Metabolite Traits Research Articles

Evaluation on the Efficacy of Farrerol in Inhibiting Shoot Blight of Larch (Neofusicoccum laricinum)

Neofusicoccum laricinum is the causal agent of larch shoot blight, a fungal disease affecting several species of larch. It causes severe damage, including stunting and mortality. This study aims to address the severe impact of larch shoot blight by evaluating the effect of farrerol on the inhibition of Neofusicoccum laricinum in Larix olgensis. We used LC-MS/MS and weighted gene co-expression network analysis to investigate farrerol’s effects on Neofusicoccum laricinum and identify associated genes in resistant and susceptible larch. Our study identified significant differences in metabolite profiles between resistant and susceptible cultivars, with higher concentrations of farrerol showing complete inhibition of N. laricinum. Additionally, specific genes associated with farrerol content were up-regulated in resistant larch. Farrerol at higher concentrations completely inhibited N. laricinum, showing a strong correlation with increased disease resistance. This research suggests that farrerol enhances disease resistance in larch and provides a foundation for developing disease-resistant larch varieties based on antifungal metabolite traits.

Estimation of Direct and Indirect Polygenic Effects and Gene-Environment Interactions using Polygenic Scores in Case-Parent Trio Studies.

Family-based studies provide a unique opportunity to characterize genetic risks of diseases in the presence of population structure, assortative mating, and indirect genetic effects. We propose a novel framework, PGS-TRI, for the analysis of polygenic scores (PGS) in case-parent trio studies for estimation of the risk of an index condition associated with direct effects of inherited PGS, indirect effects of parental PGS, and gene-environment interactions. Extensive simulation studies demonstrate the robustness of PGS-TRI in the presence of complex population structure and assortative mating compared to alternative methods. We apply PGS-TRI to multi-ancestry trio studies of autism spectrum disorders (N trio = 1,517) and orofacial clefts (N trio = 1,904) to establish the first transmission-based estimates of risk associated with pre-defined PGS for these conditions and other related traits. For both conditions, we further explored offspring risk associated with polygenic gene-environment interactions, and direct and indirect effects of genetically predicted levels of gene expression and metabolite traits.

Effect of the immune cells and plasma metabolites on rheumatoid arthritis: a mediated mendelian randomization study.

Increasing evidence indicates a close relationship between alterations in human immune cells and plasma metabolites with Rheumatoid Arthritis (RA). However, limited studies have left the causal relationships behind these links unclear. A bidirectional Mendelian Randomization (MR) study was conducted, combined with mediation analysis, using data from genome-wide association study database covering 731 immune cell phenotypes and 1,400 plasma metabolite traits to explore their causal relationships with RA and potential mediating effects. The primary method used for MR analysis was inverse-variance weighted and False Discovery Rate (FDR) correction was applied to verify the robustness of our results. HLA DR on CD33- HLA DR+ (myeloid cell group) (OR, 1.422; 95% CI, 1.194-1.694; P < 0.001; PFDR = 0.012) increased the risk of developing RA. CD19 on IgD+ CD38- naive (B cell group) (OR, 0.969; 95% CI, 0.954-0.985; P < 0.001; PFDR = 0.021) reduced the risk of developing RA. RA was a risk factor for HLA DR on CD14- CD16+ monocytes (monocyte group) (OR, 1.242; 95% CI, 1.102-1.401; P < 0.001; PFDR = 0.047). RA was a protective factor for memory B cell %lymphocyte (B cell group) (OR, 0.861; 95% CI, 0.795-0.933; P < 0.001; PFDR = 0.050), CD4+ CD8dim T cell %lymphocyte (TBNK group) (OR, 0.802; 95% CI, 0.711-0.904; P < 0.001; PFDR = 0.043), CD4+ CD8dim T cell %leukocyte (TBNK group) (OR, 0.814; 95% CI, 0.726-0.913; P < 0.001; PFDR = 0.046), CD24 on IgD+ CD24+ B cells (B cell group) (OR, 0.857; 95% CI, 0.793-0.927; P < 0.001; PFDR = 0.038), and CD24 on unswitched memory B cells (B cell group) (OR, 0.867; 95% CI, 0.797-0.942; P < 0.001; PFDR = 0.050). Increasing levels of docosatrienoate (22:3n3) (OR, 0.886; 95% CI, 0.838-0.936; P < 0.001; PFDR = 0.023) significantly reduced the risk of developing RA. The mediating effect of plasma metabolites in this context was not established. This study provides genetic evidence for the intricate relationships between immune cells, plasma metabolites, and RA, highlighting the potential mechanisms involved. This will contribute to future directions in precision medicine and research.

The mediating role of metabolites between gut microbiome and Hirschsprung disease: a bidirectional two-step Mendelian randomization study.

Gut microbiome (GM) was observed to be associated with the incidence of Hirschsprung disease (HD). However, the effect and mechanism of GM in HD is still unclear. To investigate the relationship between GM and HD and the effect of metabolites as mediators, a bidirectional two-step Mendelian randomization (MR) study was conducted. The study selected instrument variables (IVs) from summary-level genome-wide association studies (GWAS). The MiBioGen consortium provided the GWAS data for GM, while the GWAS data for metabolites and HD were obtained from the GWAS Catalog consortium. Two-sample MR analyses were performed to estimate bidirectional correlations between IVs associated with GM and HD. Then, genetic variants related to 1,400 metabolite traits were selected for further mediation analyses using the Product method. This study found that seven genus bacteria had a significant causal relationship with the incidence of HD but not vice versa. 27 metabolite traits were significantly correlated with HD. After combining the significant results, three significant GM-metabolites-HD lines have been identified. In the Peptococcus-Stearoyl sphingomyelin (d18:1/18:0)-HD line, the Stearoyl sphingomyelin (d18:1/18:0) levels showed a mediation proportion of 14.5%, while in the Peptococcus-lysine-HD line, the lysine levels had a mediation proportion of 12.9%. Additionally, in the Roseburia-X-21733-HD line, the X-21733 levels played a mediation proportion of 23.5%. Our MR study indicates a protective effect of Peptococcus on HD risk that is partially mediated through serum levels of stearoyl sphingomyelin (d18:1/18:0) and lysine, and a risk effect of Roseburia on HD that is partially mediated by X-21733 levels. These findings could serve as novel biomarkers and therapeutic targets for HD.

Causal association of circulating metabolites with diabetic retinopathy: a bidirectional Mendelian randomization analysis.

The retina is a highly metabolically active tissue, and there is a lack of clarity about the relationship between metabolites and diabetic retinopathy (DR). This study used two-sample bidirectional Mendelian randomization (MR) analyses to identify causal relationships between metabolites and DR. Genetic variants were selected from the open-access Genome-Wide Association Studies (GWAS) summary database as proxies for the 1400 most recently published metabolites. MR analysis was performed to examine associations between these metabolite traits and DR. Single nucleotide polymorphism (SNP) data that were significantly associated with exposure were screened through association analysis. Validated instrumental variables (IVs) were obtained by removing SNPs with linkage disequilibrium (LD) and F-statistic values below 10. MR analyses were performed using the inverse variance weighted (IVW) method as the primary approach. The robustness of the results was verified by sensitivity analyses, including assessments of heterogeneity, horizontal pleiotropy, and the leave-one-out method. In the IVW approach and in the primary analysis of several sensitivity analyses, genetically determined glycolithocholate sulfate levels, androstenediol (3 beta, 17 beta) monosulfate (1) levels, 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4) levels, 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels, 1-oleoyl-2-linoleoyl-GPE (18:1/18:2) levels, X-26109 levels, N6-methyllysine levels, (N6,N6-dimethyllysine levels), and (N2-acetyl,N6,N6-dimethyllysine levels) were negatively associated with the risk of DR. 5-hydroxymethyl-2-furoylcarnitine levels and the glutamate-to-alanine ratio were positively associated with the risk of DR. No reverse causal association was found between DR and metabolites. This MR study suggests that nine metabolites may have a protective effect in DR, while two metabolites may be associated with an increased risk of DR. However, further research is needed to confirm these findings. Supplementation with beneficial metabolites may reduce DR risk and could potentially be a novel therapeutic approach to DR treatment.

The association between body mass index and metabolite response to a liquid mixed meal challenge: a Mendelian randomization study

BackgroundMetabolite abundance is a dynamic trait that varies in response to environmental stimuli and phenotypic traits, such as food consumption and body mass index (BMI, kg/m2). ObjectivesIn this study, we used the Netherlands Epidemiology of Obesity (NEO) study data to identify observational and causal associations between BMI and metabolite response to a liquid meal. MethodsA liquid meal challenge was performed, and Nightingale Health metabolite profiles were collected in 5744 NEO participants. Observational and one-sample Mendelian randomization (MR) analysis were conducted to estimate the effect of BMI on metabolites (n = 229) in the fasting, postprandial, and response (or change in abundance) states. ResultsWe observed 473 associations with BMI (175 fasting, 188 postprandial, and 110 response) in observational analyses. In MR analyses, we observed 20 metabolite traits (5 fasting, 12 postprandial, and 3 response) to be associated with BMI. MR associations included the glucogenic amino acid alanine, which was inversely associated with BMI in the response state (β: −0.081; SE: 0.023; P = 5.91 × 10−4), suggesting that as alanine increased in postprandial abundance, that increase was attenuated with increasing BMI. ConclusionsOverall, this study showed that MR estimates were strongly correlated with observational effect estimates, suggesting that the broad associations seen between BMI and metabolite variation has a causal underpinning. Specific effects in previously unassessed postprandial and response states are detected, and these may likely mark novel life course risk exposures driven by regular nutrition.

Effect of the gut microbiome, plasma metabolome, peripheral cells, and inflammatory cytokines on obesity: a bidirectional two-sample Mendelian randomization study and mediation analysis.

Obesity is a metabolic and chronic inflammatory disease involving genetic and environmental factors. This study aimed to investigate the causal relationship among gut microbiota abundance, plasma metabolomics, peripheral cell (blood and immune cell) counts, inflammatory cytokines, and obesity. Summary statistics of 191 gut microbiota traits (N = 18,340), 1,400 plasma metabolite traits (N = 8,299), 128 peripheral cell counts (blood cells, N = 408,112; immune cells, N = 3,757), 41 inflammatory cytokine traits (N = 8,293), and 6 obesity traits were obtained from publicly available genome-wide association studies. Two-sample Mendelian randomization (MR) analysis was applied to infer the causal links using inverse variance-weighted, maximum likelihood, MR-Egger, weighted median, weighted mode, and Wald ratio methods. Several sensitivity analyses were also utilized to ensure reliable MR results. Finally, we used mediation analysis to identify the pathway from gut microbiota to obesity mediated by plasma metabolites, peripheral cells, and inflammatory cytokines. MR revealed a causal effect of 44 gut microbiota taxa, 281 plasma metabolites, 27 peripheral cells, and 8 inflammatory cytokines on obesity. Among them, five shared causal gut microbiota taxa belonged to the phylum Actinobacteria, order Bifidobacteriales, family Bifidobacteriaceae, genus Lachnospiraceae UCG008, and species Eubacterium nodatum group. Furthermore, we screened 42 shared causal metabolites, 7 shared causal peripheral cells, and 1 shared causal inflammatory cytokine. Based on known causal metabolites, we observed that the metabolic pathways of D-arginine, D-ornithine, linoleic acid, and glycerophospholipid metabolism were closely related to obesity. Finally, mediation analysis revealed 20 mediation relationships, including the causal pathway from gut microbiota to obesity, mediated by 17 metabolites, 2 peripheral cells, and 1 inflammatory cytokine. Sensitivity analysis represented no heterogeneity or pleiotropy in this study. Our findings support a causal relationship among gut microbiota, plasma metabolites, peripheral cells, inflammatory cytokines, and obesity. These biomarkers provide new insights into the mechanisms underlying obesity and contribute to its prevention, diagnosis, and treatment.

Exogenous glycine betaine alleviates water-deficit stress in Indian pennywort (Centella asiatica) under greenhouse conditions.

Centella asiatica (Indian pennywort) is a green leafy vegetable containing centelloside' (triterpenoid), a key phytochemical component in traditional medicine. Being a glycophytic species, they exhibit decline in growth performance and yield traits when subjected to water-deficit (WD) conditions. Glycine betaine (GB) is a low molecular-weight organic metabolite that plays a crucial role in abiotic stress conditions in higher plants. The objective of this study was to investigate the potential of GB in alleviating water-deficit stress (in terms of morphological and physiological responses) in two different genotypes of Indian pennywort, "Nakhon Pathom" (NP; high centelloside-yielding genotype) and "Pathum Thani" (PT; low centelloside-yielding genotype). The genotypes of Indian pennywort were propagated by stolon cutting and transplanted into plastic bags containing 2kg of garden soil. At the flower-initiation stage (30days after transplantation), uniform plant material was treated exogenously with 0 (control), 25, and 50mMGB at 100mL per plant (one-time foliar spray) and then divided into two groups, 1) well watered (WW; irrigated daily with 400mL fresh water; 98% field capacity) and 2) water deficit (WD; withheld water for 14days; 72% field capacity). Foliar application of GB (25mM) significantly improved leaf osmotic potential in NP under WD conditions via osmotic adjustment by free proline and fructose. Differences in leaf temperature (Tleaf) between WD and WW in NP were maximized (+ 1.93°C) and the gap of Tleaf was reduced in the case of 25-50mMGB application. Similarly, crop water stress index (CWSI) in NP and PT plants under WD condition was significantly increased by 1.95- and 1.86-fold over the control, respectively; however, it was significantly decreased by exogenous GB application. Increasing Tleaf and CWSI in drought-stressed plants was closely related to stomatal closure, leading to reduced gas exchange parameters, i.e., stomatal conductance (gs), transpiration rate (E), net photosynthetic rate (Pn), and intercellular CO2 concentration (Ci), and consequently decreased plant biomass and total centelloside yield. Overall physiological, morphological, and secondary metabolite traits were enhanced in NP under WD conditions using 25mMGB exogenous application compared with the control. The study highlights the significance of GB in Indian pennywort production under limited water irrigation (water deficit) with higher vegetable yield and phytochemical stabilization.

Causal associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases: a multivariable Mendelian randomization study.

Mounting evidence has demonstrated the associations between gut microbiota, gut microbiota-derived metabolites, and cerebrovascular diseases (CVDs). The major categories of CVD are ischemic stroke (IS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). However, the causal relationship is still unclear. A two-sample Mendelian randomization (MR) study was conducted leveraging the summary data from genome-wide association studies. The inverse variance-weighted, maximum likelihood, weighted median, and MR.RAPS methods were performed to detect the causal relationship. Several sensitivity analyses were carried out to evaluate potential horizontal pleiotropy and heterogeneity. Finally, reverse MR analysis was conducted to examine the likelihood of reverse causality, and multivariable MR was performed to adjust the potential confounders. We collected 1,505 host single nucleotide polymorphisms (SNPs) linked to 119 gut microbiota traits and 1,873 host SNPs associated with 81 gut metabolite traits as exposure data. Among these, three gut bacteria indicated an elevated risk of IS, two of ICH, and one of SAH. In contrast, five gut bacteria were associated with a reduced risk of IS, one with ICH, and one with SAH. Our study also demonstrated the potential causal associations between 11 gut microbiota-derived metabolites and CVD. This study provided evidence of the causal relationship between gut microbiota, gut microbiota-derived metabolites, and CVD, thereby offering novel perspectives on gut biomarkers and targeted prevention and treatment for CVD.

Relationship of Planting Dates and Spraying with Conventional and Nano NPK Fertilizer to some Secondary Metabolite Traits of Fenugreek Plant Trigonella foenum-graecum

This study was carried out in the agricultural fields of the College of Agriculture - Karbala University for the period from October of the year 2021 AD until May of the year 2022 AD, The study included two factors, the first was the cultivation of fenugreek seeds on three loops (20/10/2021) (10/11/2021) (30/11/2021) and the second factor was the use of conventional and nano fertilizer NPK by spraying on the leaves with five combinations, including the comparison treatment (Distilled water only, 1g.L-1 neutralized conventional fertilizer, 2g.L-1 neutralized conventional fertilizer, 100ml.L-1 of nano-fertilizer, 200mlL-1 of nano-fertilizer. The treatment were distributed using a split-plot method in a randomized complete block design (RCBD) with three replications, planting dates were set in the main-plots and fertilizer combinations in the sub-plots, so the number of experimental units is 45 units, The results were analyzed statistically using the Genstst program, and the averages were tested according to the L.S.D test at a probability level of 0.05. The results showed that the third planting date was superior to the rest of the dates in trait of the percentage of protein with an average of 23.30% and the percentage of alkaloids in the leaves with an average of 2.9333%, while the comparison treatment recorded the least significant difference of 22.78% and 1.8467%, respectively, While the fertilizer treatment recorded the superiority of most of the fertilizer combinations used over the comparison treatment, the fifth combination of fertilizer used outperformed the rest of the combinations by giving it the highest rate for all the studied characteristics, including the percentage of oil in the seeds and the percentage of alkaloids in the seeds, which amounted to (5.2444% and 9.211%), respectively, While the comparison treatment recorded the lowest average of 5.1400% and 5.844% respectively, as for the treatment of overlap between the fertilizer combinations used with the planting dates, the overlap affected significantly in the treatment of the fifth combination of the second planting date in the characteristics of the secondary metabolites compounds, which are the percentage of alkaloids in the leaves and the percentage of the alkaloids in the seeds and the percentage of oil in the seeds were (2.8000%, 9.867%, and 5.2633%), respectively, While the interaction between the first date and the fertilizer combination (distilled water only) was the lowest average for all studied traits.

Compartmentalization of specialized metabolites across vegetative and reproductive tissues in two sympatric Psychotria species.

The specialized metabolites of plants are recognized as key chemical traits in mediating the ecology and evolution of sundry plant-biotic interactions, from pollination to seed predation. Intra- and interspecific patterns of specialized metabolite diversity have been studied extensively in leaves, but the diverse biotic interactions that contribute to specialized metabolite diversity encompass all plant organs. Focusing on two species of Psychotria shrubs, we investigated and compared patterns of specialized metabolite diversity in leaves and fruit with respect to each organ's diversity of biotic interactions. To evaluate associations between biotic interaction diversity and specialized metabolite diversity, we combined UPLC-MS metabolomic analysis of foliar and fruit specialized metabolites with existing surveys of leaf- and fruit-centered biotic interactions. We compared patterns of specialized metabolite richness and variance among vegetative and reproductive tissues, among plants, and between species. In our study system, leaves interact with a far larger number of consumer species than do fruit, while fruit-centric interactions are more ecologically diverse in that they involve antagonistic and mutualistic consumers. This aspect of fruit-centric interactions was reflected in specialized metabolite richness-leaves contained more than fruit, while each organ contained over 200 organ-specific specialized metabolites. Within each species, leaf- and fruit-specialized metabolite composition varied independently of one another across individual plants. Contrasts in specialized metabolite composition were stronger between organs than between species. As ecologically disparate plant organs with organ-specific specialized metabolite traits, leaves and fruit can each contribute to the tremendous overall diversity of plant specialized metabolites.

Association between circulating fatty acid metabolites and asthma risk: a two-sample bidirectional Mendelian randomization study

BackgroundFatty acids are involved in a wide range of immunological responses in humans. Supplementation of polyunsaturated fatty acids has been reported to help alleviate symptoms and airway inflammation in asthma patients, whereas the effects of fatty acids on the actual risk of asthma remain controversial. This study comprehensively investigated the causal effects of serum fatty acids on asthma risk using two-sample bidirectional Mendelian Randomization (MR) analysis.MethodsGenetic variants strongly associated with 123 circulating fatty acid metabolites were extracted as instrumental variables, and a large GWAS data of asthma was used to test effects of the metabolites on this outcome. The inverse-variance weighted method was used for primary MR analysis. The weighted median, MR-Egger regression, MR-PRESSO, and leave-one-out analyses were utilized to evaluate heterogeneity and pleiotropy. Potential confounders were adjusted by performing multivariable MR analyses. Reverse MR analysis was also conducted to estimate the causal effect of asthma on candidate fatty acid metabolites. Further, we performed colocalization analysis to examine the pleiotropy of variants within the fatty acid desaturase 1 (FADS1) locus between the significant metabolite traits and the risk of asthma. Cis-eQTL-MR and colocalization analysis were also performed to determine the association between RNA expression of FADS1 and asthma.ResultsGenetically instrumented higher average number of methylene groups was causally associated with a lower risk of asthma in primary MR analysis, while inversely, the higher ratio of bis-allylic groups to double bonds and the higher ratio of bis-allylic groups to total fatty acids, were associated with higher probabilities of asthma. Consistent results were obtained in multivariable MR when adjusted for potential confounders. However, these effects were completely eliminated after SNPs correlated with the FADS1 gene were excluded. The reverse MR also found no causal association. The colocalization analysis suggested that the three candidate metabolite traits and asthma likely share causal variants within the FADS1 locus. In addition, the cis-eQTL-MR and colocalization analyses demonstrated a causal association and shared causal variants between FADS1 expression and asthma.ConclusionsOur study supports a negative association between several PUFA traits and the risk of asthma. However, this association is largely attributed to the influence of FADS1 polymorphisms. The results of this MR study should be carefully interpreted given the pleiotropy of SNPs associated with FADS1.

Genetic Mimicry Analysis Reveals the Specific Lipases Targeted by the ANGPTL3-ANGPTL8 Complex and ANGPTL4

Angiopoietin-like proteins, ANGPTL3, ANGPTL4, and ANGPTL8, are involved in regulating plasma lipids. In vitro and animal-based studies point to LPL and endothelial lipase (EL, LIPG) as key targets of ANGPTLs. To examine the ANGPTL mechanisms for plasma lipid modulation in humans, we pursued a genetic mimicry analysis of enhancing or suppressing variants in the LPL, LIPG, lipase C hepatic type (LIPC), ANGPTL3, ANGPTL4, and ANGPTL8 genes using data on 248 metabolic parameters derived from over 110,000 nonfasted individuals in the UK Biobank and validated in over 13,000 overnight fasted individuals from 11 other European populations. ANGPTL4 suppression was highly concordant with LPL enhancement but not HL or EL, suggesting ANGPTL4 impacts plasma metabolic parameters exclusively via LPL. The LPL-independent effects of ANGPTL3 suppression on plasma metabolic parameters showed a striking inverse resemblance with EL suppression, suggesting ANGPTL3 not only targets LPL but also targets EL. Investigation of the impact of the ANGPTL3-ANGPTL8 complex on plasma metabolite traits via the ANGPTL8 R59W substitution as an instrumental variable showed a much higher concordance between R59W and EL activity than between R59W and LPL activity, suggesting the R59W substitution more strongly affects EL inhibition than LPL inhibition. Meanwhile, when using a rare and deleterious protein-truncating ANGPTL8 variant as an instrumental variable, the ANGPTL3-ANGPTL8 complex was very LPL specific. In conclusion, our analysis provides strong human genetic evidence that the ANGPTL3-ANGPTL8 complex regulates plasma metabolic parameters, which is achieved by impacting LPL and EL. By contrast, ANGPTL4 influences plasma metabolic parameters exclusively via LPL.

Genetic variation underlying differential ammonium and nitrate responses in Arabidopsis thaliana.

Nitrogen is an essential element required for plant growth and productivity. Understanding the mechanisms and natural genetic variation underlying nitrogen use in plants will facilitate the engineering of plant nitrogen use to maximize crop productivity while minimizing environmental costs. To understand the scope of natural variation that may influence nitrogen use, we grew 1,135 Arabidopsis thaliana natural genotypes on two nitrogen sources, nitrate and ammonium, and measured both developmental and defense metabolite traits. By using different environments and focusing on multiple traits, we identified a wide array of different nitrogen responses. These responses are associated with numerous genes, most of which were not previously associated with nitrogen responses. Only a small portion of these genes appear to be shared between environments or traits, while most are predominantly specific to a developmental or defense trait under a specific nitrogen source. Finally, by using a large population, we were able to identify unique nitrogen responses, such as preferring ammonium or nitrate, which appear to be generated by combinations of loci rather than a few large-effect loci. This suggests that it may be possible to obtain novel phenotypes in complex nitrogen responses by manipulating sets of genes with small effects rather than solely focusing on large-effect single gene manipulations.

Multi-omics analyses of 398 foxtail millet accessions reveal genomic regions associated with domestication, metabolite traits, and anti-inflammatory effects

Foxtail millet (Setaria italica), which was domesticated from the wild species green foxtail (Setaria viridis), is a rich source of phytonutrients for humans. To evaluate how breeding changed the metabolome of foxtail millet grains, we generated and analyzed the datasets encompassing the genomes, transcriptomes, metabolomes, and anti-inflammatory indices from 398 foxtail millet accessions. We identified hundreds of common variants that influence numerous secondary metabolites. We observed tremendous differences in natural variations of the metabolites and their underlying genetic architectures between distinct sub-groups of foxtail millet. Furthermore, we found that the selection of the gene alleles associated with yellow grains led to altered profiles of metabolites such as carotenoids and endogenous phytohormones. Using CRISPR-mediated genome editing we validated the function of PHYTOENE SYNTHASE 1 (PSY1) gene in affecting millet grain color and quality. Interestingly, our in vitro cell inflammation assays showed that 83 metabolites in millet grains have anti-inflammatory effects. Taken together, our multi-omics study illustrates how the breeding history of foxtail millet has shaped its metabolite profile. The datasets we generated in this study also provide important resources for further understanding how millet grain quality is affected by different metabolites, laying the foundations for future millet genetic research and metabolome-assisted improvement.

Graph pangenome captures missing heritability and empowers tomato breeding

Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits1,2. The solution to this problem is to identify all causal genetic variants and to measure their individual contributions3,4. Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used for genome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding.

Generalizable approaches for genomic prediction of metabolites in plants.

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Genome-wide mediation analysis: an empirical study to connect phenotype with genotype via intermediate transcriptomic data in maize.

Mapping genotype to phenotype is an essential topic in genetics and genomics research. As the Omics data become increasingly available, 2-variable methods have been widely applied to associate genotype with the phenotype (genome-wide association study), gene expression with the phenotype (transcriptome-wide association study), and genotype with gene expression. However, signals detected by these 2-variable association methods suffer from low mapping resolution or inexplicit causality between genotype and phenotype, making it challenging to interpret and validate the molecular mechanisms of the underlying genomic variations and the candidate genes. Under the context of genetics research, we hypothesized a causal chain from genotype to phenotype partially mediated by intermediate molecular processes, i.e. gene expression. To test this hypothesis, we applied the high-dimensional mediation analysis, a class of causal inference method with an assumed causal chain from the exposure to the mediator to the outcome, and implemented it with a maize association panel (N = 280 lines). Using 40 publicly available agronomy traits, 66 newly generated metabolite traits, and published RNA-seq data from 7 different tissues, our empirical study detected 736 unique mediating genes. Noticeably, 83/736 (11%) genes were identified in mediating more than 1 trait, suggesting the prevalence of pleiotropic mediating effects. We demonstrated that several identified mediating genes are consistent with their known functions. In addition, our results provided explicit hypotheses for functional validation and suggested that the mediation analysis is a powerful tool to integrate Omics data to connect genotype to phenotype.

Association analysis, genetic diversity and population structure of barley (Hordeum vulgare L.) under heat stress conditions using SSR and ISSR markers linked to primary and secondary metabolites.

Barley is one of the major cereal crops, which can provide a significant source of genes for stress tolerance due to its high diversity and adaptability. Metabolite traits are considered to be significant for adaptation of barley to heat stress. In the present study, genetic relationships between 120 barley genotypes were determined with 50 simple sequence repeat (SSR) and 26 inter simple sequence repeat (ISSR) markers under heat stress and non-stress conditions. Moreover, genetic diversity of barley accessions was investigated using the studied markers covering 7 chromosomes of barley. In general, 153 and 85 polymorphic alleles were detected for SSR and ISSR and number of the observed polymorphic allele varied between 2-9 and 2-6, with an average of 3.26 and 3.26 alleles per locus, respectively. Markers of Bmag0223, GBMS180/180, HVM7, ISSR22, ISSR25, and ISSR48 were the most informative due to their high polymorphism information content value demonstrating that putative techniques utilized in this research can be powerful and valuable tools in breeding program of barley. Association analysis was performed between 9 important traits and SSR and ISSR markers using four statistical models. The results revealed that the model containing both population structure (Q) and general similarity in genetic background arising from shared kinship (K) factors reduced false positive associations between markers and phenotypes. According to the results, some of markers related to more than one trait under normal conditions (ISSR31-2, HVM62, and GBMS180/180) and heat stress conditions (ISSR20-5, EBmac635, HVM14, and ISSR37-3) were determined, which can be considered to be the most interesting candidates for further studies and simultaneously will provide a useful target for the future breeding programs, such as marker-assisted selection (MAS).

Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation

BackgroundPrediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders.MethodsWe used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24–28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women.ResultsMaternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24–28 weeks and 15–18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated.ConclusionsOur results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.