TY - JOUR
AU - Bauerle, Taryn, L
AB - Abstract Plants require the capacity for quick and precise recognition of external stimuli within their environment for survival. Upon exposure to biotic (herbivores and pathogens) or abiotic stressors (environmental conditions), plants can activate hydraulic, chemical, or electrical long-distance signals to initiate systemic stress responses. A plant’s stress reactions can be highly precise and orchestrated in response to different stressors or stress combinations. To date, an array of information is available on plant responses to single stressors. However, information on simultaneously occurring stresses that represent either multiple, within, or across abiotic and biotic stress types is nascent. Likewise, the crosstalk between hydraulic, chemical, and electrical signaling pathways and the importance of each individual signaling type requires further investigation in order to be fully understood. The overlapping presence and speed of the signals upon plant exposure to various stressors makes it challenging to identify the signal initiating plant systemic stress/defense responses. Furthermore, it is thought that systemic plant responses are not transmitted by a single pathway, but rather by a combination of signals enabling the transmission of information on the prevailing stressor(s) and its intensity. In this review, we summarize the mode of action of hydraulic, chemical, and electrical long-distance signals, discuss their importance in information transmission to biotic and abiotic stressors, and suggest future research directions. Abiotic stress, biotic stress, chemical signal, defense response, electrical signal, hydraulic signal, long-distance signaling, stress combinations Introduction The immobility of plants distinguishes their lifestyle from that of animals, requiring their recognition of external stimuli within their environment necessary for survival. Plants regularly encounter a wide range of abiotic and biotic stresses, whose individual elicited plant responses are abundant in the scientific literature (Mohr and Cahill, 2003; Wang et al., 2003; Choi et al., 2014; Torres-Ruiz et al., 2015). Less represented, although perhaps more realistic in natural settings, are simultaneous stresses that represent either multiple, within, or across abiotic and biotic stress types. Such stress combinations can result in either specific or integrated signaling cascades that warrant further empirical attention in an attempt to gain a more realistic representation of plant response(s) to their environment. Typical environmental stressors are commonly classified as abiotic or biotic. Abiotic stresses are caused by physical conditions such as salt, water, light, heat, and cold stress. Abiotic stress alone can reduce the yield of major crop plants by >50% (Bray et al., 2000). A plant’s ability to react to these stressors and survive in light of changing environmental conditions depends on effective defense mechanism(s) and signaling pathways leading to an increased tolerance to their surrounding (Zebelo and Maffei, 2015). Biotic stressors can be either herbivorous or pathogenic in nature (Maffei and Bossi, 2006), with both herbivore and plant–pathogen interactions often highly specific and dependent on both the plant species and the stressor type (Bonaventure et al., 2011; Bricchi et al., 2012). Defense mechanisms can be extremely costly for the plant. Therefore, plants have developed a defense response system which can be quickly activated in response to stressors and can affect the entire plant body. This so-called systemic acquired resistance (SAR) is accomplished either by the transport of defense metabolites or through the production of new defense components (Heil and Ton, 2008; Mittler and Blumwald, 2015). SAR is acquired by a modification of gene transcription patterns leading to an overall increase in plant fitness to a broader spectrum of biota as well as environmental conditions (Ryals et al., 1996; Gilroy et al., 2014). To date, there is a multitude of information available on stress/defense responses of plants when exposed to an individual stressor. However, in the field, plants are rarely exposed to single stressors, but instead often face a combination of stressors which may result in unique plant responses (Hewezi et al., 2008). The importance of deciphering the complexity of stress responses to combined stressors is exemplified in the context of current climate change predictions. Warmer temperatures and changes in precipitation events produce not only assorted stress intensities but also an array of stress combinations (Jia and Davies, 2007), including enhanced herbivore pressure due to faster growth rates (Bale et al., 2002), and increased plant water stress levels resulting from an increased evaporative demand (Barber et al., 2000) caused by increased temperature, to name but a few. It is not surprising that the impact of both abiotic and biotic stresses on a plant’s life can be severe, and often result in either decreased plant productivity or weakened plant defense mechanisms (Ewers and Fisher, 1989; Wang et al., 2001). Regardless of whether a stressor is solitary or in combination with other stressors, plants have developed several long-distance signaling pathways enabling the plant to react quickly and cope appropriately with imposed stress(s). To date, we recognize three distinct long-distance signaling types: (i) hydraulic; (ii) chemical; and (iii) electrical, that differ not only in their chemical nature but also in their propagation speeds (Figs 1, 2). To respond adequately to stressors, plants may employ different signaling pathways and use ‘master regulators’ such as transcription factors or turgor/osmosensors to interconnect these pathways (Wang et al., 2003; Christmann et al., 2013). However, the complementation and fine tuning of these signals, both their specificity and generality, especially in response to combined stressors, require critical further examination. Fig. 1. Open in new tabDownload slide Overview of electrical, hydraulic, and chemical long-distance signals in plants. Boxes represent different subcategories of signaling types. All three signaling pathways are most probably interwoven (black arrows). (This figure is available in colour at JXB online.) Fig. 1. Open in new tabDownload slide Overview of electrical, hydraulic, and chemical long-distance signals in plants. Boxes represent different subcategories of signaling types. All three signaling pathways are most probably interwoven (black arrows). (This figure is available in colour at JXB online.) Fig. 2. Open in new tabDownload slide Signal speed ranges plus standard errors for chemical, hydraulic, and electrical signals [action potentials (APs), slow wave potentials (SWPs)] in plants (in cm s−1). Plus symbols represent outliers of the box plot. Data points >4000cm s−1 were not included in the boxplot statistics, but are represented on the graph by circles. Note the break in the x-axis in order to accommodate the range of data points. Fig. 2. Open in new tabDownload slide Signal speed ranges plus standard errors for chemical, hydraulic, and electrical signals [action potentials (APs), slow wave potentials (SWPs)] in plants (in cm s−1). Plus symbols represent outliers of the box plot. Data points >4000cm s−1 were not included in the boxplot statistics, but are represented on the graph by circles. Note the break in the x-axis in order to accommodate the range of data points. In the following paragraphs we focus on hydraulic, chemical, and electrical signaling pathways, and their interaction and integration. We discuss overlapping signaling cascades and physiological factors influencing plant stress responses, highlight the importance of stress intensity and accumulating stressors on signaling within plants, and elaborate on the question of how plants orchestrate long-distance signals as a means to react to biotic and abiotic stressors accordingly. Signal types Hydraulic signals Water is the connecting medium between plant organs and is responsible for nutrient exchange and maintenance of metabolic processes, making water an excellent medium for fast information exchange. Water in plants is transported under tension along the soil–plant–air continuum (Zimmermann, 1983) due to an increasing water potential difference, largely determined by the soil water availability and the vapor pressure deficit (Comstock, 2002). In most climates, the driest (most negative) component in the soil–plant–air continuum is the atmosphere and the least negative the soil, causing the water to be pulled through the plant to the leaves (Steudle, 2001). In light of hydraulic signal transmission, this same pathway is utilized and integrated with adjacent living cells. Hydraulic signals orchestrate the physiological behavior of plants on a daily basis, through the regulation of cell expansion rates (Westgate and Boyer, 1984; Tang and Boyer, 2002, 2003) which are mainly controlled by the cell’s turgor pressure (Taiz, 1984) and fluctuate with a decrease in soil water status, an increase in evaporation demand (Bouchabké et al., 2006), or through herbivore feeding (Alarcon and Malone, 1994). These pressure changes originate in the xylem vessel conduits and, because of low axial resistance, can be propagated rapidly into surrounding cells (Bramley et al., 2007) and, potentially, throughout the whole plant. However, pressure changes cannot be perceived by dead cells, such as xylem conduits, and therefore must be decoded by adjacent parenchyma cells. Moreover, parenchyma cells can perceive and undergo pressure changes (turgor changes) themselves via elastic and resistant cell walls (Tyree and Yang, 1990). Due to the high elasticity modulus, higher volumes of water must be moved in and/or out the parenchyma cells in order to detect a pressure change in the system (Malone, 1993). Additionally, it is likely that parenchyma cells are also able to translate hydraulic signals into a physiological signal with the help of mechano-sensitive channels in the plasma membrane (for reviews, see Árnadóttir and Chalfie, 2010; Christmann et al., 2013) Depending on the imposed stressor, pressure changes can be either positive, through an expansion of the xylem fluid, or negative, due to a tension increase in the xylem sap column as a result of a decrease in the water potential gradient within the plant. It is important to mention that, in most cases, the change in cell turgor is just a secondary by-product of initial changes in the tension within the xylem conduit, and occurs after the xylem sap tension is altered (Lopez et al., 2014), resulting in water movement into or out of cells seeking water potential equilibrium (Tyree and Yang, 1990). Xylem tension can also be positively altered (decreased xylem tension or even pressure pulses) either by expanding the water in the vascular tissue, by heating or burning the leaves (Stahlberg and Cosgrove, 1997a), or by relocation of water through mechanical bending by wind or artificial means (Lopez et al., 2014). Drought can also influence cell turgor pressure positively through the displacement of water in the surrounding tissues or even by preceding pressure pulses within the xylem (pressure travels faster than fluid) provoked by cavitation events or conduit collapse (Bramley et al., 2007; Vandeleur et al., 2014). However, this mechanism is not well explored, and more hypotheses than data currently exist. A strong correlation has been recorded between cavitation events and the initiation of stomatal closure during drought periods (Cochard et al., 1996; Salleo et al., 2001), but it is unclear if the increasing pressure pulses or the displaced water initiate stomatal closure during progressive increases in xylem tension. Hydraulic pulses might also help to explain why a decrease in stomatal conductance during drought can occur in some plants irrespective of leaf turgor (Gollan et al., 1986) or Ψleaf (Gowing et al., 1990; Davies and Zhang, 1991; Yao et al., 2001). Negative alteration of the xylem tension (increased tension) can be caused by abiotic stressors such as salt and drought stress (Neumann et al., 1988; Bréda et al., 2006) that initiate similar signaling pathways (Zhu, 2001). During osmotic or drought stress, the initiation of the hydraulic signal is in the root, the organ first exposed to the water shortage, transmitted through the stem to the leaves where parenchyma cells perceive the signal (Endo et al., 2008; Christmann et al., 2013) and trigger the production of the phytohormone abscisic acid (ABA) to initiate stomatal closure (Bauer et al., 2013). The importance of ABA as the short-distance signal initiating stomatal closure and not the hydraulic signal was shown by Christmann et al. (2007). The authors suppressed the hydraulic signal in Arabidopsis (Arabidopsis thaliana) plants by maintaining the leaf turgor pressure during root exposure to drought stress. However, as soon as ABA was exogenously added to the leaves, the stomata closed. Interestingly, both the increase in xylem tension potential and the decrease in xylem tension through water displacement during drought stress conditions result in a reduction in net photosynthesis, transpiration, and stomatal conductance (Kim et al., 2004), indicating the complexity of signal transduction pathways and emphasizing the need for further research on deciphering the mechanism plants use to decode hydraulic signals (Christmann et al., 2013). Chemical signals Chemical signals are the most frequently discussed signals in plants, underlining their importance in plant stress response(s) (see Table 1). However, despite their indispensability in plant stress/defense response initiation, it is still questionable whether chemicals are exclusively a short-distance stress signal. Evidence against their long-distance transport ability is provided in several experiments showing that plant hormones crucial for plant stress/defense responses are incapable of traveling over longer distances and have propagation speeds that are slow in comparison with hydraulic and/or electrical signals (see Table 2). For example, ABA is highly correlated with stomatal closure during drought stress (Zhang and Davies, 1989; Loewenstein and Pallardy, 1998; Davies et al., 2002). However, given the xylem’s sluggish transport rate of 0.056cm s−1 (Zimmermann and Brown, 1971), it is unlikely that root-sourced ABA accounts for the rapid initiation of stomatal closure during drought events, strongly suggesting the existence of alternative and/or an additional long-distance signaling pathway (Zhang and Davies, 1991; Saliendra et al., 1995; Whitehead et al., 1996; Whitehead, 1998). These inferences are reinforced by experiments that failed to detect root-sourced ABA as a mechanism for stomatal closure in grafted tomato (Holbrook et al., 2002) and ABA-deficient Arabidopsis plants (Christmann et al., 2007). Reciprocal grafting could provide a means to help evaluate the significance of root hormone synthesis for increasing plant performance in terms of growth efficiency and plant resistance via long-distance communication between the rootstock and the scion (Albacete et al., 2015). Table 1. Molecules involved in chemical signaling pathways and the respective references Chemical signal type . References (reviews) . Secondary messengers Calcium fluxes Pandey et al. (2000); Sathyanarayanan and Poovaiah (2004); Medvedev (2005) Potassium fluxes Zebelo and Maffei (2015) Anion fluxes Garcia-Brugger et al. (2006) Inositol triphosphate Xiong and Zhu (2003); Tuteja and Sopory (2008) Reactive oxygen compounds Fujita et al. (2006); Laloi et al. (2007); Maffei et al. (2007a) Signaling cascade MAP kinase Nakagami et al. (2005); Fujita et al. (2006) Chemical response Volatile compounds Holopainen (2004); Baldwin (2006); Maffei et al. (2007a); Dicke and Baldwin (2010); Zebelo and Maffei (2015) Plant hormones Reymond and Farmer (1998); Kunkel and Brooks (2002); Fujita et al. (2006) Chemical signal type . References (reviews) . Secondary messengers Calcium fluxes Pandey et al. (2000); Sathyanarayanan and Poovaiah (2004); Medvedev (2005) Potassium fluxes Zebelo and Maffei (2015) Anion fluxes Garcia-Brugger et al. (2006) Inositol triphosphate Xiong and Zhu (2003); Tuteja and Sopory (2008) Reactive oxygen compounds Fujita et al. (2006); Laloi et al. (2007); Maffei et al. (2007a) Signaling cascade MAP kinase Nakagami et al. (2005); Fujita et al. (2006) Chemical response Volatile compounds Holopainen (2004); Baldwin (2006); Maffei et al. (2007a); Dicke and Baldwin (2010); Zebelo and Maffei (2015) Plant hormones Reymond and Farmer (1998); Kunkel and Brooks (2002); Fujita et al. (2006) Open in new tab Table 1. Molecules involved in chemical signaling pathways and the respective references Chemical signal type . References (reviews) . Secondary messengers Calcium fluxes Pandey et al. (2000); Sathyanarayanan and Poovaiah (2004); Medvedev (2005) Potassium fluxes Zebelo and Maffei (2015) Anion fluxes Garcia-Brugger et al. (2006) Inositol triphosphate Xiong and Zhu (2003); Tuteja and Sopory (2008) Reactive oxygen compounds Fujita et al. (2006); Laloi et al. (2007); Maffei et al. (2007a) Signaling cascade MAP kinase Nakagami et al. (2005); Fujita et al. (2006) Chemical response Volatile compounds Holopainen (2004); Baldwin (2006); Maffei et al. (2007a); Dicke and Baldwin (2010); Zebelo and Maffei (2015) Plant hormones Reymond and Farmer (1998); Kunkel and Brooks (2002); Fujita et al. (2006) Chemical signal type . References (reviews) . Secondary messengers Calcium fluxes Pandey et al. (2000); Sathyanarayanan and Poovaiah (2004); Medvedev (2005) Potassium fluxes Zebelo and Maffei (2015) Anion fluxes Garcia-Brugger et al. (2006) Inositol triphosphate Xiong and Zhu (2003); Tuteja and Sopory (2008) Reactive oxygen compounds Fujita et al. (2006); Laloi et al. (2007); Maffei et al. (2007a) Signaling cascade MAP kinase Nakagami et al. (2005); Fujita et al. (2006) Chemical response Volatile compounds Holopainen (2004); Baldwin (2006); Maffei et al. (2007a); Dicke and Baldwin (2010); Zebelo and Maffei (2015) Plant hormones Reymond and Farmer (1998); Kunkel and Brooks (2002); Fujita et al. (2006) Open in new tab Table 2. Propagation speed and mode of action of hydraulic, chemical, and electrical signals Signal type . Speed . Mode of action . Plant . Reference . Hydraulic <0.03cm s −1 Changes in cell turgor (cell pressure probe) upon water stress treatment Arabidopsis thaliana Christmann et al. (2007) 10cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Prunus avium, Tilia cordata, Solanum lycopersicum, Zea mays (10 additional species) Boari and Malone (1993) 150 000cm s−1 Theoretical pressure wave propagation Any plant Malone (1993) Almost simultaneously with mechanical bending Pressure sensors in apoplast correlated with bending Arpinus betulus L., Ilex aquifolium L., Pinus sylvestris L., Cupressus sempervirens L., Taxus baccata L. Lopez et al. (2014) Transportation speeds of sap 0.7cm s−1 Phloem transportation rate Triticum aestivum L. Fisher (1990) 25cm s−1 Xylem flow rate Triticum aestivum cv. Gabo Passioura (1972) Chemical ~0.04cm s−1 Calcium wave upon salt stress Arabidopsis thaliana Choi et al. (2014) 0.7cm s−1 Solute transport Zea mays Boari and Malone (1993) 3–5min (signal speed 0.05cm s−1) Increase in JA after wounding Arabidopsis thaliana Glauser et al. (2008) a 0.14cm s −1 Reactive oxygen species in apoplast upon wounding, heat, cold, high-intensity light, and salinity stresses Arabidopsis thaliana Miller et al. (2009) Electrical 0.08–0.2cm s−1 SP initiated by wounding Vicia faba, Hordeum vulgare Zimmermann et al. (2009) 0.08–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Cucumis sativus L. cv. Burpee Pickler Stahlberg and Cosgrove (1997a) 0.9cm s−1 AP upon ice shock Vitis discolor Houwink (1935) 0.1–0.2cm s−1 SWP caused by inflammation Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.3–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Pisum sativum L. Stahlberg and Cosgrove (1997b) 0.4cm s−1 Electrically induced AP Drosera rotundifolia Pickard (1973) 0.4–0.8cm s−1 AP caused by cooling Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.5–0.6cm s−1 SWP upon cutting Mimosa pudica Fromm and Lautner (2007) 1–2cm s−1 SWP upon cutting Most plants Fromm (2006) 2cm s−1 AP upon ice shock Mimosa pudica Houwink (1935) 2–3cm s−1 AP upon ice shock or touching Mimosa pudica Fromm and Lautner (2007) At least 10cm s− 1 SWP upon inflaming Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 AP during leaf closing Dionaea muscipula EllisEllis Sanderson (1888) Up to 4 000cm s−1 AP upon the uncoupler carbonyl cyanide-p-trifluoromethoxyphenyl hydrazone (FCCP) Glycine max (L.) Merrill Volkov (2000) Signal type . Speed . Mode of action . Plant . Reference . Hydraulic <0.03cm s −1 Changes in cell turgor (cell pressure probe) upon water stress treatment Arabidopsis thaliana Christmann et al. (2007) 10cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Prunus avium, Tilia cordata, Solanum lycopersicum, Zea mays (10 additional species) Boari and Malone (1993) 150 000cm s−1 Theoretical pressure wave propagation Any plant Malone (1993) Almost simultaneously with mechanical bending Pressure sensors in apoplast correlated with bending Arpinus betulus L., Ilex aquifolium L., Pinus sylvestris L., Cupressus sempervirens L., Taxus baccata L. Lopez et al. (2014) Transportation speeds of sap 0.7cm s−1 Phloem transportation rate Triticum aestivum L. Fisher (1990) 25cm s−1 Xylem flow rate Triticum aestivum cv. Gabo Passioura (1972) Chemical ~0.04cm s−1 Calcium wave upon salt stress Arabidopsis thaliana Choi et al. (2014) 0.7cm s−1 Solute transport Zea mays Boari and Malone (1993) 3–5min (signal speed 0.05cm s−1) Increase in JA after wounding Arabidopsis thaliana Glauser et al. (2008) a 0.14cm s −1 Reactive oxygen species in apoplast upon wounding, heat, cold, high-intensity light, and salinity stresses Arabidopsis thaliana Miller et al. (2009) Electrical 0.08–0.2cm s−1 SP initiated by wounding Vicia faba, Hordeum vulgare Zimmermann et al. (2009) 0.08–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Cucumis sativus L. cv. Burpee Pickler Stahlberg and Cosgrove (1997a) 0.9cm s−1 AP upon ice shock Vitis discolor Houwink (1935) 0.1–0.2cm s−1 SWP caused by inflammation Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.3–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Pisum sativum L. Stahlberg and Cosgrove (1997b) 0.4cm s−1 Electrically induced AP Drosera rotundifolia Pickard (1973) 0.4–0.8cm s−1 AP caused by cooling Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.5–0.6cm s−1 SWP upon cutting Mimosa pudica Fromm and Lautner (2007) 1–2cm s−1 SWP upon cutting Most plants Fromm (2006) 2cm s−1 AP upon ice shock Mimosa pudica Houwink (1935) 2–3cm s−1 AP upon ice shock or touching Mimosa pudica Fromm and Lautner (2007) At least 10cm s− 1 SWP upon inflaming Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 AP during leaf closing Dionaea muscipula EllisEllis Sanderson (1888) Up to 4 000cm s−1 AP upon the uncoupler carbonyl cyanide-p-trifluoromethoxyphenyl hydrazone (FCCP) Glycine max (L.) Merrill Volkov (2000) aInconclusive results. The authors could not determine the signal pathway leading to the increase in JA. Open in new tab Table 2. Propagation speed and mode of action of hydraulic, chemical, and electrical signals Signal type . Speed . Mode of action . Plant . Reference . Hydraulic <0.03cm s −1 Changes in cell turgor (cell pressure probe) upon water stress treatment Arabidopsis thaliana Christmann et al. (2007) 10cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Prunus avium, Tilia cordata, Solanum lycopersicum, Zea mays (10 additional species) Boari and Malone (1993) 150 000cm s−1 Theoretical pressure wave propagation Any plant Malone (1993) Almost simultaneously with mechanical bending Pressure sensors in apoplast correlated with bending Arpinus betulus L., Ilex aquifolium L., Pinus sylvestris L., Cupressus sempervirens L., Taxus baccata L. Lopez et al. (2014) Transportation speeds of sap 0.7cm s−1 Phloem transportation rate Triticum aestivum L. Fisher (1990) 25cm s−1 Xylem flow rate Triticum aestivum cv. Gabo Passioura (1972) Chemical ~0.04cm s−1 Calcium wave upon salt stress Arabidopsis thaliana Choi et al. (2014) 0.7cm s−1 Solute transport Zea mays Boari and Malone (1993) 3–5min (signal speed 0.05cm s−1) Increase in JA after wounding Arabidopsis thaliana Glauser et al. (2008) a 0.14cm s −1 Reactive oxygen species in apoplast upon wounding, heat, cold, high-intensity light, and salinity stresses Arabidopsis thaliana Miller et al. (2009) Electrical 0.08–0.2cm s−1 SP initiated by wounding Vicia faba, Hordeum vulgare Zimmermann et al. (2009) 0.08–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Cucumis sativus L. cv. Burpee Pickler Stahlberg and Cosgrove (1997a) 0.9cm s−1 AP upon ice shock Vitis discolor Houwink (1935) 0.1–0.2cm s−1 SWP caused by inflammation Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.3–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Pisum sativum L. Stahlberg and Cosgrove (1997b) 0.4cm s−1 Electrically induced AP Drosera rotundifolia Pickard (1973) 0.4–0.8cm s−1 AP caused by cooling Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.5–0.6cm s−1 SWP upon cutting Mimosa pudica Fromm and Lautner (2007) 1–2cm s−1 SWP upon cutting Most plants Fromm (2006) 2cm s−1 AP upon ice shock Mimosa pudica Houwink (1935) 2–3cm s−1 AP upon ice shock or touching Mimosa pudica Fromm and Lautner (2007) At least 10cm s− 1 SWP upon inflaming Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 AP during leaf closing Dionaea muscipula EllisEllis Sanderson (1888) Up to 4 000cm s−1 AP upon the uncoupler carbonyl cyanide-p-trifluoromethoxyphenyl hydrazone (FCCP) Glycine max (L.) Merrill Volkov (2000) Signal type . Speed . Mode of action . Plant . Reference . Hydraulic <0.03cm s −1 Changes in cell turgor (cell pressure probe) upon water stress treatment Arabidopsis thaliana Christmann et al. (2007) 10cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 Changes in leaf thickness (pressure transducer) upon wounding Prunus avium, Tilia cordata, Solanum lycopersicum, Zea mays (10 additional species) Boari and Malone (1993) 150 000cm s−1 Theoretical pressure wave propagation Any plant Malone (1993) Almost simultaneously with mechanical bending Pressure sensors in apoplast correlated with bending Arpinus betulus L., Ilex aquifolium L., Pinus sylvestris L., Cupressus sempervirens L., Taxus baccata L. Lopez et al. (2014) Transportation speeds of sap 0.7cm s−1 Phloem transportation rate Triticum aestivum L. Fisher (1990) 25cm s−1 Xylem flow rate Triticum aestivum cv. Gabo Passioura (1972) Chemical ~0.04cm s−1 Calcium wave upon salt stress Arabidopsis thaliana Choi et al. (2014) 0.7cm s−1 Solute transport Zea mays Boari and Malone (1993) 3–5min (signal speed 0.05cm s−1) Increase in JA after wounding Arabidopsis thaliana Glauser et al. (2008) a 0.14cm s −1 Reactive oxygen species in apoplast upon wounding, heat, cold, high-intensity light, and salinity stresses Arabidopsis thaliana Miller et al. (2009) Electrical 0.08–0.2cm s−1 SP initiated by wounding Vicia faba, Hordeum vulgare Zimmermann et al. (2009) 0.08–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Cucumis sativus L. cv. Burpee Pickler Stahlberg and Cosgrove (1997a) 0.9cm s−1 AP upon ice shock Vitis discolor Houwink (1935) 0.1–0.2cm s−1 SWP caused by inflammation Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.3–0.5cm s−1 SWP preceded by hydraulic signals (positive pressure steps) Pisum sativum L. Stahlberg and Cosgrove (1997b) 0.4cm s−1 Electrically induced AP Drosera rotundifolia Pickard (1973) 0.4–0.8cm s−1 AP caused by cooling Populus trichocarpa cv. Trichobel; Populus tremula×P. tremuloides Michx Lautner et al. (2005) 0.5–0.6cm s−1 SWP upon cutting Mimosa pudica Fromm and Lautner (2007) 1–2cm s−1 SWP upon cutting Most plants Fromm (2006) 2cm s−1 AP upon ice shock Mimosa pudica Houwink (1935) 2–3cm s−1 AP upon ice shock or touching Mimosa pudica Fromm and Lautner (2007) At least 10cm s− 1 SWP upon inflaming Triticum durum Desf. cv. Iva Malone (1992) 20cm s−1 AP during leaf closing Dionaea muscipula EllisEllis Sanderson (1888) Up to 4 000cm s−1 AP upon the uncoupler carbonyl cyanide-p-trifluoromethoxyphenyl hydrazone (FCCP) Glycine max (L.) Merrill Volkov (2000) aInconclusive results. The authors could not determine the signal pathway leading to the increase in JA. Open in new tab The uncertainty of chemical long-distance signals initiating rapid physiological plant responses is also confirmed for jasmonic acid (JA) and salicylic acid (SA) (Vernooij et al., 1994; Stratmann, 2003, respectively). JA is an important hormone in activating SAR, a non-specific resistance response upon biotic attack, and long thought to be the long-distance signal activating these responses. Likewise, SA has also been considered a long-distance wound response. However, in both cases, even though JA and SA are important for triggering wound responses, they are not long-distance communicators. It is important to stress that chemical signals can indeed act as long-distance signals in gradual metabolic responses, for example in the integration of the external and internal nitrogen status of plants in which nitrate or the phytohormon cytokinin act as communicators throughout the whole plant (Sakakibara et al., 2006). Additionally, major plant hormones such as ABA, JA, ethylene, SA, auxin, and cytokinins are all widely discussed in terms of plant stress resistance/susceptibility to biotic and abiotic stressors, including their interaction and overlapping pathway utilization (Bostock, 2005). Volatiles are also widely discussed in the literature as a systemic long-distance signal both between and within plants in response to herbivore or pathogen attacks. They can induce systemic defense responses in distant parts of plants which lack vascular connectivity (Frost et al., 2007) within hours (Howe and Jander, 2008) after insect attack, and are therefore a good mechanism for long-distance signaling. Their chemical diversity, mode of action, and their role in plant defense are nicely summarized in several reviews (Kessler and Baldwin, 2001, 2002; Dudareva et al., 2006; Howe and Jander, 2008; Zimmermann et al., 2009; Arimura et al., 2011; Wortemann et al., 2011; Mithöfer and Boland, 2012). Despite it being unlikely that chemical signals act in rapid long-distance signaling, reactive oxygen species (ROS) were recently identified as possible autopropagation chemical signals suited for traveling longer distances, by which the signal travels from cell to cell (Miller et al., 2009; Mittler et al., 2011). Their fast propagation speed (up to 0.14cm s−1), their ability to spread to the entire plant from the site of initiation (root, stem, or leaf) (Miller et al., 2009), and their potential interconnectivity with other signaling pathways (electrical, calcium waves, plant hormones, and hydraulic waves; Mittler and Blumwald, 2015) make them suitable biotic or abiotic stress communicators. ROS are mainly known as toxic by-products of aerobic metabolism which harm plant tissue. However, through evolution, plants acquired detoxifying/scavenging enzymes and several antioxidants to handle the toxic effect of ROS, before ROS developed as a signaling mechanism. The ROS signaling mechanism is mainly based on a balance between ROS production and ROS scavenging, which occur concurrently in plants, to maintain a balanced intercellular ROS concentration. Despite research demonstrating the suitability of ROS as signaling molecules, there are many unanswered questions regarding the signal specificity, transport, and sensing mechanisms (Mittler et al., 2011). Electrical signals Electrical signals were first recorded in Venus fly trap (Dionaea muscipula) (Haberland, 1890) and Mimosa (Mimosa pudica) (Applewhite, 1972), so-called ‘sensitive’ plants. For a long time, scientists found it hard to believe that electrical signals could also manifest in ‘silent’ plants; that is, plants with no outward movement in response to a stimulus. This misconception hindered the early establishment of electrical signals in the literature as a common long-distance signal (Stahlberg et al., 2006). Nowadays, electrical signals in plants are established as a rapidly propagated signal in response to both biotic and abiotic stimuli (Maffei and Bossi, 2006), and are defined as an ion imbalance across the plasma membrane leading to a voltage transient. The voltage transient’s shape is dependent on the stimulus type and the resulting ion/anion fluxes. In general, four different kinds of electrical signals are recognized in plants: action potentials (APs), slow wave potentials (SWPs), also called variation potentials (VPs), wound potentials (WPs), and system potentials (SPs) (Fig. 3). Fig. 3. Open in new tabDownload slide Illustrated representation of electrical signals in plants. Action potentials (APs), slow wave potentials (SWPs), system potentials (SPs), and wound potentials (WPs) are common electrical signals in plants. Expanded black boxes show the path of travel for each respective signal type: APs (red dots) are propagated in the phloem; SWPs (yellow dots) through functional xylem (blue xylem is water filled and white xylem is air filled); SPs (orange dots) are propagated in the apoplast upon wounding; and WPs (purple dots) are propagated through cell turgor changes initiating the depolarization of the plasma membrane. SWPs are preceded and closely linked to hydraulic signals in the xylem tissue caused by either cavitation events (white xylem elements) or changes in turgor (represented by swollen/plasmolysed cell). Red, yellow, orange, and purple circles represent the point of signal origin. Arrow length represents signal transmission ability. Short arrows indicates the inability of the signal to act as a long-distance signal. Arrow diameter represents signal strength intensity during signal propagation and transport. Original artwork by Alex Paya. Fig. 3. Open in new tabDownload slide Illustrated representation of electrical signals in plants. Action potentials (APs), slow wave potentials (SWPs), system potentials (SPs), and wound potentials (WPs) are common electrical signals in plants. Expanded black boxes show the path of travel for each respective signal type: APs (red dots) are propagated in the phloem; SWPs (yellow dots) through functional xylem (blue xylem is water filled and white xylem is air filled); SPs (orange dots) are propagated in the apoplast upon wounding; and WPs (purple dots) are propagated through cell turgor changes initiating the depolarization of the plasma membrane. SWPs are preceded and closely linked to hydraulic signals in the xylem tissue caused by either cavitation events (white xylem elements) or changes in turgor (represented by swollen/plasmolysed cell). Red, yellow, orange, and purple circles represent the point of signal origin. Arrow length represents signal transmission ability. Short arrows indicates the inability of the signal to act as a long-distance signal. Arrow diameter represents signal strength intensity during signal propagation and transport. Original artwork by Alex Paya. APs are propagated in the phloem, characterized by a rapid depolarization phase of the membrane potential followed by a rapid repolarization phase, and are elicited by non-invasive stimuli including electrical stimuli, acid rain, irradiation, and cold shock, (Stankovic et al., 1998; Shvetsova et al., 2002; Volkov et al., 2004; Trebacz et al., 2006; Fromm and Lautner, 2007). The ionic initiation of the depolarization phase is due to voltage-gated Ca+ channels which release calcium from internal [mitochondria, vacuole, and endoplasmic reticulum (ER)] and external (apoplast) storage into the cytoplasm (Reddy et al., 2011). In response to the calcium influx, Cl− channels open and Cl− ions diffuse down an electrochemical gradient out of the cell (Lunevsky et al., 1983) until voltage-gated K+ channels are activated and K+ efflux occurs (repolarization phase) (Trebacz et al., 2006). All stimuli triggering APs must meet a critical intensity in order to reach a pivotal threshold to trigger the AP. As soon as the threshold is reached (all-or-nothing rule), the signal is self-perpetuated through plasmodesmata of adjacent parenchyma cells or sieve pores in phloem cells (Trebacz et al., 2006). APs can be propagated with a constant amplitude and velocity. Average AP propagation speeds range from 1cm s−1 to 2cm s−1 (Fromm, 2006), but can also reach faster velocities up to 3000cm s−1, as found in soybean (Volkov et al., 2000) (Fig. 2). SWPs are characterized by an initial rapid depolarization phase and followed by a longer lasting and varying repolarization phase compared with APs. SWPs are mainly elicited by interfering abiotic and biotic stimuli such as mechanical wounding, tissue burning, or herbivore attack (Stankovic et al., 1998; Maffei and Bossi, 2006; Gallé et al., 2015). While the depolarization phase is caused by the same ionic mechanism as for APs, the slow repolarization phase is initiated by a transient shut down of H+ pumps causing the repolarization phase to last up to 30min (Stahlberg et al., 2006). SWPs differ from APs in that they are propagated in the xylem and therefore can be transmitted through dead tissue (Stahlberg and Cosgrove, 1996), they do not follow an all-or-nothing rule, and their propagation speed and amplitude attenuate 2.5% per cm (Stahlberg et al., 2005) the farther the signal travels from the stimulus location (Oyarce and Gurovich, 2011), resulting in a range of different SWP intensities (Maffei and Bossi, 2006). It is important to mention here that SWPs are not self-perpetuating because they are always preceded by a positive hydraulic pressure change and therefore their depolarization is hydraulically induced (Stahlberg and Cosgrove, 1996, 1997b). For example, positive pressure steps are followed by a SWP in pea epicotyls (Pisum staivum) and, depending on the applied pressure, can delay SWPs. However, if the hydraulic signal, in the form of positive pressure, reaches ≥80 kPa, then the electrical and hydraulic signals will occur simultaneously (Stahlberg and Cosgrove, 1997b). The preceding hydraulic signal is the primary cause of SWP attenuation; water can travel radially from the xylem vessel into epidermal cells, resulting in attenuation of the hydraulic signal and, consequently, also the intensity of the SWP (Westgate and Boyer, 1984). This interconnection between the hydraulic signal and SWPs was demonstrated in pea, which ceased to elicit a SWP response when their epicotyls were submerged under water (Mancuso, 1999). The range of SWP intensities allows substantially more information about the intensity of the injury compared with APs, enabling plants to decipher the distance of the wounding source from the small differences in pressure, shape, and intensity of the signal (Stahlberg et al., 2006). WPs are similar to SWPs in their ionic mechanisms inducing the depolarization and repolarization phases, resulting in a similar signal shape (Stahlberg et al., 2006). Clearly, their name is derived from the stressor (wounding) commonly initiating the potentials. Wounding, whether caused by biotic or abiotic stressors, is propagated through cell turgor changes initiating the depolarization of the plasma membrane (Shimmen, 2001). Therefore, WPs, like SWPs, are not self-perpetuating (Zebelo and Maffei, 2012) and always follow pressure changes. However, unlike SWPs, it is unclear whether WPs should be formally considered long-distance signals since to date they have only been recorded in the vicinity of injured cells (1.0–40.0mm from the injured or dead cell) (Stahlberg and Cosgrove, 1994). SPs are a recently suggested fourth electrical potential type (Zimmermann et al., 2009) that are propagated in the apoplast upon wounding. SPs can be distinguished from other electrical signals by their mode of action. While APs, SWPs, and WPs are caused by calcium streaming across the plasma membrane, SP signals are initiated by the activation of H+ pumps (K+, Cl−, and Ca2+ streaming are initiated through SPs). The advantage of the self-propagating SPs is that they do not follow an all-or-nothing rule. Therefore, SP intensity can be modulated in order to carry information about wounding severity (Zimmermann et al., 2009). Hydraulic, chemical, and electrical signal integration: understanding signal speed Appropriate physiological plant responses to stressors require the rapid relay of information between distant plant organs. While we have discussed three individual signal pathways above, it seems likely that signal pathway integration is necessary for a response to multiple concurrent stresses (Fig. 1). Nonetheless, decoding all three signal types to co-ordinate and activate plant defense mechanisms has yet to be fully understood. Reaching a consensus on which signal type relays primary information is inherently difficult, largely a result of the unresolved discrepancy around in situ long-distance signaling speeds and by the relatively few experiments that have measured all three major signaling pathways concurrently as a means to establish the influence of each pathway on the final response. Hydraulic and electrical signals are unequivocal in their ability to transmit long-distance signals for rapid plant response to short-term environmental perturbations by virtue of their fast propagation (Christmann et al., 2013; Gallé et al., 2015); however, over longer time periods these effects may be negated by other signaling mechanisms (Munns, 2000). Hydraulic pressure waves caused by biotic as well as abiotic stressors can be propagated in water almost instantaneously and travel up to 150 000cm s−1 (the speed of sound) (Malone, 1993), while hydraulic mass flow, caused by wounding events, is estimated to travel on average 20cm s−1 in herbaceous plants (Boari and Malone, 1993). It is important to note that hydraulic signals are two-component signals: a rapidly propagated pressure wave followed by xylem mass flow (Boari and Malone, 1993). Unfortunately, the literature does not often distinguish between the two components and instead reports only hydraulic mass flow determined with a pressure transducer and recorded via leaf swelling (e.g. fluid translocation), and not the preceding pressure wave. The variation in recorded speeds may result from the measurement technique as well as other factors including plant size/age. A lack of information on hydraulic signaling events makes it challenging to integrate hydraulic signals into the plant response signaling pathway timeline. Additionally, it is unclear to what degree information about the stress intensity is carried through hydraulic signals. We do know that through the wounding process, cell turgor pressure decreases and pressure waves are elicited (Malone et al., 1994). In fact, Alarcon and Malone (1994) showed that small feeding bouts of Spodoptera caterpillars at the base of tomato (Solanum lycopersium) leaflets induced a significant hydraulic signal, measured as an increase in distal leaflet thickness. Moreover, when petioles were girdled with heat, the signal could still be recorded in the distal leaf, indicating that a functioning membrane is not required and therefore hydraulic signals are responsible for proteinase inhibitor activation (Malone et al., 1994). However, we know that hydraulic signals precede SWPs, and thus electrical signals could also activate proteinase inhibitors. Therefore, despite evidence that both signal types exist, it remains elusive which signal type carries the critical information at a threshold required to elicit a particular plant response. Chemical signals are often considered the slowest long-distance signal in plants (Baydoun and Fry, 1985) and are therefore not favored as the fundamental initial long-distance signaling mechanism. However further consideration is warranted because, depending on the transportation pathway (xylem versus phloem), the transmission velocity can differ widely. For example, carbon isotope labeling of mature tree canopies has shown photosynthate transportation speeds from leaves to the roots within the phloem of on average 1–5 d (Mikan et al., 2000; Johnson et al., 2002; Steinmann et al., 2004), too moderate a velocity to support chemical signals for primary long-distance information transmission. However, considerably higher propagation speeds have been recorded in the xylem during leaf transpiration, 25cm s−1 in wheat (Passioura, 1972). Nonetheless, during the night and under stress conditions, when stomata are closed, these propagation speeds decrease dramatically. Water translocation occurs very rapidly (see above) and is a suitable medium for carrying wound signals throughout the entire plant (Boari and Malone, 1993). Chemical signal transport from a wounding site can quickly travel systemically through the plant via hydraulic signals as long as the wound signals can be produced in, or released into, the xylem sap within the time interval of the hydraulic signal propagation. Coupled hydraulic and chemical signaling within plants indicates that the integration of two signaling pathways might favor the faster pathway and thus the faster plant response. Nevertheless, it remains unknown if crucial chemical signals, utilized for plant stress responses, are indeed able to travel over longer distances. Glauser et al. (2008) demonstrated in Arabidopsis that the mechanical wounding signal leading to a systemic increase in JA must be propagated with a velocity of at least 0.05cm s−1. However, considering the propagation speed of up to 0.7cm s−1 for small molecules such as 32PO4 in the phloem of wheat plants (Fisher, 1990), it is not likely that JA is transported throughout the plant but rather accumulated in the leaves in response to a systemic signal. Proteinase inhibitor genes are activated through chemical signaling pathways (Farmer and Ryan, 1990). Yet, research shows that hydraulic as well as electrical signals are able to initiate proteinase inhibitor induction in tomato plants before an increase in proteinase inhibitor genes could be recorded (Stanković and Davies, 1996, 1997). Despite the debate on suitability of chemical signals for long-distance transport, chemical signals are certainly part of the first signaling cascades occurring in plant–herbivore interactions (Maffei et al., 2007a; Bruce, 2015). Electrical signals in plants can have a range of propagation speeds depending on the type of signal deployed (APs or SWPs) (Fig. 2). Some experiments have found that the propagation speed of APs is similar to that of hydraulic signals. For example, poplar trees exposed to a cold shock elicited propagation speeds of 0.4–0.8cm s−1 (Fromm and Lautner, 2007); higher speeds up to 4000cm s−1 were found in soybean after treatment with uncoupling agents, impacting oxidative phosphorylation (Volkov, 2000; Volkov et al., 2000), and velocities of 2–3cm s−1 were recorded in M. pudica during leaf closure in response to cold or touch stimulation (Fromm and Lautner, 2007). The propagation speeds of SWPs elicited through leaf burning on poplar trees fell in the range of 0.1–0.2cm s−1 (Lautner et al., 2005), whereas cutting leaflets in M. pudica resulted in speeds of only 0.5–0.6cm s−1 (Fromm and Lautner, 2007). This diversity in SWP propagation speed demonstrates the impact of the stressor as well as the plant species on the velocity of the electrical signal and therefore carries more information than APs. Additionally, SWPs are closely connected to, and in this case preceded by, hydraulic signals, making SWPs, in combination with hydraulic signals, a suitable candidate for information propagation over longer distances. Electrical signals have been elucidated as one of the primary responses to plant biotic attack, occurring within seconds to minutes after biotic wounding events, followed by a chemical signaling cascade (for reviews, see Maffei et al., 2007b; Zebelo and Maffei, 2015). In the course of the membrane potential elicitation, secondary messengers are initiated, leading to a downstream signaling cascade that terminates in the production of phytohormones and changes in plant metabolism, a process which can take anywhere from hours to days (Maffei et al., 2007a). During these cascades, calcium signaling is initiated (Reddy et al., 2011), along with the production of nitric oxide (NO) and its derivatives (Leitner et al., 2009), ROS (Mittler, 2006), and increases in JA, SA, and ethylene hormone production (Bari and Jones, 2009). However, the initiation of a calcium signal must precede other signals since it initiates the ion fluxes responsible for electrical signals (Zebelo and Maffei, 2012). Maffei et al. (2004 and 2006) showed that a thin layer of calcium signaling was recorded after lima bean (Phaseolus lunatus L.) leaves were wounded by the climbing cutworm (Spodoptera littoralis). Interestingly, calcium signaling was only initiated as a response to biotrophic wounding and could not be recorded after artificial mechanical wounding (Bricchi et al., 2010). Calcium is not the only secondary messenger involved in fast signaling responses. Recently ROS have also been correlated with electrical signals. ROS signals can travel in the xylem at up to 0.14cm s−1 (Miller et al., 2009), comparable with electrical signal propagation speeds. Therefore, current hypotheses support a close connection between ROS and electrical signals. However, whether ROS activate or increase the signal intensity has yet to be uncovered. The application of a calcium channel blocker does not hinder the ability of ROS to travel through the plant, indicating that Ca2+ is not required for fast signal propagation (Miller et al., 2009), a surprising result considering ROS are integrated with calcium networks (Kobayashi et al., 2007; Ogasawara et al., 2008). The ROS signaling pathway is initiated by several stressors, indicating that ROS is a general signaling molecule, and diversity in its oscillations (frequency and amplitude) allows the plant to decipher signals (Mittler et al., 2011) in the same way that distinct calcium oscillation signatures are deciphered in response to stimuli (Evans et al., 2001). There is distinct evidence that plants have several closely interconnected signaling pathways. However, we lack information on the integrated timing of hydraulic, electrical, and chemical signaling pathways, and the importance of each pathway for information transmission. For example, while chemical signals are necessary for the initiation of metabolic responses, the long-distance communication signal has to be either electrical or hydraulic in nature (Stratmann and Ryan, 1997). Conversely, some evidence supports hydraulic signals as essential for signal propagation, even though, in some cases, they are not able to initiate chemical signaling cascades independently (Malone et al., 1994). Furthermore, the connectivity among signaling pathways is shown by the inseparable connectivity of hydraulic signals and SWPs. Additionally, electrical signals can initiate chemical signals, and chemical signals in turn can intensify hydraulic signals, resulting in essentially a feedback loop of signal types. A perfect example of this cascade is found in ABA’s amplified decline in cell turgor pressure by down-regulating the permeability of the vascular tissue (Pantin et al., 2013) which in turn intensifies ABA production. Combined stressors: how do simultaneous stressors affect plant stress/defense responses? Distinguishing between different stressors Plants are able to differentiate between both biotic and abiotic stressors and orchestrate signaling pathways despite very similar stress responses. A plant’s ability to distinguish between specific biotic stressor types is frequently based on the chemical component of the attack mechanism. For example, herbivores, sucking insects, and pathogens have unique chemicals within their saliva, regurgitates, or exudates through which many plants are able to detect the identity of attacker. Bricchi et al. (2010) wounded lima beans (P. lunatus) in three ways: a single mechanical wounding event; multiple mechanical wounding events by a robotic worm; and multiple wounding events by the herbivore (S. littoralis). After wounding, only an increase in volatile compound emission in response to multiple wounding by the robotic worm and the herbivore was recorded, pinpointing that the repetition of wounding might be a trigger for stress response. The compound fraction of emitted volatiles unveiled a difference between the robotic worm and the herbivore indicating that the plants were able to differentiate between different wounding effects (Bricchi et al., 2010). Further investigation revealed that only the saliva of S. littoralis or multiple wounding events were able to induce calcium signaling and membrane polarization. Interestingly, these results suggest that more than one signaling pathway leading to volatile emissions exists in plants. However, experiments focused on plant wounding by different herbivores (the caterpillar S. littoralis and the snail Cepaea hortensis) found that lima beans released very similar compositions of volatiles regardless of herbivore type (Mithöfer et al., 2005). Wounding by three different biotic stressors (the caterpillar S. littoralis, the aphid Myzus persicae, and the pathogen Pseudomonas syringae) was directly correlated to the amount of damage caused (Bricchi et al., 2012), with membrane depolarization occurring 30min to 2h after feeding by S. littoralis, 4–6h after feeding by M. persicae, and 16h after infection by P. syringae. Interestingly, despite the same intensity of the membrane potential (Vm) change, the plant deciphered the pathogen/herbivore causing the damage as exhibited in their gene expression. Myzus persicae regulated a 10-fold higher number of genes than S. littoralis, but had higher levels of suppressed gene expression compared with P. syringae (Bricchi et al., 2012). These findings emphasize the ability of plants not only to identify the nature of the stressor, but also to fine-tune the stress response and match the intensity of the response with the inflicted stress intensity. Plants are able to distinguish between abiotic stresses despite a range of overlapping signal cascades. Among an array of abiotic stressors, cold, salt, and drought stress elicit similar responses by altering plant water relations, thus affecting the osmotic potential (Shinozaki and Yamaguchi-Shinozaki, 1997; Mahajan and Tuteja, 2005). Based on the osmotic component, plants respond with overlapping signal cascades such as calcium signaling (Sanders et al., 2002), ROS production (Hasegawa et al., 2000), or MAPK (mitogen-activated protein kinase) activation (Wu and Zong, 2011). One example of crosstalk between signaling pathways is the induction of ABA in response to drought and/or salinity. Interestingly, it is thought that ABA induction is due to changes in water potential caused by each stressor, and not the high salt concentrations occurring during salinity (Zhang et al., 2006). However, salt stress has an additional ionic component which often leads to ion toxicity and thus initiation of a special signaling cascade, SOS (salt overly sensitive) (Mahajan et al., 2008). Cold stress is unique due to the low temperature initiation of the ICE–CBF–COR signaling cascade, important for the up-regulation of cold-responsive genes (Huang et al., 2012). The receptors for the stress perception have not been identified; however, the plasma membrane plays a key role in the perception and transmission of stress responses, and the cell wall participates in the triggering process because of its role in regulating tension of the plasma membrane (Jia et al., 2001). Stress combinations The study of combinatory stresses is inherently difficult in that plants can respond in a number of ways, including a response characteristic to only one stress, an increased response intensity, or a unique response unlike any elicited by individual stressors (Rizhsky et al., 2002). Generally speaking, we can look to results from gene expression studies as an indicator of typical plant responses to multiple stressors. Gene level responses can be (i) additive, (ii) synergistic (more than the sum of individual stresses), (iii) idiosyncratic (completely different from the single stress responses), (iv) dominant (response very close to one of the stressors) (Prasch and Sonnewald, 2015), or (v) even antagonisitc (Bostock, 2005). Below we outline common stress interactions and examine typical plant responses. Abiotic–abiotic Abiotic stressors are inherently tightly linked in the natural environment. Heat stress, one of the most commonly observed stress factors, has been examined extensively, mainly in light of predicted future climatic temperature increases (Meehl et al., 2007) and their subsequent impact on plant productivity (Schlenker and Roberts, 2009). Elevated temperatures in combination with drought stress have a significantly greater detrimental effect on the growth and productivity of field crops compared with only elevated temperature or water shortage alone (Heyne and Brunson, 1940; Craufurd and Peacock, 1993; Savin and Nicolas, 1996; Jagtap et al., 1998; Mittler, 2006). Likewise, in turf grasses, high temperature and drought drastically impact plant health due to a decline in the activity of stress antioxidant enzyme activity (Jiang and Huang, 2001). It is important to note that xylem sap hormone concentrations can differ from those found within roots or leaves and may provide a more informative measurement when deciphering the effects of single stress versus combined stresses. The difficulty arises when the stressor alters the plant response in a manner that contradicts the typical trend. Examples include mitigation of negative drought and salt stress effects when CO2 concentrations are increased, resulting in elevated plant gas exchange, plant growth, and plant nutrition (Qaderi et al., 2006; Piñero et al., 2014); increased leaf NO3− concentration in pepper plants (Capsicum annuum) exposed to elevated CO2; and decreased Cl− levels under ambient CO2 concentrations, a finding that contradicts the prevailing trend found in plant leaves (Tuna et al., 2008; Del Amor and Cuadra-Crespo, 2012). In addition, plants exposed to salt stress and elevated CO2 had decreased ABA levels in their roots in comparison with when they were exposed to salt stress and ambient CO2 (Piñero et al., 2014). It is important to consider that in these cases the plants were exposed to the treatment (drought, temperature, and CO2) for 11 d (Qaderi et al., 2006) and 30 d (Piñero et al., 2014) before sampling, making it difficult to determine what role hydraulic and chemical signals played in the acclimation process. In trees, increased temperature in combination with drought stress weakens tree defense mechanisms, resulting in a reduction in stored sugars and starch, and consequently increased tree susceptibility to herbivore attack (Zvereva and Kozlov, 2006). Furthermore, the shortage of water supply limits the transportation of defense compounds to the wounding site, only compounding the tree’s vulnerability to decline (Guerard et al., 2007). From a whole-plant perspective, Niinemets and Valladares (2006) studied 806 North American, European, and West Asian temperate tree and shrub species. The authors examined waterlogging, shade, and drought tolerance, and found that only 2.6–10.3% of the species were tolerant to two simultaneous environmental stresses, and only three tree species were tolerant to all three stresses, suggesting that woody plants might be limited in their adaptability to environmental stressors. Hallik identified leaf physiological characteristics of several temperate broadleaf tree species based on their shade and drought tolerance, and demonstrated that the combination of drought and shade has opposing effects on foliar traits, in agreement with Niinemets and Valladares’s inference (Hallik et al., 2009). Interestingly, other researchers found similar conclusions to the cross-continental observations of Niimentes and Vaaladares (2006) in carefully controlled experiments comparing gene transcription patterns between single and combined stressors (Rizhsky et al., 2002, 2004; Hewezi et al., 2008; Rasmussen et al., 2013; Prasch and Sonnewald, 2015). Results to date reveal an anticipated distinct set of stress responses produced by coinciding stresses. For example, sunflowers exposed to high light intensity, elevated temperature, and the combination of both expressed 129 genes after exposure to the combination treatment while only nine of these genes were expressed upon exposure to a single stress (Hewezi et al., 2008). Arabidopsis exposure to elevated temperature, salt, and osmotic stress, or the combination of these stresses induced a total of 967 genes of which only about half were also induced by each stress individually (51, 42, and 57% to salt, osmotic, and heat stress, respectively), demonstrating that the stress combinations activated more genes. However, multiple stressor exposure repressed 719 genes, of which 25, 22, and 66% were also reduced by salt, osmotic, and heat stress individually, emphasizing the variable response of plants under different stress conditions. Despite the detailed transcription analysis of genes in response to different/multiple stressors, the spatial component of this analysis is missing. According to our knowledge, we are lacking information on how the transcription level of genes in roots versus the shoot differ in response to multiple stressors. Abiotic–biotic The effect of abiotic and biotic stress combinations has been well summarized (Prasch and Sonnewald, 2015). However, we would like to highlight general observed trends that emphasize that simultaneous abiotic and biotic stress occurrences may result in either synergistic or antagonistic interactions. Most interesting perhaps is the plant’s increased susceptibility to pathogens when preceded by mild episodic stress (Bostock, 2014). For example, abiotic factors such as increased temperature benefit virus as well as bacterial growth conditions, promoting the abundance of the bacterium P. syringe in tobacco (Nicotiana tabacum L.) and Arabidopsis (Wang et al., 2009). In addition, tobacco plants infected with the Tobacco mosaic virus have reduced resistance when exposed to temperatures >28 ºC through reduced R-gene-mediated resistance (Király et al., 2008). Furthermore, plant viruses benefit from elevated temperatures by enhanced virus survival and spread, an increased availability of insect vectors, and possibly suppressed host resistance (Moury et al., 1998; Király et al., 2008). Elevated temperatures can also cause protein denaturation or aggregation leading to a lower affinity between interacting factors affecting the recognition of pathogen molecules (Fraire-Velázquez et al., 2012). However, high temperatures can also increase plant defense response. For example, the rice blast Pib resistance gene is up-regulated in plants grown under 25 ºC (Wang et al., 2001). Likewise, sunflowers become more resistant to parasitic herbaceous plants (Orobanche cumana and O. aegyptiaca) under elevated temperatures through the increased ability to denature the parasitic tissue in their roots (Eizenberg, 2003). Determining the effects of temperature becomes even more challenging when changes in plant resistance are recorded on a genotype level. Twenty-seven lines of wheat (Triticum aestivum) were exposed to a range of temperatures, and the plant resistance to leaf rust (Puccinia recondita) was highly dependent on the plant line, with both increases and decreases with changes in temperature (Dyck and Johnson, 1983). Similarly, drought stress can also affect pathogen infection both positively and negatively. Mohr and Cahill (2003) demonstrated that Arabidopsis resistance to the bacterium P. syringae decreased when the root system was exposed to air drying, whereas the plant’s resistance to the oomycete Peronospora parasitica did not change. However, in tomato plants, three cycles of drought stress (fully hydrated until the plant was wilted) with alternating recovery periods showed subsequent enhanced inoculation resistance to the fungus Botrytis cinerea (Achuo et al., 2006), indicating that there is often an abiotic–biotic stress relationship. Interestingly, biotic factors can also affect plant resistance to abiotic stressors such as drought and freezing tolerance (Xu et al., 2008). Several vegetable seedlings infected with four different viruses had improved drought and freezing tolerance, suggesting that infection with viruses can actually aid the plant in retaining water (Xu et al., 2008). Viruses can form mutualistic relationships with plants under extreme stress conditions, even though viruses are considered parasitic symbionts under normal conditions. Under stress conditions, virus-infected plants show higher above-ground water content as well as higher water retention abilities (Xu et al., 2008), which can correlate with lower transpiration rates leading to less water loss by these plants (Keller et al., 1989). Additionally, virus-infected plants showed increased SA content as well as increased levels of osmoprotectants and antioxidants which serve as defense mediators (Xu et al., 2008). Considering the contrasting effect of stressors on plants, in a recently published review, the authors caution against the generalization of abiotic stress factors including high temperature, humidity, drought, and salinity in weakening plant defenses (Sharma et al., 2013), despite other studies having found increased pathogen susceptibility during periods of abiotic stress (Mohr and Cahill, 2003; Koga et al., 2004). Biotic–biotic As with other stress combinations, plants are often attacked by multiple herbivores and are capable of producing an integrated response (Bostock, 1999; Bruxelles et al., 2001). The simultaneous plant intrusion by the beet armyworm (BAW) Spodoptera exigua Hübner and the phloem feeder silverleaf whitefly Bemisia tabaci Gennadius reduced plant volatile emission by 60% compared with plants damaged by BAW alone (Rodriguez-Saona et al., 2003). The authors hypothesize that the reduction in volatiles can weaken the plant’s attractiveness to parasitoids. This kind of plant signal integration can not only occur during simultaneous biotic stressor encounters but is also evident when biotic stressors are temporally separated. In other words, individual biotic agents can induce a signaling cascade in favor of future stressors. When the mold fungus (Sclerotium rolfsii Saccodes) infects peanut plants, the plants change their volatile compound signature to increase their attractiveness to BAW, thus making the plants more attractive to BAW larval parasitoids (Cotesia marginiventris) as well. Additionally, previously attacked plants were able to alter their resistance in subsequent attacks to other similar stressors (Bruce and Pickett, 2007). Rice plants damaged by the white-backed plant hopper (Sogatella furicifera) increased resistance to the fungus, with rice blast (Magnaporthe grises) only when the plants were previously exposed to the hopper (Kanno and Fujita, 2003; Kanno et al., 2005). Clearly, it is difficult to generalize plant defense response(s) because the regulation of plant defense to different biotic agents depends on the plant–pathogen/herbivore interaction and is highly specific (Stout et al., 1999). Pest resistance to three different biotic agents on tomato plants found that depending on the agent combination, pest resistance could increase or impair the development of other pathogens (Stout et al., 1999), stressing the importance and need for empirical research focusing on multiple combined biotic stresses on plants to determine the patterns in plant signal integration and to enable future predictions of plant responses and their resistance to biotic stresses in the field. Other factors influencing stress response interaction Even though the combination of stressors can determine overall stress susceptibility (Atkinson and Urwin, 2012), it is important to recognize that there are several other physiological factors influencing plant stress responses. Plant nutritional state, plant age, and the time of the day at which the plants are stressed can influence, enhance, or dampen stress responses in plants. Plant size and age Plant developmental stage can partially dictate a plant’s susceptibility to particular stresses (Niinemets, 2010). The shallow root systems of tree saplings are restricted to the upper soil layers and therefore have a higher dependence on precipitation events. Older, larger trees, on the other hand, have deeper root systems allowing access to deeper ground water and a greater uncoupling from minor drought events (Dawson, 1996; Drake et al., 2009). However, as trees grow and gain in height, greater light interception leads to higher leaf temperatures and potential photoinhibition (for a review, see Niinemets and Valladares, 2004). Consequently, leaf cooling through transpiration is increased, requiring more water, a decrease in leaf water potential, and probably an increase in cavitation events if the water supply is not sufficient. Trees within distinct ontogenic stages are therefore prone to different stress combinations. The simultaneous occurrence of drought, photoinhibition, heat stress, and nutrient limitation is expected to increase with increasing plant size, while shading and drought stress interactions are expected to be more prevalent in seedlings and saplings (Valladares and Pearcy, 1997, 2002; Niinemets and Valladares, 2004; Niinemets, 2010). Nutrients The effect of nutrients on plant signaling relate to their role in supporting enzyme production and functioning (Ranieri et al., 2001). The composition of herbivore-induced volatiles strongly depends on other abiotic environmental factors, such as nitrogen and phosphorus availability (Schmelz et al., 2003), soil salinity and pH, and air humidity (Vallat et al., 2005). However, in addition to the chemical stress response, nutrients can have a contrary effect on the plant defense status. Predicting the plant susceptibility to different pathogens remains challenging, because plant stress resistance is dependent on several factors including plant–pathogen interaction, environmental conditions, plant developmental stage, as well as nutrient availability, whereby each nutrient element can impact the plant differently (Huber and Haneklaus, 2007; Dordas, 2009). Additionally, land management practices can affect nutrient availability for both the plant and the pathogen, thus affecting plant disease severity (Huber and Graham, 1999). A recent meta-analysis comparing the severity of fungal pathogen infection relative to the addition of commercial fertilizer in herbaceous plants revealed that in general, the addition of N fertilizer increased the severity of fungal pathogen infection, suggesting that a slightly depleted nutrient status would benefit plants. However, species-specific differences exist and there are some plants, such as potato (Solanum spp.), which show decreased fungal infection levels in response to N fertilizer (Veresoglou et al., 2013). A compilation of several studies has shown that an adequate K+ nutrient status in plants tends to decrease susceptibility to aphids, herbivores, viruses, and bacteria (Prabhu et al., 2007; Amtmann et al., 2008). This is most probably a result of K+ affecting the membrane potentials due to high K+ permeability. Additionally, K+ also plays a role in electrical signaling (Amtmann et al., 2005) and can differentially affect hormone pathways (for a review, see Amtmann et al., 2008). Despite advancement in our knowledge of the effect of K+ on signaling cascades as well as how other nutrients influence metabolic pathways, it still remains unknown how the plant’s nutrient status fine-tunes the signaling cascades leading to lower or higher susceptibility to stressors. Circadian clock A less explored field in plant signaling and stress response is the influence of circadian rhythm on plants (Piechulla, 1993). The circadian clock is involved in a multitude of physiological processes: stomatal conductance, photosynthesis, stem elongation, flowering time, leaf movement, pathogen and herbivore resistance, response to abiotic stress, and plant immunity (Zhang et al., 2013; for a review, see Hsu and Harmer, 2014). Covington et al. (2008) integrated information from microarray experiments and found that one-third of Arabidopsis genes are controlled by the circadian clock. Furthermore, plant hormones are also up- or down-regulated depending on the time of day. For example, 40% of the genes responsible for ABA production are up-regulated in the morning. The authors speculate that the difference in transcriptome abundance over the course of the day reflects the plant ABA level and therefore also the activity of the ABA signaling pathway. The rhythm of gene expression can be highly affected by temperature. The expression of bPRP (soybean proline-rich protein) in soybeans [Glycine max (L.) Merr.] is enhanced by several factors including drought, salt, plant hormones, and viruses. Under non-stressed conditions, bPRP expression follows a circadian rhythm, which becomes disturbed at low temperatures, demonstrating that stress can affect the circadian rhythm of gene expression. Regulation of gene transcription patterns according to a circadian rhythm helps optimize the plant’s resources and increases plant resistance to stressful environmental conditions when needed. This regulation has been shown for oxidative stress and freezing tolerance in Arabidopsis plants (Nakamichi et al., 2009; Dong et al., 2011; Lai et al., 2012) and heat shock resistance in cotton plants (Gossypium hirsutum L.cv. Deltapine 50) (Rikin, 1992). These experiments collectively show that plants use their resources efficiently by limiting signaling pathways at certain times of the day, emphasizing the importance of including different time points in experiments focusing on plant stress responses (Rienth et al., 2014). Future directions Undoubtedly plants respond rapidly and distinctly to changing environmental conditions and biotic assaults despite their sessile lifestyle. Here, we presented the mode of action of each signaling pathway, the signaling speed, and potential interconnections between signaling pathways. Yet, many unknowns remain regarding signaling pathways, ranging from the importance of each pathway individually to the integration of signals. Specifically, hydraulic signals seem to play an important role in information transmission (Malone et al., 1994). However, the lack of information on hydraulic signaling events makes integration of hydraulic signals into the plant response signaling pathway timeline challenging. Even though there is still a debate as to whether chemical signals are suitable as long-distance signaling pathways, the importance and presence of hydraulic, electric, and chemical pathways is beyond dispute. Additional investigation into the integration of all three signaling pathways along with deciphering the role of each signal type in information transfer remains an area in great need of future attention. Furthermore, it is important to include environmental variation and a more diverse spectrum of plant species into the experimental design in order to account for unique species responses to changing environmental conditions. High variability of stress resistance to abiotic stressors exists even within lines from the same species (Dyck and Johnson, 1983), and susceptibility towards different stressors can vary depending on the ontogenetic stage of plants (Niinemets, 2010). In the field, plants are rarely exposed to a single stressor, but instead face a combination of stressors that probably vary in intensity. Gene transcription analyses have emphasized the uniqueness of stress responses to combined stressors. Therefore, we call for research that moves beyond highly controlled settings and examines stress/defense responses of field-grown plants. Finally, the severe lack of information on combined stressors hinders our ability to predict stress responses under changing environmental conditions. The future of efficient crop production is highly dependent on our ability to predict stresses accurately. 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TI - Long-distance plant signaling pathways in response to multiple stressors: the gap in knowledge
JF - Journal of Experimental Botany
DO - 10.1093/jxb/erw099
DA - 2016-03-01
UR - https://www.deepdyve.com/lp/oxford-university-press/long-distance-plant-signaling-pathways-in-response-to-multiple-EOOMUS0UH6
SP - 2063
EP - 2079
VL - 67
IS - 7
DP - DeepDyve
ER -