Metabolomic study of the genetic regulation of biochemical individuality

Metabolomic study of the genetic regulation of biochemical individuality

In a recently published study in Naturopathy Researchers systematically examined the genomes of almost 20,000 women and men with regard to >900 metabolites.

Lernen: Seltene und häufige genetische Determinanten der metabolischen Individualität und ihre Auswirkungen auf die menschliche Gesundheit. Bildnachweis: PopTika/Shutterstock

Metabolites circulating in the human body reflect human physiology and the chemical uniqueness of an individual. Human metabolism is dysregulated in several diseases and is influenced by multiple dietary, genetic, drug-related, and disease-associated factors. A wide range of high-throughput biomedical technologies are available that enable assessment of the genetic factors that influence human physiology; however, co-regulation data of various metabolites are limited.

About studying

In the present study, researchers examined genetic determinants of variation in human physiology using untargeted metabolomic data.

The team analyzed the genetic architecture of 913 metabolites in >14,000 individuals. The data were used to define genetically influenced metabotypes (GIMs), or groups of metaboites influenced by a common genetic signal of ≥1.0. Samples from two United Kingdom (UK)-based cohort studies were analyzed: INTERVAL and EPIC-Norfolk. Metabolites were measured by liquid chromatography and mass spectrometry and classified as related to lipid, amino acid, xenobiotic, nucleotide, peptide, carbohydrate, cofactor, and vitamin and energy metabolism.

Compounds with uncertain chemical identity were designated as unannotated compounds. Multivariable linear regression modeling was performed for the analysis. Metabolomic measurements were performed between 2015 and 2017 for the EPIC-Norfolk samples. Metabolite levels were assessed in two sets of approximately 6,000 samples each. The team validated regional sentinel variant-metabolite associations through meta-analysis of the discovery set and validation set data.

Among participants in the EPIC-Norfolk study, 5,698 and 5,841 individuals were assigned to the validation and discovery sets, respectively. Genotyping and imputation analyzes were carried out, where the team imputed genetically predicted metabolite levels ('metabolite scores') in UK Biobank participants using weighted genetic scores and estimated their associations with 1,457 sorted disease terms ('phecodes'). A genome-wide association analysis (GWAS) was performed for each metabolite separately for the samples. Furthermore, conditional analyses, colocalization analyses, and enrichment analyzes for IEM (inborn errors of metabolism)-causing genes were performed.

Allelic heterogeneity was assessed and genetic co-regulation of different metabolites was assessed. The team also performed phenotypic analyzes for metabolite-associated genetic variants, and phenome-wide metabolic associations were determined. Results were technically validated using Whole Exome Sequence (WES) data from 3,924 INTERVAL study samples.

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The most likely causal genes were determined and the novelty of the variant association was assessed based on comparing the results with those from two previously conducted studies. Based on the identified genetic associations and manually curated scientific literature, high-confidence causative genes regulating the metabolites were refuted and their clinical relevance was assessed for more than 1,400 phenotypes.

Results

Convergence of phenotypic and metabolic presentations of rare IEM-causing genes with genetic variants of genes identified in the general population was observed. A total of 423 GIMs were identified, mainly including ≤15 genetic variants and ≤89 metabolites. In 62% (n=264) GIMs, one gene out of 253 likely causal genes was assigned based on extensive data mining. GIMs such as steroid 5α-reductase 2 (SRD5A2) and dihydropyrimidine dehydrogenase (DPYD) showed important clinical implications.

Higher SRD5A2 activity was associated with a greater risk of baldness in men. Genetic associations were consistent with lower SRD5A2 activity and lower androsterone, epiandrosterone, 3α-androstanediol, and 3β-androstanediol conjugates. Common genetic signals were observed between different androgen metabolites and male pattern baldness, with rs112881196 being the causal variant. The fatty acid desaturase (FAD) S1/S2 locus was associated with the most frequently annotated metabolites.

The mean phenotypic variance explained by conditionally independent variants was 5.2%, the highest for amino acid and energy classes. Lower SRD5A inhibitor levels were associated with more significant depression risks, with rs62142080 being the likely causal variant. The rs72977723 variant involved uracil degradation, while rs184097503 and rs28933981 increased thyroxine transport capabilities. GIMs capturing multiple gene functions, such as those of SLC7A2 (Slc7a2 solute carrier family 7) transporters associated with arginine or lysine levels, were observed.

An 8.0-fold enrichment of IEM-causing genes was observed among IEM variants mapped to genes causing disorders related to mitochondria, amino acids, and fatty acids. Lower vanillyl almond levels were associated with a lower risk of hypertension, with rs6271 being the causal variant. Causative genes have also been identified for coronary heart disease [PCSK9 (Proprotein convertase subtilisin/kexin type 9), SORT1 (Sortiliin 1) and LDLR (low-density lipoprotein receptor)]Macular degeneration [LIPC (hepatic lipase) and apolipoprotein E (APOE)/apolipoprotein C (APOC) 1,2,4]Crohn's disease [GCKR (glucokinase regulator) and FADS2] and chronic kidney disease [GATM (Glycine amidinotransferase)].

Association between metabolites and diseases, e.g. B. Uric acid levels in gout [odds ratio (OR) of 2.2], bile acids in cholelithiasis (OR of 0.6 for glycohyocholate) and complex lipids in hypercholesterolemia [OR of 1.8 for 1-dihomo-linoleoyl-GPC (20:2)] were observed. Plasma homoarginine has been found to play a key role in the pathology of chronic kidney disease, and 3-methylglutarylcarnitine protects against the development of benign neoplasms in the colon.

Overall, the study results highlighted the genetic determinants of human metabolite variation and may guide future metabolome-wide association assessments.

Reference:

• Surendran, P., Stewart, ID, Au Yeung, VPW et al. (2022). Seltene und häufige genetische Determinanten der metabolischen Individualität und ihre Auswirkungen auf die menschliche Gesundheit. Naturheilkunde. doi: https://doi.org/10.1038/s41591-022-02046-0 https://www.nature.com/articles/s41591-022-02046-0

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