Metabolite Quantitative Trait Loci Research Articles

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

Urinary metabolite quantitative trait loci in children and their interaction with dietary factors.

Human metabolism is influenced by genetic and environmental factors. Previous studies have identified over 23 loci associated with more than 26 urine metabolites levels in adults, which are known as urinary metabolite quantitative trait loci (metabQTLs). The aim of the present study is the identification for the first time of urinary metabQTLs in children and their interaction with dietary patterns. Association between genome-wide genotyping data and 44 urine metabolite levels measured by proton nuclear magnetic resonance spectroscopy was tested in 996 children from the Human Early Life Exposome project. Twelve statistically significant urine metabQTLs were identified, involving 11 unique loci and 10 different metabolites. Comparison with previous findings in adults revealed that six metabQTLs were already known, and one had been described in serum and three were involved the same locus as other reported metabQTLs but had different urinary metabolites. The remaining two metabQTLs represent novel urine metabolite-locus associations, which are reported for the first time in this study [single nucleotide polymorphism (SNP) rs12575496 for taurine, and the missense SNP rs2274870 for 3-hydroxyisobutyrate]. Moreover, it was found that urinary taurine levels were affected by the combined action of genetic variation and dietary patterns of meat intake as well as by the interaction of this SNP with beverage intake dietary patterns. Overall, we identified 12 urinary metabQTLs in children, including two novel associations. While a substantial part of the identified loci affected urinary metabolite levels both in children and in adults, the metabQTL for taurine seemed to be specific to children and interacted with dietary patterns.

Quantitative trait loci analysis of seed-specialized metabolites reveals seed-specific flavonols and differential regulation of glycoalkaloid content in tomato.

Given the potential health benefits (and adverse effects), of polyphenolic and steroidal glycoalkaloids in the diet there is a growing interest in fully elucidating the genetic control of their levels in foodstuffs. Here we carried out profiling of the specialized metabolites in the seeds of the Solanum pennellii introgression lines identifying 338 putative metabolite quantitative trait loci (mQTL) for flavonoids, steroidal glycoalkaloids and further specialized metabolites. Two putative mQTL for flavonols and one for steroidal glycoalkaloids were cross-validated by evaluation of the metabolite content of recombinants harboring smaller introgression in the corresponding QTL interval or by analysis of lines from an independently derived backcross inbred line population. The steroidal glycoalkaloid mQTL was localized to a chromosomal region spanning 14 genes, including a previously defined steroidal glycoalkaloid gene cluster. The flavonoid mQTL was further validated via the use of transient and stable overexpression of the Solyc12g098600 and Solyc12g096870 genes, which encode seed-specific uridine 5'-diphosphate-glycosyltransferases. The results are discussed in the context of our understanding of the accumulation of polyphenols and steroidal glycoalkaloids, and how this knowledge may be incorporated into breeding strategies aimed at improving nutritional aspects of plants as well as in fortifying them against abiotic stress.

Metabolite Quantitative Trait Loci for Flavonoids Provide New Insights into the Genetic Architecture of Strawberry (Fragaria × ananassa) Fruit Quality.

Flavonoids are products from specialized metabolism that contribute to fruit sensorial (color) and nutritional (antioxidant properties) quality. Here, using a pseudo full-sibling F1 progeny previously studied for fruit sensorial quality of cultivated strawberry (Fragaria × ananassa), we explored over two successive years the genetic architecture of flavonoid-related traits using liquid chromatography electrospray ionization tandem mass spectrometry (13 compounds including anthocyanins, flavonols, and flavan-3-ols) and colorimetric assays (anthocyanins, flavonoids, phenolics, and total antioxidant capacity (ferric reducing antioxidant power and Trolox equivalent antioxidant capacity)). Network correlation analysis highlighted the high connectivity of flavonoid compounds within each chemical class and low correlation with colorimetric traits except for anthocyanins. Mapping onto the female and male linkage maps of 152 flavonoid metabolic quantitative trait loci (mQTLs) and of 26 colorimetric QTLs indicated colocalization on few linkage groups of major flavonoid- and taste-related QTLs previously uncovered. These results pave the way for the discovery of genetic variations underlying flavonoid mQTLs and for marker-assisted selection of strawberry varieties with improved sensorial and nutritional quality.

Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans.

The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite-locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research.

Genetic Variation for Seed Metabolite Levels in Brachypodium distachyon.

Metabolite composition and concentrations in seed grains are important traits of cereals. To identify the variation in the seed metabolotypes of a model grass, namely Brachypodium distachyon, we applied a widely targeted metabolome analysis to forty inbred lines of B. distachyon and examined the accumulation patterns of 183 compounds in the seeds. By comparing the metabolotypes with the population structure of these lines, we found signature metabolites that represent different accumulation patterns for each of the three B. distachyon subpopulations. Moreover, we found that thirty-seven metabolites exhibited significant differences in their accumulation between the lines Bd21 and Bd3-1. Using a recombinant inbred line (RIL) population from a cross between Bd3-1 and Bd21, we identified the quantitative trait loci (QTLs) linked with this variation in the accumulation of thirteen metabolites. Our metabolite QTL analysis illustrated that different genetic factors may presumably regulate the accumulation of 4-pyridoxate and pyridoxamine in vitamin B6 metabolism. Moreover, we found two QTLs on chromosomes 1 and 4 that affect the accumulation of an anthocyanin, chrysanthemin. These QTLs genetically interacted to regulate the accumulation of this compound. This study demonstrates the potential for metabolite QTL mapping in B. distachyon and provides new insights into the genetic dissection of metabolomic traits in temperate grasses.

Large-scale metabolite quantitative trait locus analysis provides new insights for high-quality maize improvement.

It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC2 F7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling-stage leaf, grouting-stage leaf, young kernel and mature kernel) with clear tissue-specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi-tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs.

Large‐scale metabolite quantitative trait locus analysis provides new insights for high‐quality maize improvement

Large‐scale metabolite quantitative trait locus analysis provides new insights for high‐quality maize improvement

Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds

IntroductionTo date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism.ObjectiveThis study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues.MethodsThe analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses.ResultsChanges in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism.ConclusionsOverall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits.

Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds

Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci.

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the ‘Prioritization of candidate causal Genes at Molecular QTLs’ (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of ‘true positive’ causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

Natural genetic variation in C. elegans identified genomic loci controlling metabolite levels.

Metabolic homeostasis is sustained by complex biological networks that respond to nutrient availability. Genetic and environmental factors may disrupt this equilibrium, leading to metabolic disorders, including obesity and type 2 diabetes. To identify the genetic factors controlling metabolism, we performed quantitative genetic analysis using a population of 199 recombinant inbred lines (RILs) in the nematode Caenorhabditis elegans. We focused on the genomic regions that control metabolite levels by measuring fatty acid (FA) and amino acid (AA) composition in the RILs using targeted metabolomics. The genetically diverse RILs showed a large variation in their FA and AA levels with a heritability ranging from 32% to 82%. We detected strongly co-correlated metabolite clusters and 36 significant metabolite quantitative trait loci (mQTL). We focused on mQTL displaying highly significant linkage and heritability, including an mQTL for the FA C14:1 on Chromosome I, and another mQTL for the FA C18:2 on Chromosome IV. Using introgression lines (ILs), we were able to narrow down both mQTL to a 1.4-Mbp and a 3.6-Mbp region, respectively. RNAi-based screening focusing on the Chromosome I mQTL identified several candidate genes for the C14:1 mQTL, including lagr-1, Y87G2A.2, nhr-265, nhr-276, and nhr-81. Overall, this systems approach provides us with a powerful platform to study the genetic basis of C. elegans metabolism. Furthermore, it allows us to investigate interventions such as nutrients and stresses that maintain or disturb the regulatory network controlling metabolic homeostasis, and identify gene-by-environment interactions.

Genetic variation in phytochemicals in leaves of pepper (Capsicum) in relation to thrips resistance

Phytochemicals not only determine the taste and smell of plants and their products, they also play a crucial role in resistance against pests and pathogens. In previous work, we identified a form of resistance to thrips (Frankliniella occidentalis) expressed in leaves of pepper (Capsicum annuum). In the current study, we characterized leaves of an interspecific C. annuum × C. chinense F2 population for variation in their global phytochemical composition by an untargeted metabolomics approach. Quantitative trait locus (QTL) mapping resulted in metabolite QTLs (mQTLs) for 304 of the 674 metabolites detected. We compared the QTL mapping results to those obtained earlier on fruits in the same population. Very different QTL hotspots were found, suggesting that the metabolite composition of leaves and fruits is regulated independently. Six leaf mQTLs co-located with the major QTL for resistance to thrips, which we previously identified in the same F2 population. Four of them were significantly correlated to thrips resistance, including two diterpene glycosides and a flavonoid compound which may indicate a possible role of these metabolites in thrips resistance. If a causal role of some of these metabolites in resistance can be proven this will help in the identification of the causal gene(s) and it may provide leads for the identification of other sources of thrips resistance in Capsicum and in other species.

Canalization of Tomato Fruit Metabolism.

To explore the genetic robustness (canalization) of metabolism, we examined the levels of fruit metabolites in multiple harvests of a tomato introgression line (IL) population. The IL partitions the whole genome of the wild species Solanum pennellii in the background of the cultivated tomato (Solanum lycopersicum). We identified several metabolite quantitative trait loci that reduce variability for both primary and secondary metabolites, which we named canalization metabolite quantitative trait loci (cmQTL). We validated nine cmQTL using an independent population of backcross inbred lines, derived from the same parents, which allows increased resolution in mapping the QTL previously identified in the ILs. These cmQTL showed little overlap with QTL for the metabolite levels themselves. Moreover, the intervals they mapped to harbored few metabolism-associated genes, suggesting that the canalization of metabolism is largely controlled by regulatory genes.

Genome-Wide Association Mapping Reveals That Specific and Pleiotropic Regulatory Mechanisms Fine-Tune Central Metabolism and Growth in Arabidopsis.

Central metabolism is a coordinated network that is regulated at multiple levels by resource availability and by environmental and developmental cues. Its genetic architecture has been investigated by mapping metabolite quantitative trait loci (QTL). A more direct approach is to identify enzyme activity QTL, which distinguishes between cis-QTL in structural genes encoding enzymes and regulatory trans-QTL. Using genome-wide association studies, we mapped QTL for 24 enzyme activities, nine metabolites, three structural components, and biomass in Arabidopsis thaliana We detected strong cis-QTL for five enzyme activities. A cis-QTL for UDP-glucose pyrophosphorylase activity in the UGP1 promoter is maintained through balancing selection. Variation in acid invertase activity reflects multiple evolutionary events in the promoter and coding region of VAC-INVcis-QTL were also detected for ADP-glucose pyrophosphorylase, fumarase, and phosphoglucose isomerase activity. We detected many trans-QTL, including transcription factors, E3 ligases, protein targeting components, and protein kinases, and validated some by knockout analysis. trans-QTL are more frequent but tend to have smaller individual effects than cis-QTL. We detected many colocalized QTL, including a multitrait QTL on chromosome 4 that affects six enzyme activities, three metabolites, protein, and biomass. These traits are coordinately modified by different ACCELERATED CELL DEATH6 alleles, revealing a trade-off between metabolism and defense against biotic stress.

Mapping the Arabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions

Metabolic genome-wide association studies (mGWAS), whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309 Arabidopsis accessions were grown under two independent environmental conditions (control and stress) and subjected to untargeted LC–MS-based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two-condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci (p ≤ 1.0E-08), 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was followed by integration with network-derived metabolite–transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between structural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different abiotic environments for identifying metabolite–gene associations, providing novel global insights into the metabolic landscape of Arabidopsis.

SFlt-1 upregulates endoplasmic reticulum stress marker XBP1 in trophoblast cells

Insulin resistance (IR) is a complex trait with multiple genetic and environmental components. Confounded by large differences between the sexes, environment, and disease pathology, the genetic basis of IR has been difficult to dissect. Here we examine IR and related traits in a diverse population of more than 100 unique male and female inbred mouse strains after feeding a diet rich in fat and refined carbohydrates. Our results show dramatic variation in IR among strains of mice and widespread differences between sexes that are dependent on genotype. We uncover more than 15 genome-wide significant loci and validate a gene, Agpat5, associated with IR. We also integrate plasma metabolite levels and global gene expression from liver and adipose tissue to identify metabolite quantitative trait loci (mQTL) and expression QTL (eQTL), respectively. Our results provide a resource for analysis of interactions between diet, sex, and genetic background in IR.

Causal Genetic Variation Underlying Metabolome Differences.

An ongoing challenge in biology is to predict the phenotypes of individuals from their genotypes. Genetic variants that cause disease often change an individual's total metabolite profile, or metabolome. In light of our extensive knowledge of metabolic pathways, genetic variants that alter the metabolome may help predict novel phenotypes. To link genetic variants to changes in the metabolome, we studied natural variation in the yeast Saccharomyces cerevisiae We used an untargeted mass spectrometry method to identify dozens of metabolite Quantitative Trait Loci (mQTL), genomic regions containing genetic variation that control differences in metabolite levels between individuals. We mapped differences in urea cycle metabolites to genetic variation in specific genes known to regulate amino acid biosynthesis. Our functional assays reveal that genetic variation in two genes, AUA1 and ARG81, cause the differences in the abundance of several urea cycle metabolites. Based on knowledge of the urea cycle, we predicted and then validated a new phenotype: sensitivity to a particular class of amino acid isomers. Our results are a proof-of-concept that untargeted mass spectrometry can reveal links between natural genetic variants and metabolome diversity. The interpretability of our results demonstrates the promise of using genetic variants underlying natural differences in the metabolome to predict novel phenotypes from genotype.

Identification of Loci Affecting Accumulation of Secondary Metabolites in Tomato Fruit of a Solanum lycopersicum × Solanum chmielewskii Introgression Line Population.

Semi-polar metabolites such as flavonoids, phenolic acids, and alkaloids are very important health-related compounds in tomato. As a first step to identify genes responsible for the synthesis of semi-polar metabolites, quantitative trait loci (QTLs) that influence the semi-polar metabolite content in red-ripe tomato fruit were identified, by characterizing fruits of a population of introgression lines (ILs) derived from a cross between the cultivated tomato Solanum lycopersicum and the wild species Solanum chmielewskii. By analyzing fruits of plants grown at two different locations, we were able to identify robust metabolite QTLs for changes in phenylpropanoid glycoconjugation on chromosome 9, for accumulation of flavonol glycosides on chromosome 5, and for alkaloids on chromosome 7. To further characterize the QTLs we used a combination of genome sequencing, transcriptomics and targeted metabolomics to identify candidate key genes underlying the observed metabolic variation.

Quantitative resistance against Bemisia tabaci in Solanum pennellii: Genetics and metabolomics.

The whitefly Bemisia tabaci is a serious threat in tomato cultivation worldwide as all varieties grown today are highly susceptible to this devastating herbivorous insect. Many accessions of the tomato wild relative Solanum pennellii show a high resistance towards B. tabaci. A mapping approach was used to elucidate the genetic background of whitefly-resistance related traits and associated biochemical traits in this species. Minor quantitative trait loci (QTLs) for whitefly adult survival (AS) and oviposition rate (OR) were identified and some were confirmed in an F2 BC1 population, where they showed increased percentages of explained variance (more than 30%). Bulked segregant analyses on pools of whitefly-resistant and -susceptible F2 plants enabled the identification of metabolites that correlate either with resistance or susceptibility. Genetic mapping of these metabolites showed that a large number of them co-localize with whitefly-resistance QTLs. Some of these whitefly-resistance QTLs are hotspots for metabolite QTLs. Although a large number of metabolite QTLs correlated to whitefly resistance or susceptibility, most of them are yet unknown compounds and further studies are needed to identify the metabolic pathways and genes involved. The results indicate a direct genetic correlation between biochemical-based resistance characteristics and reduced whitefly incidence in S. pennellii.