Metabolomics analysis and metabolite‐agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines

Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high‐density genetic map, we conducted a comprehensive metabolome study via widely targeted LC‐MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty‐four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite‐agronomic traits with the co‐localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co‐localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.     

Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach

Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low‐cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker‐assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.     

Identification of enzymatic and regulatory genes of plant metabolism through QTL analysis in Arabidopsis

The biochemical diversity in the plant kingdom is estimated to well exceed 100,000 distinct compounds (Weckwerth, 2003) and 4000 to 20,000 metabolites per species seem likely (Fernie et al., 2004). In recent years extensive progress has been made towards the identification of enzymes and regulatory genes working in a complex network to generate this large arsenal of metabolites. Genetic loci influencing quantitative traits, e.g. metabolites or biomass, may be mapped to associated molecular markers, a method called quantitative trait locus mapping (QTL mapping), which may facilitate the identification of novel genes in biochemical pathways. Arabidopsis thaliana, as a model organism for seed plants, is a suitable target for metabolic QTL (mQTL) studies due to the availability of highly developed molecular and genetic tools, and the extensive knowledge accumulated on the metabolite profile. While intensely studied, in particular since the availability of its complete sequence, the genome of Arabidopsis still comprises a large proportion of genes with only tentative function based on sequence homology. From a total number of 33,518 genes currently listed (TAIR 9, http://www.arabidopsis.org), only about 25% have direct experimental evidence for their molecular function and biological process, while for more than 30% no biological data are available. Modern metabolomics approaches together with continually extended genomic resources will facilitate the task of assigning functions to those genes. In our previous study we reported on the identification of mQTL (Lisec et al., 2008). In this paper, we summarize the current status of mQTL analyses and causal gene identification in Arabidopsis and present evidence that a candidate gene located within the confidence interval of a fumarate mQTL (AT5G50950) encoding a putative fumarase is likely to be the causal gene of this QTL. The total number of genes molecularly identified based on mQTL studies is still limited, but the advent of multi-parallel analysis techniques for measurement of gene expression, as well as protein and metabolite abundances and for rapid gene identification will assist in the important task of assigning enzymes and regulatory genes to the growing network of known metabolic reactions.     

Genome‐wide association of drought‐related and biomass traits with HapMap SNPs in Medicago truncatula

Improving drought tolerance of crop plants is a major goal of plant breeders. In this study, we characterized biomass and drought‐related traits of 220 Medicago truncatula HapMap accessions. Characterized traits included shoot biomass, maximum leaf size, specific leaf weight, stomatal density, trichome density and shoot carbon‐13 isotope discrimination (δ¹³C) of well‐watered M. truncatula plants, and leaf performance in vitro under dehydration stress. Genome‐wide association analyses were carried out using the general linear model (GLM), the standard mixed linear model (MLM) and compressed MLM (CMLM) in TASSEL, which revealed significant overestimation of P‐values by CMLM. For each trait, candidate genes and chromosome regions containing SNP markers were found that are in significant association with the trait. For plant biomass, a 0.5 Mbp region on chromosome 2 harbouring a plasma membrane intrinsic protein, PIP2, was discovered that could potentially be targeted to increase dry matter yield. A protein disulfide isomerase‐like protein was found to be tightly associated with both shoot biomass and leaf size. A glutamate‐cysteine ligase and an aldehyde dehydrogenase family protein with Arabidopsis homologs strongly expressed in the guard cells were two of the top genes identified by stomata density genome‐wide association studies analysis.     

Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement

Tomato represents an important source of fiber and nutrients in the human diet and is a central model for the study of fruit biology. To identify components of fruit metabolic composition, here we have phenotyped tomato introgression lines (ILs) containing chromosome segments of a wild species in the genetic background of a cultivated variety. Using this high-diversity population, we identify 889 quantitative fruit metabolic loci and 326 loci that modify yield-associated traits. The mapping analysis indicates that at least 50% of the metabolic loci are associated with quantitative trait loci (QTLs) that modify whole-plant yield-associated traits. We generate a cartographic network based on correlation analysis that reveals whole-plant phenotype associated and independent metabolic associations, including links with metabolites of nutritional and organoleptic importance. The results of our genomic survey illustrate the power of genome-wide metabolic profiling and detailed morphological analysis for uncovering traits with potential for crop breeding.     

Identification of loci governing eight agronomic traits using a GBS‐GWAS approach and validation by QTL mapping in soya bean

Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome‐wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping‐by‐sequencing (GBS) approach was used to provide dense genome‐wide marker coverage (>47 000 SNPs) for a panel of 304 short‐season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNPs. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus (QTL) for these traits. In some cases, the reported QTLs were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean.     

Metabolome‐genome‐wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism

Plants produce structurally diverse secondary (specialized) metabolites to increase their fitness for survival under adverse environments. Several bioactive compounds for new drugs have been identified through screening of plant extracts. In this study, genome‐wide association studies (GWAS) were conducted to investigate the genetic architecture behind the natural variation of rice secondary metabolites. GWAS using the metabolome data of 175 rice accessions successfully identified 323 associations among 143 single nucleotide polymorphisms (SNPs) and 89 metabolites. The data analysis highlighted that levels of many metabolites are tightly associated with a small number of strong quantitative trait loci (QTLs). The tight association may be a mechanism generating strains with distinct metabolic composition through the crossing of two different strains. The results indicate that one plant species produces more diverse phytochemicals than previously expected, and plants still contain many useful compounds for human applications.     

Genetic and evolution analysis of extrafloral nectary in cotton

Extrafloral nectaries are a defence trait that plays important roles in plant–animal interactions. Gossypium species are characterized by cellular grooves in leaf midribs that secret large amounts of nectar. Here, with a panel of 215 G. arboreum accessions, we compared extrafloral nectaries to nectariless accessions to identify a region of Chr12 that showed strong differentiation and overlapped with signals from GWAS of nectaries. Fine mapping of an F₂ population identified GaNEC1, encoding a PB1 domain‐containing protein, as a positive regulator of nectary formation. An InDel, encoding a five amino acid deletion, together with a nonsynonymous substitution, was predicted to cause 3D structural changes in GaNEC1 protein that could confer the nectariless phenotype. mRNA‐Seq analysis showed that JA‐related genes are up‐regulated and cell wall‐related genes are down‐regulated in the nectary. Silencing of GaNEC1 led to a smaller size of foliar nectary phenotype. Metabolomics analysis identified more than 400 metabolites in nectar, including expected saccharides and amino acids. The identification of GaNEC1 helps establish the network regulating nectary formation and nectar secretion, and has implications for understanding the production of secondary metabolites in nectar. Our results will deepen our understanding of plant–mutualism co‐evolution and interactions, and will enable utilization of a plant defence trait in cotton breeding efforts.     

A potato molecular-function map for carbohydrate metabolism and transport

Molecular-linkage maps based on functional gene markers (molecular-function maps) are the prerequisite for a candidate-gene approach to identify genes responsible for quantitative traits at the molecular level. Genetic linkage between a quantitative trait locus (QTL) and a candidate-gene locus is observed when there is a causal relationship between alleles of the candidate gene and the QTL effect. Functional gene markers can also be used for marker-assisted selection and as anchors for structural and functional comparisons between distantly related plant species sharing the same metabolic pathways. A first molecular-function map with 85 loci was constructed in potato based on 69 genes. Priority was given to genes operating in carbohydrate metabolism and transport. Public databases were searched for genes of interest from potato, tomato, or other plant species. DNA sequence information was used to develop PCR-based marker assays that allowed the localization of corresponding potato genes on existing RFLP linkage maps. Comparing the molecular-function map for genes operating in carbohydrate metabolism and transport with a QTL map for tuber starch content indicates a number of putative candidate genes for this important agronomic trait. Received: 19 March 2000 / Accepted: 16 May 2000     

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.     

A genetic analysis of quantitative resistance to late blight in potato: towards marker-assisted selection

Late blight caused by the oomycete Phytophthora infestans is the most important fungal disease in potato cultivation worldwide. Resistance to late blight is controlled by a few major genes (R genes) which can be easily overcome by new races of P. infestans and/or by an unknown number of genes expressing a quantitative type of resistance which may be more durable. Quantitative resistance of foliage to late blight was evaluated in five F₁ hybrid families originating from crosses among seven different diploid potato clones. Tuber resistance was evaluated in four of the families. Two of the families were scored for both foliage maturity and vigour. The five families were genotyped with DNA-based markers and tested for linkage with the traits analysed. QTL (quantitative trait locus) analysis identified at least twelve segments on ten chromosomes of potato having genes that affect reproducibly foliage resistance. Two of those segments also have major R genes for resistance to late blight. The segments are tagged by 21 markers that can be analyzed based on PCR (polymerase chain reaction) with specific oligonucleotide primers. One QTL was detected for tuber resistance and one for foliage vigour. Two QTLs were mapped for foliage maturity. Major QTL effects on foliage and tuber resistance to late blight and on foliage maturity and vigour were all linked with marker GP179 on linkage group V of potato. Plants having alleles at this QTL, which increased foliage resistance, exhibited decreased tuber resistance, later maturity and more vigour.     

Metabolic GWAS‐based dissection of genetic bases underlying the diversity of plant metabolism

Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome‐wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass‐spectrometry‐based analytical systems and genome sequencing technologies. Metabolic genome‐wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS‐based multi‐dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS‐based multi‐dimensional analysis and emerging insights into the diversity of metabolism.     

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.     

马铃薯单双三价抗病毒基因表达载体的构建

马铃薯Y病毒(PVY)、X病毒(PVX)和卷叶病毒(PLRV)引起的病害是造成我国马铃薯退化的主要原因,严重危害我国的马铃薯生产。PVY和PVX或PVY和PLRV混合侵染带来的损失远远大于各病毒单独侵染。国外科学家通过在马铃薯植株体内表达病毒外壳蛋白(CP)基因来减缓病毒病害的发生已取得相当的成功。 我们从河北省坝上地区农科所试验田中采集PLRV感病材料Burbank及87-1,参照文献提取病毒RNA并以其为模板,反转录合成cDNA。根据PLRV澳大利亚分离物已发表的序列,设计并     

QTL analysis of morphological traits in a tomato recombinant inbred line population.

Quantitative trait loci (QTLs) influencing morphological traits were identified by restriction fragment length polymorphism (RFLP) analysis in a population of recombinant inbred lines (RILs) derived from a cross of the cultivated tomato Lycopersicon esculentum with a related wild species, Lycopersicon cheesmanii. One hundred and thirty-two RFLP loci spaced throughout the tomato genome were used as DNA probes on genomic DNA from 97 RIL families. Morphological traits, including plant height, plant fresh mass, number of branches, number of nodes, first flower-bearing node, and leaf length, were evaluated in two controlled environment trials in 1992 and 1993. QTLs were detected via regression analyses at multiple marker loci for each morphological trait. A total of 41 markers were significantly associated with the traits examined. Large additive effects were measured at many of these loci. QTLs for multiple traits were detected on chromosomes 3 (TG74) and 4 (CT188), suggesting the possible association of these chromosome segments with genes controlling growth and development in tomato. These chromosomal regions were also associated with multiple morphological traits in a L. esculentum x Lycopersicon pennellii cross. A total of 13% of the QTLs identified for traits common to both studies occupied similar map positions.