Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population

Maximizing the Radiation Use Efficiency by Matching the Leaf Area and Leaf Nitrogen Vertical Distributions in a Maize Canopy: A Simulation Study

The radiation use efficiency (RUE) is one of the most important functional traits determining crop productivity. The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence. However, previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth, rarely considering the optimization of the leaf area distribution, especially for C4 crops. The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy. To test this hypothesis, various virtual maize canopies were generated by combining the leaf inclination angle, vertical leaf area distribution, and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model. We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE. The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios, particularly in dense canopies. This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.

Dissection of Common Rust Resistance in Tropical Maize Multiparent Population through GWAS and Linkage Studies

Common rust (CR), caused by Puccina sorghi, is a major foliar disease in maize that leads to quality deterioration and yield losses. To dissect the genetic architecture of CR resistance in maize, this study utilized the susceptible temperate inbred line Ye107 as the male parent crossed with three resistant tropical maize inbred lines (CML312, D39, and Y32) to generate 627 F7 recombinant inbred lines (RILs), with the aim of identifying maize disease-resistant loci and candidate genes for common rust. Phenotypic data showed good segregation between resistance and susceptibility, with varying degrees of resistance observed across different subpopulations. Significant genotype effects and genotype × environment interactions were observed, with heritability ranging from 85.7% to 92.2%. Linkage and genome-wide association analyses across the three environments identified 20 QTLs and 62 significant SNPs. Among these, seven major QTLs explained 66% of the phenotypic variance. Comparison with six SNPs repeatedly identified across different environments revealed overlap between qRUST3-3 and Snp-203,116,453, and Snp-204,202,469. Haplotype analysis indicated two different haplotypes for CR resistance for both the SNPs. Based on LD decay plots, three co-located candidate genes, Zm00001d043536, Zm00001d043566, and Zm00001d043569, were identified within 20 kb upstream and downstream of these two SNPs. Zm00001d043536 regulates hormone regulation, Zm00001d043566 controls stomatal opening and closure, related to trichome, and Zm00001d043569 is associated with plant disease immune responses. Additionally, we performed candidate gene screening for five additional SNPs that were repeatedly detected across different environments, resulting in the identification of five candidate genes. These findings contribute to the development of genetic resources for common rust resistance in maize breeding programs.

Dynamic Regulation of Lipid Droplet Biogenesis in Plant Cells and Proteins Involved in the Process

Lipid droplets (LDs) are ubiquitous, dynamic organelles found in almost all organisms, including animals, protists, plants and prokaryotes. The cell biology of LDs, especially biogenesis, has attracted increasing attention in recent decades because of their important role in cellular lipid metabolism and other newly identified processes. Emerging evidence suggests that LD biogenesis is a highly coordinated and stepwise process in animals and yeasts, occurring at specific sites of the endoplasmic reticulum (ER) that are defined by both evolutionarily conserved and organism- and cell type-specific LD lipids and proteins. In plants, understanding of the mechanistic details of LD formation is elusive as many questions remain. In some ways LD biogenesis differs between plants and animals. Several homologous proteins involved in the regulation of animal LD formation in plants have been identified. We try to describe how these proteins are synthesized, transported to the ER and specifically targeted to LD, and how these proteins participate in the regulation of LD biogenesis. Here, we review current work on the molecular processes that control LD formation in plant cells and highlight the proteins that govern this process, hoping to provide useful clues for future research.

Natural variation of respiration-related traits in plants

Plant respiration is one of the greatest global metabolic fluxes, but rates of respiration vary massively both within different cell types as well as between different individuals and different species. Whilst this is well known, few studies have detailed population-level variation of respiration until recently. The last 20 years have seen a renaissance in studies of natural variance. In this review, we describe how experimental breeding populations and collections of large populations of accessions can be used to determine the genetic architecture of plant traits. We further detail how these approaches have been used to study the rate of respiration per se as well as traits that are intimately associated with respiration. The review highlights specific breakthroughs in these areas but also concludes that the approach should be more widely adopted in the study of respiration per se as opposed to the more frequently studied respiration-related traits.

Quantitative trait locus mapping and identification of candidate genes for resistance to Verticillium wilt in four recombinant inbred line populations of Gossypium hirsutum

Improving resistance to Verticillium wilt is of great significance for achieving high and stable yields of Upland cotton (Gossypium hirsutum). To deeply understand the genetic basis of cotton resistance to Verticillium wilt, Verticillium wilt-resistant Upland Lumianyan 28 and four Verticillium wilt-susceptible Acala cotton cultivars were used to create four recombinant inbred line (RIL) populations of 469 families through nested hybridization. Phenotypic data collected in five stressful environments were used to select resistant and sensitive lines and create a mixed pool of extreme phenotypes for BSA-seq. A total of 8 QTLs associated with Verticillium wilt resistance were identified on 4 chromosomes, of which qVW-A12-5 was detected simultaneously in the RIL populations and in one of the RIL populations and was identified for the first time. According to the sequence comparison and transcriptome analysis of candidate genes in the QTL interval between parents and pools, 4 genes were identified in the qVW-A12-5 interval. qRT-PCR of parental and phenotypically extreme lines revealed that Gh_CPR30 was induced by and may be a candidate gene for resistance to Verticillium wilt in G. hirsutum. Furthermore, VIGS technology revealed that the disease severity index (DSI) of the Gh_CPR30-silenced plants was significantly higher than that of the control. These results indicate that the Gh_CPR30 gene plays an important role in the resistance of G. hirsutum to Verticillium wilt, and the study provides a molecular basis for analyzing the molecular mechanism underlying G. hirsutum resistance to Verticillium wilt.

THP9 enhances seed protein content and nitrogen-use efficiency in maize

Teosinte, the wild ancestor of maize (Zea mays subsp. mays), has three times the seed protein content of most modern inbreds and hybrids, but the mechanisms that are responsible for this trait are unknown^1,2. Here we use trio binning to create a contiguous haplotype DNA sequence of a teosinte (Zea mays subsp. parviglumis) and, through map-based cloning, identify a major high-protein quantitative trait locus, TEOSINTE HIGH PROTEIN 9 (THP9), on chromosome 9. THP9 encodes an asparagine synthetase 4 enzyme that is highly expressed in teosinte, but not in the B73 inbred, in which a deletion in the tenth intron of THP9-B73 causes incorrect splicing of THP9-B73 transcripts. Transgenic expression of THP9-teosinte in B73 significantly increased the seed protein content. Introgression of THP9-teosinte into modern maize inbreds and hybrids greatly enhanced the accumulation of free amino acids, especially asparagine, throughout the plant, and increased seed protein content without affecting yield. THP9-teosinte seems to increase nitrogen-use efficiency, which is important for promoting a high yield under low-nitrogen conditions.

Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize

Background

Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations.

Results

Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants.

Conclusions

Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (10.25739/hybz-2957).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02747-2.

Using breeding and quantitative genetics to understand the C4 pathway

Reducing photorespiration in C3 crops could significantly increase rates of photosynthesis and yield. One method to achieve this would be to integrate C4 photosynthesis into C3 species. This objective is challenging as it involves engineering incompletely understood traits into C3 leaves, including complex changes to their biochemistry, cell biology, and anatomy. Quantitative genetics and selective breeding offer underexplored routes to identify regulators of these processes. We first review examples of natural intraspecific variation in C4 photosynthesis as well as the potential for hybridization between C3 and C4 species. We then discuss how quantitative genetic approaches including artificial selection and genome-wide association could be used to better understand the C4 syndrome and in so doing guide the engineering of the C4 pathway into C3 crops.

Limitation of C4 photosynthesis by low carbonic anhydrase activity increases with temperature but does not influence mesophyll CO2 conductance

The CO2-concentrating mechanism (CCM) in C4 plants is initiated by the uptake of bicarbonate (HCO3-) via phosphoenolpyruvate carboxylase (PEPC). Generation of HCO3- for PEPC is determined by the interaction between mesophyll CO2 conductance and the hydration of CO2 to HCO3- by carbonic anhydrase (CA). Genetic reduction of CA was previously shown not to limit C4 photosynthesis under ambient atmospheric partial pressures of CO2 (pCO2). However, CA activity varies widely across C4 species and it is unknown if there are specific environmental conditions (e.g. high temperature) where CA may limit HCO3- production for C4 photosynthesis. Additionally, CA activity has been suggested to influence mesophyll conductance, but this has not been experimentally tested. We hypothesize that CA activity can limit PEPC at high temperatures, particularly at low pCO2, but does not directly influence gm. Here we tested the influence of genetically reduced CA activity on photosynthesis and gm in the C4 plant Zea mays under a range of pCO2 and temperatures. Reduced CA activity limited HCO3- production for C4 photosynthesis at low pCO2 as temperatures increased, but did not influence mesophyll conductance. Therefore, high leaf CA activity may enhance C4 photosynthesis under high temperature when stomatal conductance restricts the availability of atmospheric CO2.

Preparation and Curation of Phenotypic Datasets

Based on case studies, in this chapter we discuss the extent to which the number and identity of quantitative trait loci (QTL) identified from genome-wide association studies (GWAS) are affected by curation and analysis of phenotypic data. The chapter demonstrates through examples the impact of (1) cleaning of outliers, and of (2) the choice of statistical method for estimating genotypic mean values of phenotypic inputs in GWAS. No cleaning of outliers resulted in the highest number of dubious QTL, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false positives and the risk of missing interesting, yet rare alleles. The choice of the statistical method to estimate genotypic mean values also affected the output of GWAS analysis, with reduced QTL overlap between methods. Using mixed models that capture spatial trends, among other features, increased the narrow-sense heritability of traits, the number of identified QTL and the overall power of GWAS analysis. Cleaning and choosing robust statistical models for estimating genotypic mean values should be included in GWAS pipelines to decrease both false positive and false negative rates of QTL detection.

Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava

GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture. Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of - 0.001-0.71 for the four yield-related traits and 0.33-0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.

Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed

Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.

Genome-wide association mapping for agronomic traits in an 8-way Upland cotton MAGIC population by SLAF-seq

One sub-MAGIC population was genotyped using SLAF-seq, and QTLs and candidate genes for agronomic traits were identified in Upland cotton. The agronomic traits of Upland cotton have serious impacts on cotton production, as well as economic benefits. To discover the genetic basis of important agronomic traits in Upland cotton, a subset MAGIC (multi-parent advanced generation inter-cross) population containing 372 lines (SMLs) was selected from an 8-way MAGIC population with 960 lines. The 372 lines and 8 parents were phenotyped in six environments and deeply genotyped by SLAF-seq with 60,495 polymorphic SNPs. The genetic diversity indexes of all SNPs were 0.324 and 0.362 for the parents and MAGIC lines, respectively. The LD decay distance of the SMLs was 600 kb (r² = 0.1). Genome-wide association mapping was performed using 60,495 SNPs and the phenotypic data of the SMLs, and 177 SNPs were identified to be significantly associated with 9 stable agronomic traits in multiple environments. The identified SNPs were divided into 117 QTLs (quantitative trait loci) by LD decay distance, explaining 5.44% to 31.64% of the phenotypic variation. Among the 117 QTLs, 3 QTLs were stable in multiple environments, and 11 QTL regions were proven to have pleiotropism associated with multiple traits. Within QTL regions, 154 genes were preferentially expressed in correlated tissues, and 8 genes with known functions were identified as priori candidate genes. Two genes, GhACT1 and GhGASL3, reported to have clear functions, were, respectively, located in qFE-A05-4 and qFE-D04-3, two stable QTLs for FE. This study revealed the genetic basis of important agronomic traits of Upland cotton, and the results will facilitate molecular breeding in cotton.

The utility of metabolomics as a tool to inform maize biology

With the rise of high-throughput omics tools and the importance of maize and its products as food and bioethanol, maize metabolism has been extensively explored. Modern maize is still rich in genetic and phenotypic variation, yielding a wide range of structurally and functionally diverse metabolites. The maize metabolome is also incredibly dynamic in terms of topology and subcellular compartmentalization. In this review, we examine a broad range of studies that cover recent developments in maize metabolism. Particular attention is given to current methodologies and to the use of metabolomics as a tool to define biosynthetic pathways and address biological questions. We also touch upon the use of metabolomics to understand maize natural variation and evolution, with a special focus on research that has used metabolite-based genome-wide association studies (mGWASs).

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Maize metabolome and proteome responses to controlled cold stress partly mimic early-sowing effects in the field and differ from those of Arabidopsis

In Northern Europe, sowing maize one-month earlier than current agricultural practices may lead to moderate chilling damage. However, studies of the metabolic responses to low, non-freezing, temperatures remain scarce. Here, genetically-diverse maize hybrids (Zea mays, dent inbred lines crossed with a flint inbred line) were cultivated in a growth chamber at optimal temperature and then three decreasing temperatures for 2 days each, as well as in the field. Leaf metabolomic and proteomic profiles were determined. In the growth chamber, 50% of metabolites and 18% of proteins changed between 20 and 16°C. These maize responses, partly differing from those of Arabidopsis to short-term chilling, were mapped on genome-wide metabolic maps. Several metabolites and proteins showed similar variation for all temperature decreases: seven MS-based metabolite signatures and two proteins involved in photosynthesis decreased continuously. Several increasing metabolites or proteins in the growth-chamber chilling conditions showed similar trends in the early-sowing field experiment, including trans-aconitate, three hydroxycinnamate derivatives, a benzoxazinoid, a sucrose synthase, lethal leaf-spot 1 protein, an allene oxide synthase, several glutathione transferases and peroxidases. Hybrid groups based on field biomass were used to search for the metabolite or protein responses differentiating them in growth-chamber conditions, which could be of interest for breeding.

Metabolic Profile Discriminates and Predicts Arabidopsis Susceptibility to Virus under Field Conditions

As obligatory parasites, plant viruses alter host cellular metabolism. There is a lack of information on the variability of virus-induced metabolic responses among genetically diverse plants in a natural context with daily changing conditions. To decipher the metabolic landscape of plant-virus interactions in a natural setting, twenty-six and ten accessions of Arabidopsis thaliana were inoculated with Turnip mosaic virus (TuMV), in two field experiments over 2 years. The accessions were measured for viral accumulation, above-ground biomass, targeted and untargeted metabolic profiles. The phenotypes of the accessions ranged from susceptibility to resistance. Susceptible and resistant accessions were shown to have different metabolic routes after inoculation. Susceptible genotypes accumulate primary and secondary metabolites upon infection, at the cost of hindered growth. Twenty-one metabolic signatures significantly accumulated in resistant accessions whereas they maintained their growth as mock-inoculated plants without biomass penalty. Metabolic content was demonstrated to discriminate and be highly predictive of the susceptibility of inoculated Arabidopsis. This study is the first to describe the metabolic landscape of plant-virus interactions in a natural setting and its predictive link to susceptibility. It provides new insights on plant-virus interactions. In this undomesticated species and in ecologically realistic conditions, growth and resistance are in a permanent conversation.

Transcriptomic and genome-wide association study reveal long noncoding RNAs responding to nitrogen deficiency in maize

BACKGROUND

Long noncoding RNAs (lncRNAs) play important roles in essential biological processes. However, our understanding of lncRNAs as competing endogenous RNAs (ceRNAs) and their responses to nitrogen stress is still limited.

RESULTS

Here, we surveyed the lncRNAs and miRNAs in maize inbred line P178 leaves and roots at the seedling stage under high-nitrogen (HN) and low-nitrogen (LN) conditions using lncRNA-Seq and small RNA-Seq. A total of 894 differentially expressed lncRNAs and 38 different miRNAs were identified. Co-expression analysis found that two lncRNAs and four lncRNA-targets could competitively combine with ZmmiR159 and ZmmiR164, respectively. To dissect the genetic regulatory by which lncRNAs might enable adaptation to limited nitrogen availability, an association mapping panel containing a high-density single-nucleotide polymorphism (SNP) array (56,110 SNPs) combined with variable LN tolerant-related phenotypes obtained from hydroponics was used for a genome-wide association study (GWAS). By combining GWAS and RNA-Seq, 170 differently expressed lncRNAs within the range of significant markers were screened. Moreover, 40 consistently LN-responsive genes including those involved in glutamine biosynthesis and nitrogen acquisition in root were identified. Transient expression assays in Nicotiana benthamiana demonstrated that LNC_002923 could inhabit ZmmiR159-guided cleavage of Zm00001d015521.

CONCLUSIONS

These lncRNAs containing trait-associated significant SNPs could consider to be related to root development and nutrient utilization. Taken together, the results of our study can provide new insights into the potential regulatory roles of lncRNAs in response to LN stress, and give valuable information for further screening of candidates as well as the improvement of maize resistance to LN stress.

Recent Advances in Carbon and Nitrogen Metabolism in C3 Plants

C and N are the most important essential elements constituting organic compounds in plants. The shoots and roots depend on each other by exchanging C and N through the xylem and phloem transport systems. Complex mechanisms regulate C and N metabolism to optimize plant growth, agricultural crop production, and maintenance of the agroecosystem. In this paper, we cover the recent advances in understanding C and N metabolism, regulation, and transport in plants, as well as their underlying molecular mechanisms. Special emphasis is given to the mechanisms of starch metabolism in plastids and the changes in responses to environmental stress that were previously overlooked, since these changes provide an essential store of C that fuels plant metabolism and growth. We present general insights into the system biology approaches that have expanded our understanding of core biological questions related to C and N metabolism. Finally, this review synthesizes recent advances in our understanding of the trade-off concept that links C and N status to the plant's response to microorganisms.

Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

Effect of ammonium and nitrate supplies on nitrogen and sucrose metabolism of Cabernet Sauvignon (Vitis vinifera cv.)

BACKGROUND

The quality of wine is highly dependent on the quality of berries. Development of berries is influenced by the type and ratio of different nitrogen supplies in the soil. To understand the impact of varying sources and levels of nitrate and ammonium on sucrose and nitrogen metabolism of Vitis vinifera cv. Cabernet Sauvignon, we tested nutrient solutions with four NO₃ ^- -N:NH₄ ⁺ -N ratios (100:0, 75:25, 50:50, 0:100) through the root system.

RESULTS

The form and quantity of nitrogen affected berries and leaves with source-sink relationships. Soluble sugar levels were significantly higher in plants treated with mixed nitrogen sources (75:25 and 50:50) compared to single nitrogen sources (100:0 and 0:100). In particular, treating plants with mixed nitrogen source at a 75:25 ratio resulted in 22% higher fructose levels in berries compared to the 50:50 treatment. In addition, mixed nitrogen treatments resulted in significantly higher amino acid levels and protein content. Mixed nitrogen substrates also increased the expression of enzymes involved in both nitrogen and sucrose metabolism.

CONCLUSION

Plants did not maximize the nitrogen supply when single form nitrogen was provided, and the mixed nitrogen substrates consistently increased the amount of available carbon and nitrogen in the berries and leaves. We found that NO₃ ^- -N:NH₄ ⁺ -N ratio of 75:25 was the optimum formula for improving nitrogen and sucrose metabolism, and reducing the competition between nitrogen and sucrose. By examining the nutrient utilization of plants cultivated with different nitrogen forms, the present study provides insights into improving cultivation and production practices. © 2020 Society of Chemical Industry.

Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors

The transcription regulatory network inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex network. In this study, we use large-scale ChIP-seq to reconstruct it in the maize leaf, and train machine-learning models to predict TF binding and co-localization. The resulting network covers 77% of the expressed genes, and shows a scale-free topology and functional modularity like a real-world network. TF binding sequence preferences are conserved within family, while co-binding could be key for their binding specificity. Cross-species comparison shows that core network nodes at the top of the transmission of information being more conserved than those at the bottom. This study reveals the complex and redundant nature of the plant transcription regulatory network, and sheds light on its architecture, organizing principle and evolutionary trajectory.

How does nitrogen shape plant architecture?

Plant nitrogen (N), acquired mainly in the form of nitrate and ammonium from soil, dominates growth and development, and high-yield crop production relies heavily on N fertilization. The mechanisms of root adaptation to altered supply of N forms and concentrations have been well characterized and reviewed, while reports concerning the effects of N on the architecture of vegetative and reproductive organs are limited and are widely dispersed in the literature. In this review, we summarize the nitrate and amino acid regulation of shoot branching, flowering, and panicle development, as well as the N regulation of cell division and expansion in shaping plant architecture, mainly in cereal crops. The basic regulatory steps involving the control of plant architecture by the N supply are auxin-, cytokinin-, and strigolactone-controlled cell division in shoot apical meristem and gibberellin-controlled inverse regulation of shoot height and tillering. In addition, transport of amino acids has been shown to be involved in the control of shoot branching. The N supply may alter the timing and duration of the transition from the vegetative to the reproductive growth phase, which in turn may affect cereal crop architecture, particularly the structure of panicles for grain yield. Thus, proper manipulation of N-regulated architecture can increase crop yield and N use efficiency.

Ten Years of the Maize Nested Association Mapping Population: Impact, Limitations, and Future Directions

It has been just over a decade since the release of the maize (Zea mays) Nested Association Mapping (NAM) population. The NAM population has been and continues to be an invaluable resource for the maize genetics community and has yielded insights into the genetic architecture of complex traits. The parental lines have become some of the most well-characterized maize germplasm, and their de novo assemblies were recently made publicly available. As we enter an exciting new stage in maize genomics, this retrospective will summarize the design and intentions behind the NAM population; its application, the discoveries it has enabled, and its influence in other systems; and use the past decade of hindsight to consider whether and how it will remain useful in a new age of genomics.

Large-scale metabolite quantitative trait locus analysis provides new insights for high-quality maize improvement

It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC₂ F₇ population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling-stage leaf, grouting-stage leaf, young kernel and mature kernel) with clear tissue-specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi-tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs.

Transcriptome profiling revealed diverse gene expression patterns in poplar (Populus × euramericana) under different planting densities

Certain plant genotypes can achieve optimal growth under appropriate environmental conditions. Under high planting density conditions, plants undergo competition for uptake and utilization of light and nutrients. However, the relationship between whole-genome expression patterns and the planting density in perennial woody plants remains unknown. In this study, whole-genome RNA sequencing of poplar (Populus × euramericana) was carried out at three different sampling heights to determine gene expression patterns under high (HD) and low (LD) planting densities. As a result, 4,004 differentially expressed genes (DEGs) were detected between HD and LD, of which 2,300, 701, and 1,003 were detected at the three positions, upper, middle and bottom, respectively. Function annotation results further revealed that a large number of the DEGs were involved in distinct biological functions. There were significant changes in the expression of metabolism-related and stimulus-related genes in response to planting density. There were 37 DEGs that were found at all three positions and were subsequently screened. Several DEGs related to plant light responses and photosynthesis were observed at different positions. Meanwhile, numbers of genes related to auxin/indole-3-acetic acid, and carbon and nitrogen metabolism were also revealed, displaying overall trends of upregulation under HD. These findings provide a basis for identifying candidate genes related to planting density and could increase our molecular understanding of the effect of planting density on gene expression.

Genetic analyses of nitrogen assimilation enzymes in Brassica juncea (L.) Czern & Coss

Nitrogen (N) is a critical input for plant growth and development. A better understanding of N uptake and utilization is important to develop plant breeding strategies for improving nitrogen use efficiency (NUE). With that objective in mind, we assayed a SNP-genotyped association panel comprising 92 inbred lines for the activities of nitrate reductase (NR), nitrite reductase (NIR), glutamine synthetase (GS) and glutamate synthase (GOGAT). All these enzymes are associated with N assimilation. The experiments were carried out at two levels of N application: no added N (N₀) and agrnomically recommened dose (100 kg/ha) of N application (N₁₀₀). Genome wide association studies (GWAS) helped to identify several marker-trait associations (MTAs), involving chromosomes A01, A06, A08, B02, B04, B05 and B08. These explained high phenotypic variation (up to 32%). Annotation of the genomic region(s) in or around significant SNPs allowed prediction of genes encoding high affinity nitrate transporters, glutamine synthetase 1.3, myb-like transcription factor family protein, bidirectional amino acid transporter 1, auxin signaling F-box 3 and oxidoreductases. This is the first attempt to use GWAS for identification of enzyme QTLs to explain variation for nitrogen assimilation enzymes in Brassica juncea.

Metabolite profiles reveal interspecific variation in operation of the Calvin-Benson cycle in both C4 and C3 plants

Low atmospheric CO2 in recent geological time led to the evolution of carbon-concentrating mechanisms (CCMs) such as C4 photosynthesis in >65 terrestrial plant lineages. We know little about the impact of low CO2 on the Calvin-Benson cycle (CBC) in C3 species that did not evolve CCMs, representing >90% of terrestrial plant species. Metabolite profiling provides a top-down strategy to investigate the operational balance in a pathway. We profiled CBC intermediates in a panel of C4 (Zea mays, Setaria viridis, Flaveria bidentis, and F. trinervia) and C3 species (Oryza sativa, Triticium aestivum, Arabidopsis thaliana, Nicotiana tabacum, and Manihot esculenta). Principal component analysis revealed differences between C4 and C3 species that were driven by many metabolites, including lower ribulose 1,5-bisphosphate in C4 species. Strikingly, there was also considerable variation between C3 species. This was partly due to different chlorophyll and protein contents, but mainly to differences in relative levels of metabolites. Correlation analysis indicated that one contributory factor was the balance between fructose-1,6-bisphosphatase, sedoheptulose-1,7-bisphosphatase, phosphoribulokinase, and Rubisco. Our results point to the CBC having experienced different evolutionary trajectories in C3 species since the ancestors of modern plant lineages diverged. They underline the need to understand CBC operation in a wide range of species.

The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement

We describe the development and application of the Sorghum QTL Atlas, a high-resolution, open-access research platform to facilitate candidate gene identification across three cereal species, sorghum, maize and rice. The mechanisms governing the genetic control of many quantitative traits are only poorly understood and have yet to be fully exploited. Over the last two decades, over a thousand QTL and GWAS studies have been published in the major cereal crops including sorghum, maize and rice. A large body of information has been generated on the genetic basis of quantitative traits, their genomic location, allelic effects and epistatic interactions. However, such QTL information has not been widely applied by cereal improvement programs and genetic researchers worldwide. In part this is due to the heterogeneous nature of QTL studies which leads QTL reliability variation from study to study. Using approaches to adjust the QTL confidence interval, this platform provides access to the most updated sorghum QTL information than any database available, spanning 23 years of research since 1995. The QTL database provides information on the predicted gene models underlying the QTL CI, across all sorghum genome assembly gene sets and maize and rice genome assemblies and also provides information on the diversity of the underlying genes and information on signatures of selection in sorghum. The resulting high-resolution, open-access research platform facilitates candidate gene identification across 3 cereal species, sorghum, maize and rice. Using a number of trait examples, we demonstrate the power and resolution of the resource to facilitate comparative genomics approaches to provide a bridge between genomics and applied breeding.

Insights from transcriptome profiling on the non-photosynthetic and stomatal signaling response of maize carbonic anhydrase mutants to low CO2

BACKGROUND

Carbonic anhydrase (CA) catalyzes the hydration of CO2 in the first biochemical step of C4 photosynthesis, and has been considered a potentially rate-limiting step when CO2 availability within a leaf is low. Previous work in Zea mays (maize) with a double knockout of the two highest-expressed β-CA genes, CA1 and CA2, reduced total leaf CA activity to less than 3% of wild-type. Surprisingly, this did not limit photosynthesis in maize at ambient or higher CO2concentrations. However, the ca1ca2 mutants exhibited reduced rates of photosynthesis at sub-ambient CO2, and accumulated less biomass when grown under sub-ambient CO2 (9.2 Pa). To further clarify the importance of CA for C4 photosynthesis, we assessed gene expression changes in wild-type, ca1 and ca1ca2 mutants in response to changes in pCO2 from 920 to 9.2 Pa.

RESULTS

Leaf samples from each genotype were collected for RNA-seq analysis at high CO2 and at two time points after the low CO2 transition, in order to identify early and longer-term responses to CO2 deprivation. Despite the existence of multiple isoforms of CA, no other CA genes were upregulated in CA mutants. Although photosynthetic genes were downregulated in response to low CO2, differential expression was not observed between genotypes. However, multiple indicators of carbon starvation were present in the mutants, including amino acid synthesis, carbohydrate metabolism, and sugar signaling. In particular, multiple genes previously implicated in low carbon stress such as asparagine synthetase, amino acid transporters, trehalose-6-phosphate synthase, as well as many transcription factors, were strongly upregulated. Furthermore, genes in the CO2 stomatal signaling pathway were differentially expressed in the CA mutants under low CO2.

CONCLUSIONS

Using a transcriptomic approach, we showed that carbonic anhydrase mutants do not compensate for the lack of CA activity by upregulating other CA or photosynthetic genes, but rather experienced extreme carbon stress when grown under low CO2. Our results also support a role for CA in the CO2 stomatal signaling pathway. This study provides insight into the importance of CA for C4 photosynthesis and its role in stomatal signaling.

Crop genome-wide association study: a harvest of biological relevance

With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.

Carbonic Anhydrase Mutants in Zea mays Have Altered Stomatal Responses to Environmental Signals

Stomata regulate transpirational water loss and CO2 uptake for photosynthesis in response to changing environmental conditions. Research investigating stomatal movement has mostly been conducted in C3 eudicot species, which have very different CO2 requirements for photosynthesis relative to C4 grasses. Carbonic anhydrase (CA) catalyzes the hydration of CO2, and its activity has been linked to stomatal aperture regulation in eudicots. The number of Ca genes and their evolutionary history differ between monocots and dicots, and many questions remain unanswered about potential neofunctionalization and subfunctionalization of grass Ca paralogs and their roles in photosynthesis and stomatal conductance. To investigate the roles of different Ca genes in maize (Zea mays), we examined stomatal responses in ca1 and ca2 single mutants as well as a ca1ca2 double mutant. The ca1 and ca2 single mutants had 10% and 87% of the CA activity exhibited by the wild type, respectively, while ca1ca2 had less than 5% of wild-type CA activity. The ca mutants had higher stomatal conductance than the wild type and slower stomatal closure in response to increases in CO2 partial pressure. Contrary to previous reports in eudicots, ca mutants showed slowed stomatal closure in response to the light-dark transition and did not show differences in stomatal density compared with the wild type. These results implicate CA-mediated signaling in the control of stomatal movement but not stomatal development. Drought experiments with ca1ca2 mutant plants suggest a role for CA in water-use efficiency and reveal that Z. mays is not optimized for water-use efficiency under well-watered conditions.

Biochemical and genetic analyses of N metabolism in maize testcross seedlings: 2. Roots

Intracellular factors differentially affected enzyme activities of N assimilation in the roots of maize testcrosses where alanine aminotransferase and glutamate synthase were the main enzymes regulating the levels of glutamate. N is a key macronutrient for plant growth and development. Breeding maize with improved efficiency in N use could help reduce environmental contamination as well as increase profitability for the farmers. Quantitative trait loci (QTL) mapping of traits related to N metabolism in the root tissue was undertaken in a maize testcross mapping population grown in hydroponic cultures. N concentration was negatively correlated with root and total dry mass. Neither the enzyme activities nor metabolites were appreciably correlated between the root and leaf tissues. Repeatability measures for most of the enzymes were lower than for dry mass. Weak negative correlations between most of the enzymes and dry mass resulted likely from dilution and suggested the presence of excess of enzyme activities for maximal biomass production. Glutamate synthase and alanine aminotransferase each explained more variation in glutamate concentration than either aspartate aminotransferase or asparagine synthetase whereas glutamine synthetase was inconsequential. Twenty-six QTL were identified across all traits. QTL models explained 7-43% of the variance with no significant epistasis between the QTL. Thirteen candidate genes were identified underlying QTL within 1-LOD confidence intervals. All the candidate genes were located in trans configuration, unlinked or even on different chromosomes, relative to the known genomic positions of the corresponding structural genes. Our results have implications in improving NUE in maize and other crop plants.

Biochemical and transcriptomic analysis of maize diversity to elucidate drivers of leaf carbon isotope composition

Carbon isotope discrimination is used to study CO2 diffusion, substrate availability for photosynthesis, and leaf biochemistry, but the intraspecific drivers of leaf carbon isotope composition (δ13C) in C4 species are not well understood. In this study, the role of photosynthetic enzymes and post-photosynthetic fractionation on δ13C (‰) was explored across diverse maize inbred lines. A significant 1.3‰ difference in δ13C was observed between lines but δ13C did not correlate with in vitro leaf carbonic anhydrase (CA), phosphoenolpyruvate carboxylase (PEPC), or ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) activity. RNA-sequencing was used to identify potential differences in post-photosynthetic metabolism that would influence δ13C; however, no correlations were identified that would indicate significant differences in post-photosynthetic fractionation between lines. Variation in δ13C has been observed between C4 subtypes, but differential expression of NADP-ME and PEP-CK pathways within these lines did not correlate with δ13C. However, co-expression network analysis provided novel evidence for isoforms of C4 enzymes and putative transporters. Together, these data indicate that diversity in maize δ13C cannot be fully explained by variation in CA, PEPC, or Rubisco activity or gene expression. The findings further emphasise the need for future work exploring the influence of stomatal sensitivity and mesophyll conductance on δ13C in maize.

An integrated multi-layered analysis of the metabolic networks of different tissues uncovers key genetic components of primary metabolism in maize

Primary metabolism plays a pivotal role in normal plant growth, development and reproduction. As maize is a major crop worldwide, the primary metabolites produced by maize plants are of immense importance from both calorific and nutritional perspectives. Here a genome-wide association study (GWAS) of 61 primary metabolites using a maize association panel containing 513 inbred lines identified 153 significant loci associated with the level of these metabolites in four independent tissues. The genome-wide expression level of 760 genes was also linked with metabolite levels within the same tissue. On average, the genetic variants at each locus or transcriptional variance of each gene identified here were estimated to have a minor effect (4.4-7.8%) on primary metabolic variation. Thirty-six loci or genes were prioritized as being worthy of future investigation, either with regard to functional characterization or for their utility for genetic improvement. This target list includes the well-known opaque 2 (O2) and lkr/sdh genes as well as many less well-characterized genes. During our investigation of these 36 loci, we analyzed the genetic components and variations underlying the trehalose, aspartate and aromatic amino acid pathways, thereby functionally characterizing four genes involved in primary metabolism in maize.

The genetic architecture of photosynthesis and plant growth-related traits in tomato

To identify genomic regions involved in the regulation of fundamental physiological processes such as photosynthesis and respiration, a population of Solanum pennellii introgression lines was analyzed. We determined phenotypes for physiological, metabolic, and growth related traits, including gas exchange and chlorophyll fluorescence parameters. Data analysis allowed the identification of 208 physiological and metabolic quantitative trait loci with 33 of these being associated to smaller intervals of the genomic regions, termed BINs. Eight BINs were identified that were associated with higher assimilation rates than the recurrent parent M82. Two and 10 genomic regions were related to shoot and root dry matter accumulation, respectively. Nine genomic regions were associated with starch levels, whereas 12 BINs were associated with the levels of other metabolites. Additionally, a comprehensive and detailed annotation of the genomic regions spanning these quantitative trait loci allowed us to identify 87 candidate genes that putatively control the investigated traits. We confirmed 8 of these at the level of variance in gene expression. Taken together, our results allowed the identification of candidate genes that most likely regulate photosynthesis, primary metabolism, and plant growth and as such provide new avenues for crop improvement.

The genetic architecture of amino acids dissection by association and linkage analysis in maize

Amino acids are both constituents of proteins, providing the essential nutrition for humans and animals, and signalling molecules regulating the growth and development of plants. Most cultivars of maize are deficient in essential amino acids such as lysine and tryptophan. Here, we measured the levels of 17 different total amino acids, and created 48 derived traits in mature kernels from a maize diversity inbred collection and three recombinant inbred line (RIL) populations. By GWAS, 247 and 281 significant loci were identified in two different environments, 5.1 and 4.4 loci for each trait, explaining 7.44% and 7.90% phenotypic variation for each locus in average, respectively. By linkage mapping, 89, 150 and 165 QTLs were identified in B73/By804, Kui3/B77 and Zong3/Yu87-1 RIL populations, 2.0, 2.7 and 2.8 QTLs for each trait, explaining 13.6%, 16.4% and 21.4% phenotypic variation for each QTL in average, respectively. It implies that the genetic architecture of amino acids is relative simple and controlled by limited loci. About 43.2% of the loci identified by GWAS were verified by expression QTL, and 17 loci overlapped with mapped QTLs in the three RIL populations. GRMZM2G015534, GRMZM2G143008 and one QTL were further validated using molecular approaches. The amino acid biosynthetic and catabolic pathways were reconstructed on the basis of candidate genes proposed in this study. Our results provide insights into the genetic basis of amino acid biosynthesis in maize kernels and may facilitate marker-based breeding for quality protein maize.

Novel Loci Underlie Natural Variation in Vitamin E Levels in Maize Grain

Tocopherols, tocotrienols, and plastochromanols (collectively termed tocochromanols) are lipid-soluble antioxidants synthesized by all plants. Their dietary intake, primarily from seed oils, provides vitamin E and other health benefits. Tocochromanol biosynthesis has been dissected in the dicot Arabidopsis thaliana, which has green, photosynthetic seeds, but our understanding of tocochromanol accumulation in major crops, whose seeds are nonphotosynthetic, remains limited. To understand the genetic control of tocochromanols in grain, we conducted a joint linkage and genome-wide association study in the 5000-line U.S. maize (Zea mays) nested association mapping panel. Fifty-two quantitative trait loci for individual and total tocochromanols were identified, and of the 14 resolved to individual genes, six encode novel activities affecting tocochromanols in plants. These include two chlorophyll biosynthetic enzymes that explain the majority of tocopherol variation, which was not predicted given that, like most major cereal crops, maize grain is nonphotosynthetic. This comprehensive assessment of natural variation in vitamin E levels in maize establishes the foundation for improving tocochromanol and vitamin E content in seeds of maize and other major cereal crops.

Genome-Wide Association Mapping Reveals That Specific and Pleiotropic Regulatory Mechanisms Fine-Tune Central Metabolism and Growth in Arabidopsis

Central metabolism is a coordinated network that is regulated at multiple levels by resource availability and by environmental and developmental cues. Its genetic architecture has been investigated by mapping metabolite quantitative trait loci (QTL). A more direct approach is to identify enzyme activity QTL, which distinguishes between cis-QTL in structural genes encoding enzymes and regulatory trans-QTL. Using genome-wide association studies, we mapped QTL for 24 enzyme activities, nine metabolites, three structural components, and biomass in Arabidopsis thaliana We detected strong cis-QTL for five enzyme activities. A cis-QTL for UDP-glucose pyrophosphorylase activity in the UGP1 promoter is maintained through balancing selection. Variation in acid invertase activity reflects multiple evolutionary events in the promoter and coding region of VAC-INVcis-QTL were also detected for ADP-glucose pyrophosphorylase, fumarase, and phosphoglucose isomerase activity. We detected many trans-QTL, including transcription factors, E3 ligases, protein targeting components, and protein kinases, and validated some by knockout analysis. trans-QTL are more frequent but tend to have smaller individual effects than cis-QTL. We detected many colocalized QTL, including a multitrait QTL on chromosome 4 that affects six enzyme activities, three metabolites, protein, and biomass. These traits are coordinately modified by different ACCELERATED CELL DEATH6 alleles, revealing a trade-off between metabolism and defense against biotic stress.

Quantitative trait loci from identification to exploitation for crop improvement

Advancement in the field of genetics and genomics after the discovery of Mendel's laws of inheritance has led to map the genes controlling qualitative and quantitative traits in crop plant species. Mapping of genomic regions controlling the variation of quantitatively inherited traits has become routine after the advent of different types of molecular markers. Recently, the next generation sequencing methods have accelerated the research on QTL analysis. These efforts have led to the identification of more closely linked molecular markers with gene/QTLs and also identified markers even within gene/QTL controlling the trait of interest. Efforts have also been made towards cloning gene/QTLs or identification of potential candidate genes responsible for a trait. Further new concepts like crop QTLome and QTL prioritization have accelerated precise application of QTLs for genetic improvement of complex traits. In the past years, efforts have also been made in exploitation of a number of QTL for improving grain yield or other agronomic traits in various crops through markers assisted selection leading to cultivation of these improved varieties at farmers' field. In present article, we reviewed QTLs from their identification to exploitation in plant breeding programs and also reviewed that how improved cultivars developed through introgression of QTLs have improved the yield productivity in many crops.

Biochemical and genetic analyses of N metabolism in maize testcross seedlings: 1. Leaves

Aside from the identification of 32 QTL for N metabolism in the seedling leaves of a maize testcross population, alanine aminotransferase was found to be a central enzyme in N assimilation. Excessive application of nitrogen (N) fertilizer to grow commercial crops like maize is a cause of concern because of the runoff of excess N into streams and rivers. Breeding maize with improved N use efficiency (NUE) would reduce environmental pollution as well as input costs for the farmers. An understanding of the genetics underlying N metabolism is key to breeding for NUE. From a set of 176 testcrosses derived from the maize IBMsyn10 population grown in hydroponics, we analyzed the youngest fully expanded leaf at four-leaf stage for enzymes and metabolites related to N metabolism. Three enzymes, along with one metabolite explained 24% of the variation in shoot dry mass. Alanine aminotransferase (AlaAT) stood out as the key enzyme in maintaining the cellular level of glutamate as it alone explained 58% of the variation in this amino acid. Linkage mapping revealed 32 quantitative trait loci (QTL), all trans to the genomic positions of the structural genes for various enzymes of N assimilation. The QTL models for different traits accounted for 7-31% of the genetic variance, whereas epistasis was generally not significant. Five coding regions underlying 1-LOD QTL confidence intervals were identified for further validation studies. Our results provide evidence for the key role of AlaAT in N assimilation likely through homeostatic control of glutamate levels in the leaf cells. The two QTL identified for this enzyme would help to select desirable recombinants for improved N assimilation.

Maize YABBY Genes drooping leaf1 and drooping leaf2 Regulate Plant Architecture

Leaf architecture directly influences canopy structure, consequentially affecting yield. We discovered a maize (Zea mays) mutant with aberrant leaf architecture, which we named drooping leaf1 (drl1). Pleiotropic mutations in drl1 affect leaf length and width, leaf angle, and internode length and diameter. These phenotypes are enhanced by natural variation at the drl2 enhancer locus, including reduced expression of the drl2-Mo17 allele in the Mo17 inbred. A second drl2 allele, produced by transposon mutagenesis, interacted synergistically with drl1 mutants and reduced drl2 transcript levels. The drl genes are required for proper leaf patterning, development and cell proliferation of leaf support tissues, and for restricting auricle expansion at the midrib. The paralogous loci encode maize CRABS CLAW co-orthologs in the YABBY family of transcriptional regulators. The drl genes are coexpressed in incipient and emergent leaf primordia at the shoot apex, but not in the vegetative meristem or stem. Genome-wide association studies using maize NAM-RIL (nested association mapping-recombinant inbred line) populations indicated that the drl loci reside within quantitative trait locus regions for leaf angle, leaf width, and internode length and identified rare single nucleotide polymorphisms with large phenotypic effects for the latter two traits. This study demonstrates that drl genes control the development of key agronomic traits in maize.

Genome-wide association study of salt tolerance at the seed germination stage in rice

BACKGROUND

Improving the salt tolerance of direct-seeding rice at the seed germination stage is a major breeding goal in many Asian rice-growing countries, where seedlings must often establish in soils with a high salt content. Thus, it is important to understand the genetic mechanisms of salt tolerance in rice and to screen for germplasm with salt tolerance at the seed germination stage. Here, we investigated seven seed germination-related traits under control and salt-stress conditions and conducted a genome-wide association study based on the re-sequencing of 478 diverse rice accessions.

RESULTS

The analysis used a mixed linear model and was based on 6,361,920 single nucleotide polymorphisms in 478 rice accessions grouped into whole, indica, and non-indica panels. Eleven loci containing 22 significant salt tolerance-associated single nucleotide polymorphisms were identified based on the stress-susceptibility indices (SSIs) of vigor index (VI) and mean germination time (MGT). From the SSI of VI, six major loci were identified, explaining 20.2% of the phenotypic variation. From the SSI of MGT, five major loci were detected, explaining 26.4% of the phenotypic variation. Of these, seven loci on chromosomes 1, 5, 6, 11, and 12 were close to six previously identified quantitative gene loci/genes related to tolerance to salinity or other abiotic stresses. The strongest association region for the SSI of MGT was identified in a ~ 13.3 kb interval (15450039-15,463,330) on chromosome 1, near salt-tolerance quantitative trait loci controlling the Na⁺: K⁺ ratio, total Na⁺ uptake, and total K⁺ concentration. The strongest association region for the SSI of VI was detected in a ~ 164.2 kb interval (526662-690,854) on chromosome 2 harboring two nitrate transporter family genes (OsNRT2.1 and OsNRT2.2), which affect gene expression under salt stress. The haplotype analysis indicated that OsNRT2.2 was associated with subpopulation differentiation and its minor/rare tolerant haplotype was detected.

CONCLUSIONS

These results provide valuable information for salt tolerance-related gene cloning and for understanding the genetic mechanisms of salt tolerance at the seed germination stage. This information will be useful to improve the salt tolerance of direct-seeding rice varieties by genomic selection or marker-assisted selection.

Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield

A combined metabolomic, biochemical, fluxomic, and metabolic modeling approach was developed using 19 genetically distant maize (Zea mays) lines from Europe and America. Considerable differences were detected between the lines when leaf metabolic profiles and activities of the main enzymes involved in primary metabolism were compared. During grain filling, the leaf metabolic composition appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lines. During the same period, there was a significant correlation between the genetic distance of the lines and the activities of enzymes involved in carbon metabolism, notably glycolysis. Although large differences were observed in terms of leaf metabolic fluxes, these variations were not tightly linked to the genome structure of the lines. Both correlation studies and metabolic network analyses allowed the description of a maize ideotype with a high grain yield potential. Such an ideotype is characterized by low accumulation of soluble amino acids and carbohydrates in the leaves and high activity of enzymes involved in the C4 photosynthetic pathway and in the biosynthesis of amino acids derived from glutamate. Chlorogenates appear to be important markers that can be used to select for maize lines that produce larger kernels.

New Strategies and Tools in Quantitative Genetics: How to Go from the Phenotype to the Genotype

Quantitative genetics has a long history in plants: It has been used to study specific biological processes, identify the factors important for trait evolution, and breed new crop varieties. These classical approaches to quantitative trait locus mapping have naturally improved with technology. In this review, we show how quantitative genetics has evolved recently in plants and how new developments in phenotyping, population generation, sequencing, gene manipulation, and statistics are rejuvenating both the classical linkage mapping approaches (for example, through nested association mapping) as well as the more recently developed genome-wide association studies. These strategies are complementary in most instances, and indeed, one is often used to confirm the results of the other. Despite significant advances, an emerging trend is that the outcome and efficiency of the different approaches depend greatly on the genetic architecture of the trait in the genetic material under study.