The maize leaf lipidome shows multilevel genetic control and high predictive value for agronomic traits

Phosphorus-Mediated Transition from Vegetative to Reproductive Growth in Dwarf Coconut (Cocos nucifera L.)

Reducing the time before the flowering stage in coconut (Cocos nucifera L.) trees greatly influences yield, yet the mechanisms driving the switch from vegetative to reproductive growth are not well understood, especially the role of phosphorus in this transition. In this study, dwarf coconut plants of the same cultivation age were selected and categorized into the vegetative phase (VP) or the reproductive phase (RP). By examining the phenotypic traits, nutrient variations in the roots and soil, and the transcriptional expression of relevant genes in the roots across both phases, we investigated the potential mechanisms driving the transition from the VP to the RP in coconuts. The shoots of coconuts in the RP were significantly taller compared to those in the VP. Moreover, the phosphorus concentration in the roots of coconuts during the RP was 1.31 times higher than in the VP, which may be linked to the significant upregulation of the PT1 genes AZ11G0219160 and AZ02G0034860 in the roots of coconuts in the RP. In addition, all phosphorus-containing metabolites in the roots during the RP showed a significant increase, particularly those related to long-chain fatty acids and ribonucleotide metabolites. This suggests that coconut roots may facilitate the progression from vegetative to reproductive growth by enhancing phosphorus uptake via PT1s and promoting the synthesis and accumulation of phosphorus-containing metabolites.

Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection

Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction's potential for seed yield and nine agronomic traits using 26,171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Models yielded significant variation among predictive ability values across traits for the whole marker set. The ridge regression (RR) model covering additive gene action yielded better predictive ability for most of the traits, whereas it was higher for low heritable traits by models capturing epistatic gene action. Marker subsets based on linkage disequilibrium decay distance gave significantly higher predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.

Hello darkness, my old friend: 3-KETOACYL-COENZYME A SYNTHASE4 is a branch point in the regulation of triacylglycerol synthesis in Arabidopsis thaliana

Plant lipids are important as alternative sources of carbon and energy when sugars or starch are limited. Here, we applied combined heat and darkness or extended darkness to a panel of ∼300 Arabidopsis (Arabidopsis thaliana) accessions to study lipid remodeling under carbon starvation. Natural allelic variation at 3-KETOACYL-COENZYME A SYNTHASE4 (KCS4), a gene encoding an enzyme involved in very long chain fatty acid (VLCFA) synthesis, underlies the differential accumulation of polyunsaturated triacylglycerols (puTAGs) under stress. Ectopic expression of KCS4 in yeast and plants proved that KCS4 is a functional enzyme localized in the endoplasmic reticulum with specificity for C22 and C24 saturated acyl-CoA. Allelic mutants and transient overexpression in planta revealed the differential role of KCS4 alleles in VLCFA synthesis and leaf wax coverage, puTAG accumulation, and biomass. Moreover, the region harboring KCS4 is under high selective pressure and allelic variation at KCS4 correlates with environmental parameters from the locales of Arabidopsis accessions. Our results provide evidence that KCS4 plays a decisive role in the subsequent fate of fatty acids released from chloroplast membrane lipids under carbon starvation. This work sheds light on both plant response mechanisms and the evolutionary events shaping the lipidome under carbon starvation.

An adaptive teosinte mexicana introgression modulates phosphatidylcholine levels and is associated with maize flowering time

Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp. parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern United States, Canada, and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time.

Complex Changes in Membrane Lipids Associated with the Modification of Autophagy in Arabidopsis

Autophagy is a conserved mechanism among eukaryotes that degrades and recycles cytoplasmic components. Autophagy is known to influence the plant metabolome, including lipid content; however, its impact on the plant lipidome is not fully understood, and most studies have analyzed a single or few mutants defective in autophagy. To gain more insight into the effect of autophagy on lipid concentrations and composition, we quantitatively profiled glycerolipids from multiple Arabidopsis thaliana mutants altered in autophagy and compared them with wild-type seedlings under nitrogen replete (+N; normal growth) and nitrogen starvation (-N; autophagy inducing) conditions. Mutants include those in genes of the core autophagy pathway, together with other genes that have been reported to affect autophagy. Using Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry (MALDI-MS), we imaged the cellular distribution of specific lipids in situ and demonstrated that autophagy and nitrogen treatment did not affect their spatial distribution within Arabidopsis seedling leaves. We observed changes, both increases and decreases, in the relative amounts of different lipid species in the mutants compared to WT both in +N and -N conditions, although more changes were seen in -N conditions. The relative amounts of polyunsaturated and very long chain lipids were significantly reduced in autophagy-disrupted mutants compared to WT plants. Collectively, our results provide additional evidence that autophagy affects plant lipid content and that autophagy likely affects lipid properties such as chain length and unsaturation.

Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

Genome-Wide Association Study Reveals Key Genes for Differential Lead Accumulation and Tolerance in Natural Arabidopsis thaliana Accessions

Soil contamination by lead (Pb) has become one of the major ecological threats to the environment. Understanding the mechanisms of Pb transport and deposition in plants is of great importance to achieve a global Pb reduction. We exposed a collection of 360 Arabidopsis thaliana natural accessions to a Pb-polluted soil. Germination rates, growth, and leaf Pb concentrations showed extensive variation among accessions. These phenotypic data were subjected to genome wide association studies (GWAs) and we found a significant association on chromosome 1 for low leaf Pb accumulation. Genes associated with significant SNP markers were evaluated and we selected EXTENSIN18 (EXT18) and TLC (TRAM-LAG1-CLN8) as candidates for having a role in Pb homeostasis. Six Pb-tolerant accessions, three of them exhibiting low leaf Pb content, and three of them with high leaf Pb content; two Pb-sensitive accessions; two knockout T-DNA lines of GWAs candidate genes (ext18, tlc); and Col-0 were screened under control and high-Pb conditions. The relative expression of EXT18, TLC, and other genes described for being involved in Pb tolerance was also evaluated. Analysis of Darwinian fitness, root and leaf ionome, and TEM images revealed that Pb-tolerant accessions employ two opposing strategies: (1) low translocation of Pb and its accumulation into root cell walls and vacuoles, or (2) high translocation of Pb and its efflux to inactive organelles or intracellular spaces. Plants using the first strategy exhibited higher expression of EXT18 and HMA3, thicker root cell walls and Pb vacuolar sequestration, suggesting that these genes may contribute to the deposition of Pb in the roots. On the other hand, plants translocating high amounts of Pb showed upregulation of TLC and ABC transporters, indicating that these plants were able to properly efflux Pb in the aerial tissues. We conclude that EXT18 and TLC upregulation enhances Pb tolerance promoting its sequestration: EXT18 favors the thickening of the cell walls improving Pb accumulation in roots and decreasing its toxicity, while TLC facilitates the formation of dictyosome vesicles and the Pb encapsulation in leaves. These findings are relevant for the design of phytoremediation strategies and environment restoration.

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).

Metabolomics analysis reveals differences in evolution between maize and rice

Metabolites are the intermediate and final products of metabolism, which play essential roles in plant growth, evolution and adaptation to changing climates. However, it is unclear how evolution contributes to metabolic variation in plants. Here, we investigated the metabolomics data from leaf and seed tissues in maize and rice. Using principal components analysis based on leaf metabolites but not seed metabolites, metabolomics data could be clearly separated for rice Indica and Japonica accessions, while two maize subgroups, temperate and tropical, showed more visible admixture. Rice and maize seed exhibited significant interspecific differences in metabolic variation, while within rice, leaf and seed displayed similar metabolic variations. Among 10 metabolic categories, flavonoids had higher variation in maize than rice, indicating flavonoids are a key constituent of interspecific metabolic divergence. Interestingly, metabolic regulation was also found to be reshaped dramatically from positive to negative correlations, indicative of the differential evolutionary processes in maize and rice. Moreover, perhaps due to this divergence significantly more metabolic interactions were identified in rice than maize. Furthermore, in rice, the leaf was found to harbor much more intense metabolic interactions than the seed. Our result suggests that metabolomes are valuable for tracking evolutionary history, thereby complementing and extending genomic insights concerning which features are responsible for interspecific differentiation in maize and rice.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Hybrid Breeding for MLN Resistance: Heterosis, Combining Ability, and Hybrid Prediction

Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genetic parameters for MLN disease severity, (2) investigate the efficiency of the prediction of hybrid performance based on parental per se and general combining ability (GCA) effects, and (3) examine the potential of hybrid prediction for MLN resistance or tolerance based on markers. Fifty elite maize inbred lines were selected based on their response to MLN under artificial inoculation. Crosses were made in a half diallel mating design to produce 307 F1 hybrids. All hybrids were evaluated in MLN quarantine facility in Naivasha, Kenya for two seasons under artificial inoculation. All 50 inbreds were genotyped with genotyping-by-sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability was moderate to high. We observed that hybrids were superior to the mean performance of the parents for disease severity (−14.57%) and area under disease progress curve (AUDPC) (14.9%). Correlations were significant and moderate between line per se and GCA; and mean of parental value with hybrid performance for both disease severity and AUDPC value. Very low and negative correlation was observed between parental lines marker based genetic distance and heterosis. Nevertheless, the correlation of GCA effects was very high with hybrid performance which can suggests as a good predictor of MLN resistance. Genomic prediction of hybrid performance for MLN is high for both traits. We therefore conclude that there is potential for prediction of hybrid performance for MLN. Overall, the estimated quantitative genetic parameters suggest that through targeted approach, it is possible to develop outstanding lines and hybrids for MLN resistance.

Biomarkers for grain yield stability in rice under drought stress

Crop yield stability requires an attenuation of the reduction of yield losses caused by environmental stresses such as drought. Using a combination of metabolomics and high-throughput colorimetric assays, we analysed central metabolism and oxidative stress status in the flag leaf of 292 indica rice (Oryza sativa) accessions. Plants were grown in the field and were, at the reproductive stage, exposed to either well-watered or drought conditions to identify the metabolic processes associated with drought-induced grain yield loss. Photorespiration, protein degradation, and nitrogen recycling were the main processes involved in the drought-induced leaf metabolic reprogramming. Molecular markers of drought tolerance and sensitivity in terms of grain yield were identified using a multivariate model based on the values of the metabolites and enzyme activities across the population. The model highlights the central role of the ascorbate-glutathione cycle, particularly dehydroascorbate reductase, in minimizing drought-induced grain yield loss. In contrast, malondialdehyde was an accurate biomarker for grain yield loss, suggesting that drought-induced lipid peroxidation is the major constraint under these conditions. These findings highlight new breeding targets for improved rice grain yield stability under drought.

Genome-Wide Analyses and Prediction of Resistance to MLN in Large Tropical Maize Germplasm

Maize lethal necrosis (MLN), caused by co-infection of maize chlorotic mottle virus and sugarcane mosaic virus, can lead up to 100% yield loss. Identification and validation of genomic regions can facilitate marker assisted breeding for resistance to MLN. Our objectives were to identify marker-trait associations using genome wide association study and assess the potential of genomic prediction for MLN resistance in a large panel of diverse maize lines. A set of 1400 diverse maize tropical inbred lines were evaluated for their response to MLN under artificial inoculation by measuring disease severity or incidence and area under disease progress curve (AUDPC). All lines were genotyped with genotyping by sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability estimates were moderate to high. GWAS revealed 32 significantly associated SNPs for MLN resistance (at p < 1.0 × 10⁻⁶). For disease severity, these significantly associated SNPs individually explained 3–5% of the total phenotypic variance, whereas for AUDPC they explained 3–12% of the total proportion of phenotypic variance. Most of significant SNPs were consistent with the previous studies and assists to validate and fine map the big quantitative trait locus (QTL) regions into few markers’ specific regions. A set of putative candidate genes associated with the significant markers were identified and their functions revealed to be directly or indirectly involved in plant defense responses. Genomic prediction revealed reasonable prediction accuracies. The prediction accuracies significantly increased with increasing marker densities and training population size. These results support that MLN is a complex trait controlled by few major and many minor effect genes.

Genetic diversity and metabolic profile of Salvia officinalis populations: implications for advanced breeding strategies

As a result of this work, we were able to characterize seven indigenous to Greece Salvia officinalis populations using genetic and metabolomic tools. These tools can be used to select the most promising genotypes, capable to design future breeding programs for high valuable varieties. An initial investigation was carried out to compare the genetic and metabolic diversity in S. officinalis grown in Greece and to discern the relationship between the two sets of data. Analysis of inter-simple sequence repeats (ISSR) revealed significant genetic differences among seven sage populations, which were grouped into three main clusters according to an UPGMA ISSR data-based dendrogram and Principle Coordinate Analysis. 80 loci were scored of which up to 90% were polymorphic at species level. According to the composition of their essential oil, the populations were classified into two chemotypes: 1.8 cineole/α-thujone and α-thujone/1.8 cineole. Additionally, a targeted ultra performance liquid chromatography (UPLC-MS/MS) method was used to qualify and quantify phenolic compounds in methanolic extracts of the seven sage genotypes according to which they were districted in six clusters among the sage populations. The main compounds characterizing the seven genotypes were rosmarinic acid and carnosol, followed by apigenin-7-O-glucoside (Ap7glc), and luteolin-7-O-glucoside (Lu7glc). The correlation between matrices obtained from ISSR data and metabolic profiles was non-significant. However, based on the differences in metabolic fingerprint, we aimed to define populations using as main selection criteria the high polyphenol content and desired essential oil composition, using state to the art analytical tools for the identification of parent lines for breeding programs.

Genome-wide Association Studies in Maize: Praise and Stargaze

Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype-phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.

High spatial resolution mass spectrometry imaging reveals the genetically programmed, developmental modification of the distribution of thylakoid membrane lipids among individual cells of maize leaf

Metabolism in plants is compartmentalized among different tissues, cells and subcellular organelles. Mass spectrometry imaging (MSI) with matrix-assisted laser desorption ionization (MALDI) has recently advanced to allow for the visualization of metabolites at single-cell resolution. Here we applied 5- and 10 μm high spatial resolution MALDI-MSI to the asymmetric Kranz anatomy of Zea mays (maize) leaves to study the differential localization of two major anionic lipids in thylakoid membranes, sulfoquinovosyldiacylglycerols (SQDG) and phosphatidylglycerols (PG). The quantification and localization of SQDG and PG molecular species, among mesophyll (M) and bundle sheath (BS) cells, are compared across the leaf developmental gradient from four maize genotypes (the inbreds B73 and Mo17, and the reciprocal hybrids B73 × Mo17 and Mo17 × B73). SQDG species are uniformly distributed in both photosynthetic cell types, regardless of leaf development or genotype; however, PG shows photosynthetic cell-specific differential localization depending on the genotype and the fatty acyl chain constituent. Overall, 16:1-containing PGs primarily contribute to the thylakoid membranes of M cells, whereas BS chloroplasts are mostly composed of 16:0-containing PGs. Furthermore, PG 32:0 shows genotype-specific differences in cellular distribution, with preferential localization in BS cells for B73, but more uniform distribution between BS and M cells in Mo17. Maternal inheritance is exhibited within the hybrids, such that the localization of PG 32:0 in B73 × Mo17 is similar to the distribution in the B73 parental inbred, whereas that of Mo17 × B73 resembles the Mo17 parent. This study demonstrates the power of MALDI-MSI to reveal unprecedented insights on metabolic outcomes in multicellular organisms at single-cell resolution.

A naturally occurring promoter polymorphism of the Arabidopsis FUM2 gene causes expression variation, and is associated with metabolic and growth traits

Fumarate and malate are known intermediates of the TCA cycle, a mitochondrial metabolic pathway generating NADH for respiration. Arabidopsis thaliana and other Brassicaceae contain an additional cytosolic fumarase (FUM2) that functions in carbon assimilation and nitrogen use. Here, we report the identification of a hitherto unknown FUM2 promoter insertion/deletion (InDel) polymorphism found between the Col-0 and C24 accessions, which also divides a large number of Arabidopsis accessions carrying either the Col-0 or the C24 allele. The polymorphism consists of two stretches of 2.1 and 3.8 kb, which are both absent from the promotor region of Col-0 FUM2. By analysing mutants as well as mapping and natural populations with contrasting FUM2 alleles, the promotor insertion was linked to reduced FUM2 mRNA expression, reduced fumarase activity and reduced fumarate/malate ratio in leaves. In a large population of 174 natural accessions, the polymorphism was also found to be associated with the fumarate/malate ratio, malate and fumarate levels, and with dry weight at 15 days after sowing (DAS). The association with biomass production was confirmed in an even larger (251) accession population for dry weight at 22 DAS. The dominant Col-0 allele that results in increased fumarate/malate ratios and enhanced biomass production is predominantly found in central/eastern European accessions, whereas the C24 type allele is prevalent on the Iberian Peninsula, west of the Rhine and in the British Isles. Our findings support the role of FUM2 in diurnal carbon storage, and point to a growth advantage of accessions carrying the FUM2 Col-0 allele.

Fortune telling: metabolic markers of plant performance

BACKGROUND

In the last decade, metabolomics has emerged as a powerful diagnostic and predictive tool in many branches of science. Researchers in microbes, animal, food, medical and plant science have generated a large number of targeted or non-targeted metabolic profiles by using a vast array of analytical methods (GC-MS, LC-MS, 1H-NMR….). Comprehensive analysis of such profiles using adapted statistical methods and modeling has opened up the possibility of using single or combinations of metabolites as markers. Metabolic markers have been proposed as proxy, diagnostic or predictors of key traits in a range of model species and accurate predictions of disease outbreak frequency, developmental stages, food sensory evaluation and crop yield have been obtained.

AIM OF REVIEW

(i) To provide a definition of plant performance and metabolic markers, (ii) to highlight recent key applications involving metabolic markers as tools for monitoring or predicting plant performance, and (iii) to propose a workable and cost-efficient pipeline to generate and use metabolic markers with a special focus on plant breeding.

KEY MESSAGE

Using examples in other models and domains, the review proposes that metabolic markers are tending to complement and possibly replace traditional molecular markers in plant science as efficient estimators of performance.

Relationships of Leaf Net Photosynthesis, Stomatal Conductance, and Mesophyll Conductance to Primary Metabolism: A Multispecies Meta-Analysis Approach

Plant metabolism drives plant development and plant-environment responses, and data readouts from this cellular level could provide insights in the underlying molecular processes. Existing studies have already related key in vivo leaf gas-exchange parameters with structural traits and nutrient components across multiple species. However, insights in the relationships of leaf gas-exchange with leaf primary metabolism are still limited. We investigated these relationships through a multispecies meta-analysis approach based on data sets from 17 published studies describing net photosynthesis (A) and stomatal (gs) and mesophyll (gm) conductances, alongside the 53 data profiles from primary metabolism of 14 species grown in different experiments. Modeling results highlighted the conserved patterns between the different species. Consideration of species-specific effects increased the explanatory power of the models for some metabolites, including Glc-6-P, Fru-6-P, malate, fumarate, Xyl, and ribose. Significant relationships of A with sugars and phosphorylated intermediates were observed. While gs was related to sugars, organic acids, myo-inositol, and shikimate, gm showed a more complex pattern in comparison to the two other traits. Some metabolites, such as malate and Man, appeared in the models for both conductances, suggesting a metabolic coregulation between gs and gm The resulting statistical models provide the first hints for coregulation patterns involving primary metabolism plus leaf water and carbon balances that are conserved across plant species, as well as species-specific trends that can be used to determine new biotechnological targets for crop improvement.

Natural Variation of Plant Metabolism: Genetic Mechanisms, Interpretive Caveats, and Evolutionary and Mechanistic Insights

Combining quantitative genetics studies with metabolomics/metabolic profiling platforms, genomics, and transcriptomics is creating significant progress in identifying the causal genes controlling natural variation in metabolite accumulations and profiles. In this review, we discuss key mechanistic and evolutionary insights that are arising from these studies. This includes the potential role of transport and other processes in leading to a separation of the site of mechanistic causation and metabolic consequence. A reilluminated observation is the potential for genomic variation in the organelle to alter phenotypic variation alone and in epistatic interaction with the nuclear genetic variation. These studies are also highlighting new aspects of metabolic pleiotropy both in terms of the breadth of loci altering metabolic variation as well as the potential for broader effects on plant defense regulation of the metabolic variation than has previously been predicted. We also illustrate caveats that can be overlooked when translating quantitative genetics descriptors such as heritability and per-locus r(2) to mechanistic or evolutionary interpretations.

Genotypic variation in lysophospholipids of milled rice

Phospholipids (PLs) play a prominent role in both grain cellular structure and nutritional function of cereal crops. Their lyso forms (lysophospholipids, LPLs) often combine with cereal starch to form an amylose-lipid complex (ALC), which may influence starch properties. In this study, 20 rice accessions were grown over two seasons at the same location to explore diversity in LPLs of milled rice. Levels of specific LPLs differed significantly among rice genotypes, demonstrating there is a wide diversity in LPLs in rice grain. The main LPL components were lysophosphatidylcholine (LPC) 16:0 (ranging from 3009.7 to 4697.8 μg/g), LPC18:2 (836.6-2182.3 μg/g), lysophosphatidylethanolamine (LPE) 16:0 (625.7-1139.8 μg/g), and LPE18:2 (170.6-481.6 μg/g). Total LPC, total LPE, and total LPL ranged from 4727.1 to 7685.2 μg/g, from 882.8 to 1809.5 μg/g, and from 5609.8 to 9401.1 μg/g, respectively. Although significant (P < 0.001) environment and genotype × environment (G × E) interactions were detected by analysis of variance (ANOVA), these effects accounted for only 0.7-38.9 and 1.8-6.6% of the total variance, respectively. Correlation analysis between LPL components provided insight into the possible LPL biosynthesis pathway in plants. Hierarchical cluster analysis suggested that the 20 rice accessions could be classified into three groups, whereas principal component analysis also identified three groups, with the first two components explaining 57.7 and 16.2% of the total variance. Further genetic studies are needed to identify genes or quantitative trait loci (QTLs) underlying the genetic control of LPLs in rice grain.