Discovery of sexual dimorphisms in metabolic and genetic biomarkers

Abstract

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.

Figure 1. Two dimensional partial least square (PLS) analyses showing the contribution of 131 metabolites in males and females.

Figure 2. Systematic view of metabotype variations in the metabolism of males and females.

It also shows the suggestive locus that is located in a gene encoding an enzyme that is central in human metabolism. CPS1 is related to the amino acid metabolism. For this locus the metabolite with the strongest association is provided (green box). A blue arrow indicates metabolite concentrations which are higher in men than in women; green arrows vice versa.

Figure 3. Gaussian graphical model of all measured metabolites illustrating the correlation strength and the propagation of gender-specific effects through the underlying metabolic network.

Each node represents one metabolite whereas edge weights correspond to the strength of partial correlation. Only edges with a partial correlation above r =  0.3 are shown. Node colouring represents the strength of association (measured using β from linear regression analysis) towards either males or females. Metabolite names marked with a star * represent significantly different metabolites between genders. Yellow highlighted pairs of metabolites differ by a C18∶0 fatty acid residue.

Figure 4. Manhattan plots for gender-specific genome-wide beta-differences for the metabolite glycine.

Genome-wide significant beta-differences are plotted in red (significance level p<3.8×10⁻¹⁰).