Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers

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 x 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 x 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

Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers

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 x 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 x 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

Metabolomics hallmarks OPA1 variants correlating with their in-vitro phenotype and predicting clinical severity

Interpretation of variants of uncertain significance is an actual major challenge. We addressed this question on a set of OPA1 missense variants responsible for variable severity of neurological impairments. We used targeted metabolomics to explore the different signatures of OPA1 variants expressed in Opa1 deleted mouse embryonic fibroblasts (Opa1 12/ 12 MEFs), grown under selective conditions. Multivariate analyses of data discriminated Opa1+/+ from Opa1 12/ 12 MEFs metabolic signatures and classified OPA1 variants according to their in-vitro severity. Indeed, the mild p.I382M hypomorphic variant was segregating close to the wild-type allele, while the most severe p.R445H variant was close to Opa1 12/ 12 MEFs, and the p.D603H and p.G439V alleles, responsible for isolated and syndromic presentations respectively, were intermediary between the p.I382M and the p.R445H variants. The most discriminant metabolic features were hydroxyproline, the spermine/spermidine ratio, amino acid pool and several phospholipids, emphasizing proteostasis, endoplasmic reticulum stress and phospholipid remodeling as the main mechanisms ranking OPA1 allele impacts on metabolism. These results demonstrate the high resolving power of metabolomics in hierarchizing OPA1 missense mutations by their in-vitro severity, fitting clinical expressivity. This suggests that our methodological approach can be used to discriminate the pathological significance of variants in genes responsible for other rare metabolic diseases and may be instrumental to select possible compounds eligible for supplementation treatment

Metabolomics reveals effects of maternal smoking on endogenous metabolites from lipid metabolism in cord blood of newborns

Introduction: A general detrimental effect of smoking during pregnancy on the health of newborn children is well-documented, but the detailed mechanisms remain elusive. Objectives: Beside the specific influence of environmental tobacco smoke derived toxicants on developmental regulation the impact on the metabolism of newborn children is of particular interest, first as a general marker of foetal development and second due to its potential predictive value for the later occurrence of metabolic diseases. Methods: Tobacco smoke exposure information from a questionnaire was confirmed by measuring the smoking related metabolites S-Phenyl mercapturic acid, S-Benzyl mercapturic acid and cotinine in maternal urine by LC&ndash;MS/MS. The impact of smoking on maternal endogenous serum metabolome and children&rsquo;s cord blood metabolome was assessed in a targeted analysis of 163 metabolites by an LC&ndash;MS/MS based assay. The anti-oxidative status of maternal serum samples was analysed by chemoluminiscence based method. Results: Here we present for the first time results of a metabolomic assessment of the cordblood of 40 children and their mothers. Several analytes from the group of phosphatidylcholines, namely PCaaC28:1, PCaaC32:3, PCaeC30:1, PCaeC32:2, PCaeC40:1, and sphingomyelin SM C26:0, differed significantly in mothers and children&rsquo;s sera depending on smoking status. In serum of smoking mothers the antioxidative capacity of water soluble compounds was not significantly changed while there was a significant decrease in the lipid fraction. Conclusion: Our data give evidence that smoking during pregnancy alters both the maternal and children&rsquo;s metabolome. Whether the different pattern found in adults compared to newborn children could be related to different disease outcomes should be in the focus of future studies

Rapid growth and childhood obesity are strongly associated with lysoPC(14:0)

BACKGROUND: Despite the growing interest in the early-origins-of-later-disease hypothesis, little is known about the metabolic underpinnings linking infant weight gain and childhood obesity. OBJECTIVE: To discover biomarkers reflective of weight change in the first 6 months and overweight/obesity at age 6 years via a targeted metabolomics approach. DESIGN: This analysis comprised 726 infants from a European multicenter randomized trial (Childhood Obesity Programme, CHOP) for whom plasma blood samples at age 6 months and anthropometric data up to the age of 6 years were available. 'Rapid growth' was defined as a positive difference in weight within the first 6 months of life standardized to WHO growth standards. Weight change was regressed on each of 168 metabolites (acylcarnitines, lysophosphatidylcholines, sphingomyelins, and amino acids). Metabolites significant after Bonferroni's correction were tested as predictors of later overweight/obesity. RESULTS: Among the overall 19 significant metabolites, 4 were associated with rapid growth and 15 were associated with a less-than-ideal weight change. After adjusting for feeding group, only the lysophosphatidylcholine LPCaC14:0 remained significantly associated with rapid weight gain (\u3b2 = 0.18). Only LPCaC14:0 at age 6 months was predictive of overweight/obesity at age 6 years (OR 1.33; 95% CI 1.04-1.69). CONCLUSION: LPCa14:0 is strongly related to rapid growth in infancy and childhood overweight/obesity. This suggests that LPCaC14:0 levels may represent a metabolically programmed effect of infant weight gain on the later obesity risk. However, these results require confirmation by independent cohorts

Potential metabolic mechanism of girls' central precocious puberty: a network analysis on urine metabonomics data

BACKGROUND: Central precocious puberty (CPP) is a common pediatric endocrine disease caused by early activation of hypothalamic-putuitary-gonadal (HPG) axis, yet the exact mechanism was poorly understood. Although there were some proofs that an altered metabolic profile was involved in CPP, interpreting the biological implications at a systematic level is still in pressing need. To gain a systematic understanding of the biological implications, this paper analyzed the CPP differential urine metabolites from a network point of view. RESULTS: In this study, differential urine metabolites between CPP girls and age-matched normal ones were identified by LC-MS. Their basic topological parameters were calculated in the background network. The network decomposition suggested that CPP differential urine metabolites were most relevant to amino acid metabolism. Further proximity analysis of CPP differential urine metabolites and neuro-endocrine metabolites showed a close relationship between CPP metabolism and neuro-endocrine system. Then the core metabolic network of CPP was successfully constructed among all these differential urine metabolites. As can be demonstrated in the core network, abnormal aromatic amino acid metabolism might influence the activity of HPG and hypothalamic pituitary adrenal (HPA) axis. Several adjustments to the early activation of puberty in CPP girls could also be revealed by urine metabonomics. CONCLUSIONS: The present article demonstrated the ability of urine metabonomics to provide several potential metabolic clues for CPP's mechanism. It was revealed that abnormal metabolism of amino acid, especially aromatic amino acid, might have a close correlation with CPP's pathogenesis by activating HPG axis and suppressing HPA axis. Such a method of network-based analysis could also be applied to other metabonomics analysis to provide an overall perspective at a systematic level