Abstract The establishment of inflorescence architecture is critical for the reproduction of flowering plant species. The maize plant generates two types of inflorescences, the tassel and the ear, and their architectures have a large effect on grain yield and yield-related traits that are genetically controlled by quantitative trait loci (QTLs). Since ear and tassel architecture are deeply affected by the activity of inflorescence meristems, key QTLs and genes regulating meristematic activity have important impacts on inflorescence development and show great potential for optimizing grain yield. Isolation of yield trait-related QTLs is challenging, but these QTLs have direct application in maize breeding. Additionally, characterization and functional dissection of QTLs can provide genetic and molecular knowledge of quantitative variation in inflorescence architecture. In this review, we summarize currently identified QTLs responsible for the establishment of ear and tassel architecture and discuss the potential genetic control of four ear-related and four tassel-related traits. In recent years, several inflorescence architecture-related QTLs have been characterized at the gene level. We review the mechanisms of these characterized QTLs. Introduction Maize (Zea mays L.) is one of the most important cereal crops in the world, and a main source of food, feed, and fuel. Improvement of grain yield is a primary task in most maize breeding programs. Grain yield, a complex quantitative trait, is determined by several factors, including plant and inflorescence architecture. For example, both ear length and ear diameter are strongly correlated with kernel number per row (KNR) and kernel row number (KRN), respectively (Hallauer et al. 1988), and tassel length (TL) and tassel branch number (TBN) are correlated with tassel size (Upadyayula et al. 2006). A small tassel is desirable for increased partitioning of resources into the ear (Hunter et al. 1969). Morphological features normally have higher heritability than grain yield, thus breeders hypothesize that the strong relationship and high heritability could be used to select indirectly for increased grain yield by selecting for more easily measured inflorescence architecture-related traits (Hallauer et al. 1988, Austin and Lee 1996, Messmer et al. 2009). Long-term efforts have been undertaken to dissect the genetic basis for the inflorescence traits accompanying increased yield, and much progress has been made through classical mutant analysis and quantitative trait locus (QTL) mapping. Hundreds of loci were detected using different mapping populations, and several QTLs have been well characterized. Recently, rapid progress has been made towards the genetic dissection of grain yield-related traits through the use of powerful next-generation sequencing (NGS) technology to characterize allelic diversity in breeding populations. For example, NGS-based genome-wide association studies (GWASs) have led to a rapid explosion in the identification of trait-associated variations (TAVs) or putative candidate genes for complex traits of maize (Xiao et al. 2017). The objective of this review is to summarize the progress in dissecting the genetic basis for inflorescence architecture-related traits and to describe the potential underlying regulatory pathways that modulate inflorescence architecture, and therefore grain yield, in maize. Inflorescence Architecture and Grain Yield-Related Traits The establishment of inflorescence architecture is critical for reproduction and yield of flowering plants, particularly in crop species. The monocot maize produces two types of inflorescences: a compound spike with male flowers (tassel) and a spadix with female flowers (ear). Maize ear size determines the potential number and size of kernels, and tassel size is highly correlated with the number of pollen grains, the production of which uses energy that otherwise could be available for kernel development (Lambert and Johnson 1978, Fischer et al. 1987). Although the architectures of mature tassels and ears seem to be distinct, their developmental ontogeny and underlying organization are remarkably similar. During vegetative growth, the shoot apical meristem (SAM), harboring a population of totipotent cells at the apical tip, generates leaves and associated axillary meristems (AMs), which may develop into an ear primordium depending on their position along the main stem. When the SAM switches from the vegetative to the reproductive phase, it ceases to produce leaves, and the terminal SAM becomes an inflorescence meristem (IM) and subsequently converts to form a tassel. The tassel IM then begins producing lateral meristems called branch meristems (BMs), which develop into tassel branches (TBs) at the base of the mature tassel. Ear primordia originating from the AM possess a very similar developmental fate to tassel primordia, but BMs are absent. In both cases, the IM is indeterminate and committed to form lateral meristems called spikelet pair meristems (SPMs) at the peripheral region. Each SPM gives rise to two spikelet meristems (SMs), and each SM develops into two meristems: the upper floral meristem and the lower floral meristem. In the tassel, two floral meristems develop into a spikelet composed of two male flowers, but in the ear, the lower floral meristem usually aborts and only the upper floret develops into a kernel (Cheng et al. 1983, Irish 1997, McSteen et al. 2000, Vollbrecht et al. 2005). Thus, ear and tassel traits are deeply affected by IM activity. Therefore, maintaining the meristematic activity of the ear IM could increase the potential to produce more SPMs and SMs, leading to more kernels. The developmental ontogeny of inflorescence architecture determines the potential grain yield and yield-related traits in maize (Lopez-Reynoso and Hallauer 1998). Hence, in this review, we mainly focus on four ear traits, i.e. KRN, ear diameter (ED), KNR and ear length (EL), and four tassel traits, i.e. TBN, TL, spikelet length (SL) and branch zone length (BZ). The Genetic Basis of Inflorescence Architecture Traits Long-term research in genetics, particularly in quantitative genetics, has accumulated a large amount of data that paves the way to understanding the genetic basis of inflorescence architecture. The molecular dissection of this genetic basis is enabled by the availability of genome sequences. To date, whole-genome sequence data have been produced for diverse maize inbreds using NGS (Schnable et al. 2009, Edwards et al. 2013, Jiao et al. 2017, Zhou et al. 2017). Furthermore, NGS greatly improves the number of single nucleotide polymorphism (SNP) markers available for high-resolution mapping (Davey et al. 2011); for example, De Donato et al. (2013) increased the SNP densities in telomeric and some centromeric regions using genotyping by sequencing (GBS) technology. Below, we summarize recent findings in molecular quantitative genetics of inflorescence architecture traits. We collected a total of 834 QTLs with logarithm of odds (LOD) score >2.5, and 1,764 TAVs or SNPs for four ear-related and four tassel-related traits from publicly available databases. To give a more concise and clear picture of the genome position, QTLs with intervals >20 Mb and markers with no accurate physical locations were excluded. The QTLs with clear marker intervals were then projected onto the Maize Reference Genome (B73 RefGen V2). If a QTL was associated with only one marker, then a 5 Mb flanking region around the marker was considered the QTL interval for placement on the distribution map (Fig. 1). TAVs and known genes involving inflorescence development were also labeled based on their physical positions. In general, these traits are often referred to as quantitative traits, which are controlled by multiple loci/genes with different modes of inheritance and epistasis. Loci for inflorescence architecture-related traits cover the greater part of the maize genome. Some chromosomal regions, which are repeatedly detected in different populations or under different environments, are considered highly reliable and are referred to as QTL hotspots. Fig. 1 View largeDownload slide The distribution of QTL hotspots for inflorescence architecture traits on the maize genome. (A and B) The distribution of QTLs for four ear architecture- (A) and four tassel architecture- (B) related traits. Known inflorescence-related genes are shown on the basis of their physical position on the maize genome. HS, hotspot; EL, ear length; ED, ear diameter; KRN, kernel row number; KNR, kernel number per row; TBN, tassel branch number; TL, tassel length; SL, spikelet length; BZ, branch zone; QTL, quantitative trait locus; TAVs, trait-associated variations. Fig. 1 View largeDownload slide The distribution of QTL hotspots for inflorescence architecture traits on the maize genome. (A and B) The distribution of QTLs for four ear architecture- (A) and four tassel architecture- (B) related traits. Known inflorescence-related genes are shown on the basis of their physical position on the maize genome. HS, hotspot; EL, ear length; ED, ear diameter; KRN, kernel row number; KNR, kernel number per row; TBN, tassel branch number; TL, tassel length; SL, spikelet length; BZ, branch zone; QTL, quantitative trait locus; TAVs, trait-associated variations. Ear architecture-related traits Early on, Doebley (2004) hypothesized the existence of many minor effect loci and suggested that they could have significant effects on ear architecture. By integrating 324 QTLs and 403 TAVs for KRN, 46 QTLs and 22 TAVs for ED, 140 QTLs and 87 TAVs for EL and 47 QTLs and 14 TAVs for KNR, which we collected from published data, we found that a total of 41 QTL hotspots covering 29.8% of the maize genome are highly associated with KRN, ED, EL and KNR (Table 1). Some are shared by different traits. All of these hotspots are supported by TAVs and/or inflorescence genes identified by traditional mutational analyses (Fig. 1). Table 1 Summary of collected QTLs, TAVs (SNPs) and QTL clusters on the maize genome Trait No. of collected QTLs No. of collected TAVs No. of clusters No. of QTLs in a cluster No. of TAVs in a cluster Known genes within the cluster References EL 140 87 21 94 36 AN1, BA1, BIF4, BR2, CG1, CT2, D1, D8, KN1, LG1, LG2, RA1, RA2, RTL2, TB1, TSH4, ZAG1, ZFL1, ZFL2 Briggs et al. (2007); Brown et al. (2011); Cai et al. (2014); Calderón et al. (2016); Chen et al. (2014); Chen et al. (2016); Huo et al. (2016); Liu et al. (2012); Liu et al. (2015a); Lu et al. (2011); Sabadin et al. (2008); Tan et al. (2011); Tian et al. (2014); Upadyayula et al. (2006); Wu et al. (2016); Xiao et al. (2016); Xue et al. (2016); Yang et al. (2014); Yang et al. (2015); Zhao et al. (2011); Zhou et al. (2015). ED 46 22 4 12 6 BAD1, FEA2, LG1, WAB1, ZFL2 KRN 324 403 36 233 237 BA1, BAD1, BIF1, BR2, CG1, CT2, D1, D8, FEA2, FEA4, KN1, KRN4, LG1, LG2, NATL1, RA1, RA2, RA3, RTL2, RTL3, SID1, WAB1, TE1, TRU1, TU1, UB3, TS2, TS6, TSH1, VT2, ZFL1, ZFL2 KNR 47 14 10 30 4 BR2, D8, FEA4, KN1, LG1, RA1, TB1, TSH4, ZFL1, ZFL2 TBN 100 360 20 58 158 AN1, BA1, BAD1, BR2, CT2, D8, KN1, LG1, LG2, NATL1, RTL3, WAB1, TE1, TB1, TS2, TS6, TSH1, TU1, ZFL2 TL 91 255 18 44 111 AN1, CG1, CT2, D1, D8, KN1, LG1, RA1, RA2, RA3, RMR6, RTL2, RTL3, UB2, TB1, TS2, TS6, TSH1, TSH4, ZFL2 SL 47 320 5 10 51 AN1, D8, KN1, KRN4, LG1, TB1, UB3, ZFL2 BZ 39 303 4 8 32 KRN4, RTL3, UB3, TSH1 Trait No. of collected QTLs No. of collected TAVs No. of clusters No. of QTLs in a cluster No. of TAVs in a cluster Known genes within the cluster References EL 140 87 21 94 36 AN1, BA1, BIF4, BR2, CG1, CT2, D1, D8, KN1, LG1, LG2, RA1, RA2, RTL2, TB1, TSH4, ZAG1, ZFL1, ZFL2 Briggs et al. (2007); Brown et al. (2011); Cai et al. (2014); Calderón et al. (2016); Chen et al. (2014); Chen et al. (2016); Huo et al. (2016); Liu et al. (2012); Liu et al. (2015a); Lu et al. (2011); Sabadin et al. (2008); Tan et al. (2011); Tian et al. (2014); Upadyayula et al. (2006); Wu et al. (2016); Xiao et al. (2016); Xue et al. (2016); Yang et al. (2014); Yang et al. (2015); Zhao et al. (2011); Zhou et al. (2015). ED 46 22 4 12 6 BAD1, FEA2, LG1, WAB1, ZFL2 KRN 324 403 36 233 237 BA1, BAD1, BIF1, BR2, CG1, CT2, D1, D8, FEA2, FEA4, KN1, KRN4, LG1, LG2, NATL1, RA1, RA2, RA3, RTL2, RTL3, SID1, WAB1, TE1, TRU1, TU1, UB3, TS2, TS6, TSH1, VT2, ZFL1, ZFL2 KNR 47 14 10 30 4 BR2, D8, FEA4, KN1, LG1, RA1, TB1, TSH4, ZFL1, ZFL2 TBN 100 360 20 58 158 AN1, BA1, BAD1, BR2, CT2, D8, KN1, LG1, LG2, NATL1, RTL3, WAB1, TE1, TB1, TS2, TS6, TSH1, TU1, ZFL2 TL 91 255 18 44 111 AN1, CG1, CT2, D1, D8, KN1, LG1, RA1, RA2, RA3, RMR6, RTL2, RTL3, UB2, TB1, TS2, TS6, TSH1, TSH4, ZFL2 SL 47 320 5 10 51 AN1, D8, KN1, KRN4, LG1, TB1, UB3, ZFL2 BZ 39 303 4 8 32 KRN4, RTL3, UB3, TSH1 EL, ear length; ED, ear diameter; KRN, kernel row number; KNR, kernel number per row; TBN, tassel branch number; TL, tassel length; SL, spikelet length; BZ, branch zone; QTL, quantitative trait locus; TAV, trait-associated variation. By joint linkage analysis and GWAS, Brown et al. (2011) identified a total of 36 KRN QTLs and 261 TAVs with minor additive effects. Liu et al. (2015a) proposed that a set of >40 loci, comprising some large additive loci (effect >3%) and many small additive loci (effect <3%), dominate the natural variation for KRN, and found that an additive model can accurately predict the KRN of inbred lines and hybrids. Therefore, they claimed that QTLs with additive and partially dominant effects play a major role in genetic control for KRN. This hypothesis is supported by the analysis of 10 recombinant inbred line (RIL) populations, which were evaluated by three methods (Xiao et al. 2016). Using a mapping population derived from a maize inbred line and an ancestral teosinte line, Calderón et al (2016) detected 96 KRN QTLs, of which approximately 80% are additive or partially dominant, and approximately 20% are dominant or overdominant. These results demonstrate the importance of additive and partially dominant effects for KRN in maize. ED is phenotypically associated with KRN. However, some studies have found that gene actions of QTLs for ED clearly differ from those of KRN QTLs, and are mostly partially dominant and overdominant in F2:3 family lines (Sabadin et al. 2008, Li et al. 2009, Tan et al. 2011). EL is controlled by a set of QTLs with small to moderate effects. Approximately 40 QTLs, which are supported by 202 TAVs, have been detected for EL in the 10 RIL populations, and approximately 70% of the QTLs have small effects, with each explaining a small percentage of the phenotypic variance (Xiao et al. 2016). Notably, a dominant effect could not be estimated in RILs. Nevertheless, Brown et al. (2011) found that additive model predictions are least accurate for EL, despite inclusion of loci and/or TAVs that show a larger effect independently, implying the importance of non-additive effects and epistasis. However, by estimating pair-wise epistatic effects between QTLs, as well as between TAVs, Xiao et al. (2016) did not find additive by additive interactions, suggesting that epistasis is not important relative to the additive effect. Most studies of F2:3 family lines or backcross populations, which can be used to estimate the dominant effects of loci, suggest that EL and KNR are mainly controlled by loci acting non-additively (Sabadin et al. 2008, Tan et al. 2011, Zhao et al. 2011, Zhou et al. 2015). For example, 28 out of 34 QTLs collected for EL and KNR act non-additively, and just 17.6% of loci are additive (Austin and Lee 1996, Li et al. 2009, Huo et al. 2016). In maize breeding, strong better-parent heterosis is frequently observed for EL in hybrids. This apparent non-additivity suggests that dominant and overdominant effects are the major cause of better-parent heterosis in maize (Stuber et al. 1992, Garcia et al. 2008, Larièpe et al. 2012) and in rice (Hua et al. 2003, Huang et al. 2016). In sum, non-additive effects are important genetic factors for EL and KNR. These results highlight the complexity of the genetic basis of ear-related traits. Nevertheless, a few loci do show major effects in non-mutant genotypes. For example, by QTL mapping in combination with classical mutant analysis, FASCIATED EAR2 (FEA2), a gene controlling the size of the inflorescence meristem and thus KRN, was isolated in hotspot #19 within bin 4.05 (Bommert et al. 2013b). Another KRN-related gene, UNBRANCHED3 (UB3) (Chuck et al. 2014), and an associated intergenic element controlling its expression underlying the QTL KRN4 (Liu et al. 2015b), were isolated in hotspot #21 within bin 4.08. In addition, hotspot #25 in bin 6.02 and hotspot #27 in bin 7.02 for EL have been fine-mapped to narrow regions, qKNR6 (Liu et al. 2012) and qEL7.02 (Zhou et al. 2015), respectively. Many other ear-related QTL hotspots cover known inflorescence development-related genes. For example, BARREN INFLORESCENCE 1 and 4 (BIF1 and BIF4) (Galli et al. 2015) in hotspot #31 and hotspot #24, respectively, are involved in the regulation of the determinacy of axillary meristems. COMPACT PLANT2 (CT2) (Bommert et al. 2013a) in hotspot #1, FEA4 (Pautler et al. 2015) in hotspot #21 and TERMINAL EAR1 (TE1) (White and Doebley 1999) in hotspot #15 all function in the maintenance of IM activity. BARREN STALK1 (BA1) in hotspot #16 is required for lateral meristem initiation in the inflorescence (Skirpan et al. 2008). Tassel architecture-related traits Compared with QTL detection for ear traits, fewer studies have involved QTL mapping of tassel architecture-related traits, although phenotypic variation is abundant in maize breeding populations. Studies by Tanaka et al. (2013) and Eveland et al. (2014) suggest that male inﬂorescence architecture is controlled by a large number of genes. However, within bi-parental populations, a limited number of loci for tassel traits are often detected using low-density markers (Berke and Rocheford 1999, Mickelson et al. 2002, Upadyayula et al. 2006) and high-density SNPs (Chen et al. 2014), because of the limited allelic variation between parents. When using populations with wider genetic variation, such as association panels comprised of diverse inbred lines and the nested association mapping (NAM) population, more loci are detected. Yang et al. (2014) identified 30 TBN- and 33 TL-related loci in 368 inbred lines. Brown et al. (2011) detected 39 TBN- and 37 TL-related QTLs, and 241 TL- and 325 TBN-related TAVs in the American NAM population (using B73 as the founder line). A total of 50 QTLs for TBN and 54 QTLs for TL were detected when both the Chinese NAM (using Huangzaosi, a Chinese maize inbred, as the founder line) and the American NAM were used (Wu et al. 2016). By integrating 100 QTLs for TBN, 91 QTLs for TL, 47 QTLs for SL, 39 QTLs for BZ and 965 TAVs detected by GWAS, we found that these QTLs and TAVs are distributed over 30 hotspots accounting for approximately 24.3% of the maize genome. Early investigations showed that most QTLs for TBN are partially dominant, while QTLs for TL are normally dominant and overdominant (Gao 2007), and additive by additive epistasis has a minor effect on tassel traits in a large BC1 population (Briggs et al. 2007). In addition, Brown et al (2011) found that the genetic effects of loci for tassel traits are usually smaller than those of loci for ear traits. Most of the tassel architecture-related genes were isolated by mutant analysis (Tanaka et al. 2013). Some of them were found to be highly associated with quantitative variation of tassel traits (Wu et al. 2016). The tassel-related QTL hotspots also cover known tassel architecture-related genes (Fig. 1B). For example, TEOSINTE BRANCHED1 (TB1) (Doebley et al. 1997) in hotspot #7 is involved in the regulation of the determinacy of vegetative lateral meristems. The LIGULELESS2 (LG2) gene (Walsh and Freeling 1999) in hotspot #15 may function in vegetative to reproductive phase transition. Two genes in the RAMOSA pathway, RAMOSA2 (RA2) (Bortiri et al. 2006) in hotspot #13 and RAMOSA3 (RA3) (Satoh-Nagasawa et al. 2006) in hotspot #30, regulate the identity and determinacy of inflorescence axillary meristems, and TASSEL SHEATH4 (TSH4) (Chuck et al. 2010) in hotspot #29 is required for meristem initiation and maintenance. TUNICATE1 (TU1) (Han et al. 2012) in hotspot #20 plays a role in phase transition, branch meristem formation, spikelet initiation and sex determination. In addition to these known genes, 23 new candidates mainly involved in transcriptional regulation, phytohormone synthesis and signaling, ion transport, etc. have also been found to be associated with tassel inflorescence architecture (Wu et al. 2016), providing clues for the identification of new inflorescence-related genes. Pleiotropy of inflorescence architecture-related loci Pleiotropy is assessed from linkage analysis as well as from GWAS analysis. If a QTL or an SNP has a large positive or negative effect for two or more traits in the same mapping population, the effects will be significantly correlated and thus pleiotropy is inferred. The loci for development-related traits across male and female inflorescences frequently show pleiotropy. For example, nine QTL hotspots are shared by EL and KNR, three by KRN and ED and four by TL and SL. All of the identified QTL hotspots are pleiotropic loci for two or more traits. These shared QTL hotspots may be severely underestimated, because some of these trait pairs are rarely studied together. Zhou et al. (2015) proposed that EL shows positive pleiotropy with KNR. Bommert et al. (2013b) suggested that IM diameter is positively correlated with ED, cob diameter and KRN. Additionally, several loci for ear traits also show pleiotropy with tassel traits. EL shows positive pleiotropy with SL and TL, and leaf length shows positive pleiotropy with EL, TL, SL and BZ (Brown et al. 2011). A possible interpretation for this pleiotropy is that developmentally related traits may share the same or similar development scenarios originating from the same meristem, and/or similar genetic controls. For example, ears originate from the inflorescence meristems, and the length of the ear is determined by activity of the IM. Longer meristematic activity of the IM provides the potential to develop more kernels on the cob. Another possible interpretation for pleiotropy is that closely linked genes, such as TB1, DWARF PLANT8 (D8) and KNOTTED1 (KN1), all located within the same approximately 20 Mb genome region (hotspot #7), affect KRN, EL, TBN, TL and SL. The Molecular Mechanisms of Yield-Related QTLs Ear and tassel architecture, controlled by the activity of different meristems within developing inflorescences, has a profound influence on corn yield. Forward genetic studies have uncovered many mutants with severe phenotypic defects in ear and tassel. As more genes responsible for these defects were isolated, the underlying molecular mechanisms were discovered. Well-identified regulators are involved in CLAVATA (CLV) signaling, auxin signaling, the RAMOSA pathway and small RNA regulatory pathways (Chuck et al. 2007a, Chuck et al. 2007b, Gallavotti et al. 2008, Barazesh et al. 2009, Gallavotti et al. 2010, Phillips et al. 2011, Galli et al. 2015, Somssich et al. 2016). Frequently, these classical mutants display negative pleiotropy and therefore cannot be directly used for breeding application. For example, fasciated ear mutants such as thick tassel dwarf1 (td1), ct2 and branched ramosa mutants develop short ears with disorganized seed rows, which decrease the number of kernels per ear (Bommert et al. 2005, Gallavotti et al. 2010, Bommert et al. 2013b). QTL isolation aims to optimize ear and tassel architectures and further improve grain yield. Although many grain yield-related QTLs were detected, isolation of these QTLs was rarely successful due to the small size of the mapping populations used and high complexity of the maize genome. In recent years, however, considerable progress has been made in isolating grain yield-related QTLs and in understanding the underlying molecular mechanisms. CLAVATA signaling As the first successful case of isolating an ear trait QTL with potential applications in maize breeding, a major QTL for KRN was mapped to a locus harboring the FEA2 gene (Veldboom et al. 1994, Bommert et al. 2013a) that encodes a membrane-localized receptor-like protein (Taguchi-Shiobara et al. 2001). FEA2 is orthologous to Arabidopsis CLV2 (Jeong et al. 1999), and responds to CLV3-like peptide signals (Bommert et al. 2013a, Je et al. 2016). Loss of FEA2 function leads to strong fasciation and severely shortened ears (Taguchi-Shiobara et al. 2001), demonstrating that in CLV signaling it acts to restrict IM activity (Fig. 2). Natural variation in FEA2 explains part of the difference in KRN between inbreds Mo17 and B73, as well as among the derived RILs. Consistently, a decrease in the FEA2 expression level increases IM size and also KRN. Inspired by this, TILLING (targeting induced local lesions in genomes) was used to isolate a weak allele, fea2-1328, with a single amino acid change in the leucine-rich repeat (LRR) domain (Bommert et al. 2013a). Plants carrying this weak allele have a larger meristem size and higher KRN, but normal ear length and kernel arrangement. Therefore, this allele has the potential to increase the number of kernels per ear and thus to enhance grain yield (Fig. 3). This study highlights the power of using weak, rather than null, alleles with the potential to improve grain yields. Fig. 2 View largeDownload slide Model of the regulation of ear trait-related QTLs. In the inflorescence meristem, the genes involved in CLAVATA signaling play major roles in controlling meristematic activity. In the central region of the inflorescence meristem, FEA3 perceives the ZmFCP1 signal from the peripheral region (dotted arrows) and restricts ZmWUS1 to a small domain. ZmCLV3 is proposed to be expressed in the tip and is perceived by membrane-localized FEA2, which interacts with CT2. TD1, the ortholog of Arabidopsis CLV1, is expressed in the peripheral region. FEA2 and TD1 are proposed to regulate ZmWUS1 negatively based on the well-known model in Arabidopsis. In the process of lateral meristem development, the homologous transcription factor genes TSH4, UB2 and UB3 are initially co-expressed with miR156, but are targeted and restricted to the base of the developing spikelet meristems later on. UB3 is also regulated by an intergenic region, KRN4, via a long-distance regulation model. KRN4 and UB3 interact genetically to regulate inflorescence development. FEA3, FASCIATED EAR3; ZmFCP1, Zea mays FON2-LIKE CLE PROTEIN1; ZmWUS1, Zea mays WUSCHEL1; FEA2, FASCIATED EAR2; CT2, COMPACT PLANT2; TD1, THICK TASSEL DWARF1; CLV1, CLAVATA1; CG1, CORNGRASS1; TSH4, TASSEL SHEATH4; UB2, UNBRANCHED2; UB3, UNBRANCHED3; KRN4, KERNEL ROW NUMBER4. Fig. 2 View largeDownload slide Model of the regulation of ear trait-related QTLs. In the inflorescence meristem, the genes involved in CLAVATA signaling play major roles in controlling meristematic activity. In the central region of the inflorescence meristem, FEA3 perceives the ZmFCP1 signal from the peripheral region (dotted arrows) and restricts ZmWUS1 to a small domain. ZmCLV3 is proposed to be expressed in the tip and is perceived by membrane-localized FEA2, which interacts with CT2. TD1, the ortholog of Arabidopsis CLV1, is expressed in the peripheral region. FEA2 and TD1 are proposed to regulate ZmWUS1 negatively based on the well-known model in Arabidopsis. In the process of lateral meristem development, the homologous transcription factor genes TSH4, UB2 and UB3 are initially co-expressed with miR156, but are targeted and restricted to the base of the developing spikelet meristems later on. UB3 is also regulated by an intergenic region, KRN4, via a long-distance regulation model. KRN4 and UB3 interact genetically to regulate inflorescence development. FEA3, FASCIATED EAR3; ZmFCP1, Zea mays FON2-LIKE CLE PROTEIN1; ZmWUS1, Zea mays WUSCHEL1; FEA2, FASCIATED EAR2; CT2, COMPACT PLANT2; TD1, THICK TASSEL DWARF1; CLV1, CLAVATA1; CG1, CORNGRASS1; TSH4, TASSEL SHEATH4; UB2, UNBRANCHED2; UB3, UNBRANCHED3; KRN4, KERNEL ROW NUMBER4. Fig. 3 View largeDownload slide Genetic effects of several representative inflorescence-related genes on the ear architecture of maize. The weak allele fea2-1328 produces more kernel rows but these have a normal arrangement (Bommert et al. 2013a), and hybrids with the weak allele fea3-2 show further enhancement in ear length and kernel row number (Je et al. 2016). KRN4 produces more kernels than the wild type with normal ear length and kernel arrangement (Liu et al. 2015b). FEA2, FASCIATED EAR2; FEA3, FASCIATED EAR3; KRN4, KERNEL ROW NUMBER4; WT, wild-type line; B73 and W22 are maize inbred lines. The images were used with permission from the authors. Fig. 3 View largeDownload slide Genetic effects of several representative inflorescence-related genes on the ear architecture of maize. The weak allele fea2-1328 produces more kernel rows but these have a normal arrangement (Bommert et al. 2013a), and hybrids with the weak allele fea3-2 show further enhancement in ear length and kernel row number (Je et al. 2016). KRN4 produces more kernels than the wild type with normal ear length and kernel arrangement (Liu et al. 2015b). FEA2, FASCIATED EAR2; FEA3, FASCIATED EAR3; KRN4, KERNEL ROW NUMBER4; WT, wild-type line; B73 and W22 are maize inbred lines. The images were used with permission from the authors. In the well-known CLV signaling pathway, the secreted CLV3 and CLV3/ESR-related (CLE) peptides are perceived by LRR-type receptor kinases, and the signal is then transmitted downstream through phosphorylation of receptors (Stone et al. 1998, Zhao et al. 2011, Somssich et al. 2016). Since FEA2 lacks a kinase domain, it is not difficult to imagine an intermediate step which transmits the extracellular CLV3-derived signal to an intracellular signaling pathway. The isolation of CT2 provides strong support for this hypothesis (Bommert et al. 2013b). CT2 encodes the predicted α-subunit (Gα) of a heterotrimeric GTP-binding protein and localizes on the plasma membrane, similarly to FEA2. The reduced sensitivity of ct2 mutants to the CLV3 peptide supports the involvement of CT2 in the transmission of a CLV3-derived signal. Genetic and biochemical analyses demonstrate that CT2 and FEA2 act in a common pathway and probably interact to form a complex. Thus, it has been proposed that FEA2 perceives and transmits the CLV3 signal by interacting with the CT2/Gα protein to control stem cell proliferation (Fig. 2). Recently, a novel regulator of inflorescence architecture, encoding the LRR receptor-like protein, FEA3, has been isolated through a map-based cloning approach and shows even greater potential than FEA2 to increase grain yield (Je et al. 2016). Mutation of FEA3 leads to an enlarged IM and SAM, and thus it has been proposed that FEA3 also functions to restrict meristematic activity by controlling stem cell proliferation. FEA3 is expressed in and below the organizing center (OC) of the SAM as well as in lateral leaf primordia, showing a different pattern from other CLV-type receptors, e.g. CLV1 in Arabidopsis, which is strictly expressed in the meristem center containing the OC (Clark et al. 1997, Jeong et al. 1999). FEA3 responds to a different CLE peptide, ZmFCP1, which is strikingly different from the conventional CLV3 peptide. While CLV3 is secreted from the stem cells at the tip of the meristem in Arabidopsis and rice (Fletcher et al. 1999, Suzaki et al. 2008, Je et al. 2016), ZmFCP1 is expressed in differentiating lateral organ primordia. Furthermore, expression of the putative OC marker ZmWUS1 (Zea mays WUSCHEL 1) spreads downward to the region below the OC in fea3 mutant inflorescences, rather than the upward spread of WUS expression caused by mutations in other CLV-type receptors (Schoof et al. 2000). Therefore, FEA3 is proposed to encode a distinct receptor that transmits signals from differentiating cells back to the stem cells in the center of the meristem, whereby the ZmWUS1 protein is restricted to a certain domain (Fig. 2). This novel regulation loop provides new insights into the conserved CLV–WUS feedback loop and exposes the underlying mechanisms controlling stem cell proliferation and maize ear architecture. Genetic interactions suggest that FEA3 functions independently of FEA2, but they may converge on the same downstream targets (Je et al. 2016). However, complete loss-of-function mutations in FEA3 lead to shorter ears and irregular kernel arrangements with lower kernel yield, probably due to competition for resources (Bommert et al. 2013a). Importantly, however, plants with weak fea3 alleles have normal growth and produce very mildly fasciated ears, and hybrids harboring weak fea3 alleles show further enhancement in multiple ear traits including EL, KRN, KNR and ear weight (Je et al. 2016). Thus, this novel feedback regulation loop could be used to develop new molecular tools for the genetic improvement of maize grain yield, such as additional weak alleles, and also to illuminate the genetic mechanism of pleiotropic QTLs for inflorescence architecture-related traits (Fig. 2). MicroRNA-related pathway MicroRNAs (miRNAs) have been shown to be important for ear architecture. One early report about the dominant mutant Corngrass1 (CG1), which encodes two tandem miR156 genes, connects miRNAs with a major QTL responsible for glume differences in maize and its ancestor teosinte (Wang et al. 2005, Chuck et al. 2007a). The SBP transcription factor TEOSINTE GLUME ARCHITECTURE1 (TGA1) is responsible for this major QTL and is targeted by CG1/miR156 (Chuck et al. 2007a). CG1 mutants also develop small ears and unbranched tassels, covered by large bract leaves and lacking ordered spikelet rows, and the IM of CG1 tassels is fasciated. These defects in CG1 inflorescences could be explained by decreased expression of other targets of miR156, e.g TSH4 (Chuck et al. 2010). TSH4 encodes an SBP-box gene, with high similarity to its homologs UNBRANCHED2 (UB2) and UB3 in coding sequence, expression patterns and function in inflorescence development (Chuck et al. 2014). Loss-of-function mutations in UB2, UB3 and TSH4 result in fasciation in ear inflorescences, and consequently more KRNs, but fewer TBs. These redundant factors function to restrict initiation of lateral primordia and to allow cell renewal in the meristem region. Consistently, they are expressed throughout the base of the meristem, where cells are differentiating into lateral organs, but are excluded from the central tip of the meristem. This may be due to repression by meristem-expressed miR156, which shows a complementary pattern to TSH4 and probably targets these SBP genes (Chuck et al. 2010). A recent study has expanded our understanding of the mechanism of UB3 in negative regulation of IM activity in maize and rice (Du et al. 2017). Expression of UB3 in rice leads to suppressed panicle branching due to a reduced level of active cytokinins. UB3 was also shown to regulate auxin genes, suggesting the possibility that it controls inflorescence development through cross-talk between cytokinin and auxin. Thus, miR156, which targets UB2, UB3 and TSH4, probably regulates the IM through hormonal controls. QTLs for KRN and TBN map to the UB3 locus, and the UB3-linked KRN QTL has the largest effect of any ear trait QTL in the American NAM population (Chuck et al. 2014). Association analysis shows that the variant Ser220 > Asn within the third exon of UB3 is associated with increased KRN, and another variant Val260 > Met is associated with increased TBN. These two polymorphisms affect the inflorescence architecture differently and are not present in teosinte, suggesting that the natural variants in UB3 arose during crop improvement rather than during maize domestication. This demonstrates a high potential for UB3 to be a useful tool for crop improvement, which is also supported by the function of its rice ortholog WEALTHY FARMER’S PANICLE (WFP)/IDEAL PLANT ARCHITECTURE (IPA). Increased WFP/IPA levels lead to more panicle branches and higher grain yield in rice (Jiao et al. 2010, Miura et al. 2010). Putative long-distance regulation Recently, successful isolation of another major QTL for KRN, KRN4, illustrates the significant contribution of selection to the improvement of corn yield (Liu et al. 2015a). KRN4 maps to an approximately 3 kb intergenic region located about 60 kb downstream from UB3 harboring a 1.2 kb transposon-containing insertion responsible for the increase in KRN. The near isogenic line containing this 1.2 kb insertion has significantly increased IM size, KRN, ED, cob diameter and final grain yield. Recombinant lines containing the KRN4 locus showed lower expression of UB3 and correspondingly higher KRN (Liu et al. 2015b), consistent with the negative effect of UB3 on KRN (Chuck et al. 2014). Thus, the ear trait QTL KRN4 has a high application value for corn yield improvement (Fig. 3). Further analysis revealed selection for this 1.2 kb insertion during maize domestication and improvement. The genetic relationship between UB3 and KRN4 is supported by the fact that the association of the 1.2 kb insertion with KRN is partially dependent on an A/G SNP in the third exon of UB3. Thus, the control of ear traits by KRN4 is probably through long-distance regulation on UB3, possibly via 3-D folding of chromatin or epigenetic modifications, which need to be further investigated (Fig. 2). Such long-distance regulation has been proposed in the characterization of the TB1 locus responsible for suppression of tillers in maize (Clark et al. 2006, Studer et al. 2011). Chromatin folding in maize has been detected in two epialleles of the B1 gene, B-I and B', which are tissue specifically regulated and are involved in paramutation (Louwers et al. 2009). Perspectives In the past decades, isolation of important genes controlling yield-related traits of maize was mainly based on characterizing classical mutants in combination with transposon or map-based cloning strategies. These mutants usually display quite severe defects in agronomic traits and therefore are not of use in maize breeding. However, identification and characterization of novel inflorescence regulators will extend our knowledge of the regulatory networks of inflorescence development (Fig. 2) and provide diagnostic and germplasm tools for improving ear traits and grain yield. From the weak mutant alleles of FEA2 and FEA3, which were initially isolated using knowledge derived from null alleles (Bommert et al. 2013a, Je et al. 2016), we learn that it is feasible to improve inflorescence architecture by exploring natural allelic differences in key development genes. This weak allele approach provides valuable germplasm with the potential to increase grain yield (Fig. 3). In addition, weak mutant alleles of key inflorescence genes are frequently responsible for quantitative variations of ear-related traits in non-mutant genotypes, as illustrated by FEA2 and UB3 (Bommert et al. 2013a, Chuck et al. 2014). These cases encourage us to pay more attention to known genes regulating inflorescence development if they are located in the ear trait-related QTL intervals. In addition, isolation of KRN4 reminds us that non-coding regions also play an important role in the regulation of quantitative variation of inflorescence architecture or grain yield-related traits (Liu et al. 2015b). Such intergenic regions are often the cause of natural variation and would not be identified in classical mutants, demonstrating the importance of QTL isolation, both to expand our understanding of the genetic basis of ear-related traits and to enrich the germplasm with desirable alleles for improving the agronomic traits. Currently, isolation of genes responsible for QTLs is still challenging due to the complicated genetic interactions among loci and the high complexity of the maize genome. Furthermore, QTL intervals mapped in small populations frequently cover very large physical distances containing hundreds to thousands of genes. In addition to traditional map-based cloning, several NGS methods, such as QTL-seq (Takagi et al. 2013), GBS (De Donato et al. 2013), SHOREmap (Schneeberger et al. 2009) and restriction site associated RNA sequencing (RARseq) (Alabady et al. 2015), and the combination with CRISPR (clustered regularly interspaced short palindromic repeats) technology, will be powerful tools to accelerate maize QTL characterization and deepen our understanding of the genetic regulation and underlying molecular mechanisms of inflorescence architecture in maize. Funding The work is supported by the National Natural Science Foundation of China  and the National Key Research and Development Program of China [2016YFD0100400]. Acknowledgments We thank Dr. David Jackson and Dr. Lei Liu for kindly supplying images of fea2, fea3 and KRN4. We are grateful to Dr. David Jackson for his critical review, and Hannes Claeys for comments. Disclosures The authors have no conflicts of interest to declare. 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Google Scholar PubMed Abbreviations Abbreviations AM axillary meristem BM branch meristem BZ branch zone length CLE CLV3/ESR-related CLV CLAVATA CT2 COMPACT PLANT2 EL ear length ED ear diameter FEA FASCIATED EAR GBS genotyping by sequencing GWAS genome-wide association study IM inflorescence meristem KRN kernel row number KNR kernel number per row LRR leucine-rich repeat NAM nested association mapping NGS next-generation sequencing OC organizing center QTL quantitative trait locus RIL recombinant inbred line SAM shoot apical meristem SL spikelet length SM spikelet meristem SNP single nucleotide polymorphism SPM spikelet pair meristem TAV trait-associated variant TB tassel branch TBN tassel branch number TL tassel length TSH4 TASSEL SHEATH4 UB UNBRANCHED WUS WUSCHEL © The Author(s) 2018. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: email@example.com
Plant and Cell Physiology – Oxford University Press
Published: Mar 1, 2018
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