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.     

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.     

Genetic dissection of drought and heat‐responsive agronomic traits in wheat

High yield and wide adaptation are principal targets of wheat breeding but are hindered by limited knowledge on genetic basis of agronomic traits and abiotic stress tolerances. In this study, 277 wheat accessions were phenotyped across 30 environments with non‐stress, drought‐stressed, heat‐stressed, and drought‐heat‐stressed treatments and were subjected to genome‐wide association study using 395 681 single nucleotide polymorphisms. We detected 295 associated loci including consistent loci for agronomic traits across different treatments and eurytopic loci for multiple abiotic stress tolerances. A total of 22 loci overlapped with quantitative trait loci identified by biparental quantitative trait loci mapping. Six loci were simultaneously associated with agronomic traits and abiotic stress tolerance, four of which fell within selective sweep regions. Selection in Chinese wheat has increased the frequency of superior marker alleles controlling yield‐related traits in the four loci during past decades, which conversely diminished favourable genetic variation controlling abiotic stress tolerance in the same loci; two promising candidate paralogous genes colocalized with such loci, thereby providing potential targets for studying the molecular mechanism of stress tolerance–productivity trade‐off. These results uncovering promising alleles controlling agronomic traits and/or multiple abiotic stress tolerances, providing insights into heritable covariation between yield and abiotic stress tolerance, will accelerate future efforts for wheat improvement.     

A genome‐wide association study of 23 agronomic traits in Chinese wheat landraces

Uncovering the genetic basis of agronomic traits in wheat landraces is important for ensuring global food security via the development of improved varieties. Here, 723 wheat landraces from 10 Chinese agro‐ecological zones were evaluated for 23 agronomic traits in six environments. All accessions could be clustered into five subgroups based on phenotypic data via discriminant function analysis, which was highly consistent with genotypic classification. A genome‐wide association study was conducted for these traits using 52 303 DArT‐seq markers to identify marker‐trait associations and candidate genes. Using both the general linear model and the mixed linear model, 149 significant markers were identified for 21 agronomic traits based on best linear unbiased prediction values. Considering the linkage disequilibrium decay distance in this study, significant markers within 10 cM were combined as a quantitative trait locus (QTL), with a total of 29 QTL identified for 15 traits. Of these, five QTL for heading date, flag leaf width, peduncle length, and thousand kernel weight had been reported previously. Twenty‐five candidate genes associated with significant markers were identified. These included the known vernalization genes VRN‐B1 and vrn‐B3 and the photoperiod response genes Ppd and PRR. Overall, this study should be helpful in elucidating the underlying genetic mechanisms of complex agronomic traits and performing marker‐assisted selection in wheat.     

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large‐scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back‐crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m‐trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m‐trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin‐6,8‐di‐C‐α‐l‐ arabinoside are presented as an example of a critical mQTL identified by the analysis.     

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.     

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

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

Population structure and genetic basis of the agronomic traits of upland cotton in China revealed by a genome‐wide association study using high‐density SNPs

Gossypium hirsutum L. represents the largest source of textile fibre, and China is one of the largest cotton‐producing and cotton‐consuming countries in the world. To investigate the genetic architecture of the agronomic traits of upland cotton in China, a diverse and nationwide population containing 503 G. hirsutum accessions was collected for a genome‐wide association study (GWAS) on 16 agronomic traits. The accessions were planted in four places from 2012 to 2013 for phenotyping. The CottonSNP63K array and a published high‐density map based on this array were used for genotyping. The 503 G. hirsutum accessions were divided into three subpopulations based on 11 975 quantified polymorphic single‐nucleotide polymorphisms (SNPs). By comparing the genetic structure and phenotypic variation among three genetic subpopulations, seven geographic distributions and four breeding periods, we found that geographic distribution and breeding period were not the determinants of genetic structure. In addition, no obvious phenotypic differentiations were found among the three subpopulations, even though they had different genetic backgrounds. A total of 324 SNPs and 160 candidate quantitative trait loci (QTL) regions were identified as significantly associated with the 16 agronomic traits. A network was established for multieffects in QTLs and interassociations among traits. Thirty‐eight associated regions had pleiotropic effects controlling more than one trait. One candidate gene, Gh_D08G2376, was speculated to control the lint percentage (LP). This GWAS is the first report using high‐resolution SNPs in upland cotton in China to comprehensively investigate agronomic traits, and it provides a fundamental resource for cotton genetic research and breeding.     

猪的基因图谱及数量性状位点定位

在人类基因组计划的带动下,猪的遗传连锁图谱和细胞遗传学图谱有了较大的进步,利用目前猪基因组图谱的研究成果,通过基因组扫描法和候选基因法,可以对猪重要经济性状的主效基因位点进行区域定位,进而图位克隆,找到主效基因,为现代遗传育种奠定理论基础。     

Metabolomic profiling and genomic analysis of wheat aneuploid lines to identify genes controlling biochemical pathways in mature grain

Metabolomics is becoming an increasingly important tool in plant genomics to decipher the function of genes controlling biochemical pathways responsible for trait variation. Although theoretical models can integrate genes and metabolites for trait variation, biological networks require validation using appropriate experimental genetic systems. In this study, we applied an untargeted metabolite analysis to mature grain of wheat homoeologous group 3 ditelosomic lines, selected compounds that showed significant variation between wheat lines Chinese Spring and at least one ditelosomic line, tracked the genes encoding enzymes of their biochemical pathway using the wheat genome survey sequence and determined the genetic components underlying metabolite variation. A total of 412 analytes were resolved in the wheat grain metabolome, and principal component analysis indicated significant differences in metabolite profiles between Chinese Spring and each ditelosomic lines. The grain metabolome identified 55 compounds positively matched against a mass spectral library where the majority showed significant differences between Chinese Spring and at least one ditelosomic line. Trehalose and branched‐chain amino acids were selected for detailed investigation, and it was expected that if genes encoding enzymes directly related to their biochemical pathways were located on homoeologous group 3 chromosomes, then corresponding ditelosomic lines would have a significant reduction in metabolites compared with Chinese Spring. Although a proportion showed a reduction, some lines showed significant increases in metabolites, indicating that genes directly and indirectly involved in biosynthetic pathways likely regulate the metabolome. Therefore, this study demonstrated that wheat aneuploid lines are suitable experimental genetic system to validate metabolomics–genomics networks.     

Application of metabolite profiling to the identification of traits in a population of tomato introgression lines

Naturally occurring variation in wild species can be used to increase the genetic diversity of cultivated crops and improve agronomic value. Populations of introgression lines carrying wild species alleles afford an opportunity to identify traits associated with the introgressed regions, and facilitate characterization of the biochemistry and genetics underlying these phenotypes. Understanding plant metabolic pathways and the interactions between genes, phenotype, and environment is fundamental to functional genomics. Successful analysis of the complex network of plant metabolism requires analytical methods able to record information on as many metabolites as possible. Metabolite profiling is used to provide a snapshot of the metabolome in samples which differ in a known factor such as genetic background. Differences between the metabolite profiles can identify those metabolites/metabolic pathways affected by the introgression and allow genetic maps for metabolic alterations to be established. A Time-of-Flight Mass Spectrometry method is presented, with associated data reduction, used for profiling aqueous metabolites fom tomato. Analysis of ripe fruits of two tomato species, Lycopersicon esculentum and L. pennellii, showed differences in the amounts of many metabolites, including organic acids and sugars. Six introgression lines, L. pennellii introgressions within L. esculentum, were also examined and showed that Principal Component Analysis can reveal subtle differences in metabolism of the introgressed lines when compared to their parents.     

Nested‐association mapping (NAM)‐based genetic dissection uncovers candidate genes for seed and pod weights in peanut (Arachis hypogaea)

Multiparental genetic mapping populations such as nested‐association mapping (NAM) have great potential for investigating quantitative traits and associated genomic regions leading to rapid discovery of candidate genes and markers. To demonstrate the utility and power of this approach, two NAM populations, NAM_Tifrunner and NAM_Florida‐07, were used for dissecting genetic control of 100‐pod weight (PW) and 100‐seed weight (SW) in peanut. Two high‐density SNP‐based genetic maps were constructed with 3341 loci and 2668 loci for NAM_Tifrunner and NAM_Florida‐07, respectively. The quantitative trait locus (QTL) analysis identified 12 and 8 major effect QTLs for PW and SW, respectively, in NAM_Tifrunner, and 13 and 11 major effect QTLs for PW and SW, respectively, in NAM_Florida‐07. Most of the QTLs associated with PW and SW were mapped on the chromosomes A05, A06, B05 and B06. A genomewide association study (GWAS) analysis identified 19 and 28 highly significant SNP–trait associations (STAs) in NAM_Tifrunner and 11 and 17 STAs in NAM_Florida‐07 for PW and SW, respectively. These significant STAs were co‐localized, suggesting that PW and SW are co‐regulated by several candidate genes identified on chromosomes A05, A06, B05, and B06. This study demonstrates the utility of NAM population for genetic dissection of complex traits and performing high‐resolution trait mapping in peanut.     

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     

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.     

Induced Pluripotent Stem Cells Show Metabolomic Differences to Embryonic Stem Cells in Polyunsaturated Phosphatidylcholines and Primary Metabolism

Induced pluripotent stem cells are different from embryonic stem cells as shown by epigenetic and genomics analyses. Depending on cell types and culture conditions, such genetic alterations can lead to different metabolic phenotypes which may impact replication rates, membrane properties and cell differentiation. We here applied a comprehensive metabolomics strategy incorporating nanoelectrospray ion trap mass spectrometry (MS), gas chromatography-time of flight MS, and hydrophilic interaction- and reversed phase-liquid chromatography-quadrupole time-of-flight MS to examine the metabolome of induced pluripotent stem cells (iPSCs) compared to parental fibroblasts as well as to reference embryonic stem cells (ESCs). With over 250 identified metabolites and a range of structurally unknown compounds, quantitative and statistical metabolome data were mapped onto a metabolite networks describing the metabolic state of iPSCs relative to other cell types. Overall iPSCs exhibited a striking shift metabolically away from parental fibroblasts and toward ESCs, suggestive of near complete metabolic reprogramming. Differences between pluripotent cell types were not observed in carbohydrate or hydroxyl acid metabolism, pentose phosphate pathway metabolites, or free fatty acids. However, significant differences between iPSCs and ESCs were evident in phosphatidylcholine and phosphatidylethanolamine lipid structures, essential and non-essential amino acids, and metabolites involved in polyamine biosynthesis. Together our findings demonstrate that during cellular reprogramming, the metabolome of fibroblasts is also reprogrammed to take on an ESC-like profile, but there are select unique differences apparent in iPSCs. The identified metabolomics signatures of iPSCs and ESCs may have important implications for functional regulation of maintenance and induction of pluripotency.     

Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations

Plant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively. Two biomass QTL coincide with significantly more metabolic QTL (mQTL) than statistically expected, supporting the notion that the metabolic profile and biomass accumulation of a plant are linked. On the same basis, three out the six biomass QTL can be simulated purely on the basis of metabolic composition. QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P  ≤ 0.05, with >80% agreement on the allele effects. Some of the differences could be attributed to epistatic interactions. Depending on the search conditions, metabolic pathway-derived candidate genes were found for 24–67% of all tested mQTL in the database AraCyc 3.5. This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.