Untargeted metabolic quantitative trait loci analyses reveal a relationship between primary metabolism and potato tuber quality

Compositional analyses of diverse phytochemicals and polar metabolites from different-colored potato (Solanum tubersum L.) tubers

Lipophilic bioactive compounds and hydrophilic primary metabolites from potato (solanum tubersum L.) tubers with different-colored flesh (white-, yellow-, red-, and purple) were characterized. The carotenoid content was relatively higher in red-colored potatoes, in which lutein was most plentiful. Among the other lipophilic compounds analyzed, including policosanols, tocopherols, and phytosterols, octacosanol was measured in the largest amount, followed by β-sitosterol, irrespective of color variations. Forty-three hydrophilics consisting of amino acids, organic acids, sugars, and sugar alcohols and 18 lipophilics were subjected to data-mining processes. The results of multivariate statistical analyses clearly distincted the different varieties and separated red-fleshed potatoes from other color-fleshed potatoes according to abundance of amino acids, sugars, and carotenoids. This study confirmed the metabolic association-related biochemical pathway between metabolite characteristic and color differences in potato tubers. These results can facilitate understanding the metabolic differences among diverse colored potatoes and provide fruitful information for genetic engineering of potato cultivars.

A Multidisciplinary Phenotyping and Genotyping Analysis of a Mapping Population Enables Quality to Be Combined with Yield in Rice

In this study a mapping population (F₈) of ca 200 progeny from a cross between the commercial rice varieties Apo and IR64 has been both genotyped and phenotyped. A genotyping-by-sequencing approach was first used to identify 2,681 polymorphic SNP markers which gave dense coverage of the genome with a good distribution across all 12 chromosomes. The coefficient of parentage was also low, at 0.13, confirming that the parents are genetically distant from each other. The progeny, together with both parents, were grown under irrigated and water restricted conditions in a randomised block design. All grain was harvested to determine variation in yield across the population. The grains were then polished following standard procedures prior to performing the phenotyping analyses. A Gas Chromatography—Mass Spectrometry approach was used to determine the volatile biochemical profiles of each line and after data curation and processing, discriminatory metabolites were putatively identified based on in-house and commercial spectral libraries. These data were used to predict the potential role of these metabolites in determining differences in aroma between genotypes. A number of QTLs for yield and for individual metabolites have been identified. Following these combined multi-disciplinary analyses, it proved possible to identify a number of lines which appeared to combine the favourable aroma attributes of IR64 with the favourable (higher) yield potential of Apo. As such, these lines are excellent candidates to assess further as potential genotypes to work up into a new variety of rice which has both good yield and good quality, thus meeting the needs of both farmer and consumer alike.

Metabolite profiling of barley flag leaves under drought and combined heat and drought stress reveals metabolic QTLs for metabolites associated with antioxidant defense

Barley (Hordeum vulgare L.) is among the most stress-tolerant crops; however, not much is known about the genetic and environmental control of metabolic adaptation of barley to abiotic stresses. We have subjected a genetically diverse set of 81 barley accessions, consisting of Mediterranean landrace genotypes and German elite breeding lines, to drought and combined heat and drought stress at anthesis. Our aim was to (i) investigate potential differences in morphological, physiological, and metabolic adaptation to the two stress scenarios between the Mediterranean and German barley genotypes and (ii) identify metabolic quantitative trait loci (mQTLs). To this end, we have genotyped the investigated barley lines with an Illumina iSelect 9K array and analyzed a set of 57 metabolites from the primary C and N as well as antioxidant metabolism in flag leaves under control and stress conditions. We found that drought-adapted genotypes attenuate leaf carbon metabolism much more strongly than elite lines during drought stress adaptation. Furthermore, we identified mQTLs for flag leaf γ-tocopherol, glutathione, and succinate content by association genetics that co-localize with genes encoding enzymes of the pathways producing these antioxidant metabolites. Our results provide the molecular basis for breeding barley cultivars with improved abiotic stress tolerance.

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.

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.

Integration of multi-omics data for prediction of phenotypic traits using random forest

Background

In order to find genetic and metabolic pathways related to phenotypic traits of interest, we analyzed gene expression data, metabolite data obtained with GC-MS and LC-MS, proteomics data and a selected set of tuber quality phenotypic data from a diploid segregating mapping population of potato. In this study we present an approach to integrate these ~ omics data sets for the purpose of predicting phenotypic traits. This gives us networks of relatively small sets of interrelated ~ omics variables that can predict, with higher accuracy, a quality trait of interest.

Results

We used Random Forest regression for integrating multiple ~ omics data for prediction of four quality traits of potato: tuber flesh colour, DSC onset, tuber shape and enzymatic discoloration. For tuber flesh colour beta-carotene hydroxylase and zeaxanthin epoxidase were ranked first and forty-fourth respectively both of which have previously been associated with flesh colour in potato tubers. Combining all the significant genes, LC-peaks, GC-peaks and proteins, the variation explained was 75 %, only slightly more than what gene expression or LC-MS data explain by themselves which indicates that there are correlations among the variables across data sets. For tuber shape regressed on the gene expression, LC-MS, GC-MS and proteomics data sets separately, only gene expression data was found to explain significant variation. For DSC onset, we found 12 significant gene expression, 5 metabolite levels (GC) and 2 proteins that are associated with the trait. Using those 19 significant variables, the variation explained was 45 %. Expression QTL (eQTL) analyses showed many associations with genomic regions in chromosome 2 with also the highest explained variation compared to other chromosomes. Transcriptomics and metabolomics analysis on enzymatic discoloration after 5 min resulted in 420 significant genes and 8 significant LC metabolites, among which two were putatively identified as caffeoylquinic acid methyl ester and tyrosine.

Conclusions

In this study, we made a strategy for selecting and integrating multiple ~ omics data using random forest method and selected representative individual peaks for networks based on eQTL, mQTL or pQTL information. Network analysis was done to interpret how a particular trait is associated with gene expression, metabolite and protein data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1043-4) contains supplementary material, which is available to authorized users.

Plant Metabolomics: An Indispensable System Biology Tool for Plant Science

As genomes of many plant species have been sequenced, demand for functional genomics has dramatically accelerated the improvement of other omics including metabolomics. Despite a large amount of metabolites still remaining to be identified, metabolomics has contributed significantly not only to the understanding of plant physiology and biology from the view of small chemical molecules that reflect the end point of biological activities, but also in past decades to the attempts to improve plant behavior under both normal and stressed conditions. Hereby, we summarize the current knowledge on the genetic and biochemical mechanisms underlying plant growth, development, and stress responses, focusing further on the contributions of metabolomics to practical applications in crop quality improvement and food safety assessment, as well as plant metabolic engineering. We also highlight the current challenges and future perspectives in this inspiring area, with the aim to stimulate further studies leading to better crop improvement of yield and quality.

Galactinol as marker for seed longevity

Reduced seed longevity or storability is a major problem in seed storage and contributes to increased costs in crop production. Here we investigated whether seed galactinol contents could be predictive for seed storability behavior in Arabidopsis, cabbage and tomato. The analyses revealed a positive correlation between galactinol content and seed longevity in the three species tested, which indicates that this correlation is conserved in the Brassicaceae and beyond. Quantitative trait loci (QTL) mapping in tomato revealed a co-locating QTL for galactinol content and seed longevity on chromosome 2. A candidate for this QTL is the GALACTINOL SYNTHASE gene (Solyc02g084980.2.1) that is located in the QTL interval. GALACTINOL SYNTHASE is a key enzyme of the raffinose family oligosaccharide (RFO) pathway. To investigate the role of enzymes in the RFO pathway in more detail, we applied a reverse genetics approach using T-DNA knock-out lines in genes encoding enzymes of this pathway (GALACTINOL SYNTHASE 1, GALACTINOL SYNTHASE 2, RAFFINOSE SYNTHASE, STACHYOSE SYNTHASE and ALPHA-GALACTOSIDASE) and overexpressors of the cucumber GALACTINOL SYNTHASE 2 gene in Arabidopsis. The galactinol synthase 2 mutant and the galactinol synthase 1 galactinol synthase 2 double mutant contained the lowest seed galactinol content which coincided with lower seed longevity. These results show that galactinol content of mature dry seed can be used as a biomarker for seed longevity in Brassicaceae and tomato.

HsfA2 Controls the Activity of Developmentally and Stress-Regulated Heat Stress Protection Mechanisms in Tomato Male Reproductive Tissues

Male reproductive tissues are more sensitive to heat stress (HS) compared to vegetative tissues, but the basis of this phenomenon is poorly understood. Heat stress transcription factors (Hsfs) regulate the transcriptional changes required for protection from HS In tomato (Solanum lycopersicum), HsfA2 acts as coactivator of HsfA1a and is one of the major Hsfs accumulating in response to elevated temperatures. The contribution of HsfA2 in heat stress response (HSR) and thermotolerance was investigated in different tissues of transgenic tomato plants with suppressed HsfA2 levels (A2AS). Global transcriptome analysis and immunodetection of two major Hsps in vegetative and reproductive tissues showed that HsfA2 regulates subsets of HS-induced genes in a tissue-specific manner. Accumulation of HsfA2 by a moderate HS treatment enhances the capacity of seedlings to cope with a subsequent severe HS, suggesting an important role for HsfA2 in regulating acquired thermotolerance. In pollen, HsfA2 is an important coactivator of HsfA1a during HSR HsfA2 suppression reduces the viability and germination rate of pollen that received the stress during the stages of meiosis and microspore formation but had no effect on more advanced stages. In general, pollen meiocytes and microspores are characterized by increased susceptibility to HS due to their lower capacity to induce a strong HSR This sensitivity is partially mitigated by the developmentally regulated expression of HsfA2 and several HS-responsive genes mediated by HsfA1a under nonstress conditions. Thereby, HsfA2 is an important factor for the priming process that sustains pollen thermotolerance during microsporogenesis.

Effects of Parental Temperature and Nitrate on Seed Performance are Reflected by Partly Overlapping Genetic and Metabolic Pathways

Seed performance is affected by the seed maturation environment, and previously we have shown that temperature, nitrate and light intensity were the most influential environmental factors affecting seed performance. Seeds developed in these environments were selected to assess the underlying metabolic pathways, using a combination of transcriptomics and metabolomics. These analyses revealed that the effects of the parental temperature and nitrate environments were reflected by partly overlapping genetic and metabolic networks, as indicated by similar changes in the expression levels of metabolites and transcripts. Nitrogen metabolism-related metabolites (asparagine, γ-aminobutyric acid and allantoin) were significantly decreased in both low temperature (15 °C) and low nitrate (N0) maturation environments. Correspondingly, nitrogen metabolism genes (ALLANTOINASE, NITRATE REDUCTASE 1, NITRITE REDUCTASE 1 and NITRILASE 4) were differentially regulated in the low temperature and nitrate maturation environments, as compared with control conditions. High light intensity during seed maturation increased galactinol content, and displayed a high correlation with seed longevity. Low light had a genotype-specific effect on cell surface-encoding genes in the DELAY OF GERMINATION 6-near isogenic line (NILDOG6). Overall, the integration of phenotypes, metabolites and transcripts led to new insights into the regulation of seed performance.

Comparative analysis of phytochemicals and polar metabolites from colored sweet potato (Ipomoea batatas L.) tubers

We determined the phytochemical diversity, including carotenoids, flavonoids, anthocyanins, and phenolic acids, in sweet potatoes (Ipomoea batatas L.) with distinctive flesh colors (white, orange, and purple) and identified hydrophilic primary metabolites. Carotenoid content was considerably higher in orange-fleshed sweet potatoes, wherein β-carotene was the most plentiful, and anthocyanins were detected only in purple-fleshed sweet potatoes. The levels of phenolic acids and flavonoids were relatively higher in purple-fleshed sweet potatoes than those in the other two varieties. Forty-one primary and 18 secondary metabolite profiles were subjected to multivariate statistical analyses, which fully distinguished among the varieties and separated orange- and purple-fleshed sweet potatoes from white-fleshed sweet potatoes based on the high levels of sugars, sugar alcohols, and secondary metabolites. This is the first study to determine comprehensive metabolic differences among different color-fleshed sweet potatoes and provides useful information for genetic manipulation of sweet potatoes to influence primary and secondary metabolism.

Genomics-based strategies for the use of natural variation in the improvement of crop metabolism

Next-generation genomics holds great potential in the study of plant phenotypic variation. With several crop reference genomes now available, the affordable costs of de novo genome assembly or target resequencing offer the opportunity to mine the enormous amount of genetic diversity hidden in crop wild relatives. Wide introgressions from these wild ancestors species or land races represent a possible strategy to improve cultivated varieties. In this review, we discuss the mechanisms underlying metabolic diversity within plant species and the possible strategies (and barriers) to introgress novel metabolic traits into cultivated varieties. We show how deep genomic surveys uncover various types of structural variants from extended gene pools of major crops and highlight how this variation may be used for the improvement of crop metabolism.

Gomphrena claussenii, a novel metal-hypertolerant bioindicator species, sequesters cadmium, but not zinc, in vacuolar oxalate crystals

Gomphrena claussenii is a recently described zinc (Zn)- and cadmium (Cd)-hypertolerant Amaranthaceae species displaying a metal bioindicator Zn/Cd accumulation response. We investigated the Zn and Cd distribution in stem and leaf tissues of G. claussenii at the cellular level, and determined metabolite profiles to investigate metabolite involvement in Zn and Cd sequestration. Gomphrena claussenii plants exposed to high Zn and Cd supply were analysed by scanning electron microscopy with energy-dispersive X-ray (SEM-EDX) and micro-proton-induced X-ray emission (micro-PIXE). In addition, gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) was used to determine metabolite profiles on high Zn and Cd exposure. Stem and leaf tissues of G. claussenii plants exposed to control and high Cd conditions showed the abundant presence of calcium oxalate (CaOx) crystals, but on high Zn exposure, their abundance was strongly reduced. Ca and Cd co-localized to the CaOx crystals in Cd-exposed plants. Citrate, malate and oxalate levels were all higher in shoot tissues of metal-exposed plants, with oxalate levels induced 2.6-fold on Zn exposure and 6.4-fold on Cd exposure. Sequestration of Cd in vacuolar CaOx crystals of G. claussenii is found to be a novel mechanism to deal with Cd accumulation and tolerance.

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.

Detection of QTL for metabolic and agronomic traits in wheat with adjustments for variation at genetic loci that affect plant phenology

Mapping of quantitative trait loci associated with levels of individual metabolites (mQTL) was combined with the mapping of agronomic traits to investigate the genetic basis of variation and co-variation in metabolites, agronomic traits, and plant phenology in a field-grown bread wheat population. Metabolome analysis was performed using liquid chromatography-mass spectrometry resulting in identification of mainly polar compounds, including secondary metabolites. A total of 558 metabolic features were obtained from the flag leaves of 179 doubled haploid lines, of which 197 features were putatively identified, mostly as alkaloids, flavonoids and phenylpropanoids. Coordinated genetic control was observed for several groups of metabolites, such as organic acids influenced by two loci on chromosome 7A. Five major phenology-related loci, which were introduced as cofactors in the analyses, differed in their impact upon metabolic and agronomic traits with QZad-aww-7A having more impact on the expression of both metabolite and agronomic QTL than Ppd-B1, Vrn-A1, Eps, and QZad-aww-7D. This QTL study validates the utility of combining agronomic and metabolomic traits as an approach to identify potential trait enhancement targets for breeding selection and reinforces previous results that demonstrate the importance of including plant phenology in the assessment of useful traits in this wheat mapping population.

Identification and mode of inheritance of quantitative trait loci for secondary metabolite abundance in tomato

A large-scale metabolic quantitative trait loci (mQTL) analysis was performed on the well-characterized Solanum pennellii introgression lines to investigate the genomic regions associated with secondary metabolism in tomato fruit pericarp. In total, 679 mQTLs were detected across the 76 introgression lines. Heritability analyses revealed that mQTLs of secondary metabolism were less affected by environment than mQTLs of primary metabolism. Network analysis allowed us to assess the interconnectivity of primary and secondary metabolism as well as to compare and contrast their respective associations with morphological traits. Additionally, we applied a recently established real-time quantitative PCR platform to gain insight into transcriptional control mechanisms of a subset of the mQTLs, including those for hydroxycinnamates, acyl-sugar, naringenin chalcone, and a range of glycoalkaloids. Intriguingly, many of these compounds displayed a dominant-negative mode of inheritance, which is contrary to the conventional wisdom that secondary metabolite contents decreased on domestication. We additionally performed an exemplary evaluation of two candidate genes for glycolalkaloid mQTLs via the use of virus-induced gene silencing. The combined data of this study were compared with previous results on primary metabolism obtained from the same material and to other studies of natural variance of secondary metabolism.

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.

To grow or not to grow: a stressful decision for plants

Progress in improving abiotic stress tolerance of crop plants using classic breeding and selection approaches has been slow. This has generally been blamed on the lack of reliable traits and phenotyping methods for stress tolerance. In crops, abiotic stress tolerance is most often measured in terms of yield-capacity under adverse weather conditions. "Yield" is a complex trait and is determined by growth and developmental processes which are controlled by environmental signals throughout the life cycle of the plant. The use of model systems has allowed us to gradually unravel how plants grow and develop, but our understanding of the flexibility and opportunistic nature of plant development and its capacity to adapt growth to environmental cues is still evolving. There is genetic variability for the capacity to maintain yield and productivity under abiotic stress conditions in crop plants such as cereals. Technological progress in various domains has made it increasingly possible to mine that genetic variability and develop a better understanding about the basic mechanism of plant growth and abiotic stress tolerance. The aim of this paper is not to give a detailed account of all current research progress, but instead to highlight some of the current research trends that may ultimately lead to strategies for stress-proofing crop species. The focus will be on abiotic stresses that are most often associated with climate change (drought, heat and cold) and those crops that are most important for human nutrition, the cereals.

Genetic and molecular bases of yield-associated traits: a translational biology approach between rice and wheat

Transferring the knowledge bases between related species may assist in enlarging the yield potential of crop plants. Being cereals, rice and wheat share a high level of gene conservation; however, they differ at metabolic levels as a part of the environmental adaptation resulting in different yield capacities. This review focuses on the current understanding of genetic and molecular regulation of yield-associated traits in both crop species, highlights the similarities and differences and presents the putative knowledge gaps. We focus on the traits associated with phenology, photosynthesis, and assimilate partitioning and lodging resistance; the most important drivers of yield potential. Currently, there are large knowledge gaps in the genetic and molecular control of such major biological processes that can be filled in a translational biology approach in transferring genomics and genetics informations between rice and wheat.

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis

Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/.

Application of nontargeted metabolite profiling to discover novel markers of quality traits in an advanced population of malting barley

The process of breeding superior varieties for the agricultural industry is lengthy and expensive. Plant metabolites may act as markers of quality traits, potentially expediting the appraisal of experimental lines during breeding. Here, we evaluated the utility of metabolites as markers by assessing metabolic variation influenced by genetic and environmental factors in an advanced breeding setting and in relation to the phenotypic distribution of 20 quality traits. Nontargeted liquid chromatography-mass spectrometry metabolite profiling was performed on barley (Hordeum vulgare L.) grain and malt from 72 advanced malting barley lines grown at two distinct but climatically similar locations, with 2-row and 6-row barley as the main genetic factors. 27 420 molecular features were detected, and the metabolite and quality trait profiles were similarly influenced by genotype and environment; however, malt was more influenced by genotype compared with barley. An O2PLS model characterized molecular features and quality traits that covaried, and 1319 features associated with at least one of 20 quality traits. An indiscriminant MS/MS acquisition and novel data analysis method facilitated the identification of metabolites. The analysis described 216 primary and secondary metabolites that correlated with multiple quality traits and included amines, amino acids, alkaloids, polyphenolics and lipids. The mechanisms governing quality trait-metabolite associations were interpreted based on colocalization to genetic markers and their gene annotations. The results of this study support the hypothesis that metabolism and quality traits are co-influenced by relatively narrow genetic and environmental factors and illustrate the utility of grain metabolites as functional markers of quality traits.

LC-MS profiling to link metabolic and phenotypic diversity in plant mapping populations

Numerous studies have revealed the extent of genetic, phenotypic, and metabolic variation between different plant cultivars/varieties. We present a specialized protocol for large-scale targeted and untargeted metabolite profiling for samples from large plant mapping populations using both reversed-phase and aqueous normal-phase LC-MS. This methodology provides a fast and combined targeted/nontargeted workflow as a powerful tool to discriminate related plant phenotypes and describes methods to combine mass features and agronomic traits to link phenotypic to metabolic traits independent of putative metabolite identities. This easily reproducible analytical strategy, in combination with a sophisticated data processing and analysis workflow, can be applicable to a wide range of plant mapping populations.

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.

Metabolite profiling approach reveals the interface of primary and secondary metabolism in colored cauliflowers (Brassica oleracea L. ssp. botrytis)

In the present study, carotenoids, anthocyanins, and phenolic acids of cauliflowers ( Brassica oleracea L. ssp. botrytis) with various colored florets (white, yellow, green, and purple) were characterized to determine their phytochemical diversity. Additionally, 48 metabolites comprising amino acids, organic acids, sugars, and sugar alcohols were identified using gas chromatography-time-of-flight mass spectrometry (GC-TOFMS). Carotenoid content was considerably higher in green cauliflower; anthocyanins were detected only in purple cauliflower. Phenolic acids were higher in both green and purple cauliflower. Results of partial least-squares discriminant, Pearson correlation, and hierarchical clustering analyses showed that green cauliflower is distinct on the basis of the high levels of amino acids and clusters derived from common or closely related biochemical pathways. These results suggest that GC-TOFMS-based metabolite profiling, combined with chemometrics, is a useful tool for determining phenotypic variation and identifying metabolic networks connecting primary and secondary metabolism.

Whole-genome mapping of agronomic and metabolic traits to identify novel quantitative trait Loci in bread wheat grown in a water-limited environment

Drought is a major environmental constraint responsible for grain yield losses of bread wheat (Triticum aestivum) in many parts of the world. Progress in breeding to improve complex multigene traits, such as drought stress tolerance, has been limited by high sensitivity to environmental factors, low trait heritability, and the complexity and size of the hexaploid wheat genome. In order to obtain further insight into genetic factors that affect yield under drought, we measured the abundance of 205 metabolites in flag leaf tissue sampled from plants of 179 cv Excalibur/Kukri F1-derived doubled haploid lines of wheat grown in a field experiment that experienced terminal drought stress. Additionally, data on 29 agronomic traits that had been assessed in the same field experiment were used. A linear mixed model was used to partition and account for nongenetic and genetic sources of variation, and quantitative trait locus analysis was used to estimate the genomic positions and effects of individual quantitative trait loci. Comparison of the agronomic and metabolic trait variation uncovered novel correlations between some agronomic traits and the levels of certain primary metabolites, including metabolites with either positive or negative associations with plant maturity-related or grain yield-related traits. Our analyses demonstrate that specific regions of the wheat genome that affect agronomic traits also have distinct effects on specific combinations of metabolites. This approach proved valuable for identifying novel biomarkers for the performance of wheat under drought and could facilitate the identification of candidate genes involved in drought-related responses in bread wheat.

Identifying genotype-by-environment interactions in the metabolism of germinating arabidopsis seeds using generalized genetical genomics

A complex phenotype such as seed germination is the result of several genetic and environmental cues and requires the concerted action of many genes. The use of well-structured recombinant inbred lines in combination with "omics" analysis can help to disentangle the genetic basis of such quantitative traits. This so-called genetical genomics approach can effectively capture both genetic and epistatic interactions. However, to understand how the environment interacts with genomic-encoded information, a better understanding of the perception and processing of environmental signals is needed. In a classical genetical genomics setup, this requires replication of the whole experiment in different environmental conditions. A novel generalized setup overcomes this limitation and includes environmental perturbation within a single experimental design. We developed a dedicated quantitative trait loci mapping procedure to implement this approach and used existing phenotypical data to demonstrate its power. In addition, we studied the genetic regulation of primary metabolism in dry and imbibed Arabidopsis (Arabidopsis thaliana) seeds. In the metabolome, many changes were observed that were under both environmental and genetic controls and their interaction. This concept offers unique reduction of experimental load with minimal compromise of statistical power and is of great potential in the field of systems genetics, which requires a broad understanding of both plasticity and dynamic regulation.

Systems-integration of plant metabolism: means, motive and opportunity

System integration of metabolism is considered in analogy to the investigation of corporate misdemeanour. Motive, or goal-oriented explanation, provides hypotheses that can guide the investigation of network structure. Opportunity can be established by correlative analysis using large-scale omics resources. However, correlative approaches on their own remain inconclusive and seldom identify all the links in a network. Establishment of means, or the ability to act on other network components and contribute to a phenotype, is therefore crucial. This requires functional information. Integration of quantitative data in the context of pathway models provides a powerful approach to establish 'means'. This is illustrated by discussing: first, how protein abundance is regulated by a network including transcript abundance, translation and protein degradation and second, how a combination of experimentation and modelling provides information about pathway flux, an emergent network property that integrates changes in proteins and metabolites and determines composition and biomass.

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.

Current challenges and future potential of tomato breeding using omics approaches

As tomatoes are one of the most important vegetables in the world, improvements in the quality and yield of tomato are strongly required. For this purpose, omics approaches such as metabolomics and transcriptomics are used not only for basic research to understand relationships between important traits and metabolism but also for the development of next generation breeding strategies of tomato plants, because an increase in the knowledge improves the taste and quality, stress resistance and/or potentially health-beneficial metabolites and is connected to improvements in the biochemical composition of tomatoes. Such omics data can be applied to network analyses to potentially reveal unknown cellular regulatory networks in tomato plants. The high-quality tomato genome that was sequenced in 2012 will likely accelerate the application of omics strategies, including next generation sequencing for tomato breeding. In this review, we highlight the current studies of omics network analyses of tomatoes and other plant species, in particular, a gene coexpression network. Key applications of omics approaches are also presented as case examples to improve economically important traits for tomato breeding.

Recent progress in the development of metabolome databases for plant systems biology

Metabolomics has grown greatly as a functional genomics tool, and has become an invaluable diagnostic tool for biochemical phenotyping of biological systems. Over the past decades, a number of databases involving information related to mass spectra, compound names and structures, statistical/mathematical models and metabolic pathways, and metabolite profile data have been developed. Such databases complement each other and support efficient growth in this area, although the data resources remain scattered across the World Wide Web. Here, we review available metabolome databases and summarize the present status of development of related tools, particularly focusing on the plant metabolome. Data sharing discussed here will pave way for the robust interpretation of metabolomic data and advances in plant systems biology.

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.