Human metabolic individuality in biomedical and pharmaceutical research

Abstract

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10-60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn's disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Figure 1. Genetic basis of human metabolic individuality and its overlap with loci of biomedical and pharmaceutical interest

Over 100 years ago Archibald Garrod realized that inborn errors in human metabolism were ‘merely extreme examples of variations of chemical behaviour which are probably everywhere present in minor degrees’ and that this ‘chemical individuality [confers] predisposition to and immunities from the various mishaps which are spoken of as diseases’ . The 37 genetically determined metabotypes (GDMs) we reported here explain a highly relevant amount of the total variation in the studied population and therefore contribute substantially to the genetic part of human metabolic individuality. GDMs are presented here color-coded (a) by general metabolic pathways together with selected associated metabolic traits, highlighting the relationship between gene function and the associated metabolic trait (see column 4 in Table 1), and (b) by overlap with associations in previous GWAS with disease [red], intermediate disease risk factors [yellow], and other traits [green]. Locus overlap is defined here by the lead SNP reported in the NHGRI GWAS catalogue being highly correlated (R²>=0.8) with the most associated SNP in the metabolomics scan (see column 5 in Table 1 and Suppl. Table 7). Note that the overlap between the metabolomics loci and the loci reported by the NHGRI GWAS catalogue is highly significant when compared to a draw of 37 randomly selected SNPs with similar properties (p<3×10⁻⁶, see Methods).

Figure 2. Experimental evidence for SLC16A9 (MCT9) as a carnitine efflux transporter

When incubated in uptake medium containing [³H]-carnitine (4μCi/ml) there was no significant uptake indicating that MCT9 does not mediate carnitine uptake. As some of the previously characterised MCTs are proton-coupled , uptake was measured at both pH_out 7.4 and 5.5, but no significant difference was observed (data not shown). However, when 4.6nl of [³H]-carnitine was injected into the oocyte followed by incubation in medium for 90 minutes, efflux was significantly higher in oocytes expressing MCT9 than in the non-injected (NI, Figure a) or water-injected (WI, Figure b) controls, while again changing the pH_out had no effect (Figure a). In agreement with the lack of uptake of [³H]-carnitine, external unlabelled carnitine was unable to trans-stimulate [³H]-carnitine efflux with no significant difference in efflux between MCT9-expressing oocytes in the absence or presence of 5mM carnitine (MCT9 vs. MCT9+carn, respectively, Figure b). Data are means ± SEM of 6-10 oocytes per data point from 2 oocyte preparations. Y-axis on plots represents remaining [³H]-Carnitine (cpm/oocytes). Statistical significance was determined by the Student’s t test. Taken together, these results are consistent with MCT9 acting as a unidirectional carnitine efflux system when expressed in Xenopus oocytes. Note that additional experiments are required to establish the full substrate specificity of MCT9. If future studies show an appropriate cellular distribution, MCT9 could be responsible for carnitine efflux across the basolateral membrane of absorptive epithelial cells following absorption via the well-characterized apical epithelial proton-coupled carnitine transporters OCTNs / SLC22 family .