional analysis (Yan et al., 1998,a; Cao et al., 2001,b; Khattak et al., 2002; Sun et al., 2006; Yang et al., 2006). These studies suggested that different QTLs/genes might control PH at different developmental stages, and a genetic model based on final phenotypes might not fully reflect the reality of morphological evolution. However, correlations among different QTLs across ontogeny, the changing genetic effects during ontogeny, and the vital developmental period for PH determination were not revealed in previous studies. Also, epistatic and QE effects were inadequately examined. PH is a trait modified by environment. A limited water supply often inhibits PH development, consequently affecting yield (Sari-Gorla et al., 1999; Baum et al., 2003). QE is an important factor determining the stability of crop production in unfavourable environments (Lanceras et al., 2004; Maccaferri et al., 2008). Interactions among loci or between genes and environmental factors make substantial contributions to variation in complex traits (Carlborg and Haley, 2004). Mapping QTLs with genetic main effects and QE effects could help in understanding the nature of quantitative traits (Yan et al., 1998,b; Cao et al., 2001,b; Wu et al., 2002; Li et al., 2003; Ungerer et al., 2003; Yang et al., 2007). Therefore, epistasis and QE effects should not been ignored for studies of developmentally complex traits such as PH. The objective of the present research was to map loci with genetic main effects and QE effects underlying the developmental behaviour of PH in doubled haploid lines (DHLs) of wheat (Triticum aestivum L.) by unconditional and conditional mapping, in order to uncover the genetic basis of PH development, even in water-limited environments. This approach was considered valuable for understanding the determination of final PH and for detecting PH markers that influence PH development during key periods and might be useful for marker-assisted selection (MAS). Materials and methods Experimental material The 150 DHLs were the same as used in previous studies (Jing et al., 1999; Hao et al., 2003; Zhou et al., 2005; Yang et al., 2007). The population was derived from the cross of Hanxuan 10×Lumai 14. The two parents differed greatly in many morphological and physiological characters such as plant architecture, yield, and drought tolerance. Hanxuan 10, a tall drought-tolerant cultivar with a large number of roots, high root fresh weight and root dry weight, and a low maximum root length, ratio of root dry weight to shoot dry weight, and ratio of root fresh weight to shoot fresh weight (Zhou et al., 2005), released by Fenyang Station, Shanxi Academy of Agricultural Sciences in 1966, is still grown in arid and barren areas. Lumai 14, a short high-yielding cultivar, with a low number of roots, low root fresh weight and root dry weight, and a high maximum root length, ratio of root dry weight to shoot dry weight, and ratio of root fresh weight to shoot fresh weight (Zhou et al., 2005), adapted to abundant water and fertile conditions, was developed at the Yantai Institute of Agricultural Sciences, Shandong, and was widely grown in northern China during the 1990s. The Rht genes in both parents were detected by molecular markers (http://wheat.pw.usda.gov/cgi-bin/graingenes/). The marker Xgwm261 near Rht 8 was mapped on the short arm of chromosome 2D. Other Rht genes, such as Rht 1, Rht 2, and Rht 9 (Ellis et al., 2005), were also identified using the related markers, but could not be mapped to the linkage map because of no polymorphism between the two parents. Field experiment The 150 DHLs and their parents were grown at three sites over 3 years, The sites were Fenyang Station, Shanxi (111º47' E; 37º15' N) in 2001 (F01); Haidian, Beijing (116º28' E; 39º48' N) in 2005 (H05) and 2006 (H06); and Changping, Beijing (116º13' E; 40º13' N) in 2005 (Ch05) and 2006 (Ch06). The experimental fields at each site were managed under drought stress (DS) and well watered (WW) conditions. F01, H05, Ch05, H06, and Ch06 under DS were denoted as E1, E2, E3, E4, and E5, respectively, and those under WW management as E6, E7, E8, E9, and E10. DS treatments were represented by rain-fed conditions. The rainfall from July in the planting year to June (flowering stage) in the harvesting year was 296.7, 330.6, 395.4, 434.9, and 503.8 mm for each site. The total rainfall from mid November 2001 to mid June 2002 was only 35.4 mm in F01, a severe DS growth season. The WW treatments were irrigated with 900 m3 ha−1 at the pre-overwintering, jointing, flowering, and grain filling stages. A randomized complete block design was adopted to arrange the DHLs in each water regime, at one line one plot. The parental lines were planted alternatively at every 50th plot to evaluate the uniformity of the field. Each plot consisted of four 4 m rows at 30 cm spacing, with 180 plants per row in F01, Ch05, and Ch06, and two 2 m rows at 30 cm spacing, with 40 plants per row in H05 and H06. At the jointing stage, PH (from the soil surface to the top of the plant canopy in each plot) was measured every 7 d until flowering when full PH was attained. A total of five measurements were taken during the growing period, and they were designated S1, S2, S3, S4, and S5. Fertility and cultivation regimes were consistent with wheat production in the relevant regions. Data analysis Based on the development theory proposed by Zhu (1995), the actual PH values at the different stages were defined as unconditional PHs, and the PH values obtained by the mixed model approach for conditional genetics of developmental quantitative traits as conditional PHs. The analyses for both unconditional and conditional PHs were used to detect dynamic genetic effects for PH; that is, unconditional analysis was used to detect the total accumulated genetic effects of genes expressed during the initial point to time t (0→t), and conditional analysis to reveal the net genetic effects from genes expressed in the period from time t−1 to t. When phenotypic values were first measured, the unconditional genetic effects were equivalent to the conditional genetic effects. To investigate phenotypic variation in PH, correlation coefficients between PHs during development were analysed, and differences and relationships of PHs among environments were estimated by analysis of variance (ANOVA) and cluster methods individually. All phenotypic analyses were performed by SAS software (SAS Institute, 1996). Broadsense heritability of PH was estimated as: Both unconditional and conditional PH during development were subjected to QTL analysis. A genetic linkage map, consisting of 395 marker loci, including 263 simple sequence repeats (SSRs) and 132 amplified fragment length polymorphisms (AFLPs), was available. The map was established from data on 150 DHLs and covered 3904 cM with an average distance of 9.9 cM between adjacent markers (Hao et al., 2003; Zhou et al., 2005; Yang et al., 2007). QTL analysis was implemented using mapping software QTLNetwork-2.0 based on the mixed linear model (Yang et al., 2007) to divide genetic effects into additive main effects (a effects), epistatic main effects (aa effects), and their environment interaction effects (QE, including ae and aae effects). QTLs with genetic main effects indicated that genes in these genomic regions were expressed in the same way across environments. QTLs with QE effects suggested that gene expression at these loci was environmentally dependent. An experiment-wise type I error of 0.05 was designated for candidate interval selection and putative QTL detection. The critical F-value to declare putative QTLs and to control genome-wise type I errors was accommodated by 1000 permutation tests. Both the testing window and filtration window were set at 10 cM, with a walk speed of 2 cM. QTLs were named according to the rule ‘QTL+trait+research department+chromosome’. Results Phenotypic variation The female parent Hanxuan 10 was significantly taller than the male parent Lumai 14 at all five stages in all 10 environments (year×site×water regime combination) (P <0.05) (Table 1). The average PH of DHLs was between that of the two parents. High phenotypic variability was observed in the population, with coefficients of variation (CVs) ranging from 12.2% to 25.5%. The PHs of DHLs showed continuous variation, and transgressive segregation occurred at all stages in all environments. Most skew and kurt values were <1.0, suggestive of a quantitative trait. Table 1. Phenotypic values of PH for wheat DHLs and their parents at five growth stages in 10 environments Environment Stage H10 L14 DHLs Mean±SD Range CV (%) Skew Kurt E1 S1 25.2 19.8 21.4±3.4 14.0–29.0 15.8 –0.10 –0.63 S2 31.6 24.2 26.9±3.9 16.0–36.0 14.4 –0.13 –0.26 S3 40.8 29.8 32.9±4.8 20.0–44.0 14.6 0.11 –0.19 S4 58.0 43.6 48.1±6.9 32.0–66.0 14.4 0.06 –0.46 S5 75.8 56.0 61.1±7.9 40.0–81.0 13.0 0.00 0.04 E2 S1 39.5 34.0 35.2±5.2 17.0–48.0 14.9 0.26 0.31 S2 54.0 44.5 45.7±8.0 29.0–68.0 17.5 0.18 –0.57 S3 65.0 52.0 55.0±9.5 32.0–79.0 17.2 0.09 –0.47 S4 80.5 65.5 66.1±10.5 40.0–92.0 15.9 0.05 –0.56 S5 102.0 72.0 81.5±13.0 49.0–109.0 15.9 0.11 –0.62 E3 S1 42.8 36.3 36.5±4.5 24.0–47.0 12.2 0.13 –0.36 S2 55.0 39.5 44.2±7.2 30.0–61.0 16.2 –0.02 –1.26 S3 69.3 43.0 54.0±12.6 32.0–77.0 23.4 –0.01 –1.50 S4 90.8 53.3 68.2±15.9 38.0–93.0 23.3 –0.06 –1.64 S5 101.3 69.5 81.8±14.3 47.0–105.0 17.5 –0.14 –1.34 E4 S1 43.0 37.0 37.6±4.7 23.0–51.0 12.5 0.10 0.31 S2 58.0 42.9 46.6±8.7 27.5–68.5 18.7 0.09 –0.82 S3 78.0 50.6 61.8±14.2 35.0–86.0 22.9 –0.05 –1.59 S4 98.3 60.1 77.7±18.6 44.0–108.5 24.0 –0.11 –1.70 S5 125.0 70.9 94.7±21.2 53.0–125.0 22.4 –0.11 –1.62 E5 S1 45.9 36.8 38.5±5.5 27.0–54.5 14.2 0.12 –0.62 S2 63.3 41.3 49.2±10.5 33.4–72.0 21.4 0.07 –1.47 S3 91.0 52.8 66.5±16.9 40.0–95.0 25.5 0.00 –1.71 S4 102.3 64.3 78.9±19.1 49.0–110.0 24.2 –0.03 –1.70 S5 111.3 70.0 87.7±16.3 54.0–117.0 18.6 –0.11 –1.49 E6 S1 44.8 33.0 36.8±7.4 21.0–57.0 20.0 0.34 –0.25 S2 54.8 37.6 43.1±8.8 28.0–65.0 20.5 0.29 –0.66 S3 71.0 57.6 59.9±10.6 41.0–82.0 17.7 0.09 –1.06 S4 105.2 72.4 82.2±12.8 55.0–105.0 15.6 0.03 –1.31 S5 107.6 79.8 87.2±13.3 60.0–116.0 15.3 0.08 –1.14 E7 S1 50.3 43.0 43.3±5.7 31.0–59.0 13.2 0.08 –0.68 S2 60.8 49.0 52.3±8.7 31.0–74.0 16.6 0.07 –0.83 S3 80.5 59.8 67.2±11.7 40.0–92.0 17.4 0.00 –1.04 S4 99.5 69.85 81.9±15.5 52.0–111.0 18.9 0.06 –1.34 S5 120.5 78.0 96.4±18.5 63.0–132.0 19.2 0.07 –1.32 E8 S1 52.8 39.5 44.6±6.9 29.0–59.0 15.5 0.06 –1.02 S2 80.8 52.0 61.1±14.2 32.0–85.0 23.3 –0.05 –1.59 S3 101.8 63.0 78.3±19.8 43.0–105.0 25.3 –0.08 –1.77 S4 109.8 74.3 87.2±17.1 51.0–111.0 19.6 –0.15 –1.56 S5 113.3 79.3 93.1±13.7 54.0–117.0 14.8 –0.25 –1.01 E9 S1 54.5 40.4 42.3±6.3 26.0–55.5 14.9 –0.14 –0.70 S2 78.4 51.5 58.0±11.3 35.0–82.0 19.4 0.09 –1.23 S3 99.6 64.0 78.1±15.9 44.8–102.3 20.4 –0.07 –1.62 S4 120.0 77.5 94.3±19.5 57.0–126.0 20.7 –0.03 –1.64 S5 132.8 84.5 106.4±19.5 60.0–141.0 18.3 –0.07 –1.34 E10 S1 63.3 43.6 51.0±10.0 34.0–70.6 19.7 0.02 –1.43 S2 96.8 57.8 72.1±17.1 44.0–101.0 23.8 –0.01 –1.68 S3 100.5 66.8 82.4±18.2 49.0–115.0 22.1 –0.04 –1.69 S4 120.8 74.8 95.2±18.3 60.0–127.0 19.2 –0.06 –1.56 S5 123.1 78.1 99.2±16.1 62.0–133.1 16.3 –0.06 –1.25 Environment Stage H10 L14 DHLs Mean±SD Range CV (%) Skew Kurt E1 S1 25.2 19.8 21.4±3.4 14.0–29.0 15.8 –0.10 –0.63 S2 31.6 24.2 26.9±3.9 16.0–36.0 14.4 –0.13 –0.26 S3 40.8 29.8 32.9±4.8 20.0–44.0 14.6 0.11 –0.19 S4 58.0 43.6 48.1±6.9 32.0–66.0 14.4 0.06 –0.46 S5 75.8 56.0 61.1±7.9 40.0–81.0 13.0 0.00 0.04 E2 S1 39.5 34.0 35.2±5.2 17.0–48.0 14.9 0.26 0.31 S2 54.0 44.5 45.7±8.0 29.0–68.0 17.5 0.18 –0.57 S3 65.0 52.0 55.0±9.5 32.0–79.0 17.2 0.09 –0.47 S4 80.5 65.5 66.1±10.5 40.0–92.0 15.9 0.05 –0.56 S5 102.0 72.0 81.5±13.0 49.0–109.0 15.9 0.11 –0.62 E3 S1 42.8 36.3 36.5±4.5 24.0–47.0 12.2 0.13 –0.36 S2 55.0 39.5 44.2±7.2 30.0–61.0 16.2 –0.02 –1.26 S3 69.3 43.0 54.0±12.6 32.0–77.0 23.4 –0.01 –1.50 S4 90.8 53.3 68.2±15.9 38.0–93.0 23.3 –0.06 –1.64 S5 101.3 69.5 81.8±14.3 47.0–105.0 17.5 –0.14 –1.34 E4 S1 43.0 37.0 37.6±4.7 23.0–51.0 12.5 0.10 0.31 S2 58.0 42.9 46.6±8.7 27.5–68.5 18.7 0.09 –0.82 S3 78.0 50.6 61.8±14.2 35.0–86.0 22.9 –0.05 –1.59 S4 98.3 60.1 77.7±18.6 44.0–108.5 24.0 –0.11 –1.70 S5 125.0 70.9 94.7±21.2 53.0–125.0 22.4 –0.11 –1.62 E5 S1 45.9 36.8 38.5±5.5 27.0–54.5 14.2 0.12 –0.62 S2 63.3 41.3 49.2±10.5 33.4–72.0 21.4 0.07 –1.47 S3 91.0 52.8 66.5±16.9 40.0–95.0 25.5 0.00 –1.71 S4 102.3 64.3 78.9±19.1 49.0–110.0 24.2 –0.03 –1.70 S5 111.3 70.0 87.7±16.3 54.0–117.0 18.6 –0.11 –1.49 E6 S1 44.8 33.0 36.8±7.4 21.0–57.0 20.0 0.34 –0.25 S2 54.8 37.6 43.1±8.8 28.0–65.0 20.5 0.29 –0.66 S3 71.0 57.6 59.9±10.6 41.0–82.0 17.7 0.09 –1.06 S4 105.2 72.4 82.2±12.8 55.0–105.0 15.6 0.03 –1.31 S5 107.6 79.8 87.2±13.3 60.0–116.0 15.3 0.08 –1.14 E7 S1 50.3 43.0 43.3±5.7 31.0–59.0 13.2 0.08 –0.68 S2 60.8 49.0 52.3±8.7 31.0–74.0 16.6 0.07 –0.83 S3 80.5 59.8 67.2±11.7 40.0–92.0 17.4 0.00 –1.04 S4 99.5 69.85 81.9±15.5 52.0–111.0 18.9 0.06 –1.34 S5 120.5 78.0 96.4±18.5 63.0–132.0 19.2 0.07 –1.32 E8 S1 52.8 39.5 44.6±6.9 29.0–59.0 15.5 0.06 –1.02 S2 80.8 52.0 61.1±14.2 32.0–85.0 23.3 –0.05 –1.59 S3 101.8 63.0 78.3±19.8 43.0–105.0 25.3 –0.08 –1.77 S4 109.8 74.3 87.2±17.1 51.0–111.0 19.6 –0.15 –1.56 S5 113.3 79.3 93.1±13.7 54.0–117.0 14.8 –0.25 –1.01 E9 S1 54.5 40.4 42.3±6.3 26.0–55.5 14.9 –0.14 –0.70 S2 78.4 51.5 58.0±11.3 35.0–82.0 19.4 0.09 –1.23 S3 99.6 64.0 78.1±15.9 44.8–102.3 20.4 –0.07 –1.62 S4 120.0 77.5 94.3±19.5 57.0–126.0 20.7 –0.03 –1.64 S5 132.8 84.5 106.4±19.5 60.0–141.0 18.3 –0.07 –1.34 E10 S1 63.3 43.6 51.0±10.0 34.0–70.6 19.7 0.02 –1.43 S2 96.8 57.8 72.1±17.1 44.0–101.0 23.8 –0.01 –1.68 S3 100.5 66.8 82.4±18.2 49.0–115.0 22.1 –0.04 –1.69 S4 120.8 74.8 95.2±18.3 60.0–127.0 19.2 –0.06 –1.56 S5 123.1 78.1 99.2±16.1 62.0–133.1 16.3 –0.06 –1.25 H10, Hanxuan 10; L14, Lumai 14; CV, coefficient of variation; E1, Fenyang, Shanxi province in 2001 (F01) under drought-stressed conditions (DS); E2 and E4, Haidian, Beijing in 2005 (H05) and 2006 (H06), DS; E3 and E5, Changping, Beijing, 2005 (Ch05) and 2006 (Ch06), DS; E6, Fenyang, Shanxi, 2001 (F01) under well-watered (WW); E7 and E9, Haidian, Beijing, 2005 (H05) and 2006 (H06), WW; E8 and E10, Changping, Beijing, 2005 conditions (Ch05) and 2006 (Ch06), WW; S1, S2, S3, S4 and S5 indicate the first, second, third, fourth and fifth measuring stage, respectively. Open in new tab Table 1. Phenotypic values of PH for wheat DHLs and their parents at five growth stages in 10 environments Environment Stage H10 L14 DHLs Mean±SD Range CV (%) Skew Kurt E1 S1 25.2 19.8 21.4±3.4 14.0–29.0 15.8 –0.10 –0.63 S2 31.6 24.2 26.9±3.9 16.0–36.0 14.4 –0.13 –0.26 S3 40.8 29.8 32.9±4.8 20.0–44.0 14.6 0.11 –0.19 S4 58.0 43.6 48.1±6.9 32.0–66.0 14.4 0.06 –0.46 S5 75.8 56.0 61.1±7.9 40.0–81.0 13.0 0.00 0.04 E2 S1 39.5 34.0 35.2±5.2 17.0–48.0 14.9 0.26 0.31 S2 54.0 44.5 45.7±8.0 29.0–68.0 17.5 0.18 –0.57 S3 65.0 52.0 55.0±9.5 32.0–79.0 17.2 0.09 –0.47 S4 80.5 65.5 66.1±10.5 40.0–92.0 15.9 0.05 –0.56 S5 102.0 72.0 81.5±13.0 49.0–109.0 15.9 0.11 –0.62 E3 S1 42.8 36.3 36.5±4.5 24.0–47.0 12.2 0.13 –0.36 S2 55.0 39.5 44.2±7.2 30.0–61.0 16.2 –0.02 –1.26 S3 69.3 43.0 54.0±12.6 32.0–77.0 23.4 –0.01 –1.50 S4 90.8 53.3 68.2±15.9 38.0–93.0 23.3 –0.06 –1.64 S5 101.3 69.5 81.8±14.3 47.0–105.0 17.5 –0.14 –1.34 E4 S1 43.0 37.0 37.6±4.7 23.0–51.0 12.5 0.10 0.31 S2 58.0 42.9 46.6±8.7 27.5–68.5 18.7 0.09 –0.82 S3 78.0 50.6 61.8±14.2 35.0–86.0 22.9 –0.05 –1.59 S4 98.3 60.1 77.7±18.6 44.0–108.5 24.0 –0.11 –1.70 S5 125.0 70.9 94.7±21.2 53.0–125.0 22.4 –0.11 –1.62 E5 S1 45.9 36.8 38.5±5.5 27.0–54.5 14.2 0.12 –0.62 S2 63.3 41.3 49.2±10.5 33.4–72.0 21.4 0.07 –1.47 S3 91.0 52.8 66.5±16.9 40.0–95.0 25.5 0.00 –1.71 S4 102.3 64.3 78.9±19.1 49.0–110.0 24.2 –0.03 –1.70 S5 111.3 70.0 87.7±16.3 54.0–117.0 18.6 –0.11 –1.49 E6 S1 44.8 33.0 36.8±7.4 21.0–57.0 20.0 0.34 –0.25 S2 54.8 37.6 43.1±8.8 28.0–65.0 20.5 0.29 –0.66 S3 71.0 57.6 59.9±10.6 41.0–82.0 17.7 0.09 –1.06 S4 105.2 72.4 82.2±12.8 55.0–105.0 15.6 0.03 –1.31 S5 107.6 79.8 87.2±13.3 60.0–116.0 15.3 0.08 –1.14 E7 S1 50.3 43.0 43.3±5.7 31.0–59.0 13.2 0.08 –0.68 S2 60.8 49.0 52.3±8.7 31.0–74.0 16.6 0.07 –0.83 S3 80.5 59.8 67.2±11.7 40.0–92.0 17.4 0.00 –1.04 S4 99.5 69.85 81.9±15.5 52.0–111.0 18.9 0.06 –1.34 S5 120.5 78.0 96.4±18.5 63.0–132.0 19.2 0.07 –1.32 E8 S1 52.8 39.5 44.6±6.9 29.0–59.0 15.5 0.06 –1.02 S2 80.8 52.0 61.1±14.2 32.0–85.0 23.3 –0.05 –1.59 S3 101.8 63.0 78.3±19.8 43.0–105.0 25.3 –0.08 –1.77 S4 109.8 74.3 87.2±17.1 51.0–111.0 19.6 –0.15 –1.56 S5 113.3 79.3 93.1±13.7 54.0–117.0 14.8 –0.25 –1.01 E9 S1 54.5 40.4 42.3±6.3 26.0–55.5 14.9 –0.14 –0.70 S2 78.4 51.5 58.0±11.3 35.0–82.0 19.4 0.09 –1.23 S3 99.6 64.0 78.1±15.9 44.8–102.3 20.4 –0.07 –1.62 S4 120.0 77.5 94.3±19.5 57.0–126.0 20.7 –0.03 –1.64 S5 132.8 84.5 106.4±19.5 60.0–141.0 18.3 –0.07 –1.34 E10 S1 63.3 43.6 51.0±10.0 34.0–70.6 19.7 0.02 –1.43 S2 96.8 57.8 72.1±17.1 44.0–101.0 23.8 –0.01 –1.68 S3 100.5 66.8 82.4±18.2 49.0–115.0 22.1 –0.04 –1.69 S4 120.8 74.8 95.2±18.3 60.0–127.0 19.2 –0.06 –1.56 S5 123.1 78.1 99.2±16.1 62.0–133.1 16.3 –0.06 –1.25 Environment Stage H10 L14 DHLs Mean±SD Range CV (%) Skew Kurt E1 S1 25.2 19.8 21.4±3.4 14.0–29.0 15.8 –0.10 –0.63 S2 31.6 24.2 26.9±3.9 16.0–36.0 14.4 –0.13 –0.26 S3 40.8 29.8 32.9±4.8 20.0–44.0 14.6 0.11 –0.19 S4 58.0 43.6 48.1±6.9 32.0–66.0 14.4 0.06 –0.46 S5 75.8 56.0 61.1±7.9 40.0–81.0 13.0 0.00 0.04 E2 S1 39.5 34.0 35.2±5.2 17.0–48.0 14.9 0.26 0.31 S2 54.0 44.5 45.7±8.0 29.0–68.0 17.5 0.18 –0.57 S3 65.0 52.0 55.0±9.5 32.0–79.0 17.2 0.09 –0.47 S4 80.5 65.5 66.1±10.5 40.0–92.0 15.9 0.05 –0.56 S5 102.0 72.0 81.5±13.0 49.0–109.0 15.9 0.11 –0.62 E3 S1 42.8 36.3 36.5±4.5 24.0–47.0 12.2 0.13 –0.36 S2 55.0 39.5 44.2±7.2 30.0–61.0 16.2 –0.02 –1.26 S3 69.3 43.0 54.0±12.6 32.0–77.0 23.4 –0.01 –1.50 S4 90.8 53.3 68.2±15.9 38.0–93.0 23.3 –0.06 –1.64 S5 101.3 69.5 81.8±14.3 47.0–105.0 17.5 –0.14 –1.34 E4 S1 43.0 37.0 37.6±4.7 23.0–51.0 12.5 0.10 0.31 S2 58.0 42.9 46.6±8.7 27.5–68.5 18.7 0.09 –0.82 S3 78.0 50.6 61.8±14.2 35.0–86.0 22.9 –0.05 –1.59 S4 98.3 60.1 77.7±18.6 44.0–108.5 24.0 –0.11 –1.70 S5 125.0 70.9 94.7±21.2 53.0–125.0 22.4 –0.11 –1.62 E5 S1 45.9 36.8 38.5±5.5 27.0–54.5 14.2 0.12 –0.62 S2 63.3 41.3 49.2±10.5 33.4–72.0 21.4 0.07 –1.47 S3 91.0 52.8 66.5±16.9 40.0–95.0 25.5 0.00 –1.71 S4 102.3 64.3 78.9±19.1 49.0–110.0 24.2 –0.03 –1.70 S5 111.3 70.0 87.7±16.3 54.0–117.0 18.6 –0.11 –1.49 E6 S1 44.8 33.0 36.8±7.4 21.0–57.0 20.0 0.34 –0.25 S2 54.8 37.6 43.1±8.8 28.0–65.0 20.5 0.29 –0.66 S3 71.0 57.6 59.9±10.6 41.0–82.0 17.7 0.09 –1.06 S4 105.2 72.4 82.2±12.8 55.0–105.0 15.6 0.03 –1.31 S5 107.6 79.8 87.2±13.3 60.0–116.0 15.3 0.08 –1.14 E7 S1 50.3 43.0 43.3±5.7 31.0–59.0 13.2 0.08 –0.68 S2 60.8 49.0 52.3±8.7 31.0–74.0 16.6 0.07 –0.83 S3 80.5 59.8 67.2±11.7 40.0–92.0 17.4 0.00 –1.04 S4 99.5 69.85 81.9±15.5 52.0–111.0 18.9 0.06 –1.34 S5 120.5 78.0 96.4±18.5 63.0–132.0 19.2 0.07 –1.32 E8 S1 52.8 39.5 44.6±6.9 29.0–59.0 15.5 0.06 –1.02 S2 80.8 52.0 61.1±14.2 32.0–85.0 23.3 –0.05 –1.59 S3 101.8 63.0 78.3±19.8 43.0–105.0 25.3 –0.08 –1.77 S4 109.8 74.3 87.2±17.1 51.0–111.0 19.6 –0.15 –1.56 S5 113.3 79.3 93.1±13.7 54.0–117.0 14.8 –0.25 –1.01 E9 S1 54.5 40.4 42.3±6.3 26.0–55.5 14.9 –0.14 –0.70 S2 78.4 51.5 58.0±11.3 35.0–82.0 19.4 0.09 –1.23 S3 99.6 64.0 78.1±15.9 44.8–102.3 20.4 –0.07 –1.62 S4 120.0 77.5 94.3±19.5 57.0–126.0 20.7 –0.03 –1.64 S5 132.8 84.5 106.4±19.5 60.0–141.0 18.3 –0.07 –1.34 E10 S1 63.3 43.6 51.0±10.0 34.0–70.6 19.7 0.02 –1.43 S2 96.8 57.8 72.1±17.1 44.0–101.0 23.8 –0.01 –1.68 S3 100.5 66.8 82.4±18.2 49.0–115.0 22.1 –0.04 –1.69 S4 120.8 74.8 95.2±18.3 60.0–127.0 19.2 –0.06 –1.56 S5 123.1 78.1 99.2±16.1 62.0–133.1 16.3 –0.06 –1.25 H10, Hanxuan 10; L14, Lumai 14; CV, coefficient of variation; E1, Fenyang, Shanxi province in 2001 (F01) under drought-stressed conditions (DS); E2 and E4, Haidian, Beijing in 2005 (H05) and 2006 (H06), DS; E3 and E5, Changping, Beijing, 2005 (Ch05) and 2006 (Ch06), DS; E6, Fenyang, Shanxi, 2001 (F01) under well-watered (WW); E7 and E9, Haidian, Beijing, 2005 (H05) and 2006 (H06), WW; E8 and E10, Changping, Beijing, 2005 conditions (Ch05) and 2006 (Ch06), WW; S1, S2, S3, S4 and S5 indicate the first, second, third, fourth and fifth measuring stage, respectively. Open in new tab PH of the DHLs and their parents showed significant increases across all measuring stages in all environments (P <0.05). Positive correlations between PHs at different stages ranged from 0.63*** to 0.99*** (P <0.0001) in different environments (Supplementary Table S1 available at JXB online), indicating that PHs at different stages were closely correlated. Significant differences in PH were identified for genotypes, year×site combinations, water regimes, and all two-factor combinations except for the water regime×genotype combination which was not significant at S5 (Supplementary Table S2). The most significant difference occurred between water regimes. Highly significant differences in PH among environments were also detected at all measuring stages in the combined analysis over all 10 three-factor combination environments (P <0.0001) (Supplementary Table S3), indicating that the ontogeny of PH was influenced by environment. By cluster analysis, the 10 environments were further subdivided into three clusters: E1 stood alone as one cluster characterized as the severe DS cluster (mean PH among five stages, 38.1 cm), E8, E9, and E10 formed the second cluster, the WW group (mean PH, 76.2 cm), and the remainder formed a moderate DS cluster (mean, PH 61.8 cm). The broadsense heritabilities for unconditional PH were estimated as 34.1, 39.3, 45.4, 53.9, and 60.3%, respectively, for the different development stages. There were significant correlations between conditional PHs in some periods and environments (Supplementary Table S4 at JXB online), implying the dynamic and environmentally influenced characteristics of PH ontogeny. As for unconditional PHs, significant differences were detected for conditional PHs among genotypes, year×site combinations, water regimes, and all two-factor combinations (P <0.05) except for water regime×genotype combinations with non-significant differences in S4|S3 and S5|S4 (Supplementary Table S2). The largest difference occurred between water regimes (P <0.0001). Similarly, highly significant differences (P <0.0001) were detected among 10 combination environments of year×site×water regime (Supplementary Table S3). Broadsense heritabilities for conditional PH were 34.1, 11.7, 6.1, 3.1, and 15.9%, respectively, for the five growth periods. Unconditional QTL analysis for PH development A total of 20 A-QTLs (Table 2) and 82 epistatic pairs (Supplementary Table S5 at JXB online) with significant genetic main effects and/or QE effects controlling unconditional PH at different development stages were detected. The A-QTLs were located on all chromosomes except 1A, 1D, 2A, 2B, 3D, 5D, 6D, and 7D, whereas epistasis involved contributions from all chromosomes except 6D, indicating that unconditional QTLs for PH were associated with nearly all chromosomes. Table 2. Unconditional QTLs affecting PH of wheat detected at five growth stages in 10 environments QTL Flanking markers Stage a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1 0.93*** 2.22 QPh.cgb-1B.4 Xwmc156–P3446.1 S2 2.00*** 2.88 S3 1.12*** 3.50 S4 5.08*** 4.26 S5 4.50*** 1.14* (ae7) 4.55 0.32 QPh.cgb-1B.19 Xgwm259–Xwmc367 S3 –0.44* 0.07 S4 –2.49*** 0.07 S5 –1.79*** 0.08 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S2 3.89*** –2.44*** (ae1), 1.87*** (ae8), 2.13*** (ae10) 3.43 0.66 S3 3.02*** –1.92*** (ae1) 3.18 0.64 S4 4.70*** –2.96*** (ae1) 3.76 0.54 S5 2.84*** 4.05 QPh.cgb-2D.6 P4233.2–P6411.4 S4 1.83*** 1.85 QPh.cgb-2D.11 P3176.1–P1123.1 S1 0.92*** 0.78 S2 1.34*** 0.84 S3 2.95*** 0.81 S5 2.70*** 1.39 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2 0.72*** 0.73 S5 0.78*** 1.42 QPh.cgb-3B.9 P3622.4–P2076 S1 –1.07*** –0.59* (ae10) 0.96 0.33 S2 –1.74*** –0.96* (ae10) 1.68 0.35 QPh.cgb-4A.5 P6431.1–Xgwm160 S1 0.62*** 0.19 S2 1.91*** 0.24 S3 2.04*** 0.07 S5 1.82*** 0.36 QPh.cgb-4B.1 Xgwm368–Xgwm107 S3 –2.12*** 0.84 S4 –0.77*** 0.79 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2 3.85*** –2.66*** (ae1), 1.45* (ae8), 1.19* (ae9), 3.25*** (ae10) 3.55 0.73 S3 4.88*** –2.83*** (ae1), –1.87** (ae2), 1.61* (ae8), 1.39* (ae9), 1.93*** (ae10) 4.83 0.82 S4 5.19*** –2.66*** (ae1), –1.56* (ae2) 5.74 0.63 QPh.cgb-4D.2 Xgwm192–Xwmc331 S5 4.86*** –2.36*** (ae1), 1.65* (ae4) 6.81 0.66 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2 –2.52*** –1.10* (ae8), –1.35** (ae10) 1.50 0.26 S3 –3.89*** 1.21* (ae1), –1.26* (ae5) 1.81 0.18 S4 –3.11*** 1.42 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1 –0.96*** 0.73 QPh.cgb-6A.3 P4232.4–Xcwm306 S1 –1.54*** –0.74* (ae10) 1.10 0.19 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 S2 3.06*** –1.99*** (ae1), 1.55*** (ae10) 0.65 0.12 S3 3.16*** –2.05*** (ae1) 0.90 0.13 S4 3.72*** –1.67*** (ae1) 0.66 0.09 S5 3.95*** –1.15* (ae1) 0.43 0.06 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S2 –2.85*** 1.81*** (ae1), –1.62*** (ae10) 2.29 0.45 S3 –3.43*** 1.99*** (ae1), 1.25* (ae2), –1.53** (ae10) 3.94 0.67 S4 –6.39*** 3.07*** (ae1), –1.45* (ae5), –1.84* (ae9) 3.73 0.59 S5 –3.70*** 2.04*** (ae1) 3.39 0.56 QPh.cgb-7A.3 P3454.5–P3446.4 S1 0.78*** 1.40 S2 2.27*** –1.43*** (ae1), 1.67*** (ae10) 1.61 0.35 S3 3.07*** –1.96*** (ae1), 1.31* (ae8), 1.13* (ae10) 2.18 0.41 S4 2.38*** 2.85 S5 3.49*** –1.58* (ae1) 2.64 0.36 QPh.cgb-7B.4 Xpsp3033–Xgwm297 S3 –2.99*** 1.74** (ae1), 1.34* (ae2), –1.55* (ae8) 0.67 0.13 QTL Flanking markers Stage a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1 0.93*** 2.22 QPh.cgb-1B.4 Xwmc156–P3446.1 S2 2.00*** 2.88 S3 1.12*** 3.50 S4 5.08*** 4.26 S5 4.50*** 1.14* (ae7) 4.55 0.32 QPh.cgb-1B.19 Xgwm259–Xwmc367 S3 –0.44* 0.07 S4 –2.49*** 0.07 S5 –1.79*** 0.08 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S2 3.89*** –2.44*** (ae1), 1.87*** (ae8), 2.13*** (ae10) 3.43 0.66 S3 3.02*** –1.92*** (ae1) 3.18 0.64 S4 4.70*** –2.96*** (ae1) 3.76 0.54 S5 2.84*** 4.05 QPh.cgb-2D.6 P4233.2–P6411.4 S4 1.83*** 1.85 QPh.cgb-2D.11 P3176.1–P1123.1 S1 0.92*** 0.78 S2 1.34*** 0.84 S3 2.95*** 0.81 S5 2.70*** 1.39 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2 0.72*** 0.73 S5 0.78*** 1.42 QPh.cgb-3B.9 P3622.4–P2076 S1 –1.07*** –0.59* (ae10) 0.96 0.33 S2 –1.74*** –0.96* (ae10) 1.68 0.35 QPh.cgb-4A.5 P6431.1–Xgwm160 S1 0.62*** 0.19 S2 1.91*** 0.24 S3 2.04*** 0.07 S5 1.82*** 0.36 QPh.cgb-4B.1 Xgwm368–Xgwm107 S3 –2.12*** 0.84 S4 –0.77*** 0.79 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2 3.85*** –2.66*** (ae1), 1.45* (ae8), 1.19* (ae9), 3.25*** (ae10) 3.55 0.73 S3 4.88*** –2.83*** (ae1), –1.87** (ae2), 1.61* (ae8), 1.39* (ae9), 1.93*** (ae10) 4.83 0.82 S4 5.19*** –2.66*** (ae1), –1.56* (ae2) 5.74 0.63 QPh.cgb-4D.2 Xgwm192–Xwmc331 S5 4.86*** –2.36*** (ae1), 1.65* (ae4) 6.81 0.66 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2 –2.52*** –1.10* (ae8), –1.35** (ae10) 1.50 0.26 S3 –3.89*** 1.21* (ae1), –1.26* (ae5) 1.81 0.18 S4 –3.11*** 1.42 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1 –0.96*** 0.73 QPh.cgb-6A.3 P4232.4–Xcwm306 S1 –1.54*** –0.74* (ae10) 1.10 0.19 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 S2 3.06*** –1.99*** (ae1), 1.55*** (ae10) 0.65 0.12 S3 3.16*** –2.05*** (ae1) 0.90 0.13 S4 3.72*** –1.67*** (ae1) 0.66 0.09 S5 3.95*** –1.15* (ae1) 0.43 0.06 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S2 –2.85*** 1.81*** (ae1), –1.62*** (ae10) 2.29 0.45 S3 –3.43*** 1.99*** (ae1), 1.25* (ae2), –1.53** (ae10) 3.94 0.67 S4 –6.39*** 3.07*** (ae1), –1.45* (ae5), –1.84* (ae9) 3.73 0.59 S5 –3.70*** 2.04*** (ae1) 3.39 0.56 QPh.cgb-7A.3 P3454.5–P3446.4 S1 0.78*** 1.40 S2 2.27*** –1.43*** (ae1), 1.67*** (ae10) 1.61 0.35 S3 3.07*** –1.96*** (ae1), 1.31* (ae8), 1.13* (ae10) 2.18 0.41 S4 2.38*** 2.85 S5 3.49*** –1.58* (ae1) 2.64 0.36 QPh.cgb-7B.4 Xpsp3033–Xgwm297 S3 –2.99*** 1.74** (ae1), 1.34* (ae2), –1.55* (ae8) 0.67 0.13 a, additive main effects; ae1, the additive QTL×environment interaction effects in E1, ae2, the additive QTL×environment interaction effects in E2, and so on; E1–E10 are as shown in Table 1; a positive value indicates that the Hanxuan 10 allele has a positive effects on PH, and a negative value that the Lumai 14 allele has a positive effect on PH; S1, S2, S3, S4, S5 are as shown in Table 1; h2(a)%, phenotypic variation explained (PVE) by a effects; h2(ae)%, PVE by ae effects. * P=0.05, ** P=0.01, *** P=0.005. Only significant effects are listed. Open in new tab Table 2. Unconditional QTLs affecting PH of wheat detected at five growth stages in 10 environments QTL Flanking markers Stage a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1 0.93*** 2.22 QPh.cgb-1B.4 Xwmc156–P3446.1 S2 2.00*** 2.88 S3 1.12*** 3.50 S4 5.08*** 4.26 S5 4.50*** 1.14* (ae7) 4.55 0.32 QPh.cgb-1B.19 Xgwm259–Xwmc367 S3 –0.44* 0.07 S4 –2.49*** 0.07 S5 –1.79*** 0.08 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S2 3.89*** –2.44*** (ae1), 1.87*** (ae8), 2.13*** (ae10) 3.43 0.66 S3 3.02*** –1.92*** (ae1) 3.18 0.64 S4 4.70*** –2.96*** (ae1) 3.76 0.54 S5 2.84*** 4.05 QPh.cgb-2D.6 P4233.2–P6411.4 S4 1.83*** 1.85 QPh.cgb-2D.11 P3176.1–P1123.1 S1 0.92*** 0.78 S2 1.34*** 0.84 S3 2.95*** 0.81 S5 2.70*** 1.39 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2 0.72*** 0.73 S5 0.78*** 1.42 QPh.cgb-3B.9 P3622.4–P2076 S1 –1.07*** –0.59* (ae10) 0.96 0.33 S2 –1.74*** –0.96* (ae10) 1.68 0.35 QPh.cgb-4A.5 P6431.1–Xgwm160 S1 0.62*** 0.19 S2 1.91*** 0.24 S3 2.04*** 0.07 S5 1.82*** 0.36 QPh.cgb-4B.1 Xgwm368–Xgwm107 S3 –2.12*** 0.84 S4 –0.77*** 0.79 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2 3.85*** –2.66*** (ae1), 1.45* (ae8), 1.19* (ae9), 3.25*** (ae10) 3.55 0.73 S3 4.88*** –2.83*** (ae1), –1.87** (ae2), 1.61* (ae8), 1.39* (ae9), 1.93*** (ae10) 4.83 0.82 S4 5.19*** –2.66*** (ae1), –1.56* (ae2) 5.74 0.63 QPh.cgb-4D.2 Xgwm192–Xwmc331 S5 4.86*** –2.36*** (ae1), 1.65* (ae4) 6.81 0.66 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2 –2.52*** –1.10* (ae8), –1.35** (ae10) 1.50 0.26 S3 –3.89*** 1.21* (ae1), –1.26* (ae5) 1.81 0.18 S4 –3.11*** 1.42 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1 –0.96*** 0.73 QPh.cgb-6A.3 P4232.4–Xcwm306 S1 –1.54*** –0.74* (ae10) 1.10 0.19 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 S2 3.06*** –1.99*** (ae1), 1.55*** (ae10) 0.65 0.12 S3 3.16*** –2.05*** (ae1) 0.90 0.13 S4 3.72*** –1.67*** (ae1) 0.66 0.09 S5 3.95*** –1.15* (ae1) 0.43 0.06 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S2 –2.85*** 1.81*** (ae1), –1.62*** (ae10) 2.29 0.45 S3 –3.43*** 1.99*** (ae1), 1.25* (ae2), –1.53** (ae10) 3.94 0.67 S4 –6.39*** 3.07*** (ae1), –1.45* (ae5), –1.84* (ae9) 3.73 0.59 S5 –3.70*** 2.04*** (ae1) 3.39 0.56 QPh.cgb-7A.3 P3454.5–P3446.4 S1 0.78*** 1.40 S2 2.27*** –1.43*** (ae1), 1.67*** (ae10) 1.61 0.35 S3 3.07*** –1.96*** (ae1), 1.31* (ae8), 1.13* (ae10) 2.18 0.41 S4 2.38*** 2.85 S5 3.49*** –1.58* (ae1) 2.64 0.36 QPh.cgb-7B.4 Xpsp3033–Xgwm297 S3 –2.99*** 1.74** (ae1), 1.34* (ae2), –1.55* (ae8) 0.67 0.13 QTL Flanking markers Stage a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1 0.93*** 2.22 QPh.cgb-1B.4 Xwmc156–P3446.1 S2 2.00*** 2.88 S3 1.12*** 3.50 S4 5.08*** 4.26 S5 4.50*** 1.14* (ae7) 4.55 0.32 QPh.cgb-1B.19 Xgwm259–Xwmc367 S3 –0.44* 0.07 S4 –2.49*** 0.07 S5 –1.79*** 0.08 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S2 3.89*** –2.44*** (ae1), 1.87*** (ae8), 2.13*** (ae10) 3.43 0.66 S3 3.02*** –1.92*** (ae1) 3.18 0.64 S4 4.70*** –2.96*** (ae1) 3.76 0.54 S5 2.84*** 4.05 QPh.cgb-2D.6 P4233.2–P6411.4 S4 1.83*** 1.85 QPh.cgb-2D.11 P3176.1–P1123.1 S1 0.92*** 0.78 S2 1.34*** 0.84 S3 2.95*** 0.81 S5 2.70*** 1.39 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2 0.72*** 0.73 S5 0.78*** 1.42 QPh.cgb-3B.9 P3622.4–P2076 S1 –1.07*** –0.59* (ae10) 0.96 0.33 S2 –1.74*** –0.96* (ae10) 1.68 0.35 QPh.cgb-4A.5 P6431.1–Xgwm160 S1 0.62*** 0.19 S2 1.91*** 0.24 S3 2.04*** 0.07 S5 1.82*** 0.36 QPh.cgb-4B.1 Xgwm368–Xgwm107 S3 –2.12*** 0.84 S4 –0.77*** 0.79 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2 3.85*** –2.66*** (ae1), 1.45* (ae8), 1.19* (ae9), 3.25*** (ae10) 3.55 0.73 S3 4.88*** –2.83*** (ae1), –1.87** (ae2), 1.61* (ae8), 1.39* (ae9), 1.93*** (ae10) 4.83 0.82 S4 5.19*** –2.66*** (ae1), –1.56* (ae2) 5.74 0.63 QPh.cgb-4D.2 Xgwm192–Xwmc331 S5 4.86*** –2.36*** (ae1), 1.65* (ae4) 6.81 0.66 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2 –2.52*** –1.10* (ae8), –1.35** (ae10) 1.50 0.26 S3 –3.89*** 1.21* (ae1), –1.26* (ae5) 1.81 0.18 S4 –3.11*** 1.42 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1 –0.96*** 0.73 QPh.cgb-6A.3 P4232.4–Xcwm306 S1 –1.54*** –0.74* (ae10) 1.10 0.19 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 S2 3.06*** –1.99*** (ae1), 1.55*** (ae10) 0.65 0.12 S3 3.16*** –2.05*** (ae1) 0.90 0.13 S4 3.72*** –1.67*** (ae1) 0.66 0.09 S5 3.95*** –1.15* (ae1) 0.43 0.06 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S2 –2.85*** 1.81*** (ae1), –1.62*** (ae10) 2.29 0.45 S3 –3.43*** 1.99*** (ae1), 1.25* (ae2), –1.53** (ae10) 3.94 0.67 S4 –6.39*** 3.07*** (ae1), –1.45* (ae5), –1.84* (ae9) 3.73 0.59 S5 –3.70*** 2.04*** (ae1) 3.39 0.56 QPh.cgb-7A.3 P3454.5–P3446.4 S1 0.78*** 1.40 S2 2.27*** –1.43*** (ae1), 1.67*** (ae10) 1.61 0.35 S3 3.07*** –1.96*** (ae1), 1.31* (ae8), 1.13* (ae10) 2.18 0.41 S4 2.38*** 2.85 S5 3.49*** –1.58* (ae1) 2.64 0.36 QPh.cgb-7B.4 Xpsp3033–Xgwm297 S3 –2.99*** 1.74** (ae1), 1.34* (ae2), –1.55* (ae8) 0.67 0.13 a, additive main effects; ae1, the additive QTL×environment interaction effects in E1, ae2, the additive QTL×environment interaction effects in E2, and so on; E1–E10 are as shown in Table 1; a positive value indicates that the Hanxuan 10 allele has a positive effects on PH, and a negative value that the Lumai 14 allele has a positive effect on PH; S1, S2, S3, S4, S5 are as shown in Table 1; h2(a)%, phenotypic variation explained (PVE) by a effects; h2(ae)%, PVE by ae effects. * P=0.05, ** P=0.01, *** P=0.005. Only significant effects are listed. Open in new tab Of the 20 A-QTLs, 13 were detected at 2–5 stages and seven at only one stage apart from S2 (Table 2), indicating that QTLs detected at one specific stage did not entirely represent those active at another stage, and that genes controlling PH might be selectively expressed during development. All 20 A-QTLs had significant additive main effects (a effects), and 11 of them had a effects (0.62***–5.19***) conferred by height-enhancing alleles from Hanxuan 10, implying that alleles for PH were dispersed between the two parents. Eleven QTLs were also detected with significant additive×environment interaction effects (ae effects) in 1–5 environments at 1–5 development stages (Table 2), indicating that their genetic sensitivities to environments changed during ontogeny. The reaction of QTLs to environments showed directional effects on ontogeny. QTLs such as QPh.cgb-2D.1, QPh.cgb-4D.1, QPh.cgb-6B.5, and QPh.cgb-7A.3, with negative ae effects (–2.96*** to –0.82*) in E1 (severe DS) and positive effects (0.77*–3.25***) in 1–3 environments of E8, E9, and E10 (WW) appeared to be up-regulated by WW environments at 2–3 stages, whereas QTLs such as QPh.cgb-5A.7, QPh.cgb-6B.7, and QPh.cgb-7B.4, having positive ae effects (0.71*–3.07***) in E1 and negative effects (–1.84* to –1.21***) in either E9 or E10, were up-regulated by severe DS environments at 1–5 stages (Table 2). This capacity for different QTLs to respond to environments varied. For the A-QTLs having both a effects and ae effects, the absolute values for the a effects were larger than those for ae effects in any one environment, and correspondingly the phenotypic variation explained (PVE) by a effects was larger than that explained by ae effects. Thus a effects predominated over ae effects at different growth stages. Additionally, of the QTLs detected at more than one stage, the a effects as well as the ae effects for the same environment were always in the same direction, but with unequal magnitudes at different stages. The absolute effects at later growth stages were usually greater than those at the first stage, indicating that the genetic effects of the first stage set a cumulative foundation for those at the later stages (Table 2). On the other hand, unequal magnitudes might result from net gene expression in different periods after the first stage. For unconditional epistatic effects on PH growth, one pair of QTLs, that is QPh.cgb-1B.4 and QPh.cgb-2B.6, had significant epistatic main effects (aa effects) at two stages (S4 and S5); another pair, QPh.cgb-2D.1 and QPh.cgb-4A.5 was found at four stages (S1, S2, S3, and S5), and all other pairs identified with significant aa effects and/or epistasis×environment interaction effects (aae effects) were detected at one specific stage (Supplementary Table S5 at JXB online). Thus epistatic effects were mainly short lived during PH development. All 82 epistatic pairs had significant aa effects; 43 of these, including two detected at two or more stages, had significant negative aa effects, implying that both parents contributed alleles for PH. Twelve pairs also had significant aae effects in 1–4 environments among E1, E2, E4, E6, E8, E9, and E10 at 1–5 stages, implying that most instances of epistasis were not affected by environment. For epistasis with aae effects, six pairs of QTLs, QPh.cgb-1B.11 and QPh.cgb-1B.16 at S1; QPh.cgb-1B.8 and QPh.cgb-3B.6, and QPh.cgb-2A.9 and QPh.cgb-6A.7 at S2; QPh.cgb-1B.19 and QPh.cgb-7B.4, QPh.cgb-2A.10 and QPh.cgb-7D.4, and QPh.cgb-2D.7 and QPh.cgb-7B.3 at S3, showed positive aae effects (0.45*–1.67***) in one or both environments of E8 and E10, or were negative (–2.14*** to –1.62***) in E1. In contrast, another six pairs, QPh.cgb-6A.4 and QPh.cgb-7B.4 at S1; QPh.cgb-4B.3 and QPh.cgb-5B.4, and QPh.cgb-5A.1 and QPh.cgb-7A.8 at S2, QPh.cgb-3B.14 and QPh.cgb-3D.1 at S4; and QPh.cgb-3A.1 and QPh.cgb-6A.6, and QPh.cgb-5A.2 and QPh.cgb-5A.4 at S5, displayed positive aae effects (1.05*–2.54***) in E1, or were negative (–1.57* to 1.14*) in either of E9 and E10. These epistatic pairs conferred adaptability for water-limited environments, consistent with some A-QTLs. For these instances of epistasis with aa effects and aae effects, similar to A-QTLs, the aa effects in absolute terms were larger than aae effects in each environment, and the PVE for aa effects was correspondingly larger than that of the aae effects; hence aa effects were more important in determining PH than aae effects. Interestingly, many A-QTLs (14 in total) also interacted with other QTLs, and thereby displayed modified functions (Supplementary Table S5). Moreover, 38.8% of QTLs participating in epistatic interactions (E-QTLs), including 13 A-QTLs and 27 non-individual E-QTLs (i.e. epistatic QTLs without additive effects), took part in two or more epistatic interactions at 1–5 stages, making up a QTL functional network associated with PH development (Fig. 1). For example, during PH development, through 11 A-QTLs and eight non-individual E-QTLs participating in two or more interactions, there was a large QTL network of 31 QTLs (Fig. 1A). Within this network, there were three kinds of interaction, that is 40.5% of interactions were between different A-QTLs, 21.6% were between A-QTLs and non-individual E-QTLs, and 37.8% were between different non-individual E-QTLs. Almost 60% of the interactions showed negative aa effects. QTLs interacting with more than one other QTL displayed both positive and negative aa effects. All of these interactions suggested that the genetic control of PH development was complex. Overall, the genetic effects involved in the QTL network of unconditional PH collectively accounted for 82.1% of the phenotypic variation occurring during PH development, whereas the remaining components of 15.4% and 2.5% of phenotypic variation, respectively, were attributed to those A-QTLs not involved in epistatic interactions and those non-individual QTLs not involved in the QTL network. Therefore, the genetic effects controlling PH growth were primarily expressed as part of the QTL network. Nevertheless, due to the additive and epistatic effects involved in the QTL network, unconditional PH QTLs contributed 55.3% and 26.8% of the phenotypic variation, and additive effects apparently constituted the major genetic basis of PH development. Fig. 1. Open in new tabDownload slide Epistatic QTL network for unconditional PH at five stages in wheat DHLs. indicates a QTL with additive effects (A-QTL); indicates a QTL without individual effects but interacting with two more QTLs; other QTLs were detected only once. Solid lines indicate positive epistatic main (aa) effects to increase PH; dashed lines indicate negative aa effects to decrease PH. , , , , and refer to positive aa effects at S1, S2, S3, S4, and S5, respectively. , , , , and refer to negative aa effects at S1, S2, S3, S4, and S5, respectively. Fig. 1. Open in new tabDownload slide Epistatic QTL network for unconditional PH at five stages in wheat DHLs. indicates a QTL with additive effects (A-QTL); indicates a QTL without individual effects but interacting with two more QTLs; other QTLs were detected only once. Solid lines indicate positive epistatic main (aa) effects to increase PH; dashed lines indicate negative aa effects to decrease PH. , , , , and refer to positive aa effects at S1, S2, S3, S4, and S5, respectively. , , , , and refer to negative aa effects at S1, S2, S3, S4, and S5, respectively. Conditional QTL analysis for PH development Conditional mapping was adopted to reveal the real gene expression during different developmental periods. By conditional mapping, a total of 20 A-QTLs and 23 epistatic pairs with genetic main effects and/or QE effects associated with PH growth were identified (Table 3, and Supplementary Table S6 at JXB online). A-QTLs occurred on all chromosomes except 1A, 2A, 2B, 3D, 4B, 5D, 6D, 7B, and 7D, and E-QTLs were associated with all chromosomes apart from 1D, 3A, 3D, 4D, 5D, and 6D. Some chromosomes with unconditional QTLs were not detected as conditional QTLs. Table 3. Conditional additive QTLs (A- QTLs) affecting PH of wheat in five periods and 10 environments QTL Flanking markers Period a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1|S0 0.93*** 2.22 QPh.cgb-1B.5 P3446.1–Xcwm65 S5|S4 0.46*** –0.83** (ae1), 0.91*** (ae2), 1.05*** (ae4), –0.86** (ae5), 1.63*** (ae7), –0.95*** (ae8), 0.84** (ae9), –0.68* (ae10) 0.06 1.24 QPh.cgb-1D.3 Xwmc432–Xwmc222 S5|S4 –0.43*** 0.99*** (ae4) 0.42 0.18 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1|S0 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S3|S2 –0.50*** –0.74* (ae1), –0.86* (ae2), 0.74* (ae8) 0.15 0.28 QPh.cgb-2D.11 P3176.1–P1123.1 S1|S0 0.92*** 0.78 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2|S1 0.49*** –0.72** (ae1), 0.65* (ae5), –0.64* (ae6), –0.71** (ae7), 0.84*** (ae8) 0.15 0.44 QPh.cgb-3B.9 P3622.4–P2076 S1|S0 –1.07*** –0.59* (ae10) 0.96 0.33 QPh.cgb-3B.11 Xwmc291–P3156.1 S5|S4 0.34** 0.97** (ae1), –1.62*** (ae4), 0.81* (ae8) 0.36 0.53 QPh.cgb-4A.5 P6431.1–Xgwm160 S1|S0 0.62*** 0.19 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1|S0 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2|S1 0.36*** –1.12*** (ae1), 0.63* (ae4), 1.09*** (ae5), –1.24*** (ae6), –0.87** (ae7), 0.66* (ae8) 0.08 0.70 QPh.cgb-4D.2 Xgwm192–Xwmc331 S3|S2 –0.83** (ae2), –0.72* (ae6) 0.43 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1|S0 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2|S1 –0.53*** 0.67* (ae2), –0.61* (ae5), 0.80** (ae6), –1.05*** (ae8), –0.74* (ae10) 0.23 0.49 S3|S2 –0.33*** –0.68* (ae4), 1.27*** (ae10) 0.10 0.36 S4|S3 0.35*** 0.83* (ae2), 1.05*** (ae5), –0.80* (ae6), –0.85* (ae7), –0.75* (ae9) 0.06 0.34 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1|S0 –0.96*** 0.73 QPh.cgb-6A.1 Xgwm334–Xwmc297 S3|S2 0.59*** 0.27 QPh.cgb-6A.3 P4232.4–Xcwm306 S1|S0 –1.54*** –0.74* (ae10) 1.10 0.19 S5|S4 1.60*** 0.87 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1|S0 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1|S0 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S5|S4 –0.79** (ae2), –1.19*** (ae4), 0.90*** (ae5), 0.74* (ae8) 0.65 QPh.cgb-6B.8 Xgwm644.1–Xwmc417.2 S5|S4 0.45*** –0.61* (ae3) 0.23 0.33 QPh.cgb-7A.3 P3454.5–P3446.4 S1|S0 0.78*** 1.40 QTL Flanking markers Period a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1|S0 0.93*** 2.22 QPh.cgb-1B.5 P3446.1–Xcwm65 S5|S4 0.46*** –0.83** (ae1), 0.91*** (ae2), 1.05*** (ae4), –0.86** (ae5), 1.63*** (ae7), –0.95*** (ae8), 0.84** (ae9), –0.68* (ae10) 0.06 1.24 QPh.cgb-1D.3 Xwmc432–Xwmc222 S5|S4 –0.43*** 0.99*** (ae4) 0.42 0.18 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1|S0 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S3|S2 –0.50*** –0.74* (ae1), –0.86* (ae2), 0.74* (ae8) 0.15 0.28 QPh.cgb-2D.11 P3176.1–P1123.1 S1|S0 0.92*** 0.78 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2|S1 0.49*** –0.72** (ae1), 0.65* (ae5), –0.64* (ae6), –0.71** (ae7), 0.84*** (ae8) 0.15 0.44 QPh.cgb-3B.9 P3622.4–P2076 S1|S0 –1.07*** –0.59* (ae10) 0.96 0.33 QPh.cgb-3B.11 Xwmc291–P3156.1 S5|S4 0.34** 0.97** (ae1), –1.62*** (ae4), 0.81* (ae8) 0.36 0.53 QPh.cgb-4A.5 P6431.1–Xgwm160 S1|S0 0.62*** 0.19 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1|S0 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2|S1 0.36*** –1.12*** (ae1), 0.63* (ae4), 1.09*** (ae5), –1.24*** (ae6), –0.87** (ae7), 0.66* (ae8) 0.08 0.70 QPh.cgb-4D.2 Xgwm192–Xwmc331 S3|S2 –0.83** (ae2), –0.72* (ae6) 0.43 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1|S0 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2|S1 –0.53*** 0.67* (ae2), –0.61* (ae5), 0.80** (ae6), –1.05*** (ae8), –0.74* (ae10) 0.23 0.49 S3|S2 –0.33*** –0.68* (ae4), 1.27*** (ae10) 0.10 0.36 S4|S3 0.35*** 0.83* (ae2), 1.05*** (ae5), –0.80* (ae6), –0.85* (ae7), –0.75* (ae9) 0.06 0.34 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1|S0 –0.96*** 0.73 QPh.cgb-6A.1 Xgwm334–Xwmc297 S3|S2 0.59*** 0.27 QPh.cgb-6A.3 P4232.4–Xcwm306 S1|S0 –1.54*** –0.74* (ae10) 1.10 0.19 S5|S4 1.60*** 0.87 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1|S0 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1|S0 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S5|S4 –0.79** (ae2), –1.19*** (ae4), 0.90*** (ae5), 0.74* (ae8) 0.65 QPh.cgb-6B.8 Xgwm644.1–Xwmc417.2 S5|S4 0.45*** –0.61* (ae3) 0.23 0.33 QPh.cgb-7A.3 P3454.5–P3446.4 S1|S0 0.78*** 1.40 a, ae1–ae10, the positive and negative value in the table, *, **, ***, h2(a)%, h2(ae)% are as shown in Table 2; E1–E10 are as shown in Table 1; S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4 indicate the first, second, third, fourth, and fifth measuring period, respectively. Open in new tab Table 3. Conditional additive QTLs (A- QTLs) affecting PH of wheat in five periods and 10 environments QTL Flanking markers Period a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1|S0 0.93*** 2.22 QPh.cgb-1B.5 P3446.1–Xcwm65 S5|S4 0.46*** –0.83** (ae1), 0.91*** (ae2), 1.05*** (ae4), –0.86** (ae5), 1.63*** (ae7), –0.95*** (ae8), 0.84** (ae9), –0.68* (ae10) 0.06 1.24 QPh.cgb-1D.3 Xwmc432–Xwmc222 S5|S4 –0.43*** 0.99*** (ae4) 0.42 0.18 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1|S0 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S3|S2 –0.50*** –0.74* (ae1), –0.86* (ae2), 0.74* (ae8) 0.15 0.28 QPh.cgb-2D.11 P3176.1–P1123.1 S1|S0 0.92*** 0.78 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2|S1 0.49*** –0.72** (ae1), 0.65* (ae5), –0.64* (ae6), –0.71** (ae7), 0.84*** (ae8) 0.15 0.44 QPh.cgb-3B.9 P3622.4–P2076 S1|S0 –1.07*** –0.59* (ae10) 0.96 0.33 QPh.cgb-3B.11 Xwmc291–P3156.1 S5|S4 0.34** 0.97** (ae1), –1.62*** (ae4), 0.81* (ae8) 0.36 0.53 QPh.cgb-4A.5 P6431.1–Xgwm160 S1|S0 0.62*** 0.19 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1|S0 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2|S1 0.36*** –1.12*** (ae1), 0.63* (ae4), 1.09*** (ae5), –1.24*** (ae6), –0.87** (ae7), 0.66* (ae8) 0.08 0.70 QPh.cgb-4D.2 Xgwm192–Xwmc331 S3|S2 –0.83** (ae2), –0.72* (ae6) 0.43 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1|S0 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2|S1 –0.53*** 0.67* (ae2), –0.61* (ae5), 0.80** (ae6), –1.05*** (ae8), –0.74* (ae10) 0.23 0.49 S3|S2 –0.33*** –0.68* (ae4), 1.27*** (ae10) 0.10 0.36 S4|S3 0.35*** 0.83* (ae2), 1.05*** (ae5), –0.80* (ae6), –0.85* (ae7), –0.75* (ae9) 0.06 0.34 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1|S0 –0.96*** 0.73 QPh.cgb-6A.1 Xgwm334–Xwmc297 S3|S2 0.59*** 0.27 QPh.cgb-6A.3 P4232.4–Xcwm306 S1|S0 –1.54*** –0.74* (ae10) 1.10 0.19 S5|S4 1.60*** 0.87 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1|S0 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1|S0 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S5|S4 –0.79** (ae2), –1.19*** (ae4), 0.90*** (ae5), 0.74* (ae8) 0.65 QPh.cgb-6B.8 Xgwm644.1–Xwmc417.2 S5|S4 0.45*** –0.61* (ae3) 0.23 0.33 QPh.cgb-7A.3 P3454.5–P3446.4 S1|S0 0.78*** 1.40 QTL Flanking markers Period a ae h2(a)% h2(ae)% QPh.cgb-1B.1 Xgwm582–Xgwm273 S1|S0 0.93*** 2.22 QPh.cgb-1B.5 P3446.1–Xcwm65 S5|S4 0.46*** –0.83** (ae1), 0.91*** (ae2), 1.05*** (ae4), –0.86** (ae5), 1.63*** (ae7), –0.95*** (ae8), 0.84** (ae9), –0.68* (ae10) 0.06 1.24 QPh.cgb-1D.3 Xwmc432–Xwmc222 S5|S4 –0.43*** 0.99*** (ae4) 0.42 0.18 QPh.cgb-2D.1 Xwmc453.1–Xwmc18 S1|S0 1.85*** –0.83** (ae1), 0.77* (ae10) 3.57 0.53 S3|S2 –0.50*** –0.74* (ae1), –0.86* (ae2), 0.74* (ae8) 0.15 0.28 QPh.cgb-2D.11 P3176.1–P1123.1 S1|S0 0.92*** 0.78 QPh.cgb-3A.1 Xcwm48.1–Xwmc532 S2|S1 0.49*** –0.72** (ae1), 0.65* (ae5), –0.64* (ae6), –0.71** (ae7), 0.84*** (ae8) 0.15 0.44 QPh.cgb-3B.9 P3622.4–P2076 S1|S0 –1.07*** –0.59* (ae10) 0.96 0.33 QPh.cgb-3B.11 Xwmc291–P3156.1 S5|S4 0.34** 0.97** (ae1), –1.62*** (ae4), 0.81* (ae8) 0.36 0.53 QPh.cgb-4A.5 P6431.1–Xgwm160 S1|S0 0.62*** 0.19 QPh.cgb-4D.1 Xgwm165.2–Xgwm192 S1|S0 1.32*** –0.82* (ae1), 1.53*** (ae10) 2.79 0.57 S2|S1 0.36*** –1.12*** (ae1), 0.63* (ae4), 1.09*** (ae5), –1.24*** (ae6), –0.87** (ae7), 0.66* (ae8) 0.08 0.70 QPh.cgb-4D.2 Xgwm192–Xwmc331 S3|S2 –0.83** (ae2), –0.72* (ae6) 0.43 QPh.cgb-5A.6 Xgwm595–Xwmc410 S1|S0 –0.84*** 0.32 QPh.cgb-5A.7 Xgwm291–Xgwm410 S2|S1 –0.53*** 0.67* (ae2), –0.61* (ae5), 0.80** (ae6), –1.05*** (ae8), –0.74* (ae10) 0.23 0.49 S3|S2 –0.33*** –0.68* (ae4), 1.27*** (ae10) 0.10 0.36 S4|S3 0.35*** 0.83* (ae2), 1.05*** (ae5), –0.80* (ae6), –0.85* (ae7), –0.75* (ae9) 0.06 0.34 QPh.cgb-5B.4 Xgwm371–Xgwm335 S1|S0 –0.96*** 0.73 QPh.cgb-6A.1 Xgwm334–Xwmc297 S3|S2 0.59*** 0.27 QPh.cgb-6A.3 P4232.4–Xcwm306 S1|S0 –1.54*** –0.74* (ae10) 1.10 0.19 S5|S4 1.60*** 0.87 QPh.cgb-6B.5 Xgwm132–Xwmc104 S1|S0 1.58*** –1.00*** (ae1), 0.99*** (ae10) 0.67 0.20 QPh.cgb-6B.7 Xwmc269.3–P4232.1 S1|S0 –1.83*** 0.71* (ae1), 0.80* (ae4), –0.72* (ae6), –1.21*** (ae10) 1.89 0.58 S5|S4 –0.79** (ae2), –1.19*** (ae4), 0.90*** (ae5), 0.74* (ae8) 0.65 QPh.cgb-6B.8 Xgwm644.1–Xwmc417.2 S5|S4 0.45*** –0.61* (ae3) 0.23 0.33 QPh.cgb-7A.3 P3454.5–P3446.4 S1|S0 0.78*** 1.40 a, ae1–ae10, the positive and negative value in the table, *, **, ***, h2(a)%, h2(ae)% are as shown in Table 2; E1–E10 are as shown in Table 1; S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4 indicate the first, second, third, fourth, and fifth measuring period, respectively. Open in new tab In contrast to unconditional A-QTLs, conditional A-QTLs were rarely detected in two or more growth periods. Only one QTL, QPh.cgb-5A.7, was expressed in three periods, and four were expressed in two periods; these were QPh.cgb-2D.1, QPh.cgb-4D.1, QPh.cgb-6A.3, and QPh.cgb-6B.7. The remaining 75.0% of QTLs were detected in only one specific period. No QTL was expressed in all five periods, and most were expressed in the earliest period of PH development. This further indicated that genes for PH are expressed selectively during ontogeny (Table 3). Nineteen of the 20 (95.0%) conditional A-QTLs were identified with significant a effects in 1–3 periods. Most of the favourable alleles came from Hanxuan 10, consistent with the unconditional results, implying that Hanxuan 10 carried most of the alleles for PH. Thirteen QTLs (65%) with ae effects were found in 1–8 environments in 1–3 periods. QPh.cgb-4D.2 was identified with clearly significant negative ae effects in E1 and E8, indicating that it was completely induced by environment. Conditional ae effects across PH development had the following characteristics. First, conditional A-QTLs reacted differently to environments. Typically, QPh.cgb-2D.1, QPh.cgb-4D.1, and QPh.cgb-6B.5 expressed negative ae effects (–1.12*** to –0.74*) in E1 and were positive (0.66*–1.53***) in either E8 or E10 in 1–2 periods; QPh.cgb-6B.7 displayed positive ae effects in E1 (0.71*) and E4 (0.80*), and negative effects in E6 (–0.72*) and E10 (–1.21***) in S1|S0. The responses of these QTLs to environments were consistent with their unconditional behaviour. Secondly, conditional A-QTLs more readily responded to environments. On average each of these conditional QTLs had significant ae effects in three environments, compared with only two environments for the unconditional A-QTLs. This indicated that actual gene expression associated with PH was highly sensitive to environment. Finally, in contrast to A-QTLs, conditional A-QTLs displayed more ae effects in periods other than S1|S0. From 75% to 100% of A-QTLs were identified with ae effects in 2–5 environments in the periods after S1|S0, with each of their ae effects being larger in magnitude than the a effects. Consequently, ae effects contributed more to phenotypic variation than a effects. In contrast, only 50% of QTLs were detected with significant ae effects, and most were in E1 or E10 in S1|S0. Each ae effect for these QTLs was less than the corresponding a effect, hence a effects contributed more phenotypic variation than ae effects in S1|S0. Therefore, the real gene expression for PH in period S1|S0 was little affected by environments, whereas it was greatly affected thereafter. For conditional A-QTLs identified in more than one period, the genetic effects varied greatly, in contrast to those of unconditional A-QTLs at more than one stage. For example, QPh.cgb-2D.1, QPh.cgb-5A.7, and QPh.cgb-6A.3 exhibited a effects with opposite signs in different periods, and their ae effects also differed between different periods. Another QTL, QPh.cgb-6B.7, was detected with significant a effects (–1.83***) in period S1|S0, but with only significant ae effects in periods S5|S4 in E2, E4, E5, and E8. This indicated that gene expression was developmentally dependent. All conditional epistatic QTLs were detected in a specific period; no epistatic QTL pair was expressed in two or more periods. This further confirmed that epistatic effects were short lived. Over all five growth periods, the most (seven) epistatic QTL pairs were detected in S1|S0, whereas the least (three) pairs were in S4|S3 (Supplementary Table S6 at JXB online). Seventeen of 23 epistatic QTL pairs (73.9%) were identified with significant aa effects, 10 (58.8%) pairs of which expressed negative aa effects. There were 13 pairs (56.5%) of epistatic QTLs with significant aae effects in 1–8 environments, six of which had unique aae effects completely caused by environmental components. Among the 13 epistatic pairs with aae effects, on average one epistatic pair had aae effects in ∼3 environments compared with two environments for the unconditional epistasis. Additionally, two of seven (28.6%) epistatic pairs had significant aae effects in E10 in S1|S0, whereas 60.0–75.0% of epistatic pairs had significant aae effects in 1–6 environments (but excluding E3) in the other periods. Six epistatic pairs with unique aae effects in the later three periods were detected, with two such pairs in each period. Even for epistatic pairs with aa and aae effects in S1|S0, aa effects were clearly larger than aae effects, and the same trend existed in 66.7% of this kind of epistatic pair in S2|S1, but, in other periods, aae effects were always larger than aa effects. Therefore, for epistatic QTLs during PH development, aa effects were the important components in early periods (especially in S1|S0) of growth, whereas aae effects became predominant in the middle and later periods. This reality for epistatic QTLs was consistent with that for conditional A-QTLs, which were difficult to detect by unconditional analysis. For conditional QTLs, 30% of A-QTLs (six QTLs) were involved in epistatic interactions. About 15% of QTLs involved in epistatic interaction, including three A-QTLs and three non-individual E-QTLs, interacted with 2–3 other QTLs in the same or different periods. Hence, QTL networks were detected across various periods of PH development (Fig. 2). The character was as revealed in the preceding unconditional analysis. Collectively, the QTL network-related effects explained 34.8% of the phenotypic variation, less than the 52.2% of phenotypic variation explained by A-QTLs which were not involved in the QTL networks. Additive effects involved in QTL networks accounted for up to 22.4% of the phenotypic variation (the remaining 12.5% was attributed to epistatic effects). Therefore, additive effects were the major contributors to gene expression during PH development, in accordance with the unconditional analysis. Fig. 2. Open in new tabDownload slide QTL epistatic networks for conditional PH in five periods in wheat DHLs. , , solid lines, and dashed lines are as shown in Fig. 1. , , , , and refer to positive aa effects in S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4, respectively. , , , , and refer to negative aa effects in S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4, respectively. , , , , and refer to epistasis only with aae effects in S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4, respectively. Fig. 2. Open in new tabDownload slide QTL epistatic networks for conditional PH in five periods in wheat DHLs. , , solid lines, and dashed lines are as shown in Fig. 1. , , , , and refer to positive aa effects in S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4, respectively. , , , , and refer to negative aa effects in S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4, respectively. , , , , and refer to epistasis only with aae effects in S1|S0, S2|S1, S3|S2, S4|S3, and S5|S4, respectively. Common QTLs detected by unconditional and conditional analysis Through unconditional and conditional analysis, 15 A-QTLs (Tables 2, 3) and seven epistatic pairs (Supplementary Tables S5, Supplementary Data at JXB online) were common between the two sets of results across the whole period of PH development. For common A-QTLs, the conditional a effects at least in one early period were consistent in direction with the late unconditional a effects at 1–5 stages; that is, 12 A-QTLs were revealed with a effects in S1|S0 in the same direction as detected at 1–5 stages later, three A-QTLs in S2|S1 with 2–3 other stages, and one QTL, QPh.cgb-5A.7, in S3|S2 to S3 and S4 (Tables 2, 3). The opposite effects were also observed for one QTL in different periods which might counteract each other, resulting in the detection of little or no cumulative effects of the locus at a later stage. For example, QPh.cgb-2D.1 had an a effect of –0.50*** in S3|S2, and was opposite in direction to 1.85*** in S1|S0, decreasing its unconditional a effect from 3.89*** at S2 to 3.02*** at S3. QPh.cgb-5A.7 had an a effect of 0.35*** in S4|S3, changing its unconditional a effect from –3.89*** at S3 to –3.11*** at S4. QPh.cgb-6A.3 was identified with an a effect of 1.60*** in S5|S4, but was reversed in direction to –1.54*** in S1|S0, leading to no a effect being identified at S5. As for the magnitude of a effects for a particular QTL, the absolute value of conditional a effects was always less than or equal to that of unconditional results. Hence, the real gene expression in the early period could explain gene action at a later stage. This relationship was equally applicable for common epistatic interactions. The conditional aa effects in S1|S0 could explain unconditional aa effects at 1–4 stages later. Five unconditional A-QTLs at 1–4 stages from S1 were not revealed to have conditional additive effects, whereas the same numbers of conditional A-QTLs in either periods S3|S2 or S5|S4 had no unconditional additive effects. In the same way, 91.5% of unconditional epistatic pairs were not detected with conditional epistatic effects, whereas 69.6% of conditional epistatic pairs were similarly not detected with unconditional epistatic effects. Therefore, by combining the conditional and unconditional QTL mapping of time-dependent measures, more loci were detected; consequently, it was possible to reveal dynamic gene expression for the development of a quantitative trait (Yan et al., 1998a). With regard to all genetic components, A-QTLs, E-QTLs, and their QE effects governed the development of PH of wheat, and, by unconditional mapping, a effects were identified as playing the most important roles at different development stages, accounting for 58.1–73.6% of the phenotypic variation; aa effects took second place, accounting for 14.1–36.0% of the phenotypic variation; and ae and aae effects contributed the least, accounting for 5.9–13.0% (Fig. 3). Conditional mapping showed that most of the phenotypic variation (73.6%) was due to a effects in S1|S0, whereas in periods after S1|S0, most phenotypic variation (64.8–94.0%) was attributable to ae and aae effects (Fig. 3). This implied that the net genetic effects varied greatly across ontogeny. Thus, as with the above analysis, genetic effects determined at different developmental stages after S1|S0 were basically the outcomes of real gene expression in S1|S0. Fig. 3. Open in new tabDownload slide Contributions of genetic effects to PH development in wheat DHLs. Fig. 3. Open in new tabDownload slide Contributions of genetic effects to PH development in wheat DHLs. Superior genotype prediction based on mapped QTLs In order to better utilize mapped QTLs in genetic improvement of wheat PH, the ideal multilocus combination of all putative QTLs (superior line, SL) was selected by comparing the total genetic effect of any individual with known QTL genotypes from the DHLs (Yang and Zhu, 2005). Because genetic main effects, particularly a effects expressed mostly in S1|S0, were the foremost genetic determinants of PH, SLs for PH in S1|S0 were predicted. With drought being a universal phenomenon, and increased PH being beneficial for exploiting soil moisture at depth under DS conditions, the predicted SLs were chosen with high values. The estimated total genetic effects for the taller parent (P1, Hanxuan 10) and SLs varied greatly in different environments; the predicted total genetic effects of SLs were far higher than that of P1 in each environment (Table 4), indicating the large potential for genetic improvement of PH. In E1, the severe DS environment, the predicted total genetic effect for SLs was 15.2 cm, whereas that for the taller parent P1 was 0.8 cm. Therefore, the SLs predicted in E1 adapted well to severe DS conditions. Table 4. Predicted genetic effects (G) (cm) for P1, P2, and a superior line (SL) on wheat PH in S1|S0 Entry G G+GE1 G+GE2 G+GE3 G+GE4 G+GE5 G+GE6 G+GE7 G+GE8 G+GE9 G+GE10 P1 2.7 0.8 2.7 2.7 3.5 2.7 2.0 2.7 2.7 2.7 3.2 P2 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 SL 18.6 15.2 18.6 18.6 17.8 18.6 19.3 18.6 18.6 18.6 25.5 Entry G G+GE1 G+GE2 G+GE3 G+GE4 G+GE5 G+GE6 G+GE7 G+GE8 G+GE9 G+GE10 P1 2.7 0.8 2.7 2.7 3.5 2.7 2.0 2.7 2.7 2.7 3.2 P2 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 SL 18.6 15.2 18.6 18.6 17.8 18.6 19.3 18.6 18.6 18.6 25.5 G, general genetic effect; G+GE1 refers to the total genetic effects in E1, and so on; P1, Huanxuan 10; P2, Lumai 14; SL, superior line. Open in new tab Table 4. Predicted genetic effects (G) (cm) for P1, P2, and a superior line (SL) on wheat PH in S1|S0 Entry G G+GE1 G+GE2 G+GE3 G+GE4 G+GE5 G+GE6 G+GE7 G+GE8 G+GE9 G+GE10 P1 2.7 0.8 2.7 2.7 3.5 2.7 2.0 2.7 2.7 2.7 3.2 P2 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 SL 18.6 15.2 18.6 18.6 17.8 18.6 19.3 18.6 18.6 18.6 25.5 Entry G G+GE1 G+GE2 G+GE3 G+GE4 G+GE5 G+GE6 G+GE7 G+GE8 G+GE9 G+GE10 P1 2.7 0.8 2.7 2.7 3.5 2.7 2.0 2.7 2.7 2.7 3.2 P2 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 –1.8 SL 18.6 15.2 18.6 18.6 17.8 18.6 19.3 18.6 18.6 18.6 25.5 G, general genetic effect; G+GE1 refers to the total genetic effects in E1, and so on; P1, Huanxuan 10; P2, Lumai 14; SL, superior line. Open in new tab The same kind of QTL genotype was obtained for the predicted general superior line (GSL) and SLs in all 10 environments (Table 5), implying that the predicted SL genotype for PH in S1|S0 might be broadly adaptable; that is, the superior genotype could be adaptable to a range of environments. Compared with P1, the predicted QTL genotype for SL differed at eight loci, including six A-QTLs, QPh.cgb-3B.9, QPh.cgb-4A.5, QPh.cgb-5A.6, QPh.cgb-5B.4, QPh.cgb-6A.3, and QPh.cgb-6B.7, and two non-individual QTLs, QPh.cgb-3B.1 and QPh.cgb-6A.4, at which alleles increasing the PH were contributed by the lower value parent P2 (Lumai 14). Hence, the SL could be rapidly produced by substituting alleles of P1 by those of P2 by MAS. Table 5. QTL genotypes of the predicted general superior line (GSL) and SLs on wheat PH in S1|S0 QTL Flanking markers GSL SL E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 QPh.cgb-1B.1 Xgwm582–Xgwm273 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.1 Xwmc453.1–Xwmc18 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.11 P3176.1–P1123.1 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.9 P3622.4–P2076 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4A.5 P6431.1–Xgwm160 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4D.1 Xgwm165.2–Xgwm192 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.6 Xgwm595–Xwmc410 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-5B.4 Xgwm371–Xgwm335 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.3 P4232.4–Xcwm306 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6B.5 Xgwm132–Xwmc104 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6B.7 Xwmc269.3–P4232.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7A.3 P3454.5–P3446.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.11 P6934.3–P3446.6 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.16 Xwmc269.2–Xcwm90 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2A.2 Xwmc264.2–P8966.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7D.3 Xwmc463–Xgwm295 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2B.1 Xcwm529–Xwmc317 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7A.5 Xgwm635.1–P2454.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.7 Xwmc170–Xcwm96.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.3 P2470–Xgwm154 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.1 P2478.1–Xwmc505.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.6 Xgwm617–Xcwm487 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6A.4 Xpsp3071–Xgwm570 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7B.4 Xpsp3033–Xgwm297 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QTL Flanking markers GSL SL E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 QPh.cgb-1B.1 Xgwm582–Xgwm273 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.1 Xwmc453.1–Xwmc18 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.11 P3176.1–P1123.1 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.9 P3622.4–P2076 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4A.5 P6431.1–Xgwm160 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4D.1 Xgwm165.2–Xgwm192 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.6 Xgwm595–Xwmc410 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-5B.4 Xgwm371–Xgwm335 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.3 P4232.4–Xcwm306 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6B.5 Xgwm132–Xwmc104 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6B.7 Xwmc269.3–P4232.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7A.3 P3454.5–P3446.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.11 P6934.3–P3446.6 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.16 Xwmc269.2–Xcwm90 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2A.2 Xwmc264.2–P8966.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7D.3 Xwmc463–Xgwm295 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2B.1 Xcwm529–Xwmc317 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7A.5 Xgwm635.1–P2454.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.7 Xwmc170–Xcwm96.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.3 P2470–Xgwm154 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.1 P2478.1–Xwmc505.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.6 Xgwm617–Xcwm487 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6A.4 Xpsp3071–Xgwm570 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7B.4 Xpsp3033–Xgwm297 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ E1–E10 are as shown in Table 1. Bold indicates QTLs with additive effects (A-QTLs); Light indicates QTLs with epistatic effects (E-QTLs); GSL, general superior line; SL is as shown in Table 4. Open in new tab Table 5. QTL genotypes of the predicted general superior line (GSL) and SLs on wheat PH in S1|S0 QTL Flanking markers GSL SL E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 QPh.cgb-1B.1 Xgwm582–Xgwm273 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.1 Xwmc453.1–Xwmc18 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.11 P3176.1–P1123.1 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.9 P3622.4–P2076 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4A.5 P6431.1–Xgwm160 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4D.1 Xgwm165.2–Xgwm192 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.6 Xgwm595–Xwmc410 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-5B.4 Xgwm371–Xgwm335 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.3 P4232.4–Xcwm306 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6B.5 Xgwm132–Xwmc104 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6B.7 Xwmc269.3–P4232.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7A.3 P3454.5–P3446.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.11 P6934.3–P3446.6 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.16 Xwmc269.2–Xcwm90 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2A.2 Xwmc264.2–P8966.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7D.3 Xwmc463–Xgwm295 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2B.1 Xcwm529–Xwmc317 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7A.5 Xgwm635.1–P2454.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.7 Xwmc170–Xcwm96.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.3 P2470–Xgwm154 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.1 P2478.1–Xwmc505.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.6 Xgwm617–Xcwm487 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6A.4 Xpsp3071–Xgwm570 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7B.4 Xpsp3033–Xgwm297 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QTL Flanking markers GSL SL E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 QPh.cgb-1B.1 Xgwm582–Xgwm273 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.1 Xwmc453.1–Xwmc18 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.11 P3176.1–P1123.1 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.9 P3622.4–P2076 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4A.5 P6431.1–Xgwm160 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-4D.1 Xgwm165.2–Xgwm192 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.6 Xgwm595–Xwmc410 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-5B.4 Xgwm371–Xgwm335 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.3 P4232.4–Xcwm306 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6B.5 Xgwm132–Xwmc104 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6B.7 Xwmc269.3–P4232.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7A.3 P3454.5–P3446.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.11 P6934.3–P3446.6 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-1B.16 Xwmc269.2–Xcwm90 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2A.2 Xwmc264.2–P8966.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7D.3 Xwmc463–Xgwm295 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2B.1 Xcwm529–Xwmc317 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-7A.5 Xgwm635.1–P2454.4 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-2D.7 Xwmc170–Xcwm96.2 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-5A.3 P2470–Xgwm154 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-3B.1 P2478.1–Xwmc505.1 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-6A.6 Xgwm617–Xcwm487 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QPh.cgb-6A.4 Xpsp3071–Xgwm570 qq qq qq qq qq qq qq qq qq qq qq QPh.cgb-7B.4 Xpsp3033–Xgwm297 QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ QQ E1–E10 are as shown in Table 1. Bold indicates QTLs with additive effects (A-QTLs); Light indicates QTLs with epistatic effects (E-QTLs); GSL, general superior line; SL is as shown in Table 4. Open in new tab Discussion Gene expression during ontogeny Developmental quantitative genetics assume that development of complex morphological structures occurs through the actions and interactions of many genes acting differentially during ontogeny and whose expression is modified by interactions with other genes and by the cellular or organism environments (Atchley and Zhu, 1997; Wu et al., 2002). By unconditional and conditional analysis, a total of 129 QTLs were detected on 20 chromosomes (except 6D) associated with PH development. Of these, seven showed only individual additive effects, 104 only took part in epistatic interactions (i.e. modifying other QTLs), and the remaining 18 had both individual additive effects and modifying functions. Of these, 64.0% of the A-QTLs (16 of 25) and 23.2% of the epistatic pairs (23 of 99) also showed QE effects in 1–8 of the 10 environments. Expression of the A-QTLs and E-QTLs was developmental stage dependent; that is, most additive and interactive actions occurred in specific periods, with only a few A-QTLs being expressed in two or more periods, and no additive and interactive actions were continually active during the entire PH growth period. Consequently, most QTLs identified during the early periods were not detected in the later periods, suggesting that different genetic systems were responsible for PH development during different periods. This was also reported in previous studies (Yan et al., 1998a, b; Wu et al., 1999; Cao et al., 2001b; Ellis et al., 2004). Several A-QTLs, such as QPh.cgb-2D.1, QPh.cgb-6B.5, QPh.cgb-6B.7, and QPh.cgb-7A.3, were expressed at all five stages. This was not ambivalent because QTLs detected at stage t were attributed to the cumulative gene expression from the initial time to stage t (Yan et al., 1998,a), and not the real gene expression in the specific development period from stage t–1 to t. In reality, genetic behaviour measured at stage t is the confounded result of genes expressed before stage (t–1) and within the period from (t–1) to t (Zhu, 1995). QTL behaviour at stage t suggested the sequential and hierarchical character of development; that is, an event at stage t can have significant consequences on subsequent phenotypes later in ontogeny (Atchley and Zhu, 1997). Thus, QTLs significantly expressed in one or two periods simultaneously had significant genetic effects at all developmental stages. For example, QPh.cgb-2D.1, detected with significant a effects (1.85***) in S1|S0 and S3|S2 (–0.50***), also exhibited significant a effects (1.85*** to 4.70***) at all five stages. Such properties of QPh.cgb-4D.1 was proven in a recombinant inbred line (RIL) derived from the same cross Hanxuan 10 × Lumai 14 (Wang. et al., 2010). Therefore, genetic information disclosed by both conditional mapping in a specific growth period from t–1 to t, and by unconditional mapping at stage t is necessary for revealing the genetic expression of the developmental behaviour of a quantitative trait. Parts of the genetic actions and interactions were observed to have only unconditional effects, and not conditional effects, and vice versa. The absence of conditional effects might result from the effects being too small to be detected at the statistical level, whereas the absence of unconditional effects might be associated with the counteraction of conditional effects for the same QTL during different growth periods, as shown in other studies (Yan et al., 1998,a, b; Wu et al., 1999; Cao et al., 2001b). Inductive interactions are common in sequential and hierarchical systems. One component (e.g. a gene, protein, cell, or tissue) may ‘induce’ activities of other components and alter the eventual phenotype in a unidirectional or ‘cause and effect’ fashion (Atchley and Zhu, 1997). Epistatic QTL mapping studies in model organisms have shown that epistasis makes large contributions to the genetic regulation of complex traits (Carlborg and Haley, 2004). The effect of a gene on a phenotype is a collective property of a network of genes, rather than a property of a single gene (Wade, 2002; Malmberg et al., 2005). In the current study, a number of QTLs, including A-QTLs and E-QTLs without individual effects, participated in more than one epistatic interaction during ontogeny. Interestingly, some QTLs mapping at known Rht loci and reported in previous research, such as QPh.cgb-2D.1 mapping to Rht8 (Korzun et al., 1998), QPh.cgb-4B.1 to Rht1 (Cadalen et al., 1998; Liu et al., 2006) or Rht3 (Cadalen et al., 1998; Liu et al., 2006), QPh.cgb-4D.1 to Rht2 (Cadalen et al., 1998; Ellis et al., 2002; McCartney et al., 2005) or Rht10 (Cadalen et al., 1998; Ellis et al., 2002; McCartney et al., 2005), QPh.cgb-5A.7 to Rht9 (Ellis et al., 2005), and QPh.cgb-5A.6 to Rht12 (Ellis et al., 2005), participated in both additive and epistatic effects, and all of them, except QPh.cgb-5A.6, participated in more than one epistatic interaction, suggesting that the major Rht genes for PH acted as a network during ontogeny; that is, they display effects that depend upon genotypes at other loci. This kind of action for the major dwarfing genes was also reported for PH in rice (Jiang et al., 1994). Additionally, four of five A-QTLs detected in the present study were coincident with previously reported PH QTLs (Cadalen et al., 1998; Huang et al., 2003; Schnurbusch et al., 2003; Sourdille et al., 2003), including QPh.cgb-1B.1, QPh.cgb-1B.4, QPh.cgb-5B.4, and QPh.cgb-7B.4, and also participated in two or more epistatic interactions. Close to QPh.cgb-1B.4 (0.3 cM), QPh.cgb-1B.5, identified in S5|S4, also controlled the interval length from the flag leaf ligule to the spike base. It was detected in another study on the same population (unpublished data). This provided direct evidence for gene expression of PH during development. The remaining A-QTL, QPh.cgb-6A.3, acted individually, rather than being involved in interactions with other QTLs. Additionally, among three new QTLs, QPh.cgb-6B.5 and QPh.cgb-6B.7 had significant a effects with absolute values ranging from 1.58*** to 6.39*** at all five growth stages, and were also involved in three and two epistatic interactions, respectively. The third new QTL, QPh.cgb-7A.3, with significant a effects (0.78***–3.49***) at five stages, did not participate in epistatic interaction with other QTLs. Thus there is enough evidence to infer that the genetic architecture underlying PH development was represented as a network of epistatic and additive effects, and gene expression in ontogeny was regulatory and interactive. Genetic components of PH development Additive gene effects are the major components of PH variation (Fick and Qualset, 1973). As in the present study, PH was attributed to the major gene control model based on segregation patterns (Fick and Qualset, 1973). It was determined here that additive main effects (a effects), epistatic main effects (aa effects), and their QE effects control PH development. Among these, a effects were the major genetic determinants of PH, whereas aa effects and QE effects contributed much less. Although aa effects contributed less to phenotypic variation of PH compared with a effects, they were an important genetic component in that most loci participated in epistatic interactions, rather than functioning alone. This also occurred in other studies (Fick and Qualset, 1973; Jiang et al., 1994; Yu et al., 2002; Malmberg et al., 2005; Zhang et al, 2008). Genetic components governing PH were time dependent; a effects were much more important than aa effects and QE effects at all five growth stages. Moreover, a effects were the predominant genetic components compared with other effects in S1|S0, whereas QE effects became major contributors to phenotypic variation after S1|S0; that is, the genetic effects of phenotypic variability become modified during ontogeny. A similar trend occurred with aa effects, which played a secondary role at all five growth stages, and also were more important than QE effects in S1|S0, whereas they became less important than QE effects after S1|S0. Developmental quantitative genetics showing that age-specific genetic variation at time t was conditioned by causal genetic effects at time t–1 imply the generation of significant episodes of new genetic variation arising during the period t–1 to t (Atchley and Zhu, 1997; Wu et al., 2002), and traits at different developmental stages are due to the accumulated results of genetic main effects and QE effects at all previous stages (Cao et al., 2001b). Therefore, genetic effects emerging at different developmental periods were more variable. Thus, our conclusion that the predominance of a effects at stages after S1|S0 was mostly attributable to the additive genetic variation generated in S1|S0 was reasonable; that is, a effects expressed in S1|S0 play the most important role for genetic control of PH, allowing for efficient early selection for PH improvement. Environmental effects on QTL expression The development of a complex trait reflects the adaptation of an organism to a particular environment and represents the impacts of genetic and environmental interactions (Wu et al., 2002). GE is a very important factor determining the stability of crop varieties, and has received considerable attention in plant breeding programmes (Xing et al., 2002). Loci conditioned by environmental signals (environmentally dependent genes) provide plants with large amounts of flexibility in responding to environmental changes; that is, these environmentally dependent loci might be of great importance in some environments, although less important in other environments (Lark et al., 1995). In the present study, 64.0% of A-QTLs and 23.5% of epistatic pairs had significant interaction effects in one to eight environments. Among them, QTLs highly responsive to water-limited environments might have practical consequences for breeding programmes. QTLs such as QPh.cgb-2D.1, QPh.cgb-4D.1, QPh.cgb-6B.5, and QPh.cgb-7A.3 were distinctly more adaptable to WW conditions (E10) at growth stages 2–3 (before S3); whereas QTLs such as QPh.cgb-5A.7, QPh.cgb-6B.7, and QPh.cgb-7B.4 were more responsive under DS conditions (E1) at stages 1–5. Drought tolerance is always a major goal in wheat improvement for water-limited areas. Under drought conditions increased PH is beneficial for exploiting soil moisture at depth, and for enabling mechanical harvesting, therefore leading to increased biological as well as grain yield (Baum et al., 2003). Hence QTLs such as QPh.cgb-5A.7, QPh.cgb-7B.4, and, especially, QPh.cgb-6B.7 might be useful for improving wheat PH by MAS under DS conditions. Superior genotypes predicted from QTLs In the present study, additive effects were the primary genetic component for PH growth, but E-QTLs should not been ignored in order to attain the greatest PH improvement. Since many QTLs were dependent on other QTLs rather than acting independently, predicted superior genotypes will be unreliable if epistasis is ignored (Jiang et al., 1994). Therefore, the best multilocus combination of all the putative QTLs could be used to predict and select SLs (Yang and Zhu, 2005). QTLs detected with the conditional genetic model are associated with the main genes affecting the developmental trajectory of a morphological trait during ontogeny. A MAS programme for biologically real conditional QTLs can be designed to alter growth trajectories at particular stages and hence to achieve maximum final growth, whereas conventional MAS incorporating QTLs for final growth will be less efficient in effective early selection (Wu et al., 2002). With the knowledge that genetic effects in S1|S0, with minimal QE effects, played decisive roles in determining final PH, predicted SLs can be based on QTLs identified in S1|S0. Our predicted SL was far superior to the better parent P1, implying a large potential among the progeny for further improvement of PH. Furthermore, the estimated total genetic effects for the SL varied in different environments (15.20–25.53 cm), indicating that PH improvement might be obtained differently in different environments. Furthermore, the predicted QTL genotype for SLs in S1|S0 was stable across environments. Thus, the predicted SL should be broadly adaptable to enable the optimal PH improvement in multitude environments using the same SL. To evaluate the predicted SL, further experiments are needed to substitute P1 alleles by P2 alleles using the appropriate molecular markers, then to reveal the effects of PH on grain yields of these artificial lines, which will be rather valuable for practical breeders. Abbreviations Abbreviations a effect additive main effect aa effect additive×additive epistatic main effect ae effect additive QTL×environment interaction effect aae effect epistasis×environment interaction effect A-QTL efffect QTL with individual additive effect Ch05 and Ch06 Changping Beijing in 2005 and 2006, respectively DHLs doubled haploid lines DS drought stress E1 F01 under DS E2 H05 under DS E3 Ch05 under DS E4 H06 under DS E5 Ch06 under DS E6 F01 under WW conditions E7 H05 under WW E8 Ch05 under WW E9 H06 under WW E10 Ch06 under WW E-QTL QTL participating in epistatic effects F01 Fenyang Shanxi in 2001 GSL general superior line H05 and H06 Haidian Beijing in 2005 and 2006, respectively MAS marker-assisted selection PH plant height PVE phenotypic variation explained QE QTL×environment interaction, including ae and aae QTL quantitative trait locus S1, S2, S3, S4 and S5 the first, second, third, fourth and fifth measuring stage, respectively S1|S0, S2|S1, S3|S2, S4|S3 and S5|S4 the first, second, third, fourth and fifth measuring period, respectively SL superior line WW well watered We thank Professor Jun Zhu (College of Science, Zhejiang University) for kind advice regarding the use of mapping software and data analysis. We thank Professor Robert A McIntosh (Plant Breeding Institute, University of Sydney, NSW, Australia) for revising the manuscript. This work was supported by the National High Technology Research and Development Program of China (2006AA100201), Generation Challenge Program (G4007.06), and the National Basic Research Program of China (2010CB125905). References Atchley WR , Zhu J . Developmental quantitative genetics, conditional epigenetic variability and growth in mice , Genetics , 1997 , vol. 147 (pg. 765 - 776 ) Google Scholar PubMed OpenURL Placeholder Text WorldCat Baum M , Grando S , Backes G , Jahoor A , Sabbagh A , Ceccarelli S . QTLs for agronomic traits in the Mediterranean environment identified in recombinant inbred lines of the cross ‘Arta’ × H. spontaneum 41-1 , Theoretical and Applied Genetics , 2003 , vol. 107 (pg. 1215 - 1225 ) Google Scholar Crossref Search ADS PubMed WorldCat Börner A , Schumann E , Fürste A , Cöster H , Leithold B , Röder MS , Weber WE . 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2010 The Author(s).

Chen, Juan; Wang, Peng; Mi, Hua-ling; Chen, Gen-Yun; Xu, Da-Quan

doi: 10.1093/jxb/erq122pmid: 20478969

Abstract Ribulose-1, 5-bisphosphate carboxylase/oxygenase (Rubisco) activase (RCA) in the thylakoid membrane (TM) has been shown to play a role in protection and regulation of photosynthesis under moderate heat stress. However, the physiological significance of RCA bound to the TM (TM–RCA) without heat stress remains unknown. In this study, it is first shown, using experiments in vivo, that the TM–RCA varies in rice leaves at different development stages, under different environmental conditions, and in a rice mutant. Furthermore, it is shown that the amount of TM–RCA always increased when the Rubisco activation state and the pH gradient across the TM (ΔpH) decreased. It was then demonstrated in vitro that the RCA bound dynamically to TM and the amount of TM–RCA increased during Rubisco activation. A high level of ATP and a high pH value promoted the dissociation of RCA from the TM. Both the RCA association with and dissociation from the TM showed conformational changes related to the ATP level or pH as indicated by the changes in fluorescence intensity of 1-anilinonaphthalene-8-sulphonic acid (ANS) binding to RCA. These results suggest that the reversible association of RCA with the TM is ATP and pH (or ΔpH) dependent; it might be involved in the RCA activation of Rubisco, in addition to the previously discovered role in the protection and regulation of photosynthesis under heat stress. Activation, ATP, ΔpH, Rubisco, Rubisco activase, thylakoid membrane Introduction Ribulose-1, 5-bisphosphate (RuBP) carboxylase/oxygenase (Rubisco) activase (RCA) is a member of the AAA+ (ATPases associated with a variety of cellular activities) family (Neuwald et al., 1999). It activates Rubisco that catalyses the first reaction in photosynthetic CO2 assimilation (Spreitzer and Salvucci, 2002). Recently, enhanced thermostability of RCA has been shown to improve photosynthesis and growth under moderate heat stress (Kurek et al., 2007). Sage et al. (2008) proposed that RCA may be an important factor determining the response of boreal plants to global warming based on studies with the dominant species in the boreal forest of North America. Thus, engineering crops with RCA of high resistance to stresses represents an important option to increase photosynthetic efficiency. In some plant species, RCA has two isoforms of 41–43 kDa and 45–46 kDa, both of which are encoded by the same nuclear gene that produces two transcripts via alternative splicing (Salvucci et al., 1987; Werneke et al., 1988). However, barley has another gene encoding only a single and divergent 41 kDa isoform (Rundle and Zielinski, 1991) and cotton contains multiple genes (Law et al., 2001). The larger isoform of RCA has a unique extension containing cysteine residues at the C-terminus. Usually, RCA is a soluble protein localized in the chloroplast stroma of algae and higher plants, and requires ATP hydrolysis to remove inhibitory sugar phosphates from the active site of Rubisco so as to cause Rubisco to exhibit its maximal catalytic activity in vivo (Portis, 1990). On the other hand, RCA is inhibited by ADP. Thus, RCA is sensitive to the stromal ATP/ADP level (Robinson and Portis, 1988; Kallis et al., 2000). According to the model of RCA activation of Rubisco (Portis, 2003), ATP provides energy for both movement of RCA subunits to Rubisco and formation of a RCA–Rubisco supercomplex, and Rubisco is encircled by a ring containing 16 (or possibly eight) RCA subunits. However, the stromal ATP/ADP ratio under steady-state conditions does not vary greatly with light intensity (Brooks et al., 1988). Moreover, light and photosynthetic electron transport are required for full activation of Rubisco by RCA in lysed chloroplasts even when ATP is supplied exogenously at saturating concentrations (Campbell and Ogren, 1990a). Recently, more evidence indicates that light modulation of RCA is controlled by the redox state of thioredoxin-f via the critical cysteine residues of the C-terminal extension in the larger RCA isoform (Zhang and Portis, 1999; Portis et al., 2008). However, this regulatory mechanism is unable to explain the dependence of Rubisco activation on light intensity in some plant species, such as tobacco, in which the larger isoform of RCA is absent. Thus, the basic molecular mechanism of how RCA is regulated in the activation of Rubisco in vivo is not completely clear. Several reports demonstrated that both electron transport and a pH gradient (ΔpH) across the thylakoid membrane (TM) are required for light activation of Rubisco, and the TM may be directly involved in the light activation of Rubisco by RCA (Campbell and Ogren, 1990a, b). Moreover, an increase in the amount of RCA bound to the TM (TM–RCA) was detected under moderate heat treatment (42 °C) which reduced the activity of Rubisco (Rokka et al., 2001; Yang et al., 2005; Feng et al., 2007). The physiological significance of RCA bound to the TM is suggested to be important in protecting the thylakoid-associated translation machinery against heat inactivation (Rokka et al., 2001). However, Salvucci et al. (2001) suggested that the appearance of RCA in the TM fraction is due to the self-aggregation of thermally denatured RCA. Whether the association of RCA with the TM exists is not yet very clear. Moreover the existence of RCA in the TM fraction of control leaves (Rokka et al., 2001) may suggest another possible role for TM–RCA in non-heat-stressed leaves if it is not caused by a partial insolubilization of RCA. In this study, the amount of RCA in the TM fraction was examined in a variety of leaves without moderate heat stress and it was shown that the amount of TM–RCA varied dramatically under different environment conditions and at different leaf developmental stages. Using both in vivo and in vitro analyses, it was further demonstrated that the association of RCA with the TM without heat stress is reversible, and is ATP and pH (or ΔpH) dependent. In addition to the previously discovered role in the protection and regulation of photosynthesis under heat stress we propose a model is proposed to describe the potential role of reversible association of RCA with TM in regulation of Rubisco activation. Materials and methods Plant growth Pot-grown rice (Oryza sativa L. cultivar japonica 9522), Δlut, a rice mutant in which the gene encoding carotenoid isomerase was mutated (Fang et al., 2008), and tobacco (Nicotiana tabacum) plants were grown at a photosynthetic photon flux density (PPDF) of 500 μmol m−2 s−1 with a 12 h light/12 h dark cycle, 28 °C/20 °C (day/night), and 60–70% humidity in a phytotron. Spinach (Spinacia oleracea L.) and chili pepper (Capsicum frutescens L.) plants were grown in the field. Sampling and treatment of rice plant For experiments on different developmental stages, all rice leaves were collected at 10 am in the light. One-, 2- and >4-week-old leaves were collected from rice at the booting, heading, and filling stages, respectively. For all the experiments examining different environmental conditions and genotypes, the 4-week-old flag leaves at the filling stages were used. For the experiment involving different sampling times, leaves were collected in the daytime (10 am in the light) and at night (10 pm in the dark); for the experiments involving high CO2 (1000 μmol mol−1) and very low light (30 μmol m−2 s−1, near the light compensation point) treatments, the rice grown in pots was transferred into a NK system Biotron (NC type, Nippon medical and chemical instruments Co. Ltd. Osaka, Japan) at 28 °C and 60–70% humidity for ∼12  h, then the flag leaves were collected in the Biotron in the light, and leaves of rice grown in pots in the phytotron were used as the control. Leaves of rice plants for each experiment were collected from at least three pots to minimize the influence of different growth conditions among pots. Measurements of leaf photosynthesis The measurements of net photosynthetic rate (Pn) were made in situ using a portable LI-6400 photosynthetic gas analysis system (LI-COR, USA). For the measurements, CO2 concentration and light intensity were controlled under their growth conditions with the LI-COR CO2 injection system and by a LI-COR LED irradiation source. To produce the curve of light-saturated Pn versus the intercellular CO2 concentration (Ci), Pn values were measured at CO2 concentrations of 240, 180, 120, 60, 380, 580, 750, 900, 1050, and 1200 μmol CO2 mol−1 in turn, and the PPFD was kept at 1200 μmol m−2 s−1 during the measurement. Both the maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax) values were calculated from Pn/Ci curve data according to Farquhar et al. (1980) and von Caemmerer and Farquhar (1981). Measurements of chlorophyll (Chl) content Chl contents of leaves were determined spectrophotometrically according to Arnon (1949). Measurements of Rubisco activity Total soluble protein was rapidly extracted from liquid N2-frozen leaves with a CO2-free buffer containing 50 mM TRIS-HCl (pH 7.8), 1 mM EDTA, 50 mM NaCl, and 2 mM β-mercaptoethanol. The extract was then centrifuged at 12 000 g and 4 °C for 6 min, and the obtained supernatant was used immediately for Rubisco activity measurements. Rubisco initial activity was measured immediately using the supernatant mentioned above, while its total activity was determined after full activation. In the present study the oxygenase activity of Rubisco was determined as the indicator of Rubisco activity. The oxygenase activity of Rubisco was measured with a Clarke-type O2 electrode according to the method described by Cox et al. (1999). Measurements of Chl fluorescence Chl fluorescence was monitored with a modified PAM-2000 fluorometer (Walz, Effeltrich, Germany) as described by Gray and Lewis (2006). After a 30 min dark adaptation, continuous actinic light (growth light intensity) was applied. Fo and Fm are defined as the minimum photosystem II (PSII) fluorescence yield in dark-adapted leaves and the maximum PSII fluorescence yield reached during a saturating pulse of white light, respectively. PSII potential or maximal photochemical efficiency is the ratio Fv/Fm, where Fv is the variable part of the fluorescence emission and is equal to Fm–Fo. The effective photochemical efficiency of PSII (ΦPSII) is defined as (Fm′–F)/Fm′ as proposed by Genty et al. (1989). Non-photochemical quenching (NPQ) is quantified by Fm/Fm′–1 (van Kooten and Snel, 1990). The redox change of P700 was monitored by absorbance at 810 nm minus that at 830 nm, using a Dual-PAM-100 fluorometer (Walz, Effeltrich, Germany), and the initial rate of P700+ reduction following far-red light (>705 nm, 5.2 μmol m−2 s−1) was calculated after a 40 s illumination that allowed the oxidation of P700 to a steady state (Klughammer and Schreiber, 1998). Measurements of millisecond-delayed light emission of Chl fluorescence (ms-DLE) Measurements of ms-DLE were carried out using a phosphoroscope according to the method described by Wang et al. (2006). The leaf discs were collected in the light and dark adapted at the same temperature, and were immediately inserted into the sample cell to measure the ms-DLE. The light intensity was controlled at their growth conditions. Extraction and purification of RCA and Rubisco The leaves of rice and spinach detached from the plants in the light were immediately frozen with liquid N2. RCA was purified from the frozen leaves according to Robinson et al. (1988). The obtained RCA was then filtrated through a column of antibody against Rubisco from tobacco to remove the contaminating trace amount of rice Rubisco. The antibody column was prepared using protein A–Sepharose CL-4B (Sigma, USA) according to the method described by Gupta and Tan (1981). Rubisco was purified according to the methods described by Kung et al. (1980). The purified RCA was preserved in liquid N2, while the purified Rubisco was preserved in a suspension containing 65% saturated (NH4)2SO4. The concentration of proteins was determined according to Bradford (1976). Isolation of thylakoids Thylakoids were isolated from rice leaves according to the method described by Chen and Xu (2006). All extract solutions contained 1 mM phenylmethylsulphonyl fluoride (PMSF; Sigma, USA), and all steps were performed at 4 °C. The isolated thylakoids were stored at –40 °C. Blue-Native-polyacrylamide gel electrophoresis (BN-PAGE) BN-PAGE was performed as described by Schagger et al. (1994) and all steps were carried out on ice. Briefly, the TM (0.5 mg Chl ml−1) from rice leaves was suspended in BTH buffer (25 mM BIS-TRIS-HCl, pH 7.0, 20% glycerol). Subsequently, the TM was collected by centrifugation (at 18 000 g and 4 °C for 2  min), and the obtained pellets were dissolved in BTH buffer containing 1% (w/v, final concentration) dodecylmaltoside (DM) with gentle shaking for 30 min. Non-solubilized membranes and aggregates were removed by centrifugation (at 20 000 g and 4 °C for 30 min), the supernatant was transferred to a new tube, mixed with BN sample buffer, and loaded onto a BN-gel (BN: 5–13.5% acrylamide) using a Protean II electrophoresis system (Bio-Rad, USA). BN-polyacrylamide gels contained a 4% acrylamide stacking gel. The electrophoresis unit was chilled to 4 °C. For electrophoresis in the second dimension, a slice of the BN-gel lane was cut and treated with SDS–PAGE loading buffer at room temperature for <10 min without shaking. The treated gel slice was used for the second dimensional SDS–PAGE using a 10% running gel at room temperature. After electrophoresis, the gel was stained with Coomassie Brilliant Blue or analysed by western blotting. Assay for determining the proportion of TM–RCA Total and TM proteins of rice leaves were extracted from the frozen leaves and isolated thylakoids with extraction buffer B [25 mM TRIS-HCl, pH 7.8, 1 mM EDTA, 5 mM MgCl2, 1% (w/v) SDS, 2 mM β-mercaptoethanol] by incubating at 100 °C for 3–5 min. The extracts were centrifuged at 12 000 g and 4 °C for 10 min, and the obtained supernatant was preserved for SDS–PAGE. Total and TM proteins were separated by 10% SDS–PAGE as described by Kim et al. (1993) in the absence and presence of 4 M urea, respectively. For western blot, proteins separated electrophoretically were transferred to a nitrocellulose membrane (Amersham Pharmacia, Milton Keynes, UK) within a semi-dry transfer cell (Amersham Pharmacia) and detected with antibodies raised against RCA from tobacco leaves. For quantification of Rubisco and RCA, the bands from SDS–PAGE (Rubisco) and western blots (RCA) were analysed by Labworks 4.6 software (USA). Rubisco and RCA amounts were expressed on a leaf area basis and each loaded sample contained 1–2 μg of Chl. The quantification of the TM–RCA/total RCA ratio was obtained through a western blot of TM–RCA and a series of dilutions of total RCA on one gel. The absolute value of TM–RCA and total RCA in control leaves (4-week-old flag leaves) was quantified through a western blot of TM–RCA (or total RCA) and a series of dilutions of purified RCA from spinach on one gel (Supplementary Figure S1 available at JXB online). Treatments of thylakoids with different levels of ATP and different pH values Thylakoids isolated from rice leaves (<2 weeks old) were incubated at 25 °C for 20 min in buffer A (25 mM Tricine, pH 7.8, 10 mM NaCl, 5 mM MgCl2, 10 mM NaF, 1 mM PMSF, 0.4 M sucrose) containing different concentrations of ATP. Subsequently, the TM was collected by centrifugation (at 10 000 g and 4 °C for 10 min), and the obtained pellets were washed and dissolved twice in buffer A. The TM was also incubated buffer A with a series of different pH values at 25 °C for 20 min. Then the TM was collected again according to the method described above. The sample of TM with a Chl content of 0.4 mg ml−1 was preserved, and the amount of TM–RCA in the sample was analysed by SDS–PAGE and western blot. Rubisco activation in the presence of TM Rice Rubisco was deactivated by adding excess RuBP in a CO2-free assay, and incubated with the mixture of RCA and the TM of rice in buffer A containing 1 mM ATP at 25 °C for 20 min. Activated Rubisco, rather than deactivated Rubisco, from rice leaves was used as control. In this assay 0.1 mg ml−1 RCA, 0.2 mg ml−1 Rubisco, 0.1 mM RuBP, and TM containing 0.2 mg Chl ml−1 were used. Thereafter the TM was collected according to the method described above, and the amount of TM–RCA was analysed by SDS–PAGE and Western blot. Rice RCA, rice TM, and Rubisco from rice, spinach, tobacco, and chili pepper were used in this experiment. Measurements of the fluorescence intensity of 1-anilinonaphthalene-8-sulphonic acid (ANS) binding to RCA Samples of purified RCA (80 μg ml−1) and Rubisco (80 μg ml−1) from spinach were mixed well in the presence of 2 mM or 25 μM RuBP, then the mixture was incubated with different levels of ATP for 30 min. Afterwards, ANS solution mixed well with 0.1 M phosphate buffer (pH 7.4) was added (ANS final concentration 40 μM). After 15 min in the dark, the fluorescence intensity of ANS binding to RCA was measured with a 970 CRT fluorescence spectrophotometer (Shanghai Analytical Instrumental Plant, China) at room temperature according to the method described by Wang and Portis (1991). Fluorescence emission spectra excited by light with a wavelength of 380 nm were recorded between 400 nm and 600 nm. Statistical analysis Statistical analysis of all data was performed by the software SPSS 10.0 (SPSS Inc., USA). A least significant difference (LSD) test of one-way analysis of variance (ANOVA) was performed in a post-hoc comparison of means among the data of rice leaf gas exchange and Chl fluorescence measurements. Differences were considered significant only at P <0.05. All graphs presented herein were produced using the software SigmaPlot 9.0 (SPSS Inc., USA). Results Existence of RCA on the TM from rice leaves without moderate heat stress In order to confirm the existence of RCA on the TM without moderate heat stress, RCA on the TM was investigated using western blots of two-dimensional BN-PAGE/SDS–PAGE (Fig. 1). In order to reduce the self-aggregation of thermal denatured RCA, all steps, including the preparation of TM and solubilization of TM proteins, were performed on ice or at 4 °C. The results showed that the spot of TM–RCA was in a position identical to that of some other protein having a molecular weight of ∼320–340 kDa. Only one spot (<41 kDa) was observed in Fig. 1C, which may be due to the degradation of RCA during the solubilization of TM proteins. The above result indicates that some RCAs are bound to the TM in rice in vivo. Fig. 1. Open in new tabDownload slide BN-PAGE and western blot analysis of the TM–RCA from rice leaves. (A) BN-PAGE, (B) second dimension electrophoresis, (C) western blot analysis. Fig. 1. Open in new tabDownload slide BN-PAGE and western blot analysis of the TM–RCA from rice leaves. (A) BN-PAGE, (B) second dimension electrophoresis, (C) western blot analysis. Variation in the amount of TM–RCA in rice leaves without moderate heat stress To examine variations in the amount of RCA on the TM without moderate heat stress, the TM–RCA in rice leaves was first examined at different developmental stages and under various environmental conditions using western blots with a specific antibody against RCA from tobacco. Consistent with previous reports (To et al., 1999), two bands of 41 kDa and 45 kDa were detected in rice leaves (Figs 2, 3), and rice was dominated by the smaller isoform in the total protein extract. Importantly, variations in TM–RCA were observed among all the cases analysed (Figs 2, 3), but the TM–RCA seemed to be composed more of the larger isoform. Fig. 2. Open in new tabDownload slide Changes in the amount of Rubisco, total RCA, and TM–RCA in rice leaves at different leaf ages (A) and leaf positions (B). 1w, 2w, and 4w represent 1-, 2-, and 4-week-old flag leaves, respectively, while 1st, 2nd, and 3rd represent the first (flag, 4-week-old) leaf, second leaf (∼6-week-old), and third leaf (∼8-week-old) (counted from the top of the plant), respectively. RCA amounts are all expressed on a leaf area basis. Each value is the mean of more than three independent experiments with the SE expressed as a vertical bar. The amount of total RCA (∼2.93 μg cm−2) and TM–RCA (∼0.58 μg cm−2) in 4-week-old flag leaves in the day was defined as 100%, respectively. Fig. 2. Open in new tabDownload slide Changes in the amount of Rubisco, total RCA, and TM–RCA in rice leaves at different leaf ages (A) and leaf positions (B). 1w, 2w, and 4w represent 1-, 2-, and 4-week-old flag leaves, respectively, while 1st, 2nd, and 3rd represent the first (flag, 4-week-old) leaf, second leaf (∼6-week-old), and third leaf (∼8-week-old) (counted from the top of the plant), respectively. RCA amounts are all expressed on a leaf area basis. Each value is the mean of more than three independent experiments with the SE expressed as a vertical bar. The amount of total RCA (∼2.93 μg cm−2) and TM–RCA (∼0.58 μg cm−2) in 4-week-old flag leaves in the day was defined as 100%, respectively. Fig. 3. Open in new tabDownload slide Changes in the amount of Rubisco, total RCA, and TM–RCA of rice leaves collected at different times of the day (A), treated with high CO2 and low light (B), and in Δlut (C). Day and Night represent the rice leaves collected in the daytime and at night. LH represents the rice leaves treated with high CO2 and low light. Four-week-old flag leaves at the filling stage were used in this figure. Fig. 3. Open in new tabDownload slide Changes in the amount of Rubisco, total RCA, and TM–RCA of rice leaves collected at different times of the day (A), treated with high CO2 and low light (B), and in Δlut (C). Day and Night represent the rice leaves collected in the daytime and at night. LH represents the rice leaves treated with high CO2 and low light. Four-week-old flag leaves at the filling stage were used in this figure. The amounts of Rubisco in 1-, 2-, 6-, and 8-week-old leaves were ∼57, 105, 93, and 70% of that in 4-week-old leaves (∼0.24 mg cm−2), respectively. The amounts of total RCA in 1- and 8-week-old leaves were only ∼60% of that in 4-week-old leaves (∼2.93 μg cm−2), and there was a slight difference detected in other leaves; the amounts of TM–RCA in 1-, 2-, 6-, and 8-week-old leaves were ∼33, 113, 49, and 24% of that in 4-week-old leaves (∼0.58 μg cm−2), respectively; and the proportions of RCA in 1-, 2-, 6-, and 8-week-old leaves were 48, 116, 49, and 37% of that in 4-week-old leaves (∼20% of total RCA), respectively (Fig. 2). It seems that the proportions of TM–RCA were higher in the leaves that showed more Rubisco and total RCA contents. In addition, the ratio of smaller to larger isoforms of TM–RCA was higher in 1- and 2-week-old leaves compared with that in leaves >4 weeks old. In contrast, changes in Rubisco and total RCA are different from the change in the proportion of TM–RCA under different environmental conditions (Fig. 3). The amounts of Rubisco and total RCA in the flag leaves in the daytime were slightly higher than those at night, but the amount of TM–RCA and its proportion at night were notably higher than those in the daytime (Fig. 3A). The treatment with high CO2 (1000 μmol mol−1) and very low light intensity (30 μmol m−2 s−1) led to a slight change in Rubisco and a decrease in total RCA while the amount of TM–RCA and its proportion were much higher compared with the control (Fig. 3B). Additionally, TM–RCA and its proportion as well as total RCA increased in a rice mutant (Δlut), while Rubisco decreased a lot. Moreover, the ratio of smaller to larger isoforms of TM–RCA was higher in Δlut than in the control (Fig. 3C). It seems that there is no direct influence of Rubisco and total RCA contents on the variation of the proportion of TM–RCA. Correlation of gas exchange and Chl content with TM–RCA To elucidate the possible physiological role of TM–RCA in photosynthesis, the linkage between RCA localization and net photosynthetic parameters, that is leaf Pn, Vcmax, Jmax, and Chl content was examined. A higher proportion of TM–RCA showed a higher Pn in all leaves at different developmental stages (Table 1). However, the proportion of TM–RCA was higher while the Pn was much lower (∼2.6 μmol m−2 s−1, measured at 1000 μmol CO2 mol−1 and 30 μmol photons m−2 s−1) in leaves treated with high CO2 and low light intensity (Fig. 3B). Moreover, the higher proportion of TM–RCA was also observed at night (Fig. 3A) while the Pn was less than zero. Except for the case of Δlut, a higher proportion of TM–RCA was also linked to a higher Vcmax and Jmax (Table 1). Table 1. Pn, Vcmax, Jmax, and Chl content in rice leaves Pn (μmol m−2 s−1) Vcmax (μmol m−2 s−1) Jmax (μmol m−2 s−1) Chl (mg dm−2) Leaf age (position) 1 week 10.8±0.4 a 95.2±6.7 a,b 142.0±8.1 b 1.55±0.27 a 2 weeks 15.4±0.7 b 129.3±5.7 c 174.7±4.6 c 3.26±0.17 b 4 weeks (first) 15.7±0.6 b 126.3±4.1 c 171.7±5.0 c 4.33±0.11 c 6 weeks (second) 12.6±0.7 a,b 83.8±6.4 a 111.3±7.0 a 4.07±0.40 c 8 weeks (third) 10.4±0.8 a 80.3±4.5 a 110.8±5.7 a 2.77±0.11 b Genotype Wild type 15.2±0.9 b 88.2±2.2 b 134.5±5.2 b 4.33±0.10 b Δlut 11.4±1.4 a 57.4±3.4 a 89.0±5.4 a 2.24±0.10 a Pn (μmol m−2 s−1) Vcmax (μmol m−2 s−1) Jmax (μmol m−2 s−1) Chl (mg dm−2) Leaf age (position) 1 week 10.8±0.4 a 95.2±6.7 a,b 142.0±8.1 b 1.55±0.27 a 2 weeks 15.4±0.7 b 129.3±5.7 c 174.7±4.6 c 3.26±0.17 b 4 weeks (first) 15.7±0.6 b 126.3±4.1 c 171.7±5.0 c 4.33±0.11 c 6 weeks (second) 12.6±0.7 a,b 83.8±6.4 a 111.3±7.0 a 4.07±0.40 c 8 weeks (third) 10.4±0.8 a 80.3±4.5 a 110.8±5.7 a 2.77±0.11 b Genotype Wild type 15.2±0.9 b 88.2±2.2 b 134.5±5.2 b 4.33±0.10 b Δlut 11.4±1.4 a 57.4±3.4 a 89.0±5.4 a 2.24±0.10 a Leaf Pn measurements were made at their growth light intensity (500 μmol photons m−2 s−1) and CO2 concentration (380 μmol CO2 mol−1). Vcmax and Jmax were calculated from the data of the photosynthetic response to CO2 measured at 1200 μmol photons m−2 s−1. Each value is the mean±SE of 6–7 leaves. Different lower case letters indicate significant (P <0.05) differences between the compared values. Open in new tab Table 1. Pn, Vcmax, Jmax, and Chl content in rice leaves Pn (μmol m−2 s−1) Vcmax (μmol m−2 s−1) Jmax (μmol m−2 s−1) Chl (mg dm−2) Leaf age (position) 1 week 10.8±0.4 a 95.2±6.7 a,b 142.0±8.1 b 1.55±0.27 a 2 weeks 15.4±0.7 b 129.3±5.7 c 174.7±4.6 c 3.26±0.17 b 4 weeks (first) 15.7±0.6 b 126.3±4.1 c 171.7±5.0 c 4.33±0.11 c 6 weeks (second) 12.6±0.7 a,b 83.8±6.4 a 111.3±7.0 a 4.07±0.40 c 8 weeks (third) 10.4±0.8 a 80.3±4.5 a 110.8±5.7 a 2.77±0.11 b Genotype Wild type 15.2±0.9 b 88.2±2.2 b 134.5±5.2 b 4.33±0.10 b Δlut 11.4±1.4 a 57.4±3.4 a 89.0±5.4 a 2.24±0.10 a Pn (μmol m−2 s−1) Vcmax (μmol m−2 s−1) Jmax (μmol m−2 s−1) Chl (mg dm−2) Leaf age (position) 1 week 10.8±0.4 a 95.2±6.7 a,b 142.0±8.1 b 1.55±0.27 a 2 weeks 15.4±0.7 b 129.3±5.7 c 174.7±4.6 c 3.26±0.17 b 4 weeks (first) 15.7±0.6 b 126.3±4.1 c 171.7±5.0 c 4.33±0.11 c 6 weeks (second) 12.6±0.7 a,b 83.8±6.4 a 111.3±7.0 a 4.07±0.40 c 8 weeks (third) 10.4±0.8 a 80.3±4.5 a 110.8±5.7 a 2.77±0.11 b Genotype Wild type 15.2±0.9 b 88.2±2.2 b 134.5±5.2 b 4.33±0.10 b Δlut 11.4±1.4 a 57.4±3.4 a 89.0±5.4 a 2.24±0.10 a Leaf Pn measurements were made at their growth light intensity (500 μmol photons m−2 s−1) and CO2 concentration (380 μmol CO2 mol−1). Vcmax and Jmax were calculated from the data of the photosynthetic response to CO2 measured at 1200 μmol photons m−2 s−1. Each value is the mean±SE of 6–7 leaves. Different lower case letters indicate significant (P <0.05) differences between the compared values. Open in new tab In contrast to the changes in proportions of TM–RCA, the results of Chl measurement showed that Chl contents were higher in 4- and 6-week-old leaves (∼4.3 mg dm−2) than in 1-, 2-, and 8-week-old leaves. The Chl content was decreased by ∼48% in Δlut while the proportion of TM–RCA was increased (Table 1). The results of correlation analysis showed that there was no significant correlation among gas exchange, Chl content, and proportion of TM–RCA (Fig. 5A). Increase in the proportion of TM–RCA at a lower initial/total Rubisco activity ratio Total and initial Rubisco activities were measured to explore the relationship of Rubisco activity to the proportion of TM–RCA in vivo. The results in Table 2 show that the change in Rubisco total activity was different from that in the proportion of TM–RCA. Similar to the proportion of TM–RCA, total activities in 2- and 4-week-old leaves (∼0.06 μmol O2 mg−1 protein min−1) were higher than those in 1-, 6-, and 8-week-old leaves. However, changes of total activities under different environment conditions and in Δlut were not consistent with those of the proportions of TM–RCA. Compared with their control, total activities in the leaves at night, in leaves treated with high CO2 and low light, and in Δlut decreased by only 7.9, 6.3, and 50%, respectively. Table 2. Rubisco activity, the initial/total activity ratio, and the TM–RCA/total RCA ratio in rice leaves Total activity (μmol mg−1 min−1) Initial activity (μmol mg−1 min−1) Initial/total activity (%) TM–RCA/total RCA (%) Leaf age (position) 1 week 0.021±0.002 a 0.019±0.003 a 90.1±1.3 b 11.3±2.6 b 2 weeks 0.059±0.008 c 0.038±0.007 b 63.8±2.5 a 20.4±2.0 c 4 weeks (first) 0.063±0.006 c 0.048±0.005 b 75.9±4.3 a 20.0±1.2 c 6 weeks (second) 0.049±0.001 b 0.047±0.003 b 95.1±1.9 c 9.7±1.0 b 8 weeks (third) 0.019±0.003 a 0.018±0.004 a 99.6±2.5 c 7.5±1.7 a Time of day Day 0.063±0.006 0.048±0.005 b 75.9±4.3 b 20.0±0.6 a Night 0.058±0.005 0.017±0.009 a 29.8±9.8 a 30.6±1.2 b CO2 and light treatment Control 0.063±0.006 0.048±0.006 b 75.9±4.3 b 20.0±0.9 a LH 0.059±0.009 0.011±0.003 a 19.8±1.4 a 49.5±2.5 b Genotype Wild type 0.063±0.002 b 0.048±0.002 b 75.9±0.9 b 20.0±1.2 a Δlut 0.027±0.002 a 0.015±0.001 a 56.8±1.9 a 25.4±0.9 b Total activity (μmol mg−1 min−1) Initial activity (μmol mg−1 min−1) Initial/total activity (%) TM–RCA/total RCA (%) Leaf age (position) 1 week 0.021±0.002 a 0.019±0.003 a 90.1±1.3 b 11.3±2.6 b 2 weeks 0.059±0.008 c 0.038±0.007 b 63.8±2.5 a 20.4±2.0 c 4 weeks (first) 0.063±0.006 c 0.048±0.005 b 75.9±4.3 a 20.0±1.2 c 6 weeks (second) 0.049±0.001 b 0.047±0.003 b 95.1±1.9 c 9.7±1.0 b 8 weeks (third) 0.019±0.003 a 0.018±0.004 a 99.6±2.5 c 7.5±1.7 a Time of day Day 0.063±0.006 0.048±0.005 b 75.9±4.3 b 20.0±0.6 a Night 0.058±0.005 0.017±0.009 a 29.8±9.8 a 30.6±1.2 b CO2 and light treatment Control 0.063±0.006 0.048±0.006 b 75.9±4.3 b 20.0±0.9 a LH 0.059±0.009 0.011±0.003 a 19.8±1.4 a 49.5±2.5 b Genotype Wild type 0.063±0.002 b 0.048±0.002 b 75.9±0.9 b 20.0±1.2 a Δlut 0.027±0.002 a 0.015±0.001 a 56.8±1.9 a 25.4±0.9 b The Rubisco activity was expressed as its oxygenase activity (μmol O2 mg−1 total protein min−1) measured by a Clarke-type O2 electrode, and the rice leaves were collected at the heading stage. LH, rice was treated under high CO2 (1000 μmol mol−1) and very low light (30 μmol m−2 s−1) for ∼12 h and each value (±SE) is the average of 3–4 measurements. Different lower case letters indicate significant (P <0.05) differences between the compared values. Open in new tab Table 2. Rubisco activity, the initial/total activity ratio, and the TM–RCA/total RCA ratio in rice leaves Total activity (μmol mg−1 min−1) Initial activity (μmol mg−1 min−1) Initial/total activity (%) TM–RCA/total RCA (%) Leaf age (position) 1 week 0.021±0.002 a 0.019±0.003 a 90.1±1.3 b 11.3±2.6 b 2 weeks 0.059±0.008 c 0.038±0.007 b 63.8±2.5 a 20.4±2.0 c 4 weeks (first) 0.063±0.006 c 0.048±0.005 b 75.9±4.3 a 20.0±1.2 c 6 weeks (second) 0.049±0.001 b 0.047±0.003 b 95.1±1.9 c 9.7±1.0 b 8 weeks (third) 0.019±0.003 a 0.018±0.004 a 99.6±2.5 c 7.5±1.7 a Time of day Day 0.063±0.006 0.048±0.005 b 75.9±4.3 b 20.0±0.6 a Night 0.058±0.005 0.017±0.009 a 29.8±9.8 a 30.6±1.2 b CO2 and light treatment Control 0.063±0.006 0.048±0.006 b 75.9±4.3 b 20.0±0.9 a LH 0.059±0.009 0.011±0.003 a 19.8±1.4 a 49.5±2.5 b Genotype Wild type 0.063±0.002 b 0.048±0.002 b 75.9±0.9 b 20.0±1.2 a Δlut 0.027±0.002 a 0.015±0.001 a 56.8±1.9 a 25.4±0.9 b Total activity (μmol mg−1 min−1) Initial activity (μmol mg−1 min−1) Initial/total activity (%) TM–RCA/total RCA (%) Leaf age (position) 1 week 0.021±0.002 a 0.019±0.003 a 90.1±1.3 b 11.3±2.6 b 2 weeks 0.059±0.008 c 0.038±0.007 b 63.8±2.5 a 20.4±2.0 c 4 weeks (first) 0.063±0.006 c 0.048±0.005 b 75.9±4.3 a 20.0±1.2 c 6 weeks (second) 0.049±0.001 b 0.047±0.003 b 95.1±1.9 c 9.7±1.0 b 8 weeks (third) 0.019±0.003 a 0.018±0.004 a 99.6±2.5 c 7.5±1.7 a Time of day Day 0.063±0.006 0.048±0.005 b 75.9±4.3 b 20.0±0.6 a Night 0.058±0.005 0.017±0.009 a 29.8±9.8 a 30.6±1.2 b CO2 and light treatment Control 0.063±0.006 0.048±0.006 b 75.9±4.3 b 20.0±0.9 a LH 0.059±0.009 0.011±0.003 a 19.8±1.4 a 49.5±2.5 b Genotype Wild type 0.063±0.002 b 0.048±0.002 b 75.9±0.9 b 20.0±1.2 a Δlut 0.027±0.002 a 0.015±0.001 a 56.8±1.9 a 25.4±0.9 b The Rubisco activity was expressed as its oxygenase activity (μmol O2 mg−1 total protein min−1) measured by a Clarke-type O2 electrode, and the rice leaves were collected at the heading stage. LH, rice was treated under high CO2 (1000 μmol mol−1) and very low light (30 μmol m−2 s−1) for ∼12 h and each value (±SE) is the average of 3–4 measurements. Different lower case letters indicate significant (P <0.05) differences between the compared values. Open in new tab Unlike the change in the proportion of TM–RCA, initial activities of Rubisco in 4- and 6-week-old leaves (∼0.048 μmol O2 mg−1 protein min−1) were higher than those in 1-, 2-, and 8-week-old leaves, respectively. Initial activity in the leaves at night, in leaves treated with high CO2 and low light, and in Δlut were only ∼35, 25, and 42% of those of their control, respectively (Table 2). Interestingly, it was found that leaves with a higher proportion of TM–RCA had a lower initial/total Rubisco activity ratio. As shown in Table 2, a decrease in the initial/total Rubisco activity ratio was closely linked to an increase in the proportion of TM–RCA. When rice plants were treated with high CO2 and low light, the proportion of TM–RCA increased greatly while the initial/total Rubisco activity ratio decreased from ∼75% to 20% (Table 2). Moreover, the results of correlation analysis showed that the correlation of the proportion of TM–RCA to the initial/total Rubisco activity ratio reached a significant level (the Pearson correlation coefficients was –0.958; Fig. 5B). Increase in the proportion of TM–RCA at the lower level of NPQ and ms-DLE To explore the role of electron transport in changes of TM–RCA, some Chl fluorescence parameters of photosynthetic electron transport were measured. The results in Table 3 showed that there was no significant difference in Fv/Fm among the different rice leaves mentioned above. ΦPSII represents the effective photochemical efficiency of PSII, which is related to the rate of linear electron transport (Genty et al., 1989; Maxwell and Johnson, 2000), and the initial rate of dark reduction of P700+ (P700+) after termination of far-red illumination reflects the rate of cyclic electron flow around PSI (Maxwell and Biggins, 1976; Havaux, 1996). ΦPSII and P700+ in mature (2- and 4-week-old) leaves increased while TM–RCA proportions also increased compared with those in young and old leaves. A similar result was also observed in the leaves of rice treated with high CO2 and low light (Table 3). However, the proportion of TM–RCA increased at night (Fig. 3A) when no non-cyclic electron flow exists, and the proportion of TM–RCA increased in Δlut while the P700+ decreased by >60%. Table 3. Chl fluorescence parameters Fv/Fm, ΦPSII, NPQ, and the initial rate of P700+ reduction in rice leaves Fv/Fm ΦPSII NPQ P700+ (s−1) Leaf age (position) 1 week 0.813±0.002 0.40±0.01 b,c 1.95±0.05 c 0.80±0.06 a 2 weeks 0.813±0.002 0.45±0.01 c 1.44±0.09 a 1.23±0.08 c 4 weeks (first) 0.806±0.002 0.43±0.01 c 1.60±0.06 a 1.06±0.08 b 6 weeks (second) 0.811±0.002 0.37±0.03 b 1.79±0.07 b 0.78±0.08 a 8 weeks (third) 0.808±0.002 0.29±0.01 a 2.02±0.05 c ND CO2 and light treatment Control (4 weeks) 0.806±0.002 0.43±0.01 a 1.60±0.06 b 0.94±0.08 a LH 0.812±0.002 0.66±0.01 b 0.21±0.10 a 1.72±0.12 b Genotype Wild type (4 weeks) 0.806±0.001 b 0.43±0.01 1.60±0.06 b 1.06±0.08 b Δlut 0.711±0.024 a 0.39±0.02 0.72±0.09 a 0.41±0.07 a Fv/Fm ΦPSII NPQ P700+ (s−1) Leaf age (position) 1 week 0.813±0.002 0.40±0.01 b,c 1.95±0.05 c 0.80±0.06 a 2 weeks 0.813±0.002 0.45±0.01 c 1.44±0.09 a 1.23±0.08 c 4 weeks (first) 0.806±0.002 0.43±0.01 c 1.60±0.06 a 1.06±0.08 b 6 weeks (second) 0.811±0.002 0.37±0.03 b 1.79±0.07 b 0.78±0.08 a 8 weeks (third) 0.808±0.002 0.29±0.01 a 2.02±0.05 c ND CO2 and light treatment Control (4 weeks) 0.806±0.002 0.43±0.01 a 1.60±0.06 b 0.94±0.08 a LH 0.812±0.002 0.66±0.01 b 0.21±0.10 a 1.72±0.12 b Genotype Wild type (4 weeks) 0.806±0.001 b 0.43±0.01 1.60±0.06 b 1.06±0.08 b Δlut 0.711±0.024 a 0.39±0.02 0.72±0.09 a 0.41±0.07 a Each value (±SE) is the average of 4–6 measurements. Different lower case letters indicate significant (P <0.05) differences between the compared values. ND, not determined. Open in new tab Table 3. Chl fluorescence parameters Fv/Fm, ΦPSII, NPQ, and the initial rate of P700+ reduction in rice leaves Fv/Fm ΦPSII NPQ P700+ (s−1) Leaf age (position) 1 week 0.813±0.002 0.40±0.01 b,c 1.95±0.05 c 0.80±0.06 a 2 weeks 0.813±0.002 0.45±0.01 c 1.44±0.09 a 1.23±0.08 c 4 weeks (first) 0.806±0.002 0.43±0.01 c 1.60±0.06 a 1.06±0.08 b 6 weeks (second) 0.811±0.002 0.37±0.03 b 1.79±0.07 b 0.78±0.08 a 8 weeks (third) 0.808±0.002 0.29±0.01 a 2.02±0.05 c ND CO2 and light treatment Control (4 weeks) 0.806±0.002 0.43±0.01 a 1.60±0.06 b 0.94±0.08 a LH 0.812±0.002 0.66±0.01 b 0.21±0.10 a 1.72±0.12 b Genotype Wild type (4 weeks) 0.806±0.001 b 0.43±0.01 1.60±0.06 b 1.06±0.08 b Δlut 0.711±0.024 a 0.39±0.02 0.72±0.09 a 0.41±0.07 a Fv/Fm ΦPSII NPQ P700+ (s−1) Leaf age (position) 1 week 0.813±0.002 0.40±0.01 b,c 1.95±0.05 c 0.80±0.06 a 2 weeks 0.813±0.002 0.45±0.01 c 1.44±0.09 a 1.23±0.08 c 4 weeks (first) 0.806±0.002 0.43±0.01 c 1.60±0.06 a 1.06±0.08 b 6 weeks (second) 0.811±0.002 0.37±0.03 b 1.79±0.07 b 0.78±0.08 a 8 weeks (third) 0.808±0.002 0.29±0.01 a 2.02±0.05 c ND CO2 and light treatment Control (4 weeks) 0.806±0.002 0.43±0.01 a 1.60±0.06 b 0.94±0.08 a LH 0.812±0.002 0.66±0.01 b 0.21±0.10 a 1.72±0.12 b Genotype Wild type (4 weeks) 0.806±0.001 b 0.43±0.01 1.60±0.06 b 1.06±0.08 b Δlut 0.711±0.024 a 0.39±0.02 0.72±0.09 a 0.41±0.07 a Each value (±SE) is the average of 4–6 measurements. Different lower case letters indicate significant (P <0.05) differences between the compared values. ND, not determined. Open in new tab The intensity of ms-DLE in mature (2- and 4-week-old) leaves was ∼50% and 60% lower than that in young (1-week-old leaves) and old (>6-week-old) leaves, respectively (Fig. 4A, B). Moreover, the intensity of ms-DLE in leaves treated with high CO2 and low light decreased to ∼26% of that of control (Fig. 4C). The change in NPQ was almost the same as that in ms-DLE (Table 3). The correlation coefficients of the proportion of TM–RCA to ms-DLE and NPQ were –0.895 and –0.947, respectively (Fig. 5C). Fig. 4. Open in new tabDownload slide The intensity of ms-DLE of rice leaves at different leaf ages (A), leaf positions (B), and treated with high CO2 and low light (C). ms-DLE was measured as described in the Materials and methods, and measurements were made at the heading and filling stages. 1w, 2w, 4w, 1st, 2nd, 3rd, and LH are as defined in the legends of Figs 2 and 3. Fig. 4. Open in new tabDownload slide The intensity of ms-DLE of rice leaves at different leaf ages (A), leaf positions (B), and treated with high CO2 and low light (C). ms-DLE was measured as described in the Materials and methods, and measurements were made at the heading and filling stages. 1w, 2w, 4w, 1st, 2nd, 3rd, and LH are as defined in the legends of Figs 2 and 3. Fig. 5. Open in new tabDownload slide Correlation analysis of the TM–RCA/total RCA ratio to photosynthetic parameters (A), Rubisco activity (B), and Chl fluorescence (C). Correlation analysis of the data in Tables 1–3, and Fig.4 was performed using the software SPSS 10.0 (SPSS Inc., USA). R: Pearson correlation coefficients (two tailed). Fig. 5. Open in new tabDownload slide Correlation analysis of the TM–RCA/total RCA ratio to photosynthetic parameters (A), Rubisco activity (B), and Chl fluorescence (C). Correlation analysis of the data in Tables 1–3, and Fig.4 was performed using the software SPSS 10.0 (SPSS Inc., USA). R: Pearson correlation coefficients (two tailed). Increase in the amount of TM–RCA during Rubisco activation in vitro To explore further the relationship between TM–RCA and Rubisco activation, TM–RCA was quantified when RCA was incubated with the TM and Rubisco deactivated by excessive RuBP in a CO2-free assay. The results showed that the amount of TM–RCA was significantly higher in the assay with the deactivated Rubisco than in the control (Fig. 6A). Fig. 6. Open in new tabDownload slide The binding of RCA to the TM during activation of Rubisco from different species by rice RCA. A thylakoid sample containing 1–2 μg of Chl was loaded in each line. (A) RCA and TMs from rice leaves were incubated with activated (–RuBP) and deactivated (+RuBP) Rubisco from rice leaves in an assay system containing 1 mM ATP. (B) RCA and TMs from rice were incubated with deactivated Rubisco (+RuBP) from non-Solanaceae plants (rice and spinach) and Solanaceae plants (tobacco and chili pepper). R, S, T, and C represent Rubisco from rice, spinach, tobacco, and chili pepper, respectively. Fig. 6. Open in new tabDownload slide The binding of RCA to the TM during activation of Rubisco from different species by rice RCA. A thylakoid sample containing 1–2 μg of Chl was loaded in each line. (A) RCA and TMs from rice leaves were incubated with activated (–RuBP) and deactivated (+RuBP) Rubisco from rice leaves in an assay system containing 1 mM ATP. (B) RCA and TMs from rice were incubated with deactivated Rubisco (+RuBP) from non-Solanaceae plants (rice and spinach) and Solanaceae plants (tobacco and chili pepper). R, S, T, and C represent Rubisco from rice, spinach, tobacco, and chili pepper, respectively. The amount of TM–RCA after incubation of rice RCA and TM with deactivated Rubisco from rice, spinach, tobacco, and chili pepper was also quantified. The result showed that TM–RCA was increased in the presence of the Rubisco from rice and spinach but not that from tobacco and chili pepper (Fig. 6B). A decrease in the amount of TM–RCA induced by higher ATP and alkaline pH in vitro The above results showed that Rubisco activation leads to an increase in TM–RCA. Therefore, TM–RCA during Rubisco activation at different ATP levels and pH values was also examined. The results showed that TM–RCA increased when ATP increased from 0 mM to 1 mM, then it decreased when ATP increased from 1 mM to 5 mM (Fig. 7A). Similarly, TM–RCA increased when the pH was adjusted from 7 to 7.5, then it decreased when the pH was adjusted from 7.5 to 8.0 (Fig. 7B). It seems that the binding of RCA to the TM during Rubisco activation is reversible. Fig. 7. Open in new tabDownload slide Effects of ATP level or pH on the amount of RCA on the TM in vitro. RCA and TMs were incubated with Rubisco (+RuBP) in the assay at different ATP levels (A) and different pH values (B) at 25 °C for 20 min. TMs from fresh rice leaves incubated at different ATP levels (C) or different pH values (D) at 25 °C for 20 min. The amount of TM–RCA was detected by western blot. Fig. 7. Open in new tabDownload slide Effects of ATP level or pH on the amount of RCA on the TM in vitro. RCA and TMs were incubated with Rubisco (+RuBP) in the assay at different ATP levels (A) and different pH values (B) at 25 °C for 20 min. TMs from fresh rice leaves incubated at different ATP levels (C) or different pH values (D) at 25 °C for 20 min. The amount of TM–RCA was detected by western blot. After the incubation of TM from fresh rice leaves at different levels of ATP and different pH values, the TM–RCA was quantified. The results showed that the amount of TM–RCA gradually increased and reached a maximum at 1–2 mM ATP, but it decreased when ATP exceeded 2 mM (Fig. 7C). Furthermore, the amount of TM–RCA was maximal at pH 7.0, while it was minimal at pH 8.0 in the range of pH 6.5–8.0 (Fig. 7D). It seems that the dissociation of RCA from the TM is ATP and pH dependent. Reversible change in the fluorescence emission spectra of ANS binding to RCA during Rubisco activation at different ATP levels ANS has a low fluorescence intensity in aqueous solution; however, an increase in fluorescence intensity can be observed upon binding to accessible hydrophobic regions of proteins (Nooshin and Li-Chan, 2000). The change in the fluorescence of ANS binding to protein offers a probe to detect the effects of a conformational alteration in RCA on its reversible association with the TM. The fluorescence of ANS binding to protein was examined during Rubisco activation in the presence of 2 mM or 25 μM RuBP. The results showed that the fluorescence intensity of ANS binding to Rubisco and RCA increased dramatically when ATP was increased from 0 nm to 1 nm and 4 mM in the presence an excess of RuBP (2 mM) (Fig. 8A). The change in fluorescence intensity was mainly from the ANS binding to RCA, since Rubisco remains inactive in the presence of excessive RuBP, and there were almost no changes in fluorescence of ANS binding to Rubisco and RCA at different ATP levels (data not shown). Therefore, the above result indicated the enhanced structural alteration in RCA by the accelerated Rubisco activation at the higher ATP level. In contrast, in the presence of a low RuBP concentration (25 μM), the fluorescence intensity of ANS binding to protein was decreased when ATP was increased (Fig. 8B). Since small amounts of RuBP (25 μM) would be used up and Rubisco could be maintained in an activated state in the assay, the increase in fluorescence intensity of the ANS binding RCA induced by Rubisco activation is similar between different assays. Thus, the result in Fig. 8B showed that the change in fluorescence intensity was determined by the recovery state of the RCA conformation. The above results indicate that the conformational alteration of RCA induced by activation is reversible at higher ATP concentrations. Fig. 8. Open in new tabDownload slide Effects of ATP on the fluorescence of ANS binding to RCA during Rubisco activation. The mixture of Rubisco and RCA in the presence of 2 mM (A) and 25 μM RuBP (B) was incubated with 0, 1, and 4 mM ATP at room temperature for 30 min, then ANS was added and the fluorescence intensity was measured after 15 min in the dark according to the method described in the Materials and methods. The numbers in the figure indicate the ATP concentration (mM). Fig. 8. Open in new tabDownload slide Effects of ATP on the fluorescence of ANS binding to RCA during Rubisco activation. The mixture of Rubisco and RCA in the presence of 2 mM (A) and 25 μM RuBP (B) was incubated with 0, 1, and 4 mM ATP at room temperature for 30 min, then ANS was added and the fluorescence intensity was measured after 15 min in the dark according to the method described in the Materials and methods. The numbers in the figure indicate the ATP concentration (mM). Similarly, an additional experiment showed that the conformational alteration of RCA induced by moderate heat stress is also reversible, as shown by the fluorescence emission spectra of ANS binding to RCA at a different level of ATP and different pH (Supplementary Fig. S3 at JXB online). Discussion The increase in TM–RCA is a universal phenomenon and it is not limited to moderate heat stress It has been reported previously that RCA was observed on the TM, and its amount increased under moderate heat stress of 42 °C (Rokka et al., 2001; Yang et al., 2005; Feng et al., 2007). Consistent with these results, the existence of TM–RCA was also observed in rice leaves without moderate heat stress (Figs 2, 3). The present results also showed that increases in TM–RCA in vivo were observed in rice leaves under different environment conditions, at different developmental stages, and in the mutant Δlut without moderate heat stress. Moreover, TM–RCA increased in leaves not only in vivo (Figs 2, 3) but also in the process of Rubisco activation by RCA in vitro (Fig. 6). Obviously, this means that the increase in TM–RCA is a universal phenomenon which is not limited to heat stress. Salvucci et al. (2001) suggested that RCA in the TM fraction is the result of self-aggregation of thermally denatured RCA. However, in contrast to the thermally denatured RCA, the RCA washed out from the TM can activate Rubisco (Supplementary Fig. S2 at JXB online). This indicates that RCA in the TM fraction without heat stress may be different from that under heat stress. It has been reported that no aggregation of RCA was observed in chloroplasts of spinach and tobacco plants incubated at the growth temperature, and the smaller isoform aggregated first under heat treatment because of its sensitivity to high temperature (Crafts-Brandner et al., 1997; Salvucci et al., 2001). In the experiments described here, the preparation of TM was always performed on ice or at 4 °C, and more of the larger isoform was detected in TM–RCA (Figs 2, 3). This means that TM–RCA is not mainly due to the aggregation of RCA. Moreover, the results from BN-PAGE (Fig. 1) imply that the association of some RCA with the TM occurs. Additionally, Salvucci et al. (2001) had reported that no unfolding/aggregation of tobacco RCA was observed at 35 °C in the presence of 0.75 mM ATP in vitro. Thus, it seems that the increased in TM–RCA during Rubisco activation in vitro (Fig. 6) is also not due to the aggregation of RCA. Reversible association of RCA with the TM is related to the ATP level and pH It has been proposed that RCA is likely to act as a chaperone protecting the thylakoid-associated protein synthesis machinery against heat inactivation under heat stress (Rokka et al., 2001). However, Salvucci et al. (2001) suggested that non-specific binding to the TM may stabilize RCA during periods of heat stress and promote self-aggregation of RCA. The increase in TM–RCA under conditions without heat stress in the present experiments indicates that TM–RCA may have other unrecognized functions besides protection. A strong negative correlation between the proportion of TM–RCA and the Rubisco activation state indicates that TM–RCA may be related to light activation of Rubisco in vivo (Fig. 5B). Similar results were also observed in some previous publications under moderate heat stress (Yang et al., 2005; Feng et al., 2007). The slow phase of ms-DLE reflects the state of ΔpH and the related photophosphorylation (Wraight and Crofts, 1971; Li and Shen, 1994), and NPQ is induced by the formation of ΔpH across the TM (Lavaud et al., 2004). Moreover, it is reported that ΔpH appears necessary for maximum light activation of Rubisco (Campbell and Ogren, 1990b), and Makino and Sage (2007) also suggested that a high NPQ is generally associated with a high activation state of Rubisco in vivo. Thus, the above deduction is also supported by the increase in TM–RCA at low ΔpH as shown by changes in ms-DLE and NPQ (Fig. 5C). Since the ΔpH is utilized to synthesize ATP in the chloroplast, the requirement for ΔpH in light activation of Rubisco appeared to be identical to the requirement for ATP and alkaline pH (Streusand and Portis, 1987). Therefore, the negative correlation of TM–RCA with the ΔpH as shown by the changes of ms-DLE and NPQ (Fig. 5C) indicates that TM–RCA is related to the level ATP and pH in vivo. Furthermore, the results also showed that the reversible association of RCA is ATP and pH dependent in vitro. On the one hand, the association of soluble RCA with the TM without heat stress seems to be a result of Rubisco activation by RCA. It is supported by the finding of a considerable increase in TM–RCA when deactivated Rubisco was activated by RCA (Fig. 6A). Similarly, it is also supported by the increase in TM–RCA only when deactivated Rubisco from rice and spinach (non-Solanaceous species) is included in the assay (Fig. 6B), since Rubisco from tobacco and chili pepper (Solanaceous family plants) cannot be activated by RCA from non-Solanaceous species (Wang et al., 1992). The results also showed that the change in RCA conformation during Rubisco activation (Fig. 8A) was similar to that in RCA treated by moderate heat stress (Supplementary Fig. S3 at JXB online), and Rokka et al. (2001) suggested that the first step of the association of RCA with the TM is the structural changes of RCA induced by heat treatment. In addition, the above deduction is also consistent with the model described by Portis (2003); the RCA-ADP resulting from Rubisco activation has no activity until ADP is replaced by ATP. TM–RCA also had no Rubisco-activating activity in the presence of 1 mM ATP until it was washed from the TM (data not shown). On the other hand, binding of RCA to the TM during Rubisco activation is reversible. Although the RCA cannot be washed from the TM even at 2 M NaCl (NaBr) or 1% Triton X-100 (Rokka et al., 2001), the higher ATP and alkaline pH could induce the dissociation of TM–RCA in vitro (Fig. 7C, D), consistent with the finding that the activity of RCA disengaged from the TM is ATP and pH dependent (Supplementary Fig. S2 at JXB online). Furthermore, the results of the present study also showed that a reversible conformational change of RCA induced by Rubisco activation or heat stress depends on the level of ATP and the pH in the assay (Fig. 8, Supplementary Fig. S3). Consequently, the amount of TM–RCA seems to be the result of reversible association of RCA with the TM, which is related to the ATP level and the pH. Since TM–RCA is not the active form of RCA, this is the reason why the proportion of TM–RCA is negatively related to the activation state of Rubisco in vivo. A potential new role for TM–RCA in Rubisco activation Portis (2003) proposed a model whereby inactive Rubisco is surrounded by 16 (or eight) RCA subunits forming a Rubisco–RCA complex with a ring structure. In this model, ATP hydrolysis and Pi release in this complex cause conformational changes in the ring structure, resulting in activation of inactive Rubisco and the dissociation of the Rubisco–RCA (ring structure). When ADP in RCA-ADP is subsequently replaced by ATP, a new round of RCA oligomerization and binding of RCA to Rubisco begins. In the present study, TM–RCA increased during Rubisco activation (Fig. 6), suggesting that the dissociated RCA from the Rubisco–RCA complex might associate with the TM. The experiments also showed that the dissociation of the RCA polymer from the TM depends upon a high ATP level and alkaline pH in the stroma (Fig. 7). This process of Rubisco activation including TM–RCA is summarized in a new model (Fig. 9). Fig. 9. Open in new tabDownload slide Hypothetical model of Rubisco activation by reversible association of RCA with the TM. In the chloroplast stroma, inactive Rubisco (due to binding inhibition or another reason) is surrounded by RCA in a ring structure (RCAn, n=16 or n=8). ATP hydrolysis in the RCA–Rubisco supercomplex releases Pi and causes conformational changes in the Rubisco and RCA ring structure, resulting in activation of inactive Rubisco. Then some of the dissociated RCAs from the complex bind to the TM. Subsequently, the TM–RCA complex leaves the TM at higher ATP concentrations and alkaline pH, and forms the ring structure again on Rubisco (based on the work of Portis, 2003). Fig. 9. Open in new tabDownload slide Hypothetical model of Rubisco activation by reversible association of RCA with the TM. In the chloroplast stroma, inactive Rubisco (due to binding inhibition or another reason) is surrounded by RCA in a ring structure (RCAn, n=16 or n=8). ATP hydrolysis in the RCA–Rubisco supercomplex releases Pi and causes conformational changes in the Rubisco and RCA ring structure, resulting in activation of inactive Rubisco. Then some of the dissociated RCAs from the complex bind to the TM. Subsequently, the TM–RCA complex leaves the TM at higher ATP concentrations and alkaline pH, and forms the ring structure again on Rubisco (based on the work of Portis, 2003). In the present experiment in vivo, there is more of the smaller isoform in total RCA, but more of the larger isoform on the TM (Figs 2, 3). Previous experiments suggested that the smaller RCA isoform bound to the TM first (Rokka et al., 2001). This leads to a hypothesis that the binding of the smaller isoform of RCA to the TM precedes its redox regulation by the larger isoform. This hypothesis is consistent with the observation that in a rice mutant (Δlut), where the cyclic electron transport around PSI (Table 3) and correspondingly the redox state of the larger isoform of RCA are severely influenced, there is more of the smaller isoform of RCA bound to the TM (Fig. 3). Certainly, more experimental evidence is needed to test this hypothesis. Abbreviations Abbreviations ANS 1-anilinonaphthalene-8-sulphonic acid BN blue native NPQ non-photochemical quenching ΔpH pH gradient across the thylakoid membrane RCA Rubisco activase Rubisco ribulose-1, 5-bisphosphate carboxylase/oxygenase RuBP ribulose-1, 5-bisphosphate TM thylakoid membrane This work was supported by National Basic Research Program of China (grant no. 2009CB118504) and the Chinese Academy of Sciences (grant nos KZCX2-YW-N-59 and KZCX3-SW-440). We also thank Professors Archie R Portis Jr, Ji-Rong Huang, Xin-Guang Zhu, and Kang-Cheng Ruan for their kind revision and critical comments. References Arnon DJ . Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris , Plant Physiology , 1949 , vol. 24 (pg. 1 - 15 ) Google Scholar Crossref Search ADS PubMed WorldCat Bradford MM . 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2010 The Author(s).

Hillwig, Melissa S.; Liu, Xiaoteng; Liu, Guangyu; Thornburg, Robert W.; MacIntosh, Gustavo C.

doi: 10.1093/jxb/erq119pmid: 20460362

Abstract Plants requiring an insect pollinator often produce nectar as a reward for the pollinator's visitations. This rich secretion needs mechanisms to inhibit microbial growth. In Nicotiana spp. nectar, anti-microbial activity is due to the production of hydrogen peroxide. In a close relative, Petunia hybrida, limited production of hydrogen peroxide was found; yet petunia nectar still has anti-bacterial properties, suggesting that a different mechanism may exist for this inhibition. The nectar proteins of petunia plants were compared with those of ornamental tobacco and significant differences were found in protein profiles and function between these two closely related species. Among those proteins, RNase activities unique to petunia nectar were identified. The genes corresponding to four RNase T2 proteins from Petunia hybrida that show unique expression patterns in different plant tissues were cloned. Two of these enzymes, RNase Phy3 and RNase Phy4 are unique among the T2 family and contain characteristics similar to both S- and S-like RNases. Analysis of amino acid patterns suggest that these proteins are an intermediate between S- and S-like RNases, and support the hypothesis that S-RNases evolved from defence RNases expressed in floral parts. This is the first report of RNase activities in nectar. Nectar, nectarin, nectary, petunia, ribonuclease, RNase T2 Introduction In many angiosperms, male and female sexual organs are physically located in different places on the flower or on different flowers entirely; and many of these plants rely on animal pollinators to transfer pollen between flowers. Often these visiting pollinators are insects, however, birds, mammals, and even reptiles are known to function in pollen transfer among flowers. The visiting pollinators do not, however, do this for free. Instead, plants offer the visiting pollinators an incentive in return for pollen transfer. This reward consists of nectar, a rich concoction of sugars, amino acids, vitamins, lipids, and proteins (Nicolson and Thornburg, 2007), that is freely offered to attract the pollinators to the flower where pollen transfer takes place. The composition of floral nectar suggests that it may be a good growth medium. Floral nectar is produced from a novel floral organ termed the nectary that is generally located inside the flower, usually at its base. When pollinators scavenge inside the flower for nectar they inadvertently pick up pollen grains and transfer them when they change flowers. However, these visiting pollinators are also a hazard to the plant. By freely ranging between the reproductive tracts of many flowers, pollinators also transfer micro-organisms between flowers. However, infections of the flower are rare in plants. Initial observations identified an array of five nectarins (nectar proteins) that were secreted into the nectar of ornamental tobacco plants (Carter et al., 1999) and led to the hypothesis that a major function of the nectary is to protect the gynoecium from micro-organisms vectored to the flower by visiting pollinators (Thornburg et al., 2003). Isolation and characterization of these proteins (Carter and Thornburg, 2000, 2004b, c; Naqvi et al., 2005), helped define a novel biochemical pathway, the nectar redox cycle (Carter and Thornburg, 2004,a), that exists in soluble floral nectar of ornamental tobacco. This pathway produces high levels of hydrogen peroxide (up to 4 mM; (Carter and Thornburg, 2000)) via two independent mechanisms. The nectar redox cycle begins with the developmental expression of an NADPH oxidase in the floral nectary (Carter et al., 2007). NADPH oxidase produces superoxide at the nectary membrane surface. Subsequently, the superoxide dismutase Nectarin I (NEC1), the major nectar protein, directly converts superoxide into hydrogen peroxide (Carter and Thornburg, 2000). This accumulation of hydrogen peroxide is the main antimicrobial defence of tobacco nectar, since nectar treated with catalase becomes a good substrate for microbial growth (Carter et al., 2007). The production of a superoxide dismutase protein as a mechanism of floral defence against microbes is well established in tobacco plants (Carter et al., 2007). The nectar proteins have been characterized from only a few species of plants. In leek (Allium porrum), two nectar proteins have been characterized. The first is a mannose-binding lectin and the second is alliinase (Peumans et al., 1997). Proteins in these families have anti-herbivore and antimicrobial properties, suggesting a defensive role for the leek nectar proteins as well. Characterization of Jacaranda mimosifolia nectar identified a nectar lipase that also appears to participate in defence (Kram et al., 2008). Recently, nectarins have also been identified in extrafloral nectar. In Acacia spp. an invertase was identified in soluble extrafloral nectar that modified the hexose/sucrose ratio to benefit associated ant species (Heil et al., 2005); and later, classical defence proteins such as the pathogenesis-related PR proteins were identified in the extrafloral nectar of these plants (Gonzalez-Teuber et al., 2009). Further, the reproductive secretions of gymnosperms have also been examined and found to contain both carbohydrate-modifying enzymes and defence proteins (Poulis et al., 2005; O'Leary et al., 2007; Wagner et al., 2007). These findings suggest that the defence of plant secretions is an important and ancient feature of plant biology. While preliminary studies predicted that the presence of NEC1 in nectar may be widespread among the angiosperms (Carter and Thornburg, 2000), this has never been directly tested and the occurrence of many different defence proteins in other species suggest that perhaps there are many ways to protect nectar from microbial invasion. This can only be addressed by examining nectar defence mechanisms from other closely related species. Therefore, the nectarins of a species (hybrid petunia) that is related to ornamental tobacco have been examined. These studies, outlined below, indicate that the nectar of petunia has a novel defence that is not related to that found in ornamental tobacco, but may be mediated by ribonucleases; furthermore, nectar defences based upon H2O2 may not be as highly conserved as previously thought. Ribonucleases (RNases) are proteins that have the ability to degrade RNA. There are many different classes of RNases, all members of families with specific substrate preferences and enzymatic properties (D'Alessio and Riordan, 1997; Mishra, 2002). Ribonucleases belonging to the RNase T2 family are among those proteins enriched in flower tissues and may also have a defensive role. These proteins are normally found in the secretory pathway and many accumulate in the extracellular space (Irie, 1999; Deshpande and Shankar, 2002). The S-like RNases, a subclass of RNase T2 enzymes found in all plant species (MacIntosh et al., 2010), are commonly expressed in flowers. The three characterized S-like RNases from Arabidopsis, RNS1–3, are expressed at a higher level in flowers than in any other tissue (Taylor et al., 1993; Bariola et al., 1994, 1999), with RNS1 being detected only in flowers in the absence of stress (Bariola et al., 1994). Many other S-like RNases have been isolated from flowers, or cloned from pistil libraries, or their expression has been detected mainly in flowers in a diversity of species, including tobacco RNase NE (Dodds et al., 1996), Antirrhinum AhSL28, an S-like RNase from Japanese pear styles (Norioka et al., 2007) among others. S-like RNases are proposed to function in two main physiological processes: nutrition, through the recycling of inorganic phosphate during periods of phosphate starvation or during senescence and other developmental stages involving cell-death; and defence against pathogens (Bariola and Green, 1997; Deshpande and Shankar, 2002). S-RNases are the other class of RNase T2 enzymes found in flowers. S-RNases participate in gametophytic self-incompatibility in at least three plant families (Hua et al., 2008). S-RNases are secreted into style mucilage, where they abort the growth of pollen bearing the same S-allele (Clarke and Newbigin, 1993). This cytotoxic activity and their expression in flowers lead to the hypothesis that gametophytic self-incompatibility may have evolved through the recruitment of an ancient flower ribonuclease involved in defence mechanisms against pathogens for use in defence against ‘invasion’ by self pollen tubes (Hiscock et al., 1996; Nasrallah, 2005). Tobacco nectar has been well characterized. In addition to the identification of the defence mechanism and main protein complement of tobacco nectar, the biochemical changes and key regulators of gene expression controlling nectary development and nectar secretions have been characterized (Horner et al., 2007; Ren et al., 2007,a, b; Liu et al., 2009). However, knowledge on the conservation of these mechanisms in nectar from other related species is lacking. In a first attempt to extend the characterization of nectar to other species, an analysis is presented here of nectar proteins from Petunia hybrida, which, like tobacco, belongs to the Solanaceae family. Petunia nectar has potent antimicrobial activity, but surprisingly does not produce large amounts of hydrogen peroxide, although petunia and tobacco are closely related species. Instead, petunia nectar contains many ribonuclease activities not found in tobacco. Novel RNase T2 enzymes expressed in nectaries with characteristics intermediate between S- and S-like RNases were identified. These proteins could represent an intermediate step in the evolution of S-RNases, and support the hypothesis that S-RNases were recruited for self-incompatibility participation from an ancestral defence-related role in flowers. Materials and methods Plant material Petunia hybrida plants were obtained from a local market. Nicotiana tabacum cv. Xanthi was obtained from Dr CA Ryan, Washington State University. The ornamental tobacco hybrid L×S8 (Nicotiana langsdorffii×Nicotiana sanderae var. L×S8) was described previously (Kornaga et al., 1997; Carter et al., 1999). Plants were grown to floral maturity in a greenhouse with supplemental light (16/8 h day/night). Nectar was collected as described in Carter et al. (1999) approximately 6 h after watering to ensure adequate nectar production. For RNA and protein extraction, tissues from different floral parts were harvested at the appropriate floral stage following the classification of Koltunow et al. (1990). FOX assay for hydrogen peroxide Hydrogen peroxide was assayed in nectar essentially as described by Bleau et al. (1998). Briefly, 1 ml of fresh FOX reagent (25 mM sulphuric acid, 100 μM xylenol orange, 100 μM D-sorbitol, and 250 μM ferrous ammonium sulphate) was added to 200 μl of diluted nectar. After incubating for 20 min at room temperature, the levels of hydrogen peroxide were quantitated spectroscopically at 560 nm and calculated using a hydrogen peroxide standard curve (up to 300 μM). Bactericidal assay Raw nectar was diluted 1:1 with 10 mM phosphate buffer (pH 7.0) to improve pipetting precision. A pH of 7.0 was used because this is the normal pH of petunia nectar [determined with pH indicator strips (Merck)]. A fraction of the nectar was treated with catalase (Sigma) as described in Carter et al. (2007) for 20 min. Then, 90 μl aliquots of filter-sterilized nectar were used to test bacterial growth in a 96-well microplate. Pseudomonas fluorescens (strain A506) was grown in LB overnight at 28 °C in the presence of 50 mg l−1 rifampicin. The bacterial culture was then diluted to an OD600=0.5 using Luria Broth (LB). Ten μl of culture were added to each microplate well containing nectar from ornamental tobacco L×S8, Petunia hybrida, or phosphate buffer; with or without catalase treatment. Triplicate plates were incubated in a plate reader with agitation for 18 h at 28 °C and the OD600 was measured every 30 min. Growth was normalized (to t=0 for each well). Each treatment was assayed a minimum of three times. In vitro gel assay Raw nectar was collected from Petunia hybrida, Nicotiana tabacum cv. Xanthi, and ornamental tobacco plants L×S8, and stored at –80 °C until use. Fifty μl of nectar were analysed on RNase and DNase activity gels as described by Yen and Green (1991). Due to the presence of a compound that interfered with our standard method for protein quantification, the amount of protein loaded was estimated based on comparisons of stained proteins with molecular markers of known concentration. This estimate indicated that 5–10 μg of nectar proteins were loaded in each lane. For tissue-specific protein analysis a minimum of six flowers (stage 12) were dissected to obtain sepals, petals, stamens, stigmas, styles, and ovaries (including nectaries). Tissue was ground using a mortar and pestle with liquid N2, and extracted as described by MacIntosh et al. (1996), except that the extraction buffer did not include polyvinyl polypyrrolidone and 2-mercaptoethanol. Protein concentration was determined using the Bio-Rad Protein Assay Kit, and 100 μg of total protein were analysed in RNase or DNase activity gels. For anther/stamen analysis, at least six flowers at each stage (2, 6, 9, 11, 12a, 12b) were collected and stamens were harvested for protein isolation as stated above. Each activity gel is a representative of two independent protein isolations, and at least three replicates. Protein integrity was determined by SDS-PAGE analysis. After electrophoresis, gels were stained with Coomasie Blue using GelCode Blue Stain Reagent (Pierce/Thermo Scientific) according to the manufacturer's recommendations. Cloning of RNases Nectaries were isolated from Petunia hybrida flowers as described for ornamental tobacco (Carter et al., 1999). RNA was extracted from ovaries and nectaries using the Qiagen RNeasy Plant Mini Kit, and cDNA was synthesized using the i-Script Select Kit (Bio-Rad). To amplify cDNAs corresponding to RNase T2 homologues, primers were designed corresponding to conserved nucleotide regions by comparing sequences from Arabidopsis RNS1 (Taylor and Green, 1991), tobacco RNase NE (Dodds et al., 1996), and petunia RNase X2 (Lee et al., 1992). Primers were also designed based on petunia ESTs with homology to RNase T2 sequences. The primers used are presented in Supplementary Table S1 at JXB online. PCR products were cloned into pGEM T-EASY or pGEM T vector (Promega) for sequencing purposes. RNase Phy3 and RNase Phy4 were subjected to rapid amplification of cDNA ends (RACE)-PCR using the GeneRacer Kit (Invitrogen). DNAs were sequenced at the Iowa State University DNA Facility. The petunia RNase clone sequences were deposited in GenBank as accessions GQ465917 to GQ465920. RT-PCR Sepals, petals, stamens, stigma, styles, ovaries (with nectaries), nectaries alone, leaves, roots, and stems from Petunia hybrida were collected, and RNA was extracted as described above. Genomic DNA was removed using a DNA-free kit (Applied Biosystems), and cDNA was synthesized using the i-Script Select Kit (Bio-Rad). PCR was performed using GoTAQ 2X Master Mix (Promega) and 35 cycles of PCR products were run on 1% TBE gels and stained with ethidium bromide. Amplification of 18S RNA was used as control for loading. Phylogenetic analysis Protein sequences were aligned using the CLC bio software package, followed by manual adjustments. Only the region between the first conserved region after the signal peptide and the last conserved C residue was used in phylogenetic analyses. PAUP 4.0 software (Swofford, 2002) was used for Neighbor–Joining (1000 bootstrap replications) and parsimony analyses, using default parameters. Results Antimicrobial activity of Petunia hybrida nectar is not based on H2O2 production Ornamental tobacco nectaries are bright orange (Fig. 1a) due to the accumulation of β-carotene (Horner et al., 2007). The increase in nectary carotenoids is concomitant with the accumulation of H2O2 in the nectar (Carter and Thornburg, 2004,a; Horner et al., 2007); and it is proposed that the production of β-carotene and ascorbic acid provides the counter-balancing antioxidants needed to protect nectary cells, and probably the rest of the gynoecium, from the highly oxidative environment caused by H2O2 (Horner et al., 2007). Fig. 1. Open in new tabDownload slide Differences in nectary appearance and nectar composition between petunia and tobacco. (a) Appearance of petunia (right in upper panel, and lower panel) and the L×S8 tobacco hybrid (left, upper panel) nectaries (arrows) from flowers at stage 12 (Koltunow et al., 1990). Observe the differences in size and colour; small, light yellow nectaries in petunia, large, bright orange nectaries in tobacco. (b) Accumulation of hydrogen peroxide in petunia and tobacco nectar. Nectar collected from at least 20 different flowers was pooled and analysed for the presence of H2O2 using a colorimetric assay. A direct comparison of the nectaries of Petunia hybrida with those of the ornamental tobacco hybrid L×S8 (Nicotiana langsdorffii×Nicotiana sanderae var. L×S8) showed that, in contrast to the ornamental tobacco, the mature nectaries of Petunia hybrida do not turn bright orange, but rather remain a dull yellow. This observation suggested that the biochemical processes occurring in tobacco and petunia nectaries could be different, and that petunia may use different mechanisms of defence against micro-organisms. To test this idea, nectar was collected from both species and their H2O2 content was measured (Fig. 1b). Tobacco nectar accumulates up to 4 mM H2O2, as previously reported by Carter and Thornburg (2004b). On the other hand, H2O2 accumulation in petunia nectar is more than 10-fold lower than in tobacco. The nectar of ornamental tobacco effectively inhibits the growth of micro-organisms (Carter et al., 2007). This inhibition depends on the production of H2O2, and it is lost if nectar is treated with catalase. It was found that Petunia hybrida nectar also possesses antimicrobial activity. Petunia nectar can inhibit the growth of Pseudomonas fluorescens, Salmonella typhimurium, and Erwinia amylovora (data not shown). Petunia hybrida nectar contains low levels of H2O2; however, it could still be enough to provide antimicrobial protection. To test whether H2O2 was involved in this antimicrobial effect, the inhibitory effect of petunia and ornamental tobacco (L×S8), with or without prior treatment with catalase, was compared. The bacteria Pseudomonas fluorescens strain A506 was used in this assay because it had previously been shown to be inhibited by L×S8 tobacco nectar (Carter et al., 2007). Figure 2 shows that both tobacco and petunia nectar inhibit the growth of P. fluorescens. However, this inhibition is significantly reduced after catalase treatment of ornamental tobacco nectar. On the other hand, catalase treatment had no effect on the petunia nectar, which was still capable of inhibiting bacterial growth. This result suggests that a H2O2-independent antimicrobial mechanism exists in petunia nectar. Fig. 2. Open in new tabDownload slide Effect of tobacco (circles) and petunia (boxes) nectar on the growth of bacteria. Growth of Pseudomonas fluorescens (strain A506) in raw nectar (filled symbols) or nectar that was preincubated with catalase (empty symbols) was followed by changes in OD. Each point represents the mean ±SD (n=3). Data are representative of two independent experiments. To determine whether the potency of the antimicrobial activity of petunia nectar is comparable with that of tobacco, dilutions were made of both nectars and their ability to support P. fluorescens growth was determined. While half-strength petunia nectar is more effective than tobacco nectar at inhibiting bacterial growth, a one-sixth dilution of petunia and tobacco nectars support the same level of growth (see Supplementary Fig. S1 at JXB online). Petunia nectar is rich in ribonuclease activities Because other defensive mechanisms are suggested in petunia nectar and RNases are commonly found in flowers, it was decided to look for ribonuclease activities in nectar. To determine if RNases are present in the nectar of the tobacco and petunia plants an in gel activity assay was used (Yen and Green, 1991). Nectars from Petunia hybrida and two tobacco species (Nicotiana tabacum cv. Xanthi, and the ornamental tobacco hybrid L×S8) were collected and analysed on SDS-PAGE gels in which RNA was included. After electrophoresis the gels were incubated at three different pHs to improve the chance of detecting any RNases present. These assays detected RNase activities in all nectar samples (Fig. 3a); and, in general, RNases present in the nectar of all species had higher activity at an acidic pH. However, petunia showed a more complex RNase profile. At least 8–10 bands were detected in the petunia nectar, ranging from ∼20–40 kDa. By contrast, only two bands were detected in L×S8 (∼20 kDa and 25 kDa) and an additional 1–2 weak bands in N. tabacum cv. Xanthi in the same size range. Fig. 3. Open in new tabDownload slide Nuclease activities are present in nectar. (a) Aliquots (50 μl) of raw nectar from Petunia hybrida and two different tobaccos (Nicotiana tabacum cv. Xanthi and the hybrid Nicotiana langsdorffii×Nicotiana sanderae var. L×S8) were analysed in an in gel RNase activity assay at three different pHs. P, petunia; L, L×S8; N, Xanthi. Size (kDa) of molecular weight markers (M) is indicated. (b) Same samples as in (a), but analysed in an in gel DNase activity assay. (c) The same samples as in (a), analysed by SDS-PAGE, and stained with Coomassie Blue. Gels are representative of at least three independent experiments. The estimated protein concentration of tobacco and petunia nectar is in the same range (0.1–0.2 mg ml-1); however, it could be possible that some of the observed differences are the result of different amounts of proteins in the samples analysed. Thus, an RNase activity assay using similar amounts of protein from each sample was performed (see Supplementary Fig. S2 at JXB online). Again, petunia nectar showed a large number of RNase activities that were not present in tobacco. The different nectar samples were also tested for deoxyribonuclease (DNase) activities by the in gel activity assay (Fig. 3b). Three DNase activities were identified in petunia nectar. Two bands (approximately 30 kDa and 38 kDa) seem to coincide with RNase activities and show similar pH preference in DNA and RNA gels, suggesting that these two enzymes are bifunctional nucleases. Another activity of ∼25 kDa seems to be a basic DNase only observed in petunia nectar. By contrast, no DNase activity was detected in the ornamental tobacco nectar and a single activity at ∼37 kDa was found in the N. tabacum nectar. The differences in RNase and DNase activities between petunia and tobacco nectars are not due to protein degradation in the samples, since the protein profiles determined by Coomassie Blue and silver staining did not show signs of proteolysis (Fig. 3c). The nectarin profile of ornamental tobacco shows the major NEC1 protein at ∼29 kDa (Carter and Thornburg, 2000) and the NEC4/NEC5 doublet at ∼65 kDa (Carter and Thornburg, 2004,c; Naqvi et al., 2005). NEC3 (40 kDa) and its breakdown product, NEC2 (35 kDa) are often difficult to observe (Carter and Thornburg, 2004b). The N. tabacum nectar shows the NEC1 and the NEC4/NEC5 doublet and a number of minor bands. By contrast, the nectarin profile of petunia is clearly different from that found in either of the two tobacco species analysed. The two major proteins migrate at ∼10 kDa and 38 kDa. At least four minor bands at approximately 28 kDa, 32 kDa, 56 kDa, and 70 kDa are also present in petunia nectar. To determine if the RNases present in the nectar of petunia plants were expressed solely in the nectar or were also found in other parts of the flower as well, protein extracts from different flowers parts were assayed. Petunia and ornamental tobacco flowers were dissected into six primary organs; sepal, petal, stamens, stigma, style, and ovary (including nectaries). Protein extracts were prepared from these samples and run on RNase (Fig. 4a) and DNase (Fig. 4b) activity gels at pH 6.0. As shown in Fig. 4a, it is evident that each floral organ in the two species shows a different RNase profile. Petunia has a very complex pattern of activities in the 20–27 kDa range, and few activities larger than 27 kDa. On the other hand, ornamental tobacco flowers have a series of activities in the 27–38 kDa range not observed in petunia, but lack many of the activities in the smaller range (Fig. 4a). Many of the largest sized activities seem to coincide with DNase activities (Fig. 4b). While only one DNase activity was identified in petunia samples, up to six different bands can be seen in the various tobacco floral organs. Similarities in pattern of expression and relative intensity suggest that most of the activities detected in the 27–38 kDa range correspond to bifunctional nucleases, with the exception of an activity of ∼33 kDa expressed only in petunia stigmas and styles that clearly has only RNase activity. Fig. 4. Open in new tabDownload slide Nuclease profiles of different floral parts of petunia and ornamental tobacco plants. Flowers were harvested at stage 12, and dissected to obtain sepals (Sep), petals (P), stamens (Sta), stigmas (Sti), styles (Sty), and ovaries (including nectaries, Ov). Total protein extracts (100 μg) from each floral part were analysed in an in gel RNase activity assay (a) or DNase activity assay (b) at pH 6.0. (c) Same samples as in (a) analysed by SDS-PAGE, and stained with Coomassie Blue. Position of molecular weight markers (kDa) is indicated. Gels are representative of at least three independent experiments. Several of the smaller RNases that are enriched in petunia seem to accumulate preferentially in the reproductive organs rather than in sepals and petals. Activities of ∼18, 18.5, and 20 kDa are present only in stamens (anthers+filaments), stigmas, styles, and ovaries; and an activity of ∼22.5 kDa is present in all samples, but is highly enriched in stamens, stigmas, and styles. The stamens from both petunia and ornamental tobacco flowers contained the largest number of RNase activities as well as the most abundant DNase activity. Increased expression of RNases has been observed during senescence (Taylor et al., 1993; Liang et al., 2002; Lers et al., 2006). Thus, to determine if this increase in activities was due to senescence (dehiscence) of the anthers, proteins from anthers at various stages of flower development were prepared and analysed on RNase activity gels (Fig. 5). From our analysis it is clear that most RNases present in anthers are expressed during all stages of development and are not induced during senescence, i.e. no differences were observed between anthers from stage 12a (before dehiscence) and 12b (after dehiscence). However, the 18 kDa and 18.5 kDa doublet of activities increases during anther development, while some activities in the 30–40 kDa range are only observed in the early stages. Fig. 5. Open in new tabDownload slide RNase profile of petunia stamens during development. Stamens were collected from flowers at pre-dehiscence (2, 6, 9, 11, 12A) and post-dehiscence (12B) stages. Total protein extracts (100 μg) were analysed in an in gel RNase activity assay at pH 7.0. Position of molecular weight markers (kDa) is indicated. Gel is representative of at least three independent experiments. Novel RNase T2 genes are expressed in petunia nectaries Since RNase T2 enzymes are commonly found in flowers, a search was made for this type of transcript in petunia nectaries. RNA from isolated nectaries and ovaries was prepared and RT-PCR was used to amplify transcripts belonging to this family. BLASTP searches of the non-redundant protein database of NCBI identified many petunia S-RNases, but no petunia S-like RNases. It is hypothesized that any RNase T2 enzyme in nectar would belong to the S-like RNase class, since this class has been implicated in plant defence. Primers were designed based on conserved regions of S-like RNases, determined by sequence alignment of RNaseNE (GenBank accession number AAA21135), RNaseLX (GenBank accession number P80196), and RNS1 (GenBank accession number P42813). We also searched for petunia ESTs that could correspond to RNase T2 enzymes, and primers were designed to amplify these sequences. Primer sequences are presented in Supplementary Table S1 at JXB online. Using different primer combinations it was possible to amplify four distinct sequences that contained the conserved active site (CAS) cassettes that define enzymes belonging to the RNase T2 family (Irie, 1999). These were named RNase Phy1, RNase Phy3, RNase Phy4, and RNase Phy5, and were deposited in the GenBank as accessions GQ465920, GQ465919, GQ465918, and GQ465917, respectively. BLASTP analysis (Fig. 6) of the predicted proteins encoded by these partial sequences indicated that RNase Phy1 has 96% similarity and 90% identity to RNase NE from tobacco. Likewise, RNase Phy5 showed high homology (95% similarity, 88% identity) to tomato RNase LX. However, BLAST analyses of RNase Phy3 and RNase Phy4 resulted in hits with low sequence homology, either at the nucleotide or the amino acid levels. The closest homologue to RNase Phy3 was also RNase NE, but with only 33% identity and 52% similarity, and large gaps. The closest homologue to RNase Phy4 was an S-RNase, S42-RNase from Pyrus×bretschneideri, and the homology was even lower than for RNase Phy3 (29% identity, 48% similarity). In both cases homology was higher around the two CAS that define this family of enzymes. Due to their unique sequences RNase Phy3 and RNase Phy4 were subsequently chosen for rapid amplification of cDNA ends (RACE) analysis to determine their complete transcript sequence. Fig. 6. Open in new tabDownload slide Petunia RNases have homology to RNase T2 enzymes from other plants. BLAST analysis of predicted RNases encoded by petunia cDNAs amplified from ovaries and nectaries RNA. Alignment of each petunia RNase (RNase Phy1, RNase Phy3, RNase Phy4, and RNase Phy5) with the homologue with the highest BLAST score is shown. RACE PCR analysis of RNase Phy3 yielded a partial transcript. 5' RACE was unsuccessful in yielding a complete 5' end; however, sequencing analysis did reveal the first and second CAS sites. The partial RNase Phy3 transcript is 639 nucleotides long. The predicted protein encoded by this gene has an estimated molecular weight of 23.8 kDa, and an isoelectric point of 9.25, and it is probably N-glycosylated. RACE PCR of RNase Phy4 yielded a full-length transcript of 861 nucleotides. The encoded protein showed a putative signal peptide of 19 aa. The molecular weight of the mature protein is 25.79 kDa, with an isoelectric point of 8.98. RNase Phy4 may have up to three possible N-glycosylation sites. RNase Phy3 has a 38% identity and a 63% similarity with RNase Phy4. BLASTP analyses (not shown) indicated that these two proteins have similar homology to S-RNases and S-like RNases, and are not clear members of either class. Tobacco nectarins are expressed exclusively in nectaries that are actively secreting nectar (NEC1, NEC4, and NEC5; (Carter and Thornburg, 2003, 2004c; Naqvi et al., 2005)) or in nectaries and a few other floral tissues (NEC3; Carter and Thornburg, 2004b). The four petunia RNases were cloned from nectary and/or ovary cDNA. To analyse whether their expression was limited to these organs or found throughout the plant, RNA was extracted from different flower and vegetative tissues and tested for the presence of the corresponding transcripts using RT-PCR (Fig. 7). Each of the four RNases was expressed in ovaries and, in addition, RNases Phy1, 3, and 4 were also detected in nectaries. RNase Phy1 was expressed ubiquitously throughout the plant, and although our analysis is only semi-quantitative, its expression does seem higher in floral organs than in vegetative tissues. RNase Phy3 and RNase Phy4 had similar expression profiles. Both were expressed exclusively in flowers, with strong expression in ovaries and nectaries. RNase Phy4 was also highly expressed in petals and weakly detected in styles, while RNase Phy3 was highly expressed in stigmas, but also was detected in styles and petals. RNase Phy5 was mostly expressed in styles, although weak expression was also observed in petals, stamens (anthers), and ovaries. Thus, only RNase Phy3 and RNase Phy4 have patterns consistent with that of nectarins. These results suggest a role for these proteins in nectar. Fig. 7. Open in new tabDownload slide Expression of petunia RNases in different flower parts. Flowers were harvested at stage 12, and dissected to obtain sepals (Sep), petals (P), stamens (Sta), stigmas (Sti), styles (Sty), ovaries (including nectaries, Ov), and nectaries (N). At the same time, leaves (L), stems (S), and roots (R) were collected. Expression of the four RNase genes was analysed by RT-PCR. Amplification of 18S was used as control for loading. Gels are representative of at least three independent experiments. RNase Phy3 and RNase Phy4 have characteristics of S- and S-like RNases Plant members of the RNase T2 family are classified in three groups based on their phylogenetic relationships, their protein properties and their genomic organization (Igic and Kohn, 2001; MacIntosh et al., 2010). Classes I and II include the S-like RNases, which, in general, are acidic enzymes with either less than four introns (Class I) or more than four introns (Class II). Class III includes S-RNases, ‘relic’ S-RNases (Golz et al., 1998), and other RNases that have been proposed as ancestors of S-RNases (Yamane et al., 2003). Relic S-RNases are believed to have originated from the duplication of S-RNase genes but do not participate in self-incompatibility. Most S-RNases and relic S-RNases are basic proteins and have only one intron, with the exception that S-RNases of the genus Prunus have two introns (Yamane et al., 2003). RNase Phy3 and RNase Phy4 show low homology to both S-like and S-RNases; and they have characteristics from each of these classes. These two petunia RNases are basic proteins, as are most S-RNases; but their expression patterns do not resemble S-RNases, which are expressed mainly in the pistil. By contrast, RNase Phy3 and RNase Phy4 are also found in nectaries, ovaries, and petals. Amino acid patterns have also been used to differentiate between S-like and S-RNases. Vieira et al. (2008) described four amino acid patterns that can be used to distinguish between these two classes of RNases. Two patterns were identified exclusively in S-RNases (patterns 1 and 2, shaded yellow in Fig. 8), and also two were used to define S-like RNases (patterns 3 and 4, shaded pink in Fig. 8). In their analysis Vieira et al. identified pattern 1 in 467 of 468 S-RNases analysed, while pattern 2 was found in 689 of 691 possible S-RNase sequences. On the other hand, the amino acid pattern [HY]EW (pattern 3) was found in 54 of 69 S-like RNases and but only in 7 of 658 S-RNase sequences (each of these seven sequences belonged to the genus Prunus), and pattern 4 was found in 64 of 69 S-like RNases studied, and was not found in any of the 658 S-RNase sequences used in that study (Vieira et al., 2008). Fig. 8. Open in new tabDownload slide Presence of S- and S-like RNase-specific patterns (according to (Vieira et al., 2008)) in petunia RNases. Alignment of the petunia RNases and representative members of the S-RNase and the S-like RNase subfamilies. Patterns 1 and 2 that define S-RNases are highlighted in yellow; S-like RNase patterns are pink. The conserved active sites (CAS) I and II, typical of RNase T2 enzymes, are indicated. Petunia RNases are indicated with arrows. Accession number of other S-like RNase proteins in the alignment are AAA21135 (RNase NE), BAA95448 (RNase Nk1), X79337 (RNase LE), P42813 (RNS1), AAC49325 (ZRNaseII), CAC50874 (S-like RNase 28); S-RNases included are BAA83479 (S1-RNase), CAA65319 (S2-RNase), AAB40027 (S2 Na), BAD11006 (PA1), AAB07492 (S3-RNase), and BAA28354 (S4-RNase). We also included NP_003721 (RNASET2) from Homo sapiens. RNase Phy1 and RNase Phy5 contain the two S-like RNase patterns (Fig. 8). However, RNase Phy3 and RNase Phy4 do not match either class. RNase Phy3 contains patterns 2 and 3, corresponding to S- and S-like RNases, respectively (Fig. 8). RNase Phy4 contains only pattern 3, indicative of S-like RNases (Fig. 8), but does not have pattern 4. Thus, RNase Phy3 and RNase Phy4 show characteristics that are intermediate between S-RNases and S-like RNases, although RNase Phy4 seems to be closer to S-like RNases. A phylogenetic analysis of plant RNase T2 proteins was performed to determine the relationship of RNase Phy3 and RNase Phy4 with other RNases in this family. A Neighbor–Joining tree is shown in Fig. 9. This tree included proteins belonging to the three classes, as previously analysed by MacIntosh et al. (2010), with the addition of petunia RNases and canonical S-RNases. As expected, three clades are defined, each corresponding to one of the plant RNase T2 classes previously described. Although the bootstrap support for each clade is very strong, the internal architecture of the individual clades for Class I and Class III is less supported. RNase Phy3 and RNase Phy4 clearly belong to Class III, which includes canonical S-RNases and other RNases believed to have derived from ancentral RNases that gave origin to S-RNases, or from relic RNases that may have lost their self-incompatibility function (MacIntosh et al., 2010). Surprisingly, these two petunia RNases seem to be closer to RNases found in the Rosaceae than to other Solanaceae proteins. Moreover, RNase Phy3 and RNase Phy4 are very different from the canonical S-RNases found in Petunia hybrida (Fig. 9; and data not shown). Fig. 9. Open in new tabDownload slide Phylogenetic relationship of plant RNase T2 proteins. The Neighbor–Joining tree was estimated using only conserved regions of plant RNase T2 proteins. Bootstraps percentages greater than 50 are shown on interior branches. The tree was rooted using algae sequences. Classes I, II, and II clades are indicated, as well as algae proteins. Accession numbers of proteins included in the tree are those described in MacIntosh et al. (2010), with the addition of RNase Phy3 (arrow), RNase Phy4 (arrow), RNase PW1 (ABY86422), RNase PA1 (BAD11006), S3-RNase from P. cerasifera (CAN90133), S4-RNase (BAA28354), S26-RNase (AAB70515), S-RNase (BAA24017), S3-RNase from N. alata (AAB07492), S1-RNase (AAA60465), and S2-RNase (CAA65319). Discussion Although the importance of nectar in pollination is well-recognized, the proteins that are present in this plant secretion and, in particular,the proteins involved in antimicrobial activities are, in general, not well-studied. The best-studied example is the nectar from ornamental tobacco. Several nectarins, proteins present in nectar, have been described for this plant (Carter and Thornburg, 2000, 2004b, c; Naqvi et al., 2005). These proteins function in the nectar redox cycle, a biochemical pathway that produces high levels of hydrogen peroxide as an antimicrobial agent (Carter et al., 2007). Ornamental tobacco nectaries are bright orange due to the accumulation of β-carotene (Horner et al., 2007), which, together with ascorbic acid, provides the counter-balancing antioxidants needed to protect nectary cells, and probably the rest of the gynoecium, from the oxidative environment caused by H2O2. It was found that Petunia hybrida nectar is low in H2O2 levels and, further, that the addition of catalase has no effect on the antibacterial activity of petunia nectar. Thus, the strong antibacterial activity found in petunia nectar was not dependent on the accumulation of H2O2. It was also found that petunia nectar is rich in nuclease activities, in particular RNases, although DNases are also detected in this nectar. By contrast, while present, these enzymes are not detected at high levels in tobacco nectar. Differences in the patterns of RNase and DNase activities between these two plants are not limited to nectar. Other floral parts also show differential patterns, with enrichment in RNases in the 20–27 kDa range in petunia, and enrichment in activities probably corresponding to bifunctional nucleases in the 27–38 kDa range in tobacco. Increased levels of nuclease activities, both RNases and DNases, have been observed in many plants in response to bacterial, viral, and fungal pathogens (Lusso and Kuc, 1995; Floryszak-Wieczorek and Gniazdowska-Skoczek, 2001; Šindelářová and Šindelář, 2001; Kiba et al., 2006), suggesting that these enzymes could have antimicrobial effects. Nucleases are also involved in senescence and other programmed cell death processes (Dahiya, 2003). Thus, it is possible that some of the activities identified in our analysis are associated with senescence, which occurs rapidly for several floral tissues (O'Neill, 1997). This hypothesis, however, is not supported by the fact that most activities were found in anthers, the most RNase-rich tissue in flowers, before dehiscence. Thus, it is likely that at least some of these activities are performing biological functions not related to senescence. Analyses of gene expression have identified two families of plant RNases as part of plant defence responses, pathogenesis related PR-10 proteins (Liu and Ekramoddoullah, 2006), and S-like RNases (Bariola and Green, 1997). In this study, our attention was focused on the latter. Since S-like RNases have several highly-conserved amino acid motives, it was possible to amplify four petunia S-like RNases that had not been previously described. Two of those RNases, RNase Phy1 and RNase Phy5, were highly similar to well-characterized proteins from tobacco and tomato, respectively, and their expression patterns suggested that they may not be petunia nectarins. On the other hand, RNase Phy3 and RNase Phy4 were expressed in a pattern similar to that found for tobacco nectarins, suggesting that these enzymes may be part of the petunia nectar defence repertoire. Although only these two RNases are characterized here, they do not account for all the RNase activities found in petunia nectar (MS Hillwig, R Thornburg, GC MacIntosh, unpublished data). S-like RNases have been implicated in defence responses against a variety of pathogens. Expression of the extracellular RNase NE from tobacco is induced by Phytophthora parasitica (Galiana et al., 1997). Purified RNase NE inhibits hyphal growth from P. parasitica zoospores and from Fusarium oxysporum conidia in vitro, and co-infiltration of tobacco leaves with RNase NE and P. parasitica zoospores inhibited hyphal growth of the oomycete in vivo (Hugot et al., 2002). While a direct antibacterial role for S-like RNases has not been demonstrated, expression of two rice S-like RNases is induced by Xanthomonas oryzae (MacIntosh et al., 2010), and analysis of public microarray data indicates that Arabidopsis RNS1 and RNS2 are also induced by bacterial infections (data not shown). These data suggest that S-like RNases could have an antibacterial role. Expression of the related RNase NGR3 and RNase Nk1, from different tobacco species, is also induced in response to tobacco mosaic virus and cucumber mosaic virus, respectively (Kurata et al., 2002; Ohno and Ehara, 2005). In addition, Arabidopsis RNS1 is highly induced in response to mechanical damage both in local and systemic tissues (LeBrasseur et al., 2002; Hillwig et al., 2008). Tobacco RNase NW, Zinnia ZRNase II, and tomato RNase LE are also induced by wounding (Ye and Droste, 1996; Kariu et al., 1998; Lers et al., 1998). It has been suggested that the role of these secretory proteins during the wounding response is to block the spread of micro-organisms that could penetrate through the wound site (LeBrasseur et al., 2002). The regulation of S-like RNases by varied pathogens and wounding suggests that these enzymes could have broad-spectrum antimicrobial activity that could be associated with cytotoxic properties of these proteins. In fact, it has been proposed that S-RNases involved in self-incompatibility probably evolved from S-like RNases that had a defensive role (Hiscock et al., 1996; Nasrallah, 2005). S-RNases have a cytotoxic effect on the pollen tube during self-incompatible pollination. It is thought that, as the pollen tube elongates, the S-RNases are secreted into the extracellular matrix and may gain access into the cytoplasm of the pollen tube where they may degrade RNA from incompatible pollen (McClure and Franklin-Tong, 2006). Secretory ribonucleases also have a defence role in animals. Several members of the vertebrate-specific RNase A family have antimicrobial properties. Human RNase 2 and RNase 3, two eosinophil associated RNases, have antiviral activity, and RNase 3 also has an antibacterial function. Angiogenin and RNase 7 have antibacterial and antifungal activities (reviewed in Boix and Nogues, 2007). Similarly, several zebrafish RNases, also members of the RNase A family, were shown to have antibacterial effect (Cho and Zhang, 2007). However, enzymatic activity is not essential for eosinophil associated RNases antimicrobial activity (Rosenberg, 1995; Torrent et al., 2009). It has been proposed that their antimicrobial activity is due to the membrane destabilizing properties of these proteins. Positively charged amino acid residues in these proteins are thought to be important to disrupt negatively charged bacterial cell membranes and may be key to their bactericidal activity (Cho and Zhang, 2007, and references therein). Interestingly, while most S-like RNases are acidic proteins, RNase Phy3 and RNase Phy4 have high isoelectric points, indicating enrichment in basic amino acids. Thus, it is possible that the very basic nature of these proteins could indicate an antibacterial activity that can explain the effect on bacterial growth observed in our experiments. In plants, RNase T2 proteins are divided in two classes, S-RNases and S-like RNases, based on biological role and phylogenetic relations (Igic and Kohn, 2001). However, some proteins do not fit this classification. Relic-RNases are RNases that are no longer associated with self-incompatibility, but they are clearly derived from S-RNases through gene duplication events (Golz et al., 1998). Others, referred to as non-S RNases, seem to have intermediate characteristics between S-RNases and S-like RNases (Yamane et al., 2003). RNase Phy3 and RNase Phy4 seem to fall into the latter category. Both RNase Phy3 and RNase Phy4 are basic proteins, and RNase Phy3 has only one intron interrupting the coding region (M Hillwig, G MacIntosh, unpublished data). These are characteristics of S-RNases. However, the RNase Phy4 gene is unusual because it does not have introns (M Hillwig, G MacIntosh, unpublished data). In addition, gene expression analyses showed that the expression pattern of RNase Phy4 (petals, ovaries, and nectaries) is very different from that of S-RNases, which are mainly expressed in pistils; RNase Phy3 is also mainly expressed in ovaries and nectaries, although in this case expression in stigma is also high. Analysis of the amino acid patterns present in both proteins also show that these proteins differ from both the canonical S- and S-like RNases, since RNase Phy3 has one of the two amino acid patterns characteristic of S-RNases, and one of the two patterns belonging to S-like RNases. RNase Phy4 only has one of the two S-like patterns, and none of the S-RNase patterns. Yamane et al. (2003) identified a non-S RNase from Prunus avium, RNase PA1, that is also basic and has an expression pattern similar to S-RNases, but which has a low level of homology with this class of proteins; in addition, phylogenetic analyses placed RNase PA1 outside the S-RNase class. These authors proposed that this non-S RNase is a possible ancestral form of S-RNases. So far, this type of enzyme has been found only in other plants of the genus Prunus (Yamane et al., 2003; Banovic et al., 2009). RNase Phy3 and RNase Phy4 do not have high sequence homology to the Prunus non-S RNases; however, they cluster together among class III RNases, and they share the intermediate nature between S- and S-like RNases based on amino acid patterns. Thus, these petunia proteins could be the Solanaceae equivalent of the Prunus enzymes, and represent an ancestral form of S-RNases. Alternatively, these non-S RNases could represent relic S-RNases that lost their self-incompatibility function after gene duplication. Petunia hybrida possesses both functional and relic S-RNases proteins (Ai et al., 1992; Lee et al., 1992; Robbins et al., 2000). However, it has been shown that relic S-RNases are always more closely related to the S-RNases from the same family than to other RNases (Golz et al., 1998; Liang et al., 2003). By contrast, petunia non-S RNases cluster with Prunus non-S RNases and other Prunus proteins, and there is some evidence that these non-S RNases are conserved in tobacco and tomato (M Hillwig, G MacIntosh, unpublished data). Thus, we favour the hypothesis that these non S-RNases are ancient, although a more detailed analysis of evolutionary relationships will be necessary to solve this question. The potential role of these enzymes as antimicrobial agents in nectar is consistent with the hypothesis that S-RNases were derived from enzymes involved in defence mechanisms against invading pathogens (Hiscock et al., 1996; Nasrallah, 2005). Although additional work may be needed to demonstrate an antibacterial role of RNase T2 enzymes in flowers, our work identifies for the first time the presence of these proteins in nectar, In addition, the large number of RNases and nucleases identified in other floral tissues indicates that these enzymes probably have additional roles in flowers. Finally, the absence of hydrogen peroxide and the abundance of RNases in petunia nectar and the concomitant lack of these proteins in tobacco nectars support the hypothesis that nectar defences have evolved relatively recently. We thank Gwyn Beattie for providing the bacterial strains tested in this study. We also thank Ed Newbigin for helpful discussions. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details) © 2010 The Author(s).

Baldermann, Susanne;Kato, Masaya;Kurosawa, Miwako;Kurobayashi, Yoshiko;Fujita, Akira;Fleischmann, Peter;Watanabe, Naoharu

doi: 10.1093/jxb/erq123pmid: 20478967

Abstract Carotenoids are the precursors of important fragrance compounds in flowers of Osmanthus fragrans Lour. var. aurantiacus, which exhibit the highest diversity of carotenoid-derived volatiles among the flowering plants investigated. A cDNA encoding a carotenoid cleavage enzyme, OfCCD1, was identified from transcripts isolated from flowers of O. fragrans Lour. It is shown that the recombinant enzymes cleave carotenes to produce α-ionone and β-ionone in in vitro assays. It was also found that carotenoid content, volatile emissions, and OfCCD1 transcript levels are subjected to photorhythmic changes and principally increased during daylight hours. At the times when OfCCD1 transcript levels reached their maxima, the carotenoid content remained low or slightly decreased. The emission of ionones was also higher during the day; however, emissions decreased at a lower rate than the transcript levels. Moreover, carotenoid content increased from the first to the second day, whereas the volatile release decreased, and the OfCCD1 transcript levels displayed steady-state oscillations, suggesting that the substrate availability in the cellular compartments is changing or other regulatory factors are involved in volatile norisoprenoid formation. Furthermore, the sensory evaluation of the aroma of the model mixtures suggests that the proportionally higher contribution of α-ionone and β-ionone to total volatile emissions in the evening is probably the reason for the increased perception by humans of the scent emission of Osmanthus flowers. Carotenoid cleavage, carotenoids, CCD1, circadian rhythmicity, Osmanthus fragrans, norisoprenoids, scent perception Introduction Osmanthus fragrans Lour. is a shrub native to East Asia, and horticultural varieties can be found from Japan through China, Indo-China, Thailand, and India, to the Caucasus region. The petals of the evergreen Oleaceae flowers show by far the highest diversity of carotenoid-derived aroma compounds among the flowering plants (Kaiser, 2002). Because of its unique scent, commercial extracts are in high demand for use in the production of expensive perfumes and cosmetics. In China, the essential oils are used for flavouring tea, wine, and foods. The dominant compound in the essential oils of O. fragrans is β-ionone (Wang et al., 2009), however, how it is synthesized in these flowers is not known. The contribution of CCD1 enzymes in norisoprenoid formation in flowers and fruits of other species has been demonstrated (Schwartz et al., 2001; Simkin et al., 2004a, b, 2008; Mathieu et al., 2005; Ibdah et al., 2006; Kato et al., 2006; Vogel et al., 2008; García-Limones et al., 2009; Huang et al., 2009,b). Carotenoid cleavage dioxygenases (CCDs) typically exhibit a high degree of regio-specificity for double bond positions and can cleave multiple substrates. There are examples of CCDs that can cleave multiple double bonds, i.e. enzymes of the CCD1 family are involved in the cleavage of the 5,6 (5′,6′); 7,8 (7′,8′); and 9,10 (9′10′) double bonds to produce divergent volatiles. LCD from Bixa orellana, ZmCCD1 from Zea mays, AtCCD1 from Arabidopsis thaliana, and LeCCD1 from Lycopersicon esculentum cleave lycopene at the 5,6 (5′,6′) double bonds (Bouvier et al., 2003, Vogel et al., 2008). OsCCD1 enzymes from rice can cleave the 7,8 (7′,8′) double bonds of the non-cyclic carotenoid lycopene (Ilg et al., 2009). A substantial number of enzymes involved in the cleavage of the 9,10 (9′10′) double bonds of carotenoids have been identified, such as AtCCD1 from Arabidopsis thaliana (Schwartz et al., 2001); PhCCD1 from Petunia hybrida (Simkin et al., 2004a); LeCCD1 from Lycopersicon esculentum (Simkin et al., 2004b); VvCCD1 from Vitis vinifera (Mathieu et al., 2005); CmCCD1 from Cucumis melo (Ibdah et al., 2006); CitCCD1 from Citrus limon, Citrus sinensis, and Citrus unshiu (Kato et al., 2006); CcCCD1 from Coffea canephora and CaCCD1 from Coffea arabica (Simkin et al., 2008); ZmCCD1 from Zea mays (Vogel et al., 2008); FaCCD1 from Fragaria ananassa (García-Limones et al., 2009); or RdCCD1 from Rosa damascena (Huang et al., 2009,b). Moreover, CCD4 enzymes from Crocus sativus, Rosa damascena, Osmanthus fragrans, Malus domestica, Chrysanthemum morifolium (Rubio et al., 2008; Huang et al., 2009,a) and CCD7 and CCD8 from Arabidopsis thaliana (Schwartz et al., 2004) can cleave their carotenoid or apocarotenoid substrates at the 9,10 (9′10′) double bonds. The role of CCD7 and CCD8 in the production of downstream metabolites involved in branching was known (Schwartz et al., 2004) before the carotenoid-derived strigolactones were identified to be involved in the stimulation of the colonization of arbuscular mycorrhizal fungi (Akiyama et al., 2005), the germination of parasitic plant seeds (Bouwmeester et al., 2007), and bud outgrowth (Umehara et al., 2008). Although both CCD1 and CCD4 cleave their substrates at the same 9,10 (9′10′) double bonds, CCD4 enzymes only cleave cyclic non-polar carotenoids and apocarotenoids such as β-carotene and do not cleave xanthophylls and non-cyclic carotenoids such as zeaxanthin and lycopene (Rubio et al., 2008; Huang et al., 2009,a). Moreover CCD1 enzymes are cytoplasmic enzymes, whereas CCD4 enzymes carry a targeting sequence and are located in the plastids (Auldridge et al., 2006; Rubio et al., 2008). Hence, CCD4 enzymes have access to carotenoids located in the plastids. However, recombinant CCD4 isoforms oxidize different substrates; for example, AtCCD4 from A. thaliana and RdCCD4 from R. damascena prefer apocarotenoids and CmCCD4a from Chrysanthemum morifolium and MdCCD4 from M. domestica prefer carotenoids and are suggested to exhibit different biochemical functions (Huang et al., 2009,a). Recombinant CCD1 enzymes can utilize either carotenoids or apocarotenoids in vitro (Huang et al., 2009,b). However, it was recently suggested that the in vivo substrates of CCD1 are C27-apocarotenoids. RNAi-mediated MtCCD1 repression in mycorrhizal roots of Medicago truncatula caused an accumulation of C27-apocarotenoids and, therefore, have been suggested to be the major substrates for CCD1 enzymes in planta (Floss et al., 2009). Previously α-carotene and β-carotene were identified as the two dominant carotenoids in petals of O. fragrans flowers (Baldermann, 2008). These two carotenes contribute to more than 90% of the total amount of carotenoids in flowers of O. fragrans. β-iοnone and α-ionone, two major ionones emitted from flowers of O. fragrans (Wang et al., 2009), are the proposed reaction products of the cleavage of the 9,10 (9'10') double bond of α-carotene and β-carotene (Fig. 1). Fig. 1. View largeDownload slide Oxidative enzymatic cleavage of α-carotene by carotenoid cleavage enzymes yielding to α-ionone and β-ionone. In in vitro assays, both oxidative cleavage steps can be carried out by CCD1 and CCD4 enzymes, respectively. Fig. 1. View largeDownload slide Oxidative enzymatic cleavage of α-carotene by carotenoid cleavage enzymes yielding to α-ionone and β-ionone. In in vitro assays, both oxidative cleavage steps can be carried out by CCD1 and CCD4 enzymes, respectively. In petunia flowers, β-ionone emission was correlated with the transcript levels of PhCCD1 and in chrysanthemum flowers the white colour was associated with the transcript levels of CmCCD4a (Simkin et al., 2004,a; Ohmiya et al., 2006). None of these studies investigating the enzymatic carotenoid cleavage in flowers included the determination of the relative levels of the substrates (carotenoids) or reaction products (ionones), in addition to the analysis of the transcript levels. The OfCCD4 from O. fragrans showed only very low activity with carotenoids and apocarotenoids and it is suggested that isoforms of CCD4 enzymes probably possess different biological functions (Huang et al., 2009a). It is therefore hypothesized that a member of the CCD1 family might be involved in the C13-norisoprenoid formation in flowers of O. fragrans. Its gene was identified and the enzyme it encodes was functionally characterized. The determination of the relative levels of the substrates and reaction products in addition to the analysis of the transcript levels of OfCCD1 by quantitative real-time PCR over the flowering period provided detailed information regarding the role of OfDDC1 in fragrance formation in flowers of O. fragrans. Materials and methods Plant materials The flowers of Osmanthus fragrans Lour. var. aurantiacus were collected from the grounds of Shizuoka University, Japan during the flowering period in autumn 2006 and 2008. Flowers releasing the strongest odour during the unfurling process (stages 4 and 5), after changing colour from yellow to orange, were used for the detailed studies (Fig. 2). Fig. 2. View largeDownload slide Flowering of Osmanthus fragrans Lour. var. auranticus. Detailed studies were carried out using flowers during the unfurling period (stages 4 and 5), at the time where the flowers released the strongest odour [Odour intensity*: – no, (+) very weak, + weak, +(+) low, ++ medium, +++ high]. Fig. 2. View largeDownload slide Flowering of Osmanthus fragrans Lour. var. auranticus. Detailed studies were carried out using flowers during the unfurling period (stages 4 and 5), at the time where the flowers released the strongest odour [Odour intensity*: – no, (+) very weak, + weak, +(+) low, ++ medium, +++ high]. Freshly cut flowering branches without leaves (stage 4) were exposed to constant temperature (22 °C) and relative humidity (70%). Samples were either subjected to a 12/12 h light/dark regime for 48 h or to continuous light or dark periods for 24 h. The light intensity inside the incubator was set to 80 μmol m−2 s−1. At least 4 g of O. fragrans flowers (8.4±3.2 mg and 6.8±0.8 mm per flower) were collected in intervals of 4 h and directly frozen with liquid nitrogen. All samples were stored at –80 °C prior to analysis. Isolation and sequence analysis of OfCCD The first strand cDNA was synthesized from 1 μg total RNA using the SMART RACE cDNA Amplification Kit (Clontech, Laboratories, Palo Alto, CA) according to the manufacturer's instructions. The cDNA fragments of OfCCD1 genes were amplified by PCR with the cDNA template and the primers that have been reported previously (see Supplementary Table S1 at JXB online) (Kato et al., 2006). The PCR product was purified by Microspin™ columns (Amersham Bioscience, Piscataway, NJ) and the amplified cDNAs of the 3′ and 5′ were cloned with the TOPO TA-Cloning Kit (Invitrogen, San Diego, CA) and sequenced. End-to-end PCR was performed with primers designed from the cDNA sequences obtained by RACE-PCR. Expression and purification of the recombinant protein The cDNA of OfCCD1 for the expression of recombinant proteins was amplified by PCR with the primers shown in Supplementary Table S1 at JXB online. The cDNA fragments were cloned into EcoRI and XhoI/BamHI sites of the pGEX-6P-1 plasmid (Amersham Bioscience). The plasmids were transformed into E. coli strain XL1-Blue cells. For protein expression, 2 ml of an overnight culture was used to inoculate 200 ml of YT medium (8 g l−1 tryptone, 10 g l−1 yeast extract, 5 g l−1 NaCl) containing the appropriate antibiotics. The cultures were grown at 27 °C until an OD600 of 0.6 was reached. The expression of the proteins was induced by addition of 200 μl of 100 mM isopropyl-β-D-thiogalactoside (IPTG). To simplify the enzyme assay, cultures were alternatively grown with the addition of 1000 μl 100 mM FeSO4, and 100 μl 100 mM IPTG at 16 °C for 18 h. The E. coli cells were harvested by centrifugation and immediately frozen in liquid nitrogen. The cells were suspended in 20 ml phosphate buffered saline (140 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4) and 5 μl (7.5 U ml−1 suspension) lysozyme (rLysozyme, Novagen, Darmstadt, Germany) and 25 μl (20 U ml−1 suspension) cold active nuclease (Cryonase, Takara Bio Inc, Shiga, Japan) were added. After incubation for 30 min at room temperature the lysate was sonicated (6×30 s) (Ultrasonic Homogenizer, SMT Co., Ltd, Tokyo, Japan). Subsequently 1 ml 20% Triton X-100 (v/v) was added and the lysate moderatly shaken on ice for 30 min. The cell debris was removed by centrifugation at 20 000 g for 60 min and the recombinant protein bound to Sepharose 4B (Amersham Bioscience). The column was washed with 10 ml of phosphate buffered saline and 10 ml cleavage buffer (50 mM TRIS-HCl, 150 mM NaCl, 1 mM dithiothreitol, 0.05% Triton X-100 (v/v), and 1 mM ETDA in the case of cultivation of XL1-Blue in the absence of ferrous iron). The recombinant protein was obtained after digestion with PreScission Protease (Amersham Bioscience) at 5 °C overnight. The purity of the recombinant protein was analysed by SDS-PAGE on 12.5% polyacrylamide gel (e-PAGE 12.5%, Tokyo, Japan) using the Precission Plus Protein Dual Colour Standard (Bio-Rad, Tokyo, Japan) as marker. The proteins were stained with Bio-Safe Coomassie Blue G-250 Stain (BioRad) following the manufacturer's instructions. Analysis of carotenoids The method used to analyse the carotenoids was previously published in detail (Taylor et al., 2006). Briefly, at least 4 g flowers petals were ground in liquid nitrogen and 20 mg were transferred to a micro-centrifuge tube containing 350 ng of the internal standard β-apo-8'-carotenal. Firstly, 100 μl methanol and then 100 μl 50 mM TRIS-HCl (pH 8.0) containing 1 M NaCl were added. The carotenoids were extracted three times with 400 μl chloroform. The samples were stored under argon atmosphere at –80 °C prior to analysis. For HPLC analysis the samples were dissolved in 50 μl chloroform-methanol (1:4 v/v). The carotenoids were analysed on a Jasco HPLC-PDA system (Tokyo, Japan) and separated on a C30-column (YMC Co. Ltd Japan, 4.6×250 mm, 5 μm). Mixtures of methanol methyl-tert-butyl-ether and water in different volume ratios (solvent A: 81/15/4 and solvent B: 6/90/4) were used as the mobile phases at a flow rate of 0.8 ml min−1. The carotenoids were separated in gradient mode from 30% to 100% solvent B within 20 min. Quantification was achieved from dose–response curves and identification by co-chromatography with references substances. Total RNA extraction, reverse transcription, and real-time quantitative PCR The total RNA was extracted from at least 4 g flower petals according to the method described by Ikoma et al. (1996). The genomic DNA was removed by on-column DNA digestion during the purification of the RNA using the RNeasy Mini Kit (Qiagen, Tokyo, Japan) according to the specifications given by the manufacturer. The first strand cDNA was synthesized from 200 ng purified RNA using radom hexamers at 37 °C for 60 min and TaqMan reverse transcription reagents (Applied Biosystems, Tokyo, Japan). TaqMan MGB probes and primers were designed on based on common sequences using the Primer express software (Applied Biosystems; see Supplementary Table S1 at JXB online). For endogenous control, the TaqMan ribosomal RNA control reagent VIC probe (Applied Biosystems) was used. TaqMan real-time PCR was carried out with the TaqMan Universal PCR Master Mix (Applied Biosystems) using the ABI PRISM 7000 instrument (Applied Biosystems). The PCR program included an initial step of 50 °C for 2 min, a 10 min denaturation step at 95 °C and then 40 cycles of 15 s of denaturation at 95 °C and 1 min of hybridization/polymerization at 60 °C. The relative expression ratios were calculated using the ABI PRISM 7000 sequence detection software (Applied Biosystems) and normalized using the 18S ribosomal RNA results. Real-time quantitative PCR was performed in three replicates for each sample. Headspace sampling and analysis of volatiles The volatiles emitted by O. fragrans flowers were collected by dynamic headspace sampling. Freshly cut flowering branches, after removal of the leaves, were placed into headspace sampling units and filtered air (Charcoal filter) was pumped at a flow rate of 100 ml min−1 through the sampling unit. The volatiles were trapped on TenaxTA (180 mg) and analysed by GC-MS equipped with a thermal desorption system (TDS, Gerstel GmbH and Co. KG) under the following operating conditions: desorption temperature 260 °C, desorption time for 1 min, and split ratio of 15:1. The GC was equipped with a capillary TC-WAX column (GL Sciences Inc., Japan), 60 m×0.25 mm ID, and 0.5 μm film thickness. Helium was used as a carrier gas at a flow rate of 1.7 ml min−1. The temperature program of the oven was set to 40 °C for 5 min, then 3 °C min-1 up to 230 °C, and kept at this temperature for 60 min. The mass scan range was m/z 29–500 and the electric potential was set to EI 70 eV. Under these conditions, α- and β-ionones were detected at 59.6 min and 62.7 min, respectively. Enzyme assays of recombinant proteins The enzymatic activity of the recombinant OfCCD1 enzyme was assayed according to the method by Kato et al. (2006). For the enzymes obtained after cultivation of the E. coli cells in the presence of ferrous iron the activities were screened following the method described by Fleischmann et al. (2002). Identification of the volatile reaction products of recombinant proteins The volatile reaction products of the assay mixtures were analysed after solid phase micro-extraction (SPME) by gas chromatography mass spectrometry (GC-MS). Therefore a SPME fibre coated with 100 μm polymethylsiloxane (Supelco, Bellefonte, PA) was introduced into a headspace vial containing 2 ml enzymatic reaction mixture and 1 ml saturated sodium chloride solution and stirred for 1 h at 35 °C. The volatiles absorbed onto the fibre were analysed by GS-MS using a capillary Suplecowax column (GL Sciences Inc., Japan, 30 m×0.25 mm ID, 0.25 μm film thickness). The temperature program of the oven was set as follows: 50 °C maintained for 3 min, 5 °C min−1 up to 190 °C, 40 °C min−1 up to 240 °C, and held for 3 min. The mass scan range was set to m/z 50–300 and the electric potential to 1.00 kV. α-Ionone and β-ionone were detected at 23.1 min and 25.1 min, respectively. Sensory evaluation For sensory evaluation, three model mixtures simulating O. fragrans odour were evaluated by 23 panelists (16 male and 7 female). The three model samples contained different amounts of β-ionone, α-ionone, linalool, linalool oxides (furanoids), and γ-decalactone in ratios and concentrations comparable to the emitted volatiles at 02.00 h, 10.00 h, and 18.00 h (indicated by 1, 2, and 3 in Fig 7A). The exact compositions of the model mixtures are listed in Supplementary Table S2 at JXB online. To consider the different amounts of emitted volatiles, 0.06 g, 2.0 g, and 0.6 g of mixtures 1, 2, and 3 were diluted in ethanol (w/w) to give 10 g of stock solutions 1, 2, and 3, respectively. The three concentrates were diluted 1:10 with ethanol (w/w) and subsequently with MilliQ water until their odour intensities were felt to be the same as living flowers (100 ppm). For sensory evaluation, 10 g of samples in concentrations of 0.1 ppm, 1 ppm, and 10 ppm in ascending order were presented to the panelists in closed sensory vials (total volume 50 ml), coded by a random three-digit number. The panelists were asked to evaluate the intensity of the samples from 1 (none) to 5 (very strong). 10 g of Milli-Q water (intensity 1) and model mixture 2 in a concentration of 100 ppm (intensity 5) were provided as reference samples. Model mixture 2 (100 ppm) was used because it simulates the aroma of O. fragrans flowers at the time of highest volatiles emission and the odour of this concentration was evaluated to be similar to living flowers. The results were averaged and analysed by ANOVA (analysis of variance) and Tukey's multiple comparison test. A probability level of 5% (P <0.05) was considered as significant. Results Isolation and functional characterization of OfCCD1 To identify CCD homologues in O. fragrans flowers, degenerate oligonucletides based on conserved CCD sequences and amplified cDNA fragments of RNA isolated from O. fragrans flowers were designed. A full-length cDNA was subsequently obtained by RACE-PCR using gene-specific primers. The nucleotide sequence of this cDNA encodes a predicted protein of 563 residues. Phylogenetic analyses showed that the protein encoded by this cDNA clusters with other plant CCD1 enzymes (Fig. 3). The cDNA was therefore designated as OfCCD1. Fig. 3. View largeDownload slide Phylogenetic tree of deduced amino acid sequences of carotenoid cleavage enzymes involved in the cleavage of carotenoids (C40) or C27 apocarotenoids (CCD1, CCD4, CCD7, and CCD8) at the 9,10 (9',10') double bonds. The sequences were aligned using ClustalW (http://www.genome.jp/). The evolutionary history was inferred using the Neighbor–Joining method and drawn by Tree View (accession numbers: Arabidopsis thaliana CCD7 AK229864, CCD8 Q8VY26; Chrysanthemum morifolium CCD4a ABY60885, CCD4b BAF36656; Citrus limon CCD1 AB219168; Citrus sinensis CCD1 AB219165; Citrus unshiu CCD1 AB219164; Coffea arabica CCD1 DQ157170; Coffea canephora CCD1 DQ157166; Crocus sativus CsCCD1 AJ132927, CCD4a EU523662, CCD4b EU523663; Cucumis melo CCD1 DQ269467; Malus domestica CCD4 EU327777; Fragaria ananassa CCD1 ACA13522; Medicago truncatula CAR57918; Osmanthus fragrans CCD4 EU33443, CCD1 AB526191; Petunia hybrida CCD1 AY576003; Rosa damascena CCD4 EU334433 RdCCD1 ABY47994; Solanum lycopersicum SlCCD1a AY576001, SlCCD1b AY576002; VvCCD1 Vitis vinifera AY856353; Zea mays ACR33784). Fig. 3. View largeDownload slide Phylogenetic tree of deduced amino acid sequences of carotenoid cleavage enzymes involved in the cleavage of carotenoids (C40) or C27 apocarotenoids (CCD1, CCD4, CCD7, and CCD8) at the 9,10 (9',10') double bonds. The sequences were aligned using ClustalW (http://www.genome.jp/). The evolutionary history was inferred using the Neighbor–Joining method and drawn by Tree View (accession numbers: Arabidopsis thaliana CCD7 AK229864, CCD8 Q8VY26; Chrysanthemum morifolium CCD4a ABY60885, CCD4b BAF36656; Citrus limon CCD1 AB219168; Citrus sinensis CCD1 AB219165; Citrus unshiu CCD1 AB219164; Coffea arabica CCD1 DQ157170; Coffea canephora CCD1 DQ157166; Crocus sativus CsCCD1 AJ132927, CCD4a EU523662, CCD4b EU523663; Cucumis melo CCD1 DQ269467; Malus domestica CCD4 EU327777; Fragaria ananassa CCD1 ACA13522; Medicago truncatula CAR57918; Osmanthus fragrans CCD4 EU33443, CCD1 AB526191; Petunia hybrida CCD1 AY576003; Rosa damascena CCD4 EU334433 RdCCD1 ABY47994; Solanum lycopersicum SlCCD1a AY576001, SlCCD1b AY576002; VvCCD1 Vitis vinifera AY856353; Zea mays ACR33784). To determine whether OfCCD1 encodes a functional CCD, the cDNA was transferred into a glutathione pGEX-6P-1 fusion vector for expression in E. coli. The recombinant protein was then purified using affinity chromatography. SDS-PAGE analysis on a 12.5% acrylamide gel identified a single band with a calculated molecular size of 65 kDa (see Supplementary Fig. S2 at JXB online). This was in accordance with a predicted molecular mass of 64 kDa. Two in vitro assays were used to determine the cleavage activity of the recombinant protein. The first assay utilized ferrous iron, catalase, and ascorbic acid, and OfCCD1* purified from E. coli cells grown and induced under standard conditions (20 μM isopropyl β-D-thiogalactoside (IPTG), and 27 °C for 6 h). In the second assay, OfCCD1 was purified from E. coli cells induced by the addition of reduced amounts of IPTG (10 μM) and the bacteria were grown at 16 °C for an additional 18 h in the presence of ferrous iron (100 μM). The second enzymatic reaction buffer did not contain additional compounds (ferrous iron, catalase, and ascorbic acid) and the enzyme assay was carried out according to Fleischmann et al. (2002). After cultivation of the E. coli cells in the absence of ferrous iron, the isolated enzymes (OfCCD1*) showed no activity due to the lack of ferrous iron (Fig. 4). The rate of β-carotene degradation was similar to the chemical degradation of β-carotene under our experimental conditions (blank). After the addition of ferrous iron, catalase, and ascorbic acid to the buffer-substrate mixture, OfCCD1* activity could be detected, however, to obtain a comparable decrease of the initial amount of β-carotene for OfCCD1 and OfCCD1* longer reaction times were necessary (Fig. 4). OfCCD1 isolated from liquid cultures containing ferrous iron yielded active recombinant OfCCD1 enzymes that degraded β-carotene faster and without the supplement of additional ferrous iron (Fig. 4). However, high activities were only obtained directly after isolation and a stabilization of the enzymes with glycerol and ascorbate was necessary for storage. Because ascorbate also protects carotenoids against oxidation Km and Vmax values were not determined. Fig. 4. View largeDownload slide Relative enzymatic activities of purified recombinant OfCCD1 enzymes. OfCCD1 was purified from E. coli cells after the induction of protein expression in the presence of ferrous iron, whereas the recombinant OfCCD1* was purified after the induction of protein expression in the absence of ferrous iron. The recombinant OfCCD1* showed carotenoid cleavage ability if ferrous iron, catalase, and ascorbic acid were added to the assay mixture, however, prolonged reaction times were necessary. The assays were carried out at room temperature, 0.93 μmol l−1 initial concentration of β-carotene, 10 μg ml−1 protein in 1 ml TRIS-HCl buffer (pH 7, 50 mM TRIS-HCl, 125 mM KCl, 5 mM MgCl2, 1 mM dithiothreitol) for 20 min or 240 min, respectively. The relative activities were calculated setting the initial β-carotene concentration to 1. The blank values represent β-carotene degradation under identical assay conditions. The data are presented as mean ±SD from three replicates. Fig. 4. View largeDownload slide Relative enzymatic activities of purified recombinant OfCCD1 enzymes. OfCCD1 was purified from E. coli cells after the induction of protein expression in the presence of ferrous iron, whereas the recombinant OfCCD1* was purified after the induction of protein expression in the absence of ferrous iron. The recombinant OfCCD1* showed carotenoid cleavage ability if ferrous iron, catalase, and ascorbic acid were added to the assay mixture, however, prolonged reaction times were necessary. The assays were carried out at room temperature, 0.93 μmol l−1 initial concentration of β-carotene, 10 μg ml−1 protein in 1 ml TRIS-HCl buffer (pH 7, 50 mM TRIS-HCl, 125 mM KCl, 5 mM MgCl2, 1 mM dithiothreitol) for 20 min or 240 min, respectively. The relative activities were calculated setting the initial β-carotene concentration to 1. The blank values represent β-carotene degradation under identical assay conditions. The data are presented as mean ±SD from three replicates. The volatile enzymatic reaction products of the cleavage of β-carotene and α-carotene were analysed by SPME-GS-MS. β-Ionone was detected in the headspace of the reaction mixtures after the addition of β-carotene as substrate, and both α- and β-ionone were detected as volatiles in the headspace after applying α-carotene as the substrate (Fig. 5). Other putative volatile reaction products derived from the carotenoid cleavage, such as β-cyclocitral resulting from the cleavage of the 7,8 (7′,8′) double bond were not detected. These results indicate that the activity of OfCCD1 is similar to that of other CCD1 enzymes involved in the cleavage of the 9,10 (9′,10′) double bonds of cyclic carotenoids (Fig. 5A, B). Fig. 5. View largeDownload slide SPME-GC-MS analysis of volatiles formed in in vitro assays with the purified OfCCD1, structures and cleaving sites of the substrates. GC-chromatograms (A, B) of volatiles formed in an assay mixture containing α-carotene (A) and β-carotene (B) as substrates. α-Ionone and β-ionone were identified as reaction products (the GC-chromatograms are presented as the difference between the sample chromatogram and the chromatogram obtained in the control assay). Fig. 5. View largeDownload slide SPME-GC-MS analysis of volatiles formed in in vitro assays with the purified OfCCD1, structures and cleaving sites of the substrates. GC-chromatograms (A, B) of volatiles formed in an assay mixture containing α-carotene (A) and β-carotene (B) as substrates. α-Ionone and β-ionone were identified as reaction products (the GC-chromatograms are presented as the difference between the sample chromatogram and the chromatogram obtained in the control assay). Cleavage activity against α-carotene and β-carotene Other CCD1 enzymes from Arabidopsis thaliana (Schwartz et al., 2001), tomato (Simkin et al., 2004,b), melon (Ibdah et al., 2006), maize (Vogel et al., 2008), and roses (Huang et al., 2009,a) showed a broad substrate specificity against carotenoids and apocarotenoids. All CCD1 enzymes cleave at the 9,10 (9′,10′) double bonds. The widest range of substrates was tested with the rose CCD1, which cleaved symmetric carotenoids at both ends simultaneously (Huang et al., 2009b). The RdCCD1 exhibited different affinities against the end group moieties of pseudo-symmetric molecules, except for the pseudo-symmetric xanthophyl lutein where similar levels of the reaction products 3-hydroxy-α-ionone and 3-hydroxy-β-ionone were observed. So far, in no study were the symmetric and pseudo-symmetric carotenes β- and α-carotene used as substrates. The ratio of α- and β-ionone of the reaction by OfCCD1 with α-carotene was approximately 1.7:1, indicating that the preferred cleavage site was the α-ionone ring moiety (Fig. 5). Changes in OfCCD1 transcript levels To determine the change in the OfCCD1 transcript levels over time, RNA was isolated from petals harvested in intervals of 4 h over 48 h. In addition, RNA was isolated from petals of cut flowering branches subjected either to 24 h light or to 24 h continuous dark periods. OfCCD1 transcript levels were determined by qRT-PCR. OfCCD1 steady-state transcript levels increased during the light periods and reached their maximal levels either at 12.00 h (noon) or 16.00 h (Fig. 6A). When the branches were subjected to constant darkness for 24 h, the transcript levels increased over time (Fig. 6B), even though at a reduced level compared with the OfCCD1 transcript level changes detected during the 12/12 h (dark/light) photoperiods (Fig. 6A). The maximum transcript levels were observed after 20 h incubation in continuous darkness, which was somewhat delayed compared with the flowers subjected in parallel to 12/12 h (dark/light) photorhythmic conditions (Fig. 6A, B). When the flowers were placed in constant light, lower steady-state transcript levels and changes at lower amplitude were observed (Fig. 6B). The maximal OfCCD1 transcript levels in flower petals of branches placed into 24 h continuous light were detected after an 8 h incubation period (Fig. 6B). At this time the flowers which were subjected to 12/12 h (dark/light) photoperiods exhibited the lowest transcript levels during the dark period (Fig. 6A). Fig. 6. View largeDownload slide Changes in OfCCD1 transcript levels (A, B), α-carotene and β-carotene concentrations (C, D), volatile emission (E, F), and α-ionone and β-ionone release (G, H) of flowers of Osmanthus fragrans Lour. var. auranticus exposed to constant temperature (22 °C), humidity (70%), and different photoperiods (12/12 h light/dark regime or 24 h continuous dark or 24 h light). The photoperiods are indicated by shading of the background. The data of RT-qPCR and analysis of carotenoids are presented as mean ±SD from three replicates. The volatile emission profiles were comparable over different flowering periods (years, data not shown). Fig. 6. View largeDownload slide Changes in OfCCD1 transcript levels (A, B), α-carotene and β-carotene concentrations (C, D), volatile emission (E, F), and α-ionone and β-ionone release (G, H) of flowers of Osmanthus fragrans Lour. var. auranticus exposed to constant temperature (22 °C), humidity (70%), and different photoperiods (12/12 h light/dark regime or 24 h continuous dark or 24 h light). The photoperiods are indicated by shading of the background. The data of RT-qPCR and analysis of carotenoids are presented as mean ±SD from three replicates. The volatile emission profiles were comparable over different flowering periods (years, data not shown). Fig. 7. View large Download slide (A) The ratios of selected volatiles emitted by flowers of Osmanthus fragrans. Lour. var. auranticus. The emitted volatiles were collected by dynamic headspace sampling over 48 h at intervals of 4 h and the concentrations were determined by GC-MS. The cut flowering branches were subjected to constant temperature (22 °C), humidity (70%), and 12/12 h (dark/light) photoperiods as indicated by shading of the background. (B) Results of sensory evaluation of model mixtures reflecting the flower scent at 02.00, 10.00, and 18.00 h (marked as 1, 2, and 3 in A). The model mixtures were prepared by mixing different ratios of α-ionone, β-ionone, linalool, linalool oxides (furanoids), and γ-decalactone (see Supplementary Table S2 at JXB online). The odour intensities of the model mixtures were evaluated by 23 panelists according to the procedure described in the Materials and methods. Fig. 7. View large Download slide (A) The ratios of selected volatiles emitted by flowers of Osmanthus fragrans. Lour. var. auranticus. The emitted volatiles were collected by dynamic headspace sampling over 48 h at intervals of 4 h and the concentrations were determined by GC-MS. The cut flowering branches were subjected to constant temperature (22 °C), humidity (70%), and 12/12 h (dark/light) photoperiods as indicated by shading of the background. (B) Results of sensory evaluation of model mixtures reflecting the flower scent at 02.00, 10.00, and 18.00 h (marked as 1, 2, and 3 in A). The model mixtures were prepared by mixing different ratios of α-ionone, β-ionone, linalool, linalool oxides (furanoids), and γ-decalactone (see Supplementary Table S2 at JXB online). The odour intensities of the model mixtures were evaluated by 23 panelists according to the procedure described in the Materials and methods. To confirm that the OfCCD1 peak equals the transcript levels during day, the OfCCD1 transcripts of flowers grown outside and picked at 14.00 h were analysed. The transcript levels in O. fragrans flowers picked outside at the same times at various flowering stages (2, 4, 7, and 10; Fig. 2) changed 0.2-fold (2 arbitrary units; see Supplementary Fig. S1 at JXB online), whereas the transcripts varied up to 3.5-fold (21 arbitrary units) between the light and dark periods (Fig. 7A). Changes in the carotenoid content in Osmanthus fragrans flowers To determine the changes in the concentrations of α- and β-carotene, previously identified as the two major carotenoids (Baldermann, 2008), cut flowering branches were subjected to controlled environmental conditions and the concentrations of α- and β-carotene were analysed at intervals of 4 h. The concentrations of both α- and β-carotene increased in the presence of light (Fig. 6C, D), indicating that carotenoid biosynthesis in the flowers of O. fragrans is influenced by light. During the dark period, little change in carotenoid concentrations were observed and the levels remained nearly at the values reached during the previous light period. Although OfCCD1 transcript levels and carotenoid concentrations peak with an offset of 4 h, the carotenoid content decreased or remained at a relatively low level, or example, at 12.00 h and 16.00 h of the first and second days, respectively (12/12 h (dark/light) photoperiods, Fig. 6A, C). To test the effect of light on the carotenoid content, flowers were incubated under continuous 24 h dark or light (Fig. 6D). A nearly steady increase in carotenoids was obtained inside the flowers in the absence of light and only small changes were observed under continuous photoemission (Fig. 6D). Lower carotenoid concentrations in flowers subjected to 24 h continuous light or dark were detected compared with the flowers subjected to 12/12 h light/dark regime. The carotenoid content decreased at the peak of the OfCCD1 transcript levels at 04.00 h and 12.00 h under continuous illumination (Fig. 6B, D). Volatile emission and α-ionone and β-ionone release in flowers of O. fragrans The cut flowering branches subjected to 12/12 h (dark/light) photoperiods released maximum amounts of volatiles shortly after the beginning of the light period, following by a decrease until the lowest release during the dark period at 06.00 h (Fig. 6E). To test if the release of volatiles was regulated by circadian mechanisms, the cut flowering branches were subjected to a regime of 24 h constant light or constant dark. Flowers subjected to constant dark (Fig. 6F) showed a similar emission pattern to those flowers subjected to 12/12 h (dark/light) photoperiods (Fig. 6E). In both cases, the maximum levels of released volatiles were detected after 12 h. The results indicate that the release of volatiles is regulated by both light and circadian mechanisms. Flowers subjected to continuous light reached their maximum emission after 12 h, followed by a decrease in the emission over the rest of the experimental period. The scent emission decreased strongly between the first and second days when the samples were subjected to 12/12 h (dark/light) photoperiods. The emission of the two primary cleavage products of the major carotenes of Osmanthus flowers, α- and β-ionones (Fig. 1) were next examined under the different photorhythmic conditions (Fig. 6G, H). As with the total emission of volatiles, the release of ionones was higher during the light periods. Flowers subjected to a 24 h continuous light regime emitted more ionones compared with those flowers treated in parallel under 12/12 h (dark/light) photoperiods (Fig. 6G, H). Compared with the total volatile emission, the emission of ionones was higher in the early evening, which means that the contribution of the ionones to the total volatiles increases during the day (Fig. 7A). Volatile norisoprenoids are characterized by extremely low odour detection thresholds in humans. To evaluate changes in the scent of flowers of Osmanthus fragrans at different day times, the odour intensities of three model mixtures reflecting the floral scent at 02.00, 10.00, and 18.00 h (1, 2, and 3 in Fig. 7A; see Supplementary Table S2 at JXB online) were subjected to sensory evaluation. Although the amount of emitted volatiles were much higher at 10.00 h, the model mixtures 2 (10.00 h) and 3 (18.00 h) were evaluated as similar, but significantly different from model mixture 1 (02.00 h) (Fig. 7B). Discussion Isolation and functional analysis of OfCCD1 It is well known that the colour of yellow flowers is often caused by the presence of large amounts of carotenoids. Some flowers also have a broad variety of carotenoid-derived scent compounds, as in the case of O. fragrans which has the highest diversity of carotenoid-derived scent compounds among 1250 flowering plants investigated (Kaiser, 2002). Hence, it was of special interest to elucidate the biosynthesis of these compounds in O. fragrans. Since a previous report indicated that a 75% decrease in β-ionone formation was observed in transgenic petunia plants in which PhCCD1 gene expression was inhibited (Simkin et al., 2004a), the possible role of OfCCD1 on ionone biosynthesis was examined in O. fragrans. Apart from the formation of volatile C13-norisoprenoids through the action of CCD1, enzymatic cleavage of the 9,10 (9′,10′) double bond has also been demonstrated for CCD4 enzymes from C. sativus, R. damascena, C. morifolium, M. domestica, and A. thaliana (Rubio et al., 2008; Huang et al., 2009a). Because the O. fragrans CCD4 showed very low activity against carotenoids and apocarotenoids (Huang et al., 2009b) this study focused on the functional characterization of the OfCCC1 enzyme. The putative amino acid sequence of OfCCD1 exhibited the conserved histidine residues of the active centre of CCDs and in the presence of ferrous iron, the recombinant enzymes showed cleavage activity towards the two dominant carotenoids (β-carotene and α-carotene) found in flowers of O. fragrans. CCD1 enzymes cleave symmetric and pseudo-symmetric carotenoids differently. Based on the observations of Kloer and Schulz (2006), it was suggested that pseudo-symmetric molecules undergo a two-step cleavage. First, the enzyme cleaves the C40 substrate once, releasing the products, and then it binds to the primary non-volatile apocarotenoid and cleaves it for a second time. It should be noted that recent studies suggest that CCD1 enzymes cleave the primary derived cleavage products (C27-apocarotenoids) in the cytosol in vivo (Floss et al., 2009). In vitro, CCD1 can cleave either carotenoids or apocarotenoids (Huang et al., 2009,b). In roses, the non-volatile reaction products (C27-apaocarotenoids) of the first cleavage were only detected when the substrates contained different moieties at their ends. In this study, the symmetric β-carotene and the pseudo-symmetric α-carotene were used as substrates and the cleavage of α-carotene resulted in higher amounts of α-ionone, suggesting that the first site of cleavage is the one with the α-ionone moiety. However, in the case of the rose enzyme (RdCCD1), the same amounts of the reaction products 3-hydroxy-α-ionone and 3-hydroxy-β-ionone were obtained from pseudo-symmetric xanthophyll lutein (Huang et al., 2009b). Photorhythmic changes of OfCCD1 transcript levels, carotenoid concentrations, and volatile emission Photorhythmic volatile emission in plants has been demonstrated in several flowering plants (Matile and Altenburger, 1988; Loughrin et al., 1990; Helsper, 1998;, Picone et al., 2004; Dudavera and Pichersky, 2006). In general, nocturnally pollinated flowers tend to have maximum scent emission during the dark period, whereas the diurnally pollinated flowers release higher amounts of volatiles during the day. Volatile emission can be regulated either by light or by endogenous circadian mechanisms, mostly controlled at the gene expression transcription level (Hendel-Rahmanim et al., 2007). One group of plant enzymes is characterized by an increase in activity in young flowers and a decline during ageing, while a second group of enzymes show little or no decline at the end of the flower's life (Dudavera and Pichersky, 2006). During the floral development of Ipomoea sp., I. obscura, and I. nil, the CCD1 and CCD4 transcript levels decreased (Yamamizo et al., 2009). In the case of OfCCD1, the steady-state transcript levels are subjected to circadian mechanisms and have a peak during the day. The concentrations of α-carotene and β-carotene also underwent photorhythmic changes. It is interesting to note that there is a negative correlation between the abundance of OfCCD1 mRNA and the concentrations of the substrates (α-carotene and β-carotene). In O. fragrans flowers, the carotenoid levels remained low or decreased if the transcript levels of OfCCD1 were high. The carotenoid content increased over the experimental interval and reached the maximal concentration under light conditions. The light/dark regulation of carotenoid biosynthesis was investigated in red pepper, where all transcript levels of genes involved in the carotenoid biosynthesis decreased under dark conditions (Simkin et al., 2003). In citrus fruits, the transcript levels of genes encoding enzymes involved in carotenoid biosynthesis as well as CCD transcript levels increased during e fruit maturation (Kato et al., 2007). In chrysanthemums, a negative correlation between CmCCD4a mRNA abundance and carotenoid content was observed. However, recently obtained results during the flower development of Ipomoea sp., I. obscura, and I. nil, suggest that the flower colour cannot be correlated to carotenoid degradation activity in Ipomoea plants (Yamamizo et al., 2009). In O. fragrans, the OfCCD4 showed very low activity against carotenoids and apocarotenoids (Huang et al., 2009a) and hence, the contribution to the biodegradation of carotenoids is unclear. However, the transcript levels were quite similar to those of OfCCD1 (M Kato, unpublished results). Hence, based on the work presented here, it might be suggested that, in Osmanthus flowers, the slight decrease in α-carotene and β-carotene levels observed in the light periods is at least partly due to the high activity of OfCCD1. In petunia corollas, a correlation between mRNA abundance and β-ionone emission was observed (Simkin et al., 2004,a). However, emission was still increasing when transcript levels began decreasing during the afternoon. This study provides a similar observation in Osmanthus flowers, where the β-ionone emission remained at high levels after the transcript levels of OfCCD1 had already decreased during the day. It was suggested that there might be some limitation due to the substrate availability. Carotenoids are synthesized in the plastids whereas the CCD1 enzymes are located in the cytosol and therefore the cytosolic CCD1 enzymes have access only to those carotenoids distributed on the outer envelope of plastids, where, for example, significant amounts of β-carotene have been detected in pea chloroplasts (Markwell et al., 1992). In O. fragrans flowers, the carotenoid concentrations increased over the flowering period and, hence, a limiting factor for the reaction of OfCCD1 with the substrates could be the access inside the cell compartments. Another regulatory factor could be the catalytic efficiency of enzymes with their substrates. Carotenoid cleavage enzymes purified from plant tissues exhibit different affinities towards β-carotene. For example the Km values for β-carotene obtained for carotenoid cleavage enzymes isolated from different fruits varied from 11.0 μM l−1 for quince fruit, 6.6 μM l−1 for nectarine, and 3.6 μM l−1 for star fruit, respectively (Fleischmann et al., 2002, 2003; Baldermann et al., 2005). In Osmanthus flowers, the carotenoid content increased and steady-state maximal transcript levels were observed under light conditions, whereas the emission of ionones, as enzymatic reaction products, decreased over the flowering period. It might be suggested that the catalytic efficiency of the OfCCD1 enzymes with their substrates is another regulatory factor. Our results demonstrate that OfCCD1 in flowers of Osmanthus fragrans Lour. is probably involved in the oxidative cleavage of carotenoids to produce the volatile scent compounds α- and β-ionone. However, detailed analysis of carotenoids as putative precursors, transcript levels of OfCCD1, and volatile emissions indicate that the activity of this enzyme is not sufficient to account for the total emission of these volatiles. Additional work is needed to clarify the contribution of other carotenoid cleavage enzymes to ionone emission and identify the in vivo substrates. Changes in β-ionone and α-ionone emission in relation to sent perception Osmanthus flowers release their volatiles under light conditions. The analysis showed that the highest total volatile emission occurs in the morning, and total emission is lower in the afternoon. The release of β-ionone and α-ionone also strongly increased in the presence of light in the morning, and remained at a high level when the total volatile emission began decreasing during the afternoon. Because β-ionone (0.007 μg l−1; Buttery et al., 1990) and α-ionone (0.4 μg l−1, Teranishi and Buttery, 1987) have very low odour perception thresholds for humans, in water those compounds exhibit a strong impact on floral scents. The sensory evaluation of model mixtures reflecting the floral scent of O. fragrans flowers at 02.00, 10.00, and 18.00 h demonstrated that the scent in the morning and early evening is considered as similar, although the total volatile emission had decreased by approximately 3-fold. A similar example is the low amount of C13 norisoprenoids in rose, which nonetheless make a strong contribution to the scent; while constituting less than 1% of the total volatiles, they contribute to more than 90% to the scent impression by humans (Ohloff and Demole, 1987). Hence, the increasing amounts of α-ionone and β-ionone in relation to the total volatiles in the early evening are likely to be responsible for the stronger smell in the afternoon or early evening. This work was supported by the Japan Society for the Promotion of Science (JSPS 07434). We thank Dr Eran Pichersky for useful comments on the manuscript and Dr Toshiyuki Ohnishi for valuable discussions. 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Liu, Yinggao; Ye, Nenghui; Liu, Rui; Chen, Moxian; Zhang, Jianhua

doi: 10.1093/jxb/erq125pmid: 20460363

Abstract H2O2 is known as a signal molecule in plant cells, but its role in the regulation of aqbscisic acid (ABA) and gibberellic acid (GA) metabolism and hormonal balance is not yet clear. In this study it was found that H2O2 affected the regulation of ABA catabolism and GA biosynthesis during seed imbibition and thus exerted control over seed dormancy and germination. As seen by quantitative RT-PCR (QRT-PCR), H2O2 up-regulated ABA catabolism genes (e.g. CYP707A genes), resulting in a decreased ABA content during imbibition. This action required the participation of nitric oxide (NO), another signal molecule. At the same time, H2O2 also up-regulated GA biosynthesis, as shown by QRT-PCR. When an ABA catabolism mutant, cyp707a2, and an overexpressing plant, CYP707A2-OE, were tested, ABA content was negatively correlated with GA biosynthesis. Exogenously applied GA was able to over-ride the inhibition of germination at low concentrations of ABA, but had no obvious effect when ABA concentrations were high. It is concluded that H2O2 mediates the up-regulation of ABA catabolism, probably through an NO signal, and also promotes GA biosynthesis. High concentrations of ABA inhibit GA biosynthesis but a balance of these two hormones can jointly control the dormancy and germination of Arabidopsis seeds. ABA, ABA catabolism, Arabidopsis, GA, GA biosynthesis, hydrogen peroxide (H2O2), nitric oxide (NO), seed dormancy Introduction Seed germination is a complex process. Germination incorporates those events that commence with the uptake of water by the quiescent dry seed and terminate with the elongation of the embryonic axis (Bewley and Black, 1994; Holdsworth et al., 2008). Seeds of most angiosperms are dormant at maturity, and the dormancy must be lost before germination can occur (Bewley, 1997). Seed dormancy has been defined by Finch-Savage and Leubner-Metzger as the incapacity of a viable seed to germinate under favourable conditions (Finch-Savage and Leubner-Metzger, 2006). Many factors are involved in seed dormancy regulation, including some plant hormones, such as abscisic acid (ABA), gibberellic acid (GA), and ethylene (Bewley, 1997; Zhou et al., 1998; Ghassemian et al., 2000; Nakajima et al., 2006; Carrera et al., 2008; Holdsworth et al., 2008), some environmental factors, such as light intensity and low temperatures (Holdsworth et al., 2008), and several signalling molecules, such as nitric oxide (NO) and some reactive oxygen species (ROS) (Batak et al., 2002; Bethke et al., 2004, 2006; Sarath et al., 2007). However, the mechanisms of dormancy holding and breaking remain unclear because it is unknown how these factors are inter-related. The mechanisms of ABA catabolism and GA biosynthesis regulation are of particular interest. H2O2 acts as a signalling molecule, participating in a series of processes including plant development, stress responses, and programmed cell death (Pei et al., 2000; Bethke and Jones, 2001; Apel and Hirt, 2004; Foyer and Noctor, 2005). In plants, H2O2 is generated in chloroplasts, mitochondria, and peroxisomes (Mittler et al., 2004). Plasma membrane NAD(P)H oxidase is reported to be the pivotal enzyme involved in H2O2 generation (Kauss and Jeblick, 1995, 1996; Mur et al., 1996; Shirasu et al., 1997). The effect of H2O2 on plant development, stress responses, and programmed cell death has been thoroughly investigated (Pei et al., 2000; Bethke and Jones, 2001; Apel and Hirt, 2004; Foyer and Noctor, 2005). The effect of H2O2 on seed germination has also been researched by some investigators. Fontaine et al. (1994) indicated that thioredoxin reduction by NADPH produced via the oxidative pentose phosphate pathway allows the mobilization of storage proteins of cereals, leading to germination. H2O2 is also regarded as having a function as a promoter of seed germination by oxidizing germination inhibitors in Zinnia elegans seeds (Ogawa and Iwabuchi, 2001). The sources of H2O2 during seed germination are not clear. Bailly et al. (2008) indicated that in the dry state enzymes are probably not active and in this case ROS probably originate from non-enzymatic reactions such as lipid peroxidation or Amadori and Maillard reactions, and in hydrated seeds can be produced during the catabolism of lipids (glyoxysomes) and purines (peroxisomes), respiratory activity (mitochondria), electron transfer in photosystems (chloroplasts), or through the activity of NADPH oxidase (plasma membrane), amine oxidase, and peroxidase (cell wall) or cytochrome P450 (cytosol). They also indicated that accumulated H2O2 during imbibition is essential for seed dormancy breaking ABA plays an important role in a number of physiological processes such as seed maturation, growth, and developmental regulation, seed dormancy, and adaptive responses to environmental stresses (Zeevaart and Creelman, 1988; Hoffmann-Benning and Kende, 1992; Kuwabara et al., 2003; Nambara and Marion-Poll, 2005). In addition, ABA has been shown to be an important positive regulator in both the induction of dormancy during seed maturation and the maintenance of the dormant state in imbibed seeds (Finkelstein et al., 2002; Himmelbach et al., 2003). ABA-deficient mutants in Arabidopsis, such as aba1, aba2, and aao3, show the absence of primary dormancy in mature seeds (Leon-Kloosterziel et al., 1996; Finkelstein et al., 2002; Himmelbach et al., 2003). Some ABA-insensitive mutants such as abi1, abi2, and abi3 also lack or have decreased primary dormancy in mature seeds (Raz et al., 2001; Finkelstein et al., 2002; Himmelbach et al., 2003; Kushiro et al., 2004; Nambara and Marion-Poll, 2005), whereas overexpression of some ABA biosynthesis genes increases seed ABA content and enhances seed dormancy or delays germination (Finkelstein et al., 2002; Kushiro et al., 2004; Nambara and Marion-Poll, 2005; Holdsworth et al., 2008). Some investigations have shown that ABA catabolism also plays a major role in seed dormancy maintenance and dormancy break. Seeds of the mutant cyp707a2, lacking the key enzyme in ABA catabolism, (+)-abscisic acid 8'-hydroxylase, accumulate much more ABA and show stronger dormancy during imbibition than the wild type (Kushiro et al., 2004; Saito et al., 2004; Okamoto et al., 2006). Earlier results (Liu et al., 2009) also indicated that CYP707A2 plays a major role in ABA catabolism during imbibition and regulates seed dormancy. GA is a major plant hormone in a number of physiological processes, such as seed germination, stem elongation, leaf expansion, flowering, and seed development (Davies, 1993; Ogawa et al., 2003; Yamauchi et al., 2004). Together with ABA, GA is also involved in seed dormancy and germination control (Ogawa et al., 2003) and is found to promote seed germination in many species (Koornneef and van der Veen, 1980; White et al., 2000; Yamauchi et al., 2004). Inhibitors of GA biosynthesis, such as paclobutrazol (PAC) and uniconazole, reduce seed germination in Arabidopsis (Jacobsen and Olszewski, 1993; Leon-Kloosterziel et al., 1996; Toh et al., 2008). Several GA-deficient mutants, such as ga1-3 and ga2-1, have also delayed seed germination (Koornneef and van der Veen, 1980). It is proposed that GA plays two major roles in stimulating germination in Arabidopsis. The first role is in inducing radicle protrusion apparently by weakening the tissue that surrounds the embryo. The second role is in increasing the growth potential of the embryo, as indicated by the reduced growth rate of GA-deficient embryos (Groot and Karssen, 1987; Ogawa et al., 2003). The roles of ABA and GA in seed germination control have been indicated to be antagonistic by some investigators (Razem et al., 2006; Weiss and Ori, 2007; Toh et al., 2008). For example, GA induces transcription of α-amylase in the aleurone layer of cereal seeds that is significantly suppressed by ABA (Rogers and Rogers, 1992; Gómez-Cadenas et al., 2001; Zentella et al., 2002). ABA is also reported to inhibit seed germination by inhibiting GA biosynthesis directly under high temperature (Toh et al., 2008). However, because of the complexity in their signalling pathways, the relationship of ABA and GA is not well understood in terms of their regulation. In this study, it was found that both GA and ABA are under the regulation of H2O2 in seed dormancy. Exogenous H2O2 increases ABA catabolism by enhancing the expression of CYP707A genes. At the same time, H2O2 enhances GA biosynthesis via enhancement of GA biosynthesis genes such as GA3ox and GAw20ox genes. The inhibition of seed germination by a low concentration of ABA is reversed by GA, but apparently GA cannot over-ride the effect of high concentration of ABA. The present results also suggest that the H2O2-enhanced ABA catabolism requires the participation of NO, another small signalling molecule. Materials and methods Plant materials The plants were grown in a growth chamber with a 16 h photoperiod at a photon flux density of ∼200 μmol m−2 s−1 at a daytime temperature of 23 °C and a night-time temperature of 20 °C. In order to minimize the effect of seed maturation and storage conditions, plants of each genotype tested were grown in different sections of the same pot and seeds were harvested at the same time. Seeds were harvested in bulk 30 d after the petals appeared on the first flowers. These seeds maintained stronger dormancy. Only freshly harvested seeds were used in the experiments. The rest of the seeds were stored at –80 °C, at which temperature dormancy can be maintained for more than a year (Millar et al., 2006; Fujii et al., 2007). T-DNA insertion line The seeds of Arabidopsis thaliana cyp707a2 (SALK_083966) generated by the Salk Institute Genomic Analysis Laboratory (http://signal.sal.edu/) were obtained from the ABRC. The seeds were planted on agar plates containing kanamycin, and the kanamycin-resistant plants were transferred to soil. Seeds were harvested separately from individual plants. Subsequently, to confirm the mutant line as homozygous, PCR was performed with the genomic DNA of cyp707a2 using gene-specific oligonucleotides (LP, AATCCCAAATATGCCTTAGGC; and RP, TATGTGGGGACTTTGATGGAC). Chemical treatments Sodium nitroprusside (SNP) was used as the NO donor to release NO steadily, and 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (c-PTIO) was used as the NO scavenger (Bright et al., 2006). Diphenyliodonium (DPI) was used to inhibit NADPH oxidase to decrease production of H2O2 (Orozco-Cardenas et al., 2001). Diniconazole and PAC were used to inhibit ABA catabolism and GA biosynthesis, respectively (Han et al., 2004; Kitahata et al., 2005; Toh et al., 2008). Germination assay Fifty seeds were placed in 55 mm diameter Petri dishes with three Whatman No. 1 filter papers and 2.2 ml of sterile double-distilled water or treatment solutions. Plates were then placed in a 21 °C growth chamber under continuous light at 100 μM m−2 s−1 for 7 d. The seeds were regarded as germinated when the radicle emerged. Experiments were performed in quadruplicate for each treatment. Determination of NO NO was detected by the Nitric Oxide (total) Detection Kit (Assay Designs, USA). About 0.2 g of seeds were put into 1.5 ml tubes, then 200 μl of reaction buffer and 100 μl final dilution of NADH were added; 100 μl of water was added to a parallel tube as a control. Then 100 μl of nitrate reductase (NR) was added to the samples and 100 μl of reaction buffer was added to the control tubes. A 400 μl aliquot of reaction buffer without seeds, NADPH, and NR acted as a blank. The blank, control, and sample tubes were mixed well and incubated at 37 °C for 30 min. After incubation they were centrifuged at 3000 g for 1 min and 300 μl of supernatant was transformed to a new tube. A 100 μl aliquot of Griess reagent I was added to the control, sample, and blank, and after being well mixed, 100 μl of Griess reagent II was added. The tubes were mixed by shaking and then they were incubated at 25 °C for 10 min. The optical density (OD) of samples and controls was measured at 540 nm. The OD of each sample was labelled as ODs and that of each control as ODc. Then the average net OD was calculate and labelled as ODn. Each average ODn=average ODs–average ODc. Each ODn could be calculated from a standard curve. Sodium nitrate at 0–100 μM was used as a standard, and a standard curve was produced. The amount of NO released was equal to the amount of nitrate. The mechanism of this kit is transformation of NO to nitrite and its measurement. Extraction and determination of ABA For estimation of endogenous ABA levels of imbibed seeds, 0.2 g of seeds was homogenized in 1 ml of distilled water and then shaken at 4 °C overnight. The homogenates were centrifuged at 12 000 g for 10 min at 4 °C and the supernatant were directly used for ABA assay. ABA analysis was carried out using the radioimmumoassay (RIA) method as described by Quarrie et al. (1988). The 450 μl reaction mixture contained 200 μl of phosphate buffer (pH 6.0), 100 μl of diluted antibody (Mac 252) solution, 100 μl of [3H]ABA (∼8000 cpm) solution, and 50 μl of crude extract. The mixture was then incubated at 4 °C for 45 min and the bound radioactivity was measured in pellets precipitated with 50% saturated (NH4)2SO4– with a liquid scintillation counter. Determination of H2O2 An Amplex Red Hydrogen Peroxide/Peroxidase Assay Kit (Invitrogen, Carlsbad, CA, USA) was used to measure H2O2 production in 2-week-old plants. Leaves were frozen in N2 and then ground. Then 500 μl of phosphate buffer (20 mM K2HPO4, pH 6.5) was added to 50 mg of ground frozen tissue. After centrifugation, 50 μl of the supernatant was incubated with 0.2 U ml−1 horseradish peroxidase and 100 μM Amplex Red reagent (10-acetyl-3,7-dihydrophenoxazine) at room temperature for 30 min in darkness. The fluorescence was quantified using FLUOStar Optima (excitation at 560 nm and emission at 590 nm) (Xing et al., 2008). QRT-PCR analysis Total RNA was isolated from seeds or leaves by an RNeasy kit (Invitrogen). DNA impurities in the isolated RNA were digested before synthesizing the cDNA by adding DNase (Invitrogen) and incubation for 30 min at 37 °C. DNase was then inactivated by incubation for 10 min at 65 °C. Then 2 μg of RNA was reverse transcribed to cDNA with SuperScriptIII RTS First-Strand cDNA Synthesis Kit (Invitrogen). After that, the cDNA was diluted 10 times, and 4 μl of cDNA was used to carry out the quantitative RT-PCR (QRT-PCR). IQ™ SYBR Green Supermix (Bio-Rad) was used for the QRT-PCR. Actin2 acted as the intramural standard. The QRT-PCR was executed with iCycle (Bio-Rad). The primers that were used in QRT-PCR are as follows: CYP707A1 (F, TTGGAAAGAGGAGACTAGAG; R, GTGAACCACAAAAGAGGAAC), CYP707A2 (F, AAATGGAGTGCACTCATGTC; R, CCTTCTTCATCTCCAATCAC), CYP707A3 (F, ATTCTTGTCCAGGCAATGAG; R, ATAGGCAATCCATTCTGAGG), CYP707A4 (F, GAAAGGAATACAGTACAGTC; R, GGATTAGATTTGGCTAACTAC), GA20ox1 (F, GCCTGTAAGAAGCACGGTTTCT; R, CTCGTGTATTCATGAGCGTCTGA), GA20ox2 (F, CCCAAGGCTTTCGTTGTCAA; R, CCGCTCTATGCAAACAGCTCT), GA20ox3 (F, TCGTGGACAACAAATGGCA; R, TGAAGGTGTCGCCTATGTTCAC), GA3ox1 (F, TCCGAAGGTTTCACCATCACT; R, TCGCAGTAGTTGAGGTGATGTTG), GA3ox2 (F, GTTTCACCGTTATTGGCTCTCC; R, TCACAGTATTTGAGGTGGTGGC), RD29A (F, TGCACCAGGCGTAACAGGTA; R, TAATCGGAAGACACGACAGGA), RD29B (F, GAGCATCCAAAGTGTTGAAGAAAGT; R, GGTCTTGCTCGTCATACTCATCAT), XTH5 (F, CACGTCGATGGATGTGAAGCT; R, CTTTCTGATCCCACCAACGTTT), EXP2 (F, CCTCCAAACTTTGCCTTAGCT; R, CGGCCAAGTCAAAGTGCTTAA), NCED6 (F, TGAGAGACGAAGAGAAAGAC; R, GTTCCTTCAACTGATTCTCG), NCED9 (F, GGAAAACGCCATGATCTCACA; R, AGGATCCGCCGTTTTAGGAT), GA2ox2 (F, CCCTCAAATTTTCCGTGAGT; R, CAGCATTTTACTCAGAGTGTC), CAT1 (F, ACACATACGTGTTTTGGTGTTGAGC; R, CACCCGAGTTTGTAGTGAAGAAAGG), CAT2 (F, CTCCAAGCTCTCTTCTCATCAAACCAT; R, GGAGCTCGGAGAAAGTCAGCACAA), CAT3 (F, GAGGGATATTCGTGGTTTTGCTGTC; R, TTTGTTTTCGGGTTAGGTTTCAACG), AT1G19230 (F, TCACTTTTACTGGGTCACAAGGGAG; R, AACTCCATGTTTGGCATG-GTTCA), AT4G11230 (F, GGTACCGCAAAACGGTATGGATGT; R, AATCATCTCCAGGGGAAGAAGTAATAGA), AT4G25090 (F, TTGGCAAAGAGTTTGGGTGATAGC; R, GGTAACAGAATTAGGGCCATGTTTAGC), Actin2 (F, TGTGCCAATCTACGAGGGT; R, GCTGGTCTTTGAGGTTTCC). Generation of the CPY707A2-overexpressing line Full-length Arabidopsis CPY707A2 cDNA was obtained by using reverse transcription-PCR and cloned into the pENTR-TOPO cloning vector (Invitrogen) and sequenced. After the LR reaction, CPY707A2 cDNA was inserted into the pGWB5 vector (a gift from Professor Liang, Yangzhou University) which had a 35S promoter; this vector was named pGWB5-CPY707A2. Transgenic Arabidopsis containing the cauliflower mosaic virus (CaMV) 35S promoter was generated using the floral dipping method (Clough and Bent, 1998) and transferred into Col-0 wild-type plants. Transformed plants were selected by growth on hygromycin-containing medium. Plants of the second generation after transformation were used for the experiments. The empty pGWB5 vector (the ccdb gene was substituted by a nonsense segment with a termination codon) which acted as control was also transferred into Col-0 wild-type plants. Accession numbers Sequence data from the article can be found in the GenBank data libraries or TIGR database (Arabidopsis thaliana Genome Project) under the following accession numbers: CYP707A1, At4g19230; CYP707A2, At2g29090; CYP707A3, At5g45340; CYP707A4, At3g19270; GA20ox1, At4g25420; GA20ox2, At5g51810; GA20ox3, At5g07200; GA3ox1, At1g15550; GA3ox2, At1g80340; XTH5, At5g13870; EXP2, At5g05290; RD29A, AT5g52310; RD29B, AT5g52300; GA2ox2, At1g30040; NCED6, At3g24220; NCED9, At1g78390; CAT1, At1g17680; CAT2, At4g35090; CAT3, At1g20620; and Actin2, At3g18780. Results H2O2 reduces dormancy of freshly harvested Arabidopsis seeds After 7 d of imbibition in 5 mM H2O2, the dormancy of freshly harvested Arabidopsis seeds was broken. Much higher H2O2 concentrations, such as 100 mM, had less effect (Fig. 1a). The H2O2 effect was further confirmed by supplying DPI, a H2O2 scavenger, to reduce the level of H2O2 (Levine et al., 1994; Alvarez et al., 1998; Lee et al., 1999). As shown in Fig. 1b, DPI enhanced seed dormancy significantly while exogenous H2O2 completely reversed the effect of DPI. When supplied to non-dormant seeds, H2O2 increased seed germination and DPI slightly slowed down the process (Fig. 1c). Fig. 1. Open in new tabDownload slide The effect of H2O2 on seed germination and dormancy break in Arabidopsis. (a). Effect of different H2O2 concentrations on dormancy break of freshly harvested wild-type seed. Freshly harvested seeds were imbibed in water or different concentrations of H2O2 and the germination ratio was counted on the seventh day. (b) Effect of H2O2 and its production inhibitor DPI on freshly harvested seed dormancy and germination. (c) Effect of H2O2 and its production inhibitor DPI on non-dormant seed germination. Data represent the means±SE of four replicates, with 50 seeds each in a, b, and c. An ANOVA test followed by a rank test was performed. Different letters in (a) are used to indicate means that are significantly different (P <0.05). H2O2 regulates genes involved in ABA catabolism and GA synthesis during imbibition ABA and GA are known to regulate seed germination and dormancy but their signalling pathways have not yet been established. In the present experiments, H2O2 acted as a regulator of genes involved in both ABA and GA metabolism. As shown in Fig. 2a, the release of H2O2 from the imbibed seeds rapidly increased in the first few hours and reached a peak at 3 h, followed by a decrease after 6 h. DPI treatment decreased this H2O2 release significantly. When expression of ABA 8'-hydroxylase (CYP707A1, CYP707A2, CYP707A3, and CYP707A4), 9-cis epoxcartenoid dioxygenase (NECD6 and NCED9), GA 20-oxidase (GA20ox1, GA20ox2, and GA20ox3), GA 3-oxidase (GA3ox1 and GA3ox2), and GA 2-oxidase (GA2ox2) was investigated following different treatments during imbibition, the expression of four ABA catabolism genes (CYP707A genes) were induced within a few hours after imbibition of water, and then decreased after 6 h. When imbibed with H2O2, the transcription level of these four genes increased much more rapidly and was maintained at a high level during the entire imbibition period (Fig. 2a). When release of H2O2 was inhibited by DPI, there was no increase in transcription levels of these four genes compared with water. Further addition of exogenous H2O2 was able to reverse the inhibitory effect of DPI on transcription of the four genes (Fig. 2b). Transcription levels of the ABA biosynthetic genes NCED6 and NCED9 decreased in the water control within the first 6 h of imbibition and then increased. Exogenous H2O2 slightly enhanced the expression of these two genes (Supplementary Fig. S1 available at JXB online). Fig. 2. Open in new tabDownload slide Effect of H2O2 on the expressions of ABA catabolism and GA biosynthesis genes during imbibition. (a) Change of H2O2 content during imbibition under different treatments in the first 48 h of imbibition. (b) Change in the transcript levels of ABA catabolism genes in the first 48 h of imbibition. All four CYP707A genes were determined by QRT-PCR. (c) Change in the transcript levels of GA biosynthesis genes in the first 48 h of imbibition. All three GA20ox genes and two GA3ox genes were determined by QRT-PCR. H2O2 (10 mM) and DPI (10 μM) were used for these experiments. Values are the means±SE (n=4 for a and n=3 for b and c). An ANOVA test followed by a rank test was performed. Different letters are used to indicate means that are significantly different (P <0.05). Expression of three GA20ox and two GA3ox genes was also significantly induced in the water control in the initial hours of imbibition. As shown in Fig. 2c, transcription levels of all three GA20ox genes increased rapidly during the first 6 h. Levels of GA20ox1 remained high for the entire imbibition period, but GA20ox2 and GA20ox3 transcription levels decreased to a lower level after 6 h. Two GA3ox genes displayed a delayed initiation compared with the GA20ox genes. GA3ox1 reached its maximum at 12 h and decreased thereafter, while GA3ox2 peaked at 24 h. H2O2 enhanced the transcription of all five GA biosynthesis genes to a high level and these elevated levels were maintained throughout the remainder of the imbibition period. DPI significantly inhibited transcription of the five genes, but addition of exogenous H2O2 was able to reverse the DPI inhibition (Fig. 2c). The transcription level of the GA catabolic gene GA2ox2 decreased during the first 6 h of imbibition and increased thereafter. Exogenous H2O2 enhanced this gene expression slightly (Supplementary Fig. S1 at JXB online). H2O2 requires NO for regulation of genes involved in ABA catabolism and breaking of seed dormancy As shown in Fig. 3a, both NO and H2O2 caused dormancy break in freshly harvested seeds. Treatment with DPI or with the NO scavenger c-PTIO enhanced seed dormancy. Treatment with the NO donor SNP substantially reversed the inhibition caused by DPI, whereas exogenous H2O2 failed to reverse the inhibition caused by c-PTIO. The effect of H2O2 and SNP on seed dormancy break could be reversed by the ABA catabolism inhibitor diniconazole, which supposedly inhibits ABA 8'-hydroxylase activity (Fig. 3a). Fig. 3. Open in new tabDownload slide The function of NO for H2O2-regulated ABA catabolism. (a) Effect of H2O2, NO, and their production inhibitor DPI or scavenger c-PTIO, or the ABA catabolism inhibitor diniconazole on freshly harvested seed dormancy and germination; the germination ratio was counted on the seventh day. (b) Change in the transcript levels of ABA catabolism genes under different treatments at 6 h imbibition. (c) Change in ABA content after 24 h imbibition. ABA contents were measured by RIA. (d) Change in the transcript levels of the ABA-regulated genes RD29A and RD29B under different treatments at 24 h imbibition. H2O2 (10 mM), DPI (10 μM), SNP (200 μM), c-PTIO (200 μM), and diniconazole (10 μM) were used for these experiments. Values are the means±SE (n=3 for a–d). An ANOVA test followed by a rank test was performed. Different letters are used to indicate means that are significantly different (P <0.05). Earlier results indicated that CYP707A2 was much more abundant than the other CPY707A genes during germination (Saito et al., 2004; Okamoto et al., 2006), consistent with what was found in the present study (Fig. 3b). Both SNP and H2O2 enhanced CYP707A gene expression, especially that of CYP707A2 (Fig. 3b). Treatment with c-PTIO or DPI decreased CYP707A gene expression. Treatment with SNP reversed the effect of DPI, while exogenous H2O2 treatment was not able to reverse the effect of c-PTIO (Fig. 3b). The changes in ABA content reflected the expression of ABA catabolic genes. SNP and exogenous H2O2 treatments enhanced ABA catabolic gene expression, while scavengers or inhibitors decreased expression of these ABA genes (Fig. 3c). SNP reversed the inhibitory effects of DPI, but exogenous H2O2 was largely ineffective at reversing the effects of c-PTIO on the expression of ABA catabolic genes. Expression of the ABA response genes RD29A and RD29B (Yamaguchi et al., 2006; Fujii et al., 2007) showed a similar response (Fig. 3d) to that shown for ABA catabolic genes (Fig. 3c). The apparent requirement for NO to elicit the H2O2-responsive expression of ABA catabolic genes and breaking of seed dormancy suggests that H2O2 may also regulate NO production. As shown in Fig. 4a, during imbibition, exogenous H2O2 increased NO production while DPI decreased it. However, SNP and its scavenger c-PTIO showed a slight effect on H2O2 production (Fig. 4b). Fig. 4. Open in new tabDownload slide Effect of H2O2 and NO on each other's production and the transcription of CAT and NADPH genes during imbibition. (a) Changes in NO release during imbibition with water, H2O2 (10 mM), and its production inhibitor DPI (10 μM). (b) Changes in H2O2 release during imbibition with water, SNP (200 μM), and its scavenger c-PTIO (200 μM). (c) The transcript levels of CAT H2O2 catabolism genes during imbibition. (d). The transcript levels of H2O2 NADPH production genes during imbibition. Values are means±SE (n=4 for a and b, and n=3 for c and d). An ANOVA test followed by a rank test was performed. Different letters are used to indicate means that are significantly different (P <0.05). Some genes involved in H2O2 production and catabolism were also measured during imbibition. Transcription of all three H2O2 catabolic genes, CAT1, CAT2, and CAT3, decreased rapidly and significantly during the first 3 h of imbibition (Fig. 4c). Transcription of the three NADPH oxidase genes, which are involved in H2O2 production, showed no change in the first 6 h of imbibition (Fig. 4d). Accumulation of H2O2 at the first stage of imbibition was apparently related primarily to decreasing H2O2 catabolism. The expression of GA biosynthesis genes is enhanced by H2O2 Treatment with PAC, which inhibited GA biosynthesis (Kitahata et al., 2005; Toh et al., 2008), enhanced seed dormancy, and the enhancement of germination by H2O2 and NO could be reversed by PAC (Fig. 5a). When expression of GA biosynthesis genes was investigated after 24 h imbibition, the transcription of the GA20ox and GA3ox genes was significantly up-regulated by exogenous H2O2, slightly up-regulated by SNP, and down-regulated by DPI and c-PTIO (Fig. 5b, c). The down-regulation by c-PTIO was reversed significantly by addition of exogenous H2O2, but this enhanced value was still low compared with seeds imbibed in H2O2 (Fig. 5b, c). The inhibition by DPI was increased slightly by addition of SNP (Fig. 5b, c). Fig. 5. Open in new tabDownload slide The function of NO in H2O2-regulated GA biosynthesis. (a) Effects of H2O2, NO, and their production inhibitor DPI or scavenger c-PTIO, or the GA biosynthesis inhibitor PAC on the dormancy and germination of freshly harvested seed; the germination ratio was counted on the seventh day. (b) Changes in the transcript levels of GA20ox GA biosynthesis genes under different treatments at 24 h imbibition. (c) Changes in the transcript levels of GA3ox GA biosynthesis genes under different treatments at 24 h imbibition. (d) Changes in the transcript levels of GA-regulated genes XTH5 and EXP2 under different treatments at 24 h imbibition. H2O2 (10 mM), DPI (10 μM), SNP (200 μM), c-PTIO (200 μM), and PAC (10 μM) were used for these experiments. Values are the means±SE (n=3 for a–d). An ANOVA test followed by a rank test was performed. Different letters are used to indicate means that are significantly different (P <0.05). Expression of two GA-regulated genes, XTH5 and EXP2, was also measured after 24 h imbibition under different treatments (Rose et al., 2002; Yamauchi et al., 2004). Both XTH3 and EXP2 transcription levels were increased significantly by H2O2 and slightly by SNP, but were decreased by DPI and c-PTIO. Exogenous H2O2 reversed the effect of c-PTIO while SNP caused only a minimal reversal of the DPI effect (Fig. 5d). H2O2 enhances and ABA suppresses the expression of GA biosynthesis genes Treatment with H2O2 enhanced expression of GA biosynthesis genes during imbibition. It was therefore hypothesized that GA biosynthesis is up-regulated by H2O2 and suppressed by ABA. To examine this possibility, the ABA catabolism mutant cyp707a2 and its overexpression line CYP707A2-OE were used. As shown in Fig. 6a, the cyp707a2 mutant showed an absence of CYP707A2 gene expression while CYP707A2-OE had a high level of CYP707A2 gene expression. Freshly harvested cyp707a2 seeds showed strong dormancy, while CYP707A2-OE seeds had a much weaker dormancy response compared with wild-type seeds (Fig. 6b). After 24 h imbibition, cyp707a2 seeds retained a high ABA content whereas ABA levels in CYP707A2-OE seeds were much lower (Fig. 6c) Fig. 6. Open in new tabDownload slide The CYP707A2 gene mediates seed dormancy and ABA catabolism. (a) Transcript levels of CYP707A2 in the wild type, cyp707a2, the CYP707A2 overexpression line, and pGB5 vector only in the wild type were analysed by RT-PCR. (b) The germination of the wild type, cyp707a2, and the CYP707A2 overexpression line in freshly harvested seeds. The germination rates were recorded after 7 d of imbibition. (c) ABA contents of the wild type, cyp707a2, and the CYP707A2 overexpression line after 24 h imbibition. Values are the means±SE (n=3 for b and c). An ANOVA test followed by a rank test was performed for b and c. Different letters are used to indicate means that are significantly different (P <0.05) H2O2 and GA (10 μM) clearly enhanced germination of freshly harvested cyp707a2 seed, while NO did not (Fig. 7a). CYP707A2-OE germination was inhibited completely by treatment with 0.5 μM exogenous ABA (Fig. 7a). QRT-PCR analysis demonstrated that the transcription of GA3ox genes was enhanced by exogenous H2O2 rather than by SNP after 24 h imbibition in cyp707a2 (Fig. 7b). These results indicated that it was H2O2 rather than NO that exerts a regulatory effect on the expression of GA biosynthetic genes. QRT-PCR analysis also indicated that the transcription of GA3ox genes was high in CYP707A2-OE and was significantly decreased by treatment with 0.5 μM ABA (Fig. 7b). Expression of GA3ox genes was much lower in cyp707a2 compared with the wild type and CYP707A2-OE (Fig. 7b, c). Thus, ABA treatment appeared to suppress expression of GA biosynthesis genes. Fig. 7. Open in new tabDownload slide H2O2 mediated GA biosynthesis and ABA suppressed GA biosynthesis. (a) The germination of cyp707a2 and the CYP707A2 overexpression line in freshly harvested seeds under different treatments. (b) Change in the transcript levels of GA3ox GA biosynthesis genes under different treatments in cyp707a2 and the CYP707A2 overexpression line after 24 h imbibition. (c) Change in the transcript levels of GA-regulated genes XTH5 and EXP2 under different treatments in cyp707a2 and the CYP707A2 overexpression line after 24 h imbibition. H2O2 (10 mM), SNP (200 μM), ABA (0.5 μM), and GA (10 μM) were used for these experiments. Values are the means±SE (n=3 for a–d). An ANOVA test followed by a rank test was performed. Different letters are used to indicate means that are significantly different (P <0.05) The empty pGWB5 vector showed no effect on expression of any gene (data not shown). The transcription levels of the GA-regulated genes XTH5 and EXP2 were similar to those of GA3ox genes. In the cyp707a2 mutant, expression of these genes was enhanced by treatment with exogenous H2O2 but not by SNP after 24 h imbibition (Fig. 7c). Transcription of XTH5 and EXP2 was down-regulated by ABA in CYP707A2-OE after 24 h imbibition (Fig. 7c), suggesting that XTH5 and EXP2 transcription was much lower in cyp707a2 compared with the wild type and CYP707A2-OE (Figs 5d, 7c). All of these results suggest that H2O2 enhances GA biosynthesis while ABA suppresses GA biosynthesis. Discussion It is well known that ABA and GA play important roles in seed dormancy and germination (Finkelstein et al., 2002; Himmelbach et al., 2003; Ogawa et al., 2003; Razem et al., 2006; Weiss et al., 2007; Toh et al., 2008). Studies on numerous mutants have demonstrated that ABA catabolism and GA biosynthesis are required for seed germination (Koornneef and van der Veen, 1980; Groot and Karssen, 1987; Ogawa et al., 2003; Kushiro et al., 2004; Saito et al., 2004; Okamoto et al., 2006). The results from the present study may have clarified a signalling pathway for the mechanisms underlying these responses. A rapid NO-induced decrease in ABA is essential to break seed dormancy in Arabidopsis. Based on the present results, H2O2, acting as a signalling molecule, could regulate seed dormancy by triggering both ABA catabolism and GA biosynthesis. The up-regulation of ABA catabolism by H2O2 would be carried out through NO. Concomitantly, as long as a high concentration of ABA exists, it inhibits the expression of GA biosynthesis genes so that a balance of these two hormones jointly controls the dormancy and germination of Arabidopsis seeds (Fig. 8). Fig. 8. Open in new tabDownload slide The relationship of ABA and GA in seed germination. The germination of non-dormant seeds under different treatments. An ANOVA test followed by a rank test was performed. Different letters are used to indicate means that are significantly different (P <0.05). As shown in Fig. 1, exogenous H2O2 decreased dormancy in freshly harvested seed. Inhibiting H2O2 production in turn enhanced seed dormancy. The action of H2O2 was associated with expression of genes related to ABA and GA biosynthesis and catabolism. Exogenous H2O2 treatment clearly increased the expression of CYP707A, GA3ox, and GA20ox (Fig. 2b, c) and decreased seed dormancy (Fig. 1b). Using DPI to inhibit H2O2 decreased the expression of CYP707A, GA3ox and GA20ox genes (Fig. 2b, c) and enhanced seed dormancy (Fig. 1b). It was also observed that the inhibition by DPI of the expression of these genes and of seed germination was completely reversed by exogenous H2O2. When ABA catabolism and GA biosynthesis were directly inhibited by diniconazole and PAC, the enhancement of seed germination by H2O2 disappeared (Figs 2, 4). H2O2 showed a slight effect on expression of genes involved in ABA biosynthesis and GA catabolism compared with its effects on expression of genes involved in ABA catabolism and GA biosynthesis (Fig. 2 and Supplementary Fig. S1). These results indicated that the effect of H2O2 on seed dormancy break might be connected with transcription of genes involved in both ABA catabolism and GA biosynthesis. A role for NO, another widespread signalling molecule, in breaking of dormancy was also apparent. Up-regulation of genes responsible for ABA catabolism by H2O2 required the participation of NO. The enhancement of seed germination by H2O2 treatment was significantly reversed by the NO scavenger c-PTIO. DPI inhibited H2O2 generation and enhanced seed dormancy, but these effects were substantially reversed by addition of the NO donor SNP (Fig. 3a). Use of c-PTIO as an NO scavenger reversed the effect of H2O2 on the expression of CYP707A genes and of ABA catabolic genes (Fig. 3b–d). Treatment with H2O2 also modulated NO release during imbibition (Fig. 4a, b), and the accumulation of H2O2 at the first stage of imbibition, primarily by decreasing H2O2 catabolism (Fig. 4c, d). The regulation by H2O2 of expression of GA biosynthetic genes is different from its effects on genes involved in ABA catabolism. Although SNP, similarly to H2O2, increased seed germination (Fig. 5a), SNP did not significantly increase transcription of GA biosynthetic genes when compared with H2O2 (Fig. 5b,c). SNP could not reverse the inhibition by DPI of transcription of GA biosynthesis genes, while exogenous H2O2 substantially restored the inhibition induced by c-PTIO (Fig. 5b, c). In particular, when the cyp707a2 mutant was used to measure the effect of H2O2 and NO on GA biosynthesis, it was found that SNP had no effect on transcription of GA biosynthesis genes, while H2O2 increased this transcription (Fig. 7b, c), It was also found that the expression of GA3ox genes was much lower in cyp707a2 than in the wild type imbibed with water or exogenous H2O2 (Figs 5c, 7b). The enhancement of seed germination induced by exogenous H2O2 and GA was also much lower in cyp707a2 compared with the effects on the wild type (Figs 3a, 7a). As shown in Fig. 6c, cyp707a2 had higher ABA levels compared with the wild type and CYP707A2-OE. The present results also support the conclusion that ABA down-regulated genes responsible for GA biosynthesis and ABA catabolism, both of which are functions that are necessary for breaking seed dormancy. The transcription of GA biosynthesis genes was suppressed by exogenous ABA (Fig. 7b). From these results, it can be hypothesized that H2O2 could directly regulate GA biosynthesis and indirectly regulate ABA catabolism. The effect of SNP on GA biosynthesis may occur via regulation of ABA catabolism during imbibition. The results also suggest that GA reverses the inhibitory effect of a low concentration of ABA on seed germination but GA alone is insufficient to reverse germination inhibition when the ABA content is high (Fig. 8). Thus, ABA may regulate seed germination by two pathways, one that acts directly on some as yet uncharacterized factors and the second that acts through the regulation of GA biosynthesis. The data presented in Figs 3 and 5 indicate that even at low concentrations of ABA and under H2O2 and SNP treatments, the seeds failed to germinate if GA biosynthesis was inhibited by diniconozole or PAC. This indicates that both ABA catabolism and GA biosynthesis are absolutely necessary for seed dormancy break. Figure 9 shows a hypothetical schematic model that could explain the results documented in the present paper. In this scheme, H2O2 may relieve dormancy of freshly harvested Arabidopsis seeds by two pathways. One pathway relies on enhancement of ABA catabolism and GA biosynthesis. The signal molecule NO does not regulate GA biosynthesis directly, but acts as an interim signalling molecule involved in H2O2 regulation of ABA catabolism. In the second pathway, ABA negatively regulates GA biosynthesis. In this way, both ABA and GA act in concert to regulate seed dormancy and germination. Fig. 9. Open in new tabDownload slide Model showing how H2O2, NO, ABA, and GA regulate seed dormancy and germination. Seed imbibition leads to increases in H2O2 and NO. H2O2 up-regulates ABA catabolism through NO, and also GA biosynthesis. A high concentration of ABA also inhibits GA biosynthesis, but a balance of these two hormones jointly controls the dormancy and germination of Arabidopsis seeds. 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