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Title: Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers}
Article:
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.
Introduction:
Metabolomics provides a powerful tool to analyse physiological and disease-induced biological states on the molecular level, taking into account both the organism's intrinsic properties, i. e. genetic factors, and the effects of lifestyle, diet, and environment. The development of sophisticated analytic platforms and modern computational tools to handle increasingly complex data now enables the quantification of hundreds of metabolites from complex biological samples with a high throughput rate. These advancements support the integration of metabolomic profiles with genetic, epigenetic, transcriptomic and proteomic data for holistic systems biology approaches. Recently, common genetic variants have been demonstrated to exert large effects on individual metabolic capacities called “genetically determined metabotypes”[1],[2]. Therefore genetic variants in metabolism-related genes led to specific and clearly differentiated metabolic phenotypes[1],[3]. Knowledge on such genetically determined metabotypes is of crucial importance to understand the contribution and complex interaction of genes, proteins and metabolites in health and disease. Consequently, genetic studies can help to elucidate the direction of effects between metabolites and a specific disease. Thus, the combination of genetic and metabolic markers is an important emerging approach for biological research. To uncover potentially confounding influences on the interpretation of metabolic results, it is important to minimize the occurring confounders on human serum metabolites in a population-based study that has not been subjected to lifestyle and dietary controls. Pointed out recently, gender inequalities are another increasingly recognized problem in both basic research and clinical medicine[4]. Nevertheless, many published studies did not analyse their data stratified by sex[4]–[6] although there is a strong correlation between sex and the incidence, prevalence, age at onset, symptoms and severity of a disease, as well as the reaction to drugs[7],[8]. A survey of studies published in 2004 of nine different medical journals found that only 37% of participants were women( 24% when restricted to drug trials), and only 13% of studies analysed data by sex[4]. Therefore we systematically assessed the effect of sex on serum metabolites in a large population-based cohort[9]. Furthermore, we investigated whether there are sex-specific differences in the genetic determination of metabotypes.
Discussion:
There have been only few studies addressing metabolic differences between males and females, and most of these studies were rather small in sample size and determined only a small number of metabolites[5],[12]. We investigated a large population-based study with sufficient statistical power to examine associations within subgroups and a large number of metabolites. Our findings shed light on sex-specific architecture of the human metabolome and provide clues on biochemical mechanisms that might explain observed differences in susceptibility and time course of the development of common diseases in males and females. Our data provided new insights into sex-specific metabotype differences. Combining results from linear regression with partial correlation analysis( resulting in a Gaussian graphical model) yielded interesting insights into how sex-specific concentration differences spread over the metabolic network( Figure 3). The analysis suggests that sex-specific concentration differences affect whole metabolic pathways rather than being randomly spread over the different metabolites. In addition, we found three interesting inter-class associations between PCaa/PCae species and lyso PC species( highlighted in yellow in Figure 3). Those pairs shared a strong partial correlation but displayed differential concentration patterns with respect to gender effects. Furthermore, these pairs displayed a fatty acid residue difference of C18∶0, indicating that this fatty acid species might be a key compound giving rise to opposing metabolic gender effects. Direct experimental evidence indicated a role for sphingolipids( sphingomyelins and ceramides) in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction( MI), cardiomyopathy, pancreatic beta cell failure, insulin resistance, coronary heart disease and type 2 diabetes( T2D)[13],[14]. Already young children( between birth and 4 years old, with low levels of sex-hormones) may reveal significant sex-specific differences in plasma sphingolipid concentrations[15]. Our observations described new sex-specific differences, while other lipid-derived molecules, like bile acids, were already demonstrated not to be sex-specific[16]. Therefore sphingomyelins represent important intermediate phenotypes. The concentration differences between males and females of acylcarnitines described in this study coincide with previous findings showing that carnitine( C0) and acetylcarnitine( C2) concentrations were higher in males than in females[17],[18]. Phosphatidylcholines, as demonstrated in this study, are another gender-specific phenotype. Ghrelin( controlling energy homeostasis and pituitary hormone secretion in humans) levels have been shown to be similar in men and women and did not vary by menopausal status or in association with cortisol levels[19]. These findings of our and other studies urgently suggest when using metabolites for disease prediction gender has to be strictly taken into account. A previously published non sex-stratified GWA study on metabolites based on the same population Geno-KORA F4 reported 15 loci which showed genome-wide significant associations with at least one metabolite concentration or ratio[1]. Besides others the locus CPS1 was found to have a significant effect on glycine concentrations. But the findings of this sex-stratified genome-wide association analysis revealed that the genetic determination of CPS1 differs significantly between males and females. Therefore it is important to analyse the data stratified by sex. SNP rs74223369 on chromosome 2 in the 3′ UTR region of the gene CPS1 showed a genome-wide significant difference in beta-estimates between men and women for association with glycine. The gender-specific effect of SNP rs7422339 was significantly replicated as the difference between the beta-estimates of men and women was of the same direction in the discovery sample and in the replication cohort Rep-KORA F4 and the p-value of the test of differences was lower than the replication significance niveau( 0. 05/8). The other SNPs of the CPS1 gene region also showed significant gender-specific effects but these effects could not be replicated in the Rep-KORA F3 cohort. As the effect-sizes and differences for the SNP rs7422339 are similar and at least for the other SNPs are pointing into the same direction as in the discovery set, the failed replication in Rep-KORA F3 might be a problem of power due to the smaller sample size. CPS1, which encodes the mitochondrial enzyme CPS1, plays a pivotal role in protein and nitrogen metabolism catalyzing the first committed step of the hepatic urea cycle. Once ammonia has entered the mitochondria via glutamine or glutamate, CPS1 adds the ammonia to bicarbonate along with a phosphate group to form carbamoyl phosphate. Carbamoyl phosphate is then put into the urea cycle. The hepatic urea cycle is responsible for the elimination of ammonia in the form of urea as well as the synthesis of arginine. Among others, Döring et al. 2008 could show that there is strong evidence that, in addition to environmental components, a strong genetic and sex-specific control influences the regulation of blood uric acid concentration. They showed that the proportion of the variance of serum uric acid concentrations explained by SLC2A9 genotypes was about 1. 2% in men and 6% in women[12]. Brandstätter et al. 2010 also observed a sex-specific interaction with genetic association of atherogenic uric acid concentrations[20]. Paré et al. 2009 described that the CPS1 SNP rs7422339, which encodes the substitution of asparagine to threonine( T1405N) in the region critical for N-acetyl-glutamate binding resulting in 20% to 30% higher enzymatic activity[21], is associated with homocysteine also in a sex-specific manner in their study[22]. Also in an Asian population the effects of the genetic variations of the CPS1 gene were stronger in women than in men[23]. Interestingly a meta-analysis of genome-wide association data in 67, 093 individuals also of European ancestry identified recently CPS1 to affect creatinine production and secretion in Chronic Kidney Disease( CKD)[24]. Hicks et al. 2009 performed a GWAS of different circulating sphingolipids in five diverse European populations[3]. They could show associations of genetic loci with several lipid species but did not analyse their data stratified by sex. There is just some evidence for loci with differential sex-effects influencing classical lipids like HDL[25]. Therefore identification of sex-specific genetic variants, that alter the homeostasis of key metabolites in males and in females, will lead to a better functional understanding of the genetics of complex disorders. As global ‘omics’-techniques are more and more refined to identify more compounds in single biological samples, the predictive power of these new technologies will greatly increase. Metabolite concentration profiles and genomic data can be used as predictive biomarkers to indicate the presence or severity of a disease depending on gender. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider gender-specific effects in design and interpretation. Our findings help to understand biochemical mechanisms underlying sexual dimorphism, a phenomenon which may explain the differential susceptibility to common diseases in males and females.
The combination of genomic and metabolic studies during the last years has provided astonishing results . However , most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by sex . The investigation of 131 serum metabolite concentrations of >3 , 300 population-based samples ( KORA F3/F4 ) revealed significant differences in the metabolite profile of males and females . Furthermore , a genome-wide picture of sex-specific genetic variations in human metabolism ( >2 , 000 subjects from KORA F3/F4 cohorts ) was investigated . Sex-specific genome-wide association studies ( GWAS ) showed differences in the effect of genetic variations on metabolites in men and women . SNPs in the CPS1 ( carbamoyl-phosphate synthase 1 ) locus showed genome-wide significant differences in beta-estimates of sex-specific association analysis ( significance level: 3 . 8×10−10 ) for glycine . As global metabolomic techniques are more and more refined to identify more compounds in single biological samples , the predictive power of this new technology will greatly increase . This suggests that metabolites , which may be used as predictive biomarkers to indicate the presence or severity of a disease , have to be used selectively depending on sex .