Integrative analyses of genetic variation in enzyme activities of primary carbohydrate metabolism reveal distinct modes of regulation in Arabidopsis thaliana

Genetic effects on molecular network states explain complex traits

The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.

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.

GABA shunt pathway is stimulated in response to early defoliation-induced carbohydrate limitation in Mandarin fruits

The regulation of sugar and organic acid metabolism during fruit development has a major effect on high-quality fruit production. The reduction of leaf area is a common feature in plant growth, induced by abiotic and biotic stresses and disturbing source/sink ratio, thus impacting fruit quality. Here, we induced carbohydrate limitation by partial leaf defoliation at the beginning of the second stage of mandarin development (before the citrate peak). Resulting changes were monitored in the short-term (48 h and 1 week) and long-term (7 weeks) after the defoliation. Short-term response to early defoliation implied metabolic settings to re-feed TCA for sustaining respiration rate. These features involved (i) vacuolar sucrose degradation (high acid invertase activity and mRNA expression level) and enhanced glycolytic flux (high ATP-phosphofructokinase activity), (ii) malic and citric acid utilization (increased phosphoenolpyruvate kinase and NADP-Isocitrate dehydrogenase) associated with vacuolar citric acid release (high mRNA expression of the transporter CsCit1) and (iii) stimulation of GABA shunt pathway (low GABA content and increased mRNA expression of succinate semialdehyde dehydrogenase). A steady-state proline level was found in ED fruits although an increase in P5CS mRNA expression level. These results contribute to a better knowledge of the molecular basis of the relationship between defoliation and sugar and organic acid metabolism in mandarin fruit.

Genetic variants of the nuclear factor erythroid 2-related factor 2/antioxidant reaction element pathway on the risk of antituberculosis drug-induced liver injury: a systematic review

Aim: To evaluate the effects of genetic variants in the nuclear factor erythroid 2-related factor 2/antioxidant reaction element signaling pathway on antituberculosis drug-induced liver injury (AT-DILI) susceptibility. Methods: The PubMed, Embase, Cochrane, Web of Science, China National Knowledge Infrastructure and Wanfang databases were searched from inception to April 2022. Results: Seven case-control studies with 4676 patients were included. Six genes with 35 SNPs in the pathway have been reported. Among 17 SNPs reported in two or more studies, the meta-analysis indicated that only one SNP (rs3735656 in MAFK) was significantly associated with a decreased risk for AT-DILI under the dominant model (odds ratio: 0.636; 95% CI: 0.519-0.780; p < 0.001). Conclusion: SNP rs3735656 in the MAFK gene was significantly associated with the risk of AT-DILI.

Enzyme activity profiling for physiological phenotyping within functional phenomics: plant growth and stress responses

High-throughput profiling of key enzyme activities of carbon, nitrogen, and antioxidant metabolism is emerging as a valuable approach to integrate cell physiological phenotyping into a holistic functional phenomics approach. However, the analyses of the large datasets generated by this method represent a bottleneck, often keeping researchers from exploiting the full potential of their studies. We address these limitations through the exemplary application of a set of data evaluation and visualization tools within a case study. This includes the introduction of multivariate statistical analyses that can easily be implemented in similar studies, allowing researchers to extract more valuable information to identify enzymatic biosignatures. Through a literature meta-analysis, we demonstrate how enzyme activity profiling has already provided functional information on the mechanisms regulating plant development and response mechanisms to abiotic stress and pathogen attack. The high robustness of the distinct enzymatic biosignatures observed during developmental processes and under stress conditions underpins the enormous potential of enzyme activity profiling for future applications in both basic and applied research. Enzyme activity profiling will complement molecular -omics approaches to contribute to the mechanistic understanding required to narrow the genotype-to-phenotype knowledge gap and to identify predictive biomarkers for plant breeding to develop climate-resilient crops.

QTL Mapping and GWAS Reveal the Genetic Mechanism Controlling Soluble Solids Content in Brassica napus Shoots

Oilseed-vegetable-dual-purpose (OVDP) rapeseed can effectively alleviate the land contradiction between crops and it supplements vegetable supplies in winter or spring. The soluble solids content (SSC) is an important index that is used to evaluate the quality and sugar content of fruits and vegetables. However, the genetic architecture underlying the SSC in Brassica napus shoots is still unclear. Here, quantitative trait loci (QTLs) for the SSC in B. napus shoots were investigated by performing linkage mapping using a recombinant inbred line population containing 189 lines. A germplasm set comprising 302 accessions was also used to conduct a genome-wide association study (GWAS). The QTL mapping revealed six QTLs located on chromosomes A01, A04, A08, and A09 in two experiments. Among them, two major QTLs, qSSC/21GY.A04-1 and qSSC/21NJ.A08-1, accounted for 12.92% and 10.18% of the phenotypic variance, respectively. In addition, eight single-nucleotide polymorphisms with phenotypic variances between 5.62% and 10.18% were identified by the GWAS method. However, no locus was simultaneously identified by QTL mapping and GWAS. We identified AH174 (7.55 °Brix and 7.9 °Brix), L166 (8.9 °Brix and 8.38 °Brix), and L380 (8.9 °Brix and 7.74 °Brix) accessions can be used as superior parents. These results provide valuable information that increases our understanding of the genetic control of SSC and will facilitate the breeding of high-SSC B. napus shoots.

Divergent patterns of selection on metabolite levels and gene expression

Background

Natural selection can act on multiple genes in the same pathway, leading to polygenic adaptation. For example, adaptive changes were found to down-regulate six genes involved in ergosterol biosynthesis—an essential pathway targeted by many antifungal drugs—in some strains of the yeast Saccharomyces cerevisiae. However, the impact of this polygenic adaptation on metabolite levels was unknown. Here, we performed targeted mass spectrometry to measure the levels of eight metabolites in this pathway in 74 yeast strains from a genetic cross.

Results

Through quantitative trait locus (QTL) mapping we identified 19 loci affecting ergosterol pathway metabolite levels, many of which overlap loci that also impact gene expression within the pathway. We then used the recently developed v-test, which identified selection acting upon three metabolite levels within the pathway, none of which were predictable from the gene expression adaptation.

Conclusions

These data showed that effects of selection on metabolite levels were complex and not predictable from gene expression data. This suggests that a deeper understanding of metabolism is necessary before we can understand the impacts of even relatively straightforward gene expression adaptations on metabolic pathways.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01915-5.

Characterization of effects of genetic variants via genome-scale metabolic modelling

Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

Regulation of sugar metabolism genes in the nitrogen-dependent susceptibility of tomato stems to Botrytis cinerea

BACKGROUND AND AIMS

The main soluble sugars are important components of plant defence against pathogens, but the underlying mechanisms are unclear. Upon infection by Botrytis cinerea, the activation of several sugar transporters, from both plant and fungus, illustrates the struggle for carbon resources. In sink tissues, the metabolic use of the sugars mobilized in the synthesis of defence compounds or antifungal barriers is not fully understood.

METHODS

In this study, the nitrogen-dependent variation of tomato stem susceptibility to B. cinerea was used to examine, before and throughout the course of infection, the transcriptional activity of enzymes involved in sugar metabolism. Under different nitrate nutrition regimes, the expression of genes that encode the enzymes of sugar metabolism (invertases, sucrose synthases, hexokinases, fructokinases and phosphofructokinases) was determined and sugar contents were measured before inoculation and in asymptomatic tissues surrounding the lesions after inoculation.

KEY RESULTS

At high nitrogen availability, decreased susceptibility was associated with the overexpression of several genes 2 d after inoculation: sucrose synthases Sl-SUS1 and Sl-SUS3, cell wall invertases Sl-LIN5 to Sl-LIN9 and some fructokinase and phosphofructokinase genes. By contrast, increased susceptibility corresponded to the early repression of several genes that encode cell wall invertase and sucrose synthase. The course of sugar contents was coherent with gene expression.

CONCLUSIONS

The activation of specific genes that encode sucrose synthase is required for enhanced defence. Since the overexpression of fructokinase is also associated with reduced susceptibility, it can be hypothesized that supplementary sucrose cleavage by sucrose synthases is dedicated to the production of cell wall components from UDP-glucose, or to the additional implication of fructose in the synthesis of antimicrobial compounds, or both.

Key metabolites associated with the onset of flowering of guar genotypes (Cyamopsis tetragonoloba (L.) Taub)

BACKGROUND

Guar (Cyamopsis tetragonoloba (L.) Taub.), a short-day plant, is an economically valuable legume crop. Seeds of guar serve as a source of galactomannan polysaccharide, known as guar gum, which is in demand in the gas and oil industries. The rapid and complete maturation of guar seeds depends on the flowering time of a particular genotype. It is known that flowering in guar is controlled by several gene systems. However, no information about the process and mechanisms that trigger flowering in guar on the molecular and biochemical levels was previously reported. The aim of the study was to investigate the metabolic landscape underlying transition to the flowering in guar using GC-MS-metabolomic analysis.

RESULTS

82 diverse guar genotypes (each in 8 replicates) from the VIR collection were grown under experimental conditions of high humidity and long photoperiod. In the stress environment some guar genotypes turned to flowering early (41 ± 1,8 days from the first true leaf appearance) while for others the serious delay of flowering (up to 95 ± 1,7 days) was observed. A total of 244 metabolites were detected by GC-MS analysis on the third true leaves stage of 82 guar genotypes. Among them some molecules were associated with the transition of the guar plants to flowering. Clear discrimination was observed in metabolomic profiles of two groups of «early flowering» and «delayed flowering» plants, with 65 metabolites having a significantly higher abundance in early flowering genotypes. Among them 7 key molecules were identified by S-plot, as potential biomarkers discriminating of «early flowering» and «delayed flowering» guar genotypes.

CONCLUSIONS

The metabolomic landscape accompanying transition to flowering in guar was firstly described. The results obtained can be used in subsequent genomic research for identifying metabolite-gene associations and revealing genes responsible for the onset of flowering and photoperiod sensitivity of guar. In addition, the detected key metabolites associated with flowering of guar can be employed as biomarkers allowing rapid screening of breeding material for the potentially early flowering genotypes.

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.

Comparative analysis of metabolome of rice seeds at three developmental stages using a recombinant inbred line population

Plants are considered an important food and nutrition source for humans. Despite advances in plant seed metabolomics, knowledge about the genetic and molecular bases of rice seed metabolomes at different developmental stages is still limited. Here, using Zhenshan 97 (ZS97) and Minghui 63 (MH63), we performed a widely targeted metabolic profiling in seeds during grain filling, mature seeds and germinating seeds. The diversity between MH63 and ZS97 was characterized in terms of the content of metabolites and the metabolic shifting across developmental stages. Taking advantage of the ultra-high-density genetic map of a population of 210 recombinant inbred lines (RILs) derived from a cross between ZS97 and MH63, we identified 4681 putative metabolic quantitative trait loci (mQTLs) in seeds across the three stages. Further analysis of the mQTLs for the codetected metabolites across the three stages revealed that the genetic regulation of metabolite accumulation was closely related to developmental stage. Using in silico analyses, we characterized 35 candidate genes responsible for 30 structurally identified or annotated compounds, among which LOC_Os07g04970 and LOC_Os06g03990 were identified to be responsible for feruloylserotonin and l-asparagine content variation across populations, respectively. Metabolite-agronomic trait association and colocation between mQTLs and phenotypic quantitative trait loci (pQTLs) revealed the complexity of the metabolite-agronomic trait relationship and the corresponding genetic basis.

ARSENATE INDUCED CHLOROSIS 1/ TRANSLOCON AT THE OUTER ENVOLOPE MEMBRANE OF CHLOROPLASTS 132 Protects Chloroplasts from Arsenic Toxicity

Arsenic (As) is highly toxic to plants and detoxified primarily through complexation with phytochelatins (PCs) and other thiol compounds. To understand the mechanisms of As toxicity and detoxification beyond PCs, we isolated an arsenate-sensitive mutant of Arabidopsis (Arabidopsis thaliana), arsenate induced chlorosis1 (aic1), in the background of the PC synthase-defective mutant cadmium-sensitive1-3 (cad1-3). Under arsenate stress, aic1 cad1-3 showed larger decreases in chlorophyll content and the number and size of chloroplasts than cad1-3 and a severely distorted chloroplast structure. The aic1 single mutant also was more sensitive to arsenate than the wild type (Columbia-0). As concentrations in the roots, shoots, and chloroplasts were similar between aic1 cad1-3 and cad1-3 Using genome resequencing and complementation, TRANSLOCON AT THE OUTER ENVOLOPE MEMBRANE OF CHLOROPLAST132 (TOC132) was identified as the mutant gene, which encodes a translocon protein involved in the import of preproteins from the cytoplasm into the chloroplasts. Proteomic analysis showed that the proteome of aic1 cad1-3 chloroplasts was more affected by arsenate stress than that of cad1-3 A number of proteins related to chloroplast ribosomes, photosynthesis, compound synthesis, and thioredoxin systems were less abundant in aic1 cad1-3 than in cad1-3 under arsenate stress. Our results indicate that chloroplasts are a sensitive target of As toxicity and that AIC1/Toc132 plays an important role in protecting chloroplasts from As toxicity.

Metabolome Analysis of Multi-Connected Biparental Chromosome Segment Substitution Line Populations

Metabolomic analysis coupled with advanced genetic populations represents a powerful tool with which to investigate the plant metabolome. However, genetic analyses of the rice (Oryza sativa) metabolome have been conducted mainly using natural accessions or a single biparental population. Here, the flag leaves from three interconnected chromosome segment substitution line populations with a common recurrent genetic background were used to dissect rice metabolic diversity. We effectively used multiple interconnected biparental populations, constructed by introducing genomic segments into Zhenshan 97 from ACC10 (A/Z), Minghui 63 (M/Z), and Nipponbare (N/Z), to map metabolic quantitative trait loci (mQTL). A total of 1,587 mQTL were generated, of which 684, 479, and 722 were obtained from the A/Z, M/Z, and N/Z chromosome segment substitution line populations, respectively, and we designated 99 candidate genes for 367 mQTL. In addition, 1,001 mQTL were generated specifically from joint linkage analysis with 25 candidate genes assigned. Several of these candidates were validated, such as LOC_Os07g01020 for the in vivo content of pyridoxine and its derivative and LOC_Os04g25980 for cis-zeatin glucosyltransferase activity. We propose a novel biosynthetic pathway for O-methylapigenin C-pentoside and demonstrated that LOC_Os04g11970 encodes a component of this pathway through fine-mapping. We postulate that the methylated apigenin may confer plant disease resistance. This study demonstrates the power of using multiple interconnected populations to generate a large number of veritable mQTL. The combined results are discussed in the context of functional metabolomics and the possible features of assigned candidates underlying respective metabolites.

Gene coexpression network analysis of fruit transcriptomes uncovers a possible mechanistically distinct class of sugar/acid ratio-associated genes in sweet orange

BACKGROUND

The ratio of sugars to organic acids, two of the major metabolites in fleshy fruits, has been considered the most important contributor to fruit sweetness. Although accumulation of sugars and acids have been extensively studied, whether plants evolve a mechanism to maintain, sense or respond to the fruit sugar/acid ratio remains a mystery. In a prior study, we used an integrated systems biology tool to identify a group of 39 acid-associated genes from the fruit transcriptomes in four sweet orange varieties (Citrus sinensis L. Osbeck) with varying fruit acidity, Succari (acidless), Bingtang (low acid), and Newhall and Xinhui (normal acid).

RESULTS

We reanalyzed the prior sweet orange fruit transcriptome data, leading to the identification of 72 genes highly correlated with the fruit sugar/acid ratio. The majority of these sugar/acid ratio-related genes are predicted to be involved in regulatory functions such as transport, signaling and transcription or encode enzymes involved in metabolism. Surprisingly, only three of these sugar/acid ratio-correlated genes are weakly correlated with sugar level and none of them overlaps with the acid-associated genes. Weighted Gene Coexpression Network Analysis (WGCNA) has revealed that these genes belong to four modules, Blue, Grey, Brown and Turquoise, with the former two modules being unique to the sugar/acid ratio control.

CONCLUSION

Our results indicate that orange fruits contain a possible mechanistically distinct class of genes that may potentially be involved in maintaining fruit sugar/acid ratios and/or responding to the cellular sugar/acid ratio status. Therefore, our analysis of orange transcriptomes provides an intriguing insight into the potentially novel genetic or molecular mechanisms controlling the sugar/acid ratio in fruits.

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.

Spatially resolved metabolic analysis reveals a central role for transcriptional control in carbon allocation to wood

The contribution of transcriptional and post-transcriptional regulation to modifying carbon allocation to developing wood of trees is not well defined. To clarify the role of transcriptional regulation, the enzyme activity patterns of eight central primary metabolism enzymes across phloem, cambium, and developing wood of aspen (Populus tremula L.) were compared with transcript levels obtained by RNA sequencing of sequential stem sections from the same trees. Enzymes were selected on the basis of their importance in sugar metabolism and in linking primary metabolism to lignin biosynthesis. Existing enzyme assays were adapted to allow measurements from ~1 mm3 sections of dissected stem tissue. These experiments provided high spatial resolution of enzyme activity changes across different stages of wood development, and identified the gene transcripts probably responsible for these changes. In most cases, there was a clear positive relationship between transcripts and enzyme activity. During secondary cell wall formation, the increases in transcript levels and enzyme activities also matched with increased levels of glucose, fructose, hexose phosphates, and UDP-glucose, emphasizing an important role for transcriptional regulation in carbon allocation to developing aspen wood. These observations corroborate the efforts to increase carbon allocation to wood by engineering gene regulatory networks.

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.

Genetic dissection of metabolite variation in Arabidopsis seeds: evidence for mQTL hotspots and a master regulatory locus of seed metabolism

To gain insight into genetic factors controlling seed metabolic composition and its relationship to major seed properties, an Arabidopsis recombinant inbred line (RIL) population, derived from accessions Col-0 and C24, was studied using an MS-based metabolic profiling approach. Relative intensities of 311 polar primary metabolites were used to identify associated genomic loci and to elucidate their interactions by quantitative trait locus (QTL) mapping. A total of 786 metabolic QTLs (mQTLs) were unequally distributed across the genome, forming several hotspots. For the branched-chain amino acid leucine, mQTLs and candidate genes were elucidated in detail. Correlation studies displayed links between metabolite levels, seed protein content, and seed weight. Principal component analysis revealed a clustering of samples, with PC1 mapping to a region on the short arm of chromosome IV. The overlap of this region with mQTL hotspots indicates the presence of a potential master regulatory locus of seed metabolism. As a result of database queries, a series of candidate regulatory genes, including bZIP10, were identified within this region. Depending on the search conditions, metabolic pathway-derived candidate genes for 40-61% of tested mQTLs could be determined, providing an extensive basis for further identification and characterization of hitherto unknown genes causal for natural variation of Arabidopsis seed metabolism.

AraQTL - workbench and archive for systems genetics in Arabidopsis thaliana

Genetical genomics studies uncover genome-wide genetic interactions between genes and their transcriptional regulators. High-throughput measurement of gene expression in recombinant inbred line populations has enabled investigation of the genetic architecture of variation in gene expression. This has the potential to enrich our understanding of the molecular mechanisms affected by and underlying natural variation. Moreover, it contributes to the systems biology of natural variation, as a substantial number of experiments have resulted in a valuable amount of interconnectable phenotypic, molecular and genotypic data. A number of genetical genomics studies have been published for Arabidopsis thaliana, uncovering many expression quantitative trait loci (eQTLs). However, these complex data are not easily accessible to the plant research community, leaving most of the valuable genetic interactions unexplored as cross-analysis of these studies is a major effort. We address this problem with AraQTL (http://www.bioinformatics.nl/Ara QTL/), an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local eQTLs that could harbour candidate genes for phenotypic QTLs, or detect gene-by-environment interactions by comparing eQTLs under different conditions.

Metabolomic analysis of tomato seed germination

INTRODUCTION

Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.

OBJECTIVE

This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.

METHODS

We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.

RESULTS

QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.

CONCLUSIONS

Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.

Homologous and heterologous expression of grapevine E-(β)-caryophyllene synthase (VvGwECar2)

E-(β)-caryophyllene is a sesquiterpene volatile emitted by plants and involved in many ecological interactions within and among trophic levels and it has a kairomonal activity for many insect species. In grapevine it is a key compound for host-plant recognition by the European grapevine moth, Lobesia botrana, together with other two sesquiterpenes. In grapevine E-(β)-caryophyllene synthase is coded by the VvGwECar2 gene, although complete characterization of the corresponding protein has not yet been achieved. Here we performed the characterization of the enzyme after heterologous expression in E. coli, which resulted to produce in vitro also minor amounts of the isomer α-humulene and of germacrene D. The pH optimum was estimated to be 7.8, and the K_m and K_cat values for farnesyl pyrophosphate were 31.4 μM and 0.19 s^-1 respectively. Then, we overexpressed the gene in the cytoplasm of two plant species, Arabidopsis thaliana and the native host Vitis vinifera. In Arabidopsis the enzyme changed the plant head space release, showing a higher selectivity for E-(β)-caryophyllene, but also the production of thujopsene instead of germacrene D. Overall plants increased the E-(β)-caryophyllene emission in the headspace collection by 8-fold compared to Col-0 control plants. In grapevine VvGwECar2 overexpression resulted in higher E-(β)-caryophyllene emissions, although there was no clear correlation between gene activity and sesquiterpene quantity, suggesting a key role by the plant regulation machinery.

Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals

The plant metabolome is characterized by extensive diversity and is often regarded as a bridge between genome and phenome. Here we report metabolic and phenotypic genome-wide studies (mGWAS and pGWAS) in rice grain that, in addition to previous metabolic GWAS in rice leaf and maize kernel, show both distinct and overlapping aspects of genetic control of metabolism within and between species. We identify new candidate genes potentially influencing important metabolic and/or morphological traits. We show that the differential genetic architecture of rice metabolism between different tissues is in part determined by tissue specific expression. Using parallel mGWAS and pGWAS we identify new candidate genes potentially responsible for variation in traits such as grain colour and size, and provide evidence of metabotype-phenotype linkage. Our study demonstrates a powerful strategy for interactive functional genomics and metabolomics in plants, especially the cloning of minor QTLs for complex phenotypic traits.

The Metabolic Signature of Biomass Formation in Barley

The network analysis of genome-wide transcriptome responses, metabolic signatures and enzymes' relationship to biomass formation has been studied in a diverse panel of 12 barley accessions during vegetative and reproductive stages. The primary metabolites and enzymes involved in central metabolism that determine the accumulation of shoot biomass at the vegetative stage of barley development are primarily being linked to sucrose accumulation and sucrose synthase activity. Interestingly, the metabolic and enzyme links which are strongly associated with biomass accumulation during reproductive stages are related to starch accumulation and tricarboxylic acid (TCA) cycle intermediates citrate, malate, trans-aconitate and isocitrate. Additional significant associations were also found for UDP glucose, ATP and the amino acids isoleucine, valine, glutamate and histidine during the reproductive stage. A network analysis resulted in a combined identification of metabolite and enzyme signatures indicative for grain weight accumulation that was correlated with the activity of ADP-glucose pyrophosphorylase (AGPase), a rate-limiting enzyme involved in starch biosynthesis, and with that of alanine amino transferase involved in the synthesis of storage proteins. We propose that the mechanism related to vegetative and reproductive biomass formation vs. seed biomass formation is being linked to distinct fluxes regulating sucrose, starch, sugars and amino acids as central resources. These distinct biomarkers can be used to engineer biomass production and grain weight in barley.

Genotypes, Networks, Phenotypes: Moving Toward Plant Systems Genetics

One of the central goals in biology is to understand how and how much of the phenotype of an organism is encoded in its genome. Although many genes that are crucial for organismal processes have been identified, much less is known about the genetic bases underlying quantitative phenotypic differences in natural populations. We discuss the fundamental gap between the large body of knowledge generated over the past decades by experimental genetics in the laboratory and what is needed to understand the genotype-to-phenotype problem on a broader scale. We argue that systems genetics, a combination of systems biology and the study of natural variation using quantitative genetics, will help to address this problem. We present major advances in these two mostly disconnected areas that have increased our understanding of the developmental processes of flowering time control and root growth. We conclude by illustrating and discussing the efforts that have been made toward systems genetics specifically in plants.

Dynamic QTLs for sugars and enzyme activities provide an overview of genetic control of sugar metabolism during peach fruit development

Knowledge of the genetic control of sugar metabolism is essential to enhance fruit quality and promote fruit consumption. The sugar content and composition of fruits varies with species, cultivar and stage of development, and is controlled by multiple enzymes. A QTL (quantitative trait locus) study was performed on peach fruit [Prunus persica (L.) Batsch], the model species for Prunus Progeny derived from an interspecific cross between P. persica cultivars and P. davidiana was used. Dynamic QTLs for fresh weight, sugars, acids, and enzyme activities related to sugar metabolism were detected at different stages during fruit development. Changing effects of alleles during fruit growth were observed, including inversions close to maturity. This QTL analysis was supplemented by the identification of genes annotated on the peach genome as enzymes linked to sugar metabolism or sugar transporters. Several cases of co-locations between annotated genes, QTLs for enzyme activities and QTLs controlling metabolite concentrations were observed and discussed. These co-locations raise hypotheses regarding the functional regulation of sugar metabolism and pave the way for further analyses to enable the identification of the underlying genes. In conclusion, we identified the potential impact on fruit breeding of the modification of QTL effect close to maturity.

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.

Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis

Natural variation can be extremely useful in unraveling the determinants of phenotypic trait evolution but has rarely been analyzed with unbiased metabolic profiling to understand how its effects are organized at the level of biochemical pathways. Native populations of Nicotiana attenuata, a wild tobacco species, have been shown to be highly genetically diverse for traits important for their interactions with insects. To resolve the chemodiversity existing in these populations, we developed a metabolomics and computational pipeline to annotate leaf metabolic responses to Manduca sexta herbivory. We selected seeds from 43 accessions of different populations from the southwestern United States--including the well-characterized Utah 30th generation inbred accession--and grew 183 plants in the glasshouse for standardized herbivory elicitation. Metabolic profiles were generated from elicited leaves of each plant using a high-throughput ultra HPLC (UHPLC)-quadrupole TOFMS (qTOFMS) method, processed to systematically infer covariation patterns among biochemically related metabolites, as well as unknown ones, and finally assembled to map natural variation. Navigating this map revealed metabolic branch-specific variations that surprisingly only partly overlapped with jasmonate accumulation polymorphisms and deviated from canonical jasmonate signaling. Fragmentation analysis via indiscriminant tandem mass spectrometry (idMS/MS) was conducted with 10 accessions that spanned a large proportion of the variance found in the complete accession dataset, and compound spectra were computationally assembled into spectral similarity networks. The biological information captured by this networking approach facilitates the mining of the mass spectral data of unknowns with high natural variation, as demonstrated by the annotation of a strongly herbivory-inducible phenolic derivative, and can guide pathway analysis.

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

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C4 metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.

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

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C4 metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.

Using metabolomic approaches to explore chemical diversity in rice

Rice (Oryza sativa) is an excellent resource; it comprises 25% of the total caloric intake of the world's population, and rice plants yield many types of bioactive compounds. To determine the number of metabolites in rice and their chemical diversity, the metabolite composition of cultivated rice has been investigated with analytical techniques such as mass spectrometry (MS) and/or nuclear magnetic resonance spectroscopy and rice metabolite databases have been constructed. This review summarizes current knowledge on metabolites in rice including sugars, amino and organic acids, aromatic compounds, and phytohormones detected by gas chromatography-MS, liquid chromatography-MS, and capillary electrophoresis-MS. The biological properties and the activities of polar and nonpolar metabolites produced by rice plants are also presented. Challenges in the estimation of the structure(s) of unknown metabolites by metabolomic approaches are introduced and discussed. Lastly, examples are presented of the successful application of metabolite profiling of rice to characterize the gene(s) that are potentially critical for improving its quality by combining metabolite quantitative trait loci analysis and to identify potential metabolite biomarkers that play a critical role when rice is grown under abiotic stress conditions.

Metabolic fingerprinting of Arabidopsis thaliana accessions

In the post-genomic era much effort has been put on the discovery of gene function using functional genomics. Despite the advances achieved by these technologies in the understanding of gene function at the genomic and proteomic level, there is still a big genotype-phenotype gap. Metabolic profiling has been used to analyze organisms that have already been characterized genetically. However, there is a small number of studies comparing the metabolic profile of different tissues of distinct accessions. Here, we report the detection of over 14,000 and 17,000 features in inflorescences and leaves, respectively, in two widely used Arabidopsis thaliana accessions. A predictive Random Forest Model was developed, which was able to reliably classify tissue type and accession of samples based on LC-MS profile. Thereby we demonstrate that the morphological differences among A. thaliana accessions are reflected also as distinct metabolic phenotypes within leaves and inflorescences.

Moving toward a comprehensive map of central plant metabolism

Decades of intensive study have led to the discovery of the main pathways involved in central metabolism but only some of the pathways and regulatory networks in which they are embedded. In this review, we discuss techniques used to assemble these pathways into a systems biology framework that can enable accurate modeling of the response of central metabolism to changes, including ways to perturb metabolic systems and assemble the resulting data into a meaningful network. Critically, these networks are of such size and complexity that it is possible to derive them only if data from different groups can be comprehensively and meaningfully combined. We conclude that it is essential to establish common standards for the description of experimental conditions and data collection and to store this information in databases to which the whole community can contribute.

Dissecting the subcellular compartmentation of proteins and metabolites in arabidopsis leaves using non-aqueous fractionation

Non-aqueous fractionation is a technique for the enrichment of different subcellular compartments derived from lyophilized material. It was developed to study the subcellular distribution of metabolites. Here we analyzed the distribution of about 1,000 proteins and 70 metabolites, including 22 phosphorylated intermediates in wild-type Arabidopsis rosette leaves, using non-aqueous gradients divided into 12 fractions. Good separation of plastidial, cytosolic, and vacuolar metabolites and proteins was achieved, but cytosolic, mitochondrial, and peroxisomal proteins clustered together. There was considerable heterogeneity in the fractional distribution of transcription factors, ribosomal proteins, and subunits of the vacuolar-ATPase, indicating diverse compartmental location. Within the plastid, sub-organellar separation of thylakoids and stromal proteins was observed. Metabolites from the Calvin-Benson cycle, photorespiration, starch and sucrose synthesis, glycolysis, and the tricarboxylic acid cycle grouped with their associated proteins of the respective compartment. Non-aqueous fractionation thus proved to be a powerful method for the study of the organellar, and in some cases sub-organellar, distribution of proteins and their association with metabolites. It remains the technique of choice for the assignment of subcellular location to metabolites in intact plant tissues, and thus the technique of choice for doing combined metabolite-protein analysis on a single tissue sample.

Analysis of short-term metabolic alterations in Arabidopsis following changes in the prevailing environmental conditions

Although a considerable increase in our knowledge concerning the importance of metabolic adjustments to unfavorable growth conditions has been recently provided, relatively little is known about the adjustments which occur in response to fluctuation in environmental factors. Evaluating the metabolic adjustments occurring under changing environmental conditions thus offers a good opportunity to increase our current understanding of the crosstalk between the major pathways which are affected by such conditions. To this end, plants growing under normal conditions were transferred to different light and temperature conditions which were anticipated to affect (amongst other processes) the rates of photosynthesis and photorespiration and characterized at the physiological, molecular, and metabolic levels following this transition. Our results revealed similar behavior in response to both treatments and imply a tight connectivity of photorespiration with the major pathways of plant metabolism. They further highlight that the majority of the regulation of these pathways is not mediated at the level of transcription but that leaf metabolism is rather pre-poised to adapt to changes in these input parameters.

Why measure enzyme activities in the era of systems biology?

Information about the abundance and biological activities of proteins is essential to reveal how genes affect phenotypes. Over the past decade, mass spectrometry (MS)-based proteomics has revolutionized the identification and quantification of proteins, and the detection of post-translational modifications. Interpretation of proteomics data depends on information about the biological activities of proteins, which has created a bottleneck in research. This review focuses on enzymes in central metabolism. We examine the methods used for measuring enzyme activities, and discuss how these methods provide information about the kinetic and regulatory properties of enzymes, their turnover, and how this information can be integrated into metabolic models. We also discuss how robotized assays could enable the genetic networks that control enzyme abundance to be analyzed.

The AT-hook motif-encoding gene METABOLIC NETWORK MODULATOR 1 underlies natural variation in Arabidopsis primary metabolism

Regulation of primary metabolism is a central mechanism by which plants coordinate their various responses to biotic and abiotic challenge. To identify genes responsible for natural variation in primary metabolism, we focused on cloning a locus from Arabidopsis thaliana that influences the level of TCA cycle metabolites in planta. We found that the Met.V.67 locus was controlled by natural variation in METABOLIC NETWORK MODULATOR 1 (MNM1), which encoded an AT-hook motif-containing protein that was unique to the Brassicales lineage. MNM1 had wide ranging effects on plant metabolism and displayed a tissue expression pattern that was suggestive of a function in sink tissues. Natural variation within MNM1 had differential effects during a diurnal time course, and this temporal dependency was supported by analysis of T-DNA insertion and over-expression lines for MNM1. Thus, the cloning of a natural variation locus specifically associated with primary metabolism allowed us to identify MNM1 as a lineage-specific modulator of primary metabolism, suggesting that the regulation of primary metabolism can change during evolution.

Deciphering genetic diversity and inheritance of tomato fruit weight and composition through a systems biology approach

Integrative systems biology proposes new approaches to decipher the variation of phenotypic traits. In an effort to link the genetic variation and the physiological and molecular bases of fruit composition, the proteome (424 protein spots), metabolome (26 compounds), enzymatic profile (26 enzymes), and phenotypes of eight tomato accessions, covering the genetic diversity of the species, and four of their F1 hybrids, were characterized at two fruit developmental stages (cell expansion and orange-red). The contents of metabolites varied among the genetic backgrounds, while enzyme profiles were less variable, particularly at the cell expansion stage. Frequent genotype by stage interactions suggested that the trends observed for one accession at a physiological level may change in another accession. In agreement with this, the inheritance modes varied between crosses and stages. Although additivity was predominant, 40% of the traits were non-additively inherited. Relationships among traits revealed associations between different levels of expression and provided information on several key proteins. Notably, the role of frucktokinase, invertase, and cysteine synthase in the variation of metabolites was highlighted. Several stress-related proteins also appeared related to fruit weight differences. These key proteins might be targets for improving metabolite contents of the fruit. This systems biology approach provides better understanding of networks controlling the genetic variation of tomato fruit composition. In addition, the wide data sets generated provide an ideal framework to develop innovative integrated hypothesis and will be highly valuable for the research community.

Predictive modelling of complex agronomic and biological systems

Biological systems are tremendously complex in their functioning and regulation. Studying the multifaceted behaviour and describing the performance of such complexity has challenged the scientific community for years. The reduction of real-world intricacy into simple descriptive models has therefore convinced many researchers of the usefulness of introducing mathematics into biological sciences. Predictive modelling takes such an approach another step further in that it takes advantage of existing knowledge to project the performance of a system in alternating scenarios. The ever growing amounts of available data generated by assessing biological systems at increasingly higher detail provide unique opportunities for future modelling and experiment design. Here we aim to provide an overview of the progress made in modelling over time and the currently prevalent approaches for iterative modelling cycles in modern biology. We will further argue for the importance of versatility in modelling approaches, including parameter estimation, model reduction and network reconstruction. Finally, we will discuss the difficulties in overcoming the mathematical interpretation of in vivo complexity and address some of the future challenges lying ahead.

Impact of the carbon and nitrogen supply on relationships and connectivity between metabolism and biomass in a broad panel of Arabidopsis accessions

Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27-0.58 and 0.21-0.51 and P values in the range of <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.

A diminution in ascorbate oxidase activity affects carbon allocation and improves yield in tomato under water deficit

The regulation of carbon allocation between photosynthetic source leaves and sink tissues in response to stress is an important factor controlling plant yield. Ascorbate oxidase is an apoplastic enzyme, which controls the redox state of the apoplastic ascorbate pool. RNA interference was used to decrease ascorbate oxidase activity in tomato (Solanum lycopersicum L.). Fruit yield was increased in these lines under three conditions where assimilate became limiting for wild-type plants: when fruit trusses were left unpruned, when leaves were removed or when water supply was limited. Several alterations in the transgenic lines could contribute to the improved yield and favour transport of assimilate from leaves to fruits in the ascorbate oxidase lines. Ascorbate oxidase plants showed increases in stomatal conductance and leaf and fruit sugar content, as well as an altered apoplastic hexose:sucrose ratio. Modifications in gene expression, enzyme activity and the fruit metabolome were coherent with the notion of the ascorbate oxidase RNAi lines showing altered sink strength. Ascorbate oxidase may therefore be a target for strategies aimed at improving water productivity in crop species.

Genetic analysis of metabolome-phenotype interactions: from model to crop species

The past decade has seen increased interest from the scientific community, and particularly plant biologists, in integrating metabolic approaches into research aimed at unraveling phenotypic diversity and its underlying genetic variation. Advances in plant metabolomics have enabled large-scale analyses that have identified qualitative and quantitative variation in the metabolic content of various species, and this variation has been linked to genetic factors through genetic-mapping approaches, providing a glimpse of the genetic architecture of the plant metabolome. Parallel analyses of morphological phenotypes and physiological performance characteristics have further enhanced our understanding of the complex molecular mechanisms regulating these quantitative traits. This review aims to illustrate the advantages of including assessments of phenotypic and metabolic diversity in investigations of the genetic basis of complex traits, and the value of this approach in studying agriculturally important crops. We highlight the ground-breaking work on model species and discuss recent achievements in important crop species.

Metabolism and growth in Arabidopsis depend on the daytime temperature but are temperature-compensated against cool nights

Diurnal cycles provide a tractable system to study the response of metabolism and growth to fluctuating temperatures. We reasoned that the response to daytime and night temperature may vary; while daytime temperature affects photosynthesis, night temperature affects use of carbon that was accumulated in the light. Three Arabidopsis thaliana accessions were grown in thermocycles under carbon-limiting conditions with different daytime or night temperatures (12 to 24 °C) and analyzed for biomass, photosynthesis, respiration, enzyme activities, protein levels, and metabolite levels. The data were used to model carbon allocation and growth rates in the light and dark. Low daytime temperature led to an inhibition of photosynthesis and an even larger inhibition of growth. The inhibition of photosynthesis was partly ameliorated by a general increase in protein content. Low night temperature had no effect on protein content, starch turnover, or growth. In a warm night, there is excess capacity for carbon use. We propose that use of this capacity is restricted by feedback inhibition, which is relaxed at lower night temperature, thus buffering growth against fluctuations in night temperature. As examples, the rate of starch degradation is completely temperature compensated against even sudden changes in temperature, and polysome loading increases when the night temperature is decreased.

Multi-dimensional regulation of metabolic networks shaping plant development and performance

The metabolome is an integral part of a plant's life cycle and determines for a large part its external phenotype. It is the final, internal product of chemical interactions, obtained through developmental, genetic, and environmental inputs, and as such, it defines the state of a plant in terms of development and performance. Understanding its regulation will provide knowledge and new insights into the biochemical pathways and genetic interactions that shape the plant and its surroundings. In this review, we will focus on four dimensions that contribute to the huge diversity of metabolomes and we will illustrate how this diversity shapes the plant in terms of development and performance: (i) temporal regulation: the metabolome is extremely dynamic and temporal changes in the environment can have an immense impact on its composition; (ii) spatial regulation: metabolites can be very specific, in both quantitative and qualitative terms, to specialized organs, tissues, and cell types; (iii) environmental regulation: the metabolic profile of plants is highly dependent on environmental signals, such as light, temperature, and nutrients, and very susceptible to biotic and abiotic stresses; and (iv) genetic regulation: the biosynthesis, structure, and accumulation of metabolites have a genetic origin, and there is quantitative and qualitative variation for metabolomes within a species. We will address the contribution of these dimensions to the wide diversity of metabolomes and highlight how the multi-dimensional regulation of metabolism defines the plant's phenotype.

Metabolic profiling of a mapping population exposes new insights in the regulation of seed metabolism and seed, fruit, and plant relations

To investigate the regulation of seed metabolism and to estimate the degree of metabolic natural variability, metabolite profiling and network analysis were applied to a collection of 76 different homozygous tomato introgression lines (ILs) grown in the field in two consecutive harvest seasons. Factorial ANOVA confirmed the presence of 30 metabolite quantitative trait loci (mQTL). Amino acid contents displayed a high degree of variability across the population, with similar patterns across the two seasons, while sugars exhibited significant seasonal fluctuations. Upon integration of data for tomato pericarp metabolite profiling, factorial ANOVA identified the main factor for metabolic polymorphism to be the genotypic background rather than the environment or the tissue. Analysis of the coefficient of variance indicated greater phenotypic plasticity in the ILs than in the M82 tomato cultivar. Broad-sense estimate of heritability suggested that the mode of inheritance of metabolite traits in the seed differed from that in the fruit. Correlation-based metabolic network analysis comparing metabolite data for the seed with that for the pericarp showed that the seed network displayed tighter interdependence of metabolic processes than the fruit. Amino acids in the seed metabolic network were shown to play a central hub-like role in the topology of the network, maintaining high interactions with other metabolite categories, i.e., sugars and organic acids. Network analysis identified six exceptionally highly co-regulated amino acids, Gly, Ser, Thr, Ile, Val, and Pro. The strong interdependence of this group was confirmed by the mQTL mapping. Taken together these results (i) reflect the extensive redundancy of the regulation underlying seed metabolism, (ii) demonstrate the tight co-ordination of seed metabolism with respect to fruit metabolism, and (iii) emphasize the centrality of the amino acid module in the seed metabolic network. Finally, the study highlights the added value of integrating metabolic network analysis with mQTL mapping.

Seeds represent 70% of the food source for man and livestock. However, as a result of millennia of domestication, crop plants have undergone major genetic deterioration, leading to a loss of important quality traits. Thus, the reintroduction of these quality traits is the key to the improvement of crops in modern agriculture. Seed quality traits include nutritional components, such as proteins and amino acids, and seed germination and storability, which are, in turn, inherently related to metabolism. To understand the genetic basis of seed metabolism—a strategic need in the improvement of seed crops—we studied a collection of offspring plants stemming from the cross between a domesticated tomato cultivar Solanum lycopersicum cv M82 and its distant wild relative S. pennellii. We monitored the changes in metabolism and studied the mode of regulation of the concentration of metabolites in the seeds as a result of genetic introgression, by taking advantage of state-of-the-art technologies and methods of data elaboration such as network-based analysis. We identified a number of candidate genes that may be useful in manipulations to enhance nutritional values in seeds. Finally, in an effort to study the relation among the seed, the fruit, and the mother plant, we determined potential yield-associated metabolic markers.

Molecular mapping of Arabidopsis thaliana lipid-related orthologous genes in Brassica napus

Quantitative Trait Loci (QTL) for oil content has been previously analyzed in a SG-DH population from a cross between a Chinese cultivar and a European cultivar of Brassica napus. Eight QTL with additive and epistatic effects, and with environmental interactions were evaluated. Here we present an integrated linkage map of this population predominantly based on informative markers derived from Brassica sequences, including 249 orthologous A. thaliana genes, where nearly half (112) are acyl lipid metabolism related genes. Comparative genomic analysis between B. napus and A. thaliana revealed 33 colinearity regions. Each of the conserved A. thaliana segments is present two to six times in the B. napus genome. Approximately half of the mapped lipid-related orthologous gene loci (76/137) were assigned in these conserved colinearity regions. QTL analysis for seed oil content was performed using the new map and phenotypic data from 11 different field trials. Nine significant QTL were identified on linkage groups A1, A5, A7, A9, C2, C3, C6 and C8, together explaining 57.79% of the total phenotypic variation. A total of 14 lipid related candidate gene loci were located in the confidence intervals of six of these QTL, of which ten were assigned in the conserved colinearity regions and felled in the most frequently overlapped QTL intervals. The information obtained from this study demonstrates the potential role of the suggested candidate genes in rapeseed kernel oil accumulation.

Genetical genomics for evolutionary studies

Genetical genomics combines acquired high-throughput genomic data with genetic analysis. In this chapter, we discuss the application of genetical genomics for evolutionary studies, where new high-throughput molecular technologies are combined with mapping quantitative trait loci (QTL) on the genome in segregating populations.The recent explosion of high-throughput data--measuring thousands of proteins and metabolites, deep sequencing, chromatin, and methyl-DNA immunoprecipitation--allows the study of the genetic variation underlying quantitative phenotypes, together termed xQTL. At the same time, mining information is not getting easier. To deal with the sheer amount of information, powerful statistical tools are needed to analyze multidimensional relationships. In the context of evolutionary computational biology, a well-designed experiment may help dissect a complex evolutionary trait using proven statistical methods for associating phenotypical variation with genomic locations.Evolutionary expression QTL (eQTL) studies of the last years focus on gene expression adaptations, mapping the gene expression landscape, and, tentatively, eQTL networks. Here, we discuss the possibility of introducing an evolutionary prior, in the form of gene families displaying evidence of positive selection, and using that in the context of an eQTL experiment for elucidating host-pathogen protein-protein interactions. Through the example of an experimental design, we discuss the choice of xQTL platform, analysis methods, and scope of results. The resulting eQTL can be matched, resulting in putative interacting genes and their regulators. In addition, a prior may help distinguish QTL causality from reactivity, or independence of traits, by creating QTL networks.

Determining novel functions of Arabidopsis 14-3-3 proteins in central metabolic processes

Background

14-3-3 proteins are considered master regulators of many signal transduction cascades in eukaryotes. In plants, 14-3-3 proteins have major roles as regulators of nitrogen and carbon metabolism, conclusions based on the studies of a few specific 14-3-3 targets.

Results

In this study, extensive novel roles of 14-3-3 proteins in plant metabolism were determined through combining the parallel analyses of metabolites and enzyme activities in 14-3-3 overexpression and knockout plants with studies of protein-protein interactions. Decreases in the levels of sugars and nitrogen-containing-compounds and in the activities of known 14-3-3-interacting-enzymes were observed in 14-3-3 overexpression plants. Plants overexpressing 14-3-3 proteins also contained decreased levels of malate and citrate, which are intermediate compounds of the tricarboxylic acid (TCA) cycle. These modifications were related to the reduced activities of isocitrate dehydrogenase and malate dehydrogenase, which are key enzymes of TCA cycle. In addition, we demonstrated that 14-3-3 proteins interacted with one isocitrate dehydrogenase and two malate dehydrogenases. There were also changes in the levels of aromatic compounds and the activities of shikimate dehydrogenase, which participates in the biosynthesis of aromatic compounds.

Conclusion

Taken together, our findings indicate that 14-3-3 proteins play roles as crucial tuners of multiple primary metabolic processes including TCA cycle and the shikimate pathway.