Plasma and ovarian metabolomics responses to chronic stress in female mice

Background: Chronic stress may affect metabolism of amino acids, lipids, and other small molecule metabolites, but these alterations may differ depending on tissue evaluated. We examined metabolomic changes in plasma and ovarian tissue samples from female mice due to chronic stress exposure. Methods: At 12 weeks old, healthy, female, C57 black mice were randomly assigned to three weeks of chronic stress using daily restraint (2 hours/day; n=9) or normal care (n=10). Metabolomic profiling was conducted on plasma and ovarian tissues. Using the Wilcoxon Rank Test, Metabolite Set Enrichment Analysis, and Differential Network Analysis we identified metabolomic alterations occurring in response to restraint stress. All p-values were corrected for multiple testing using the false discovery rate approach. Results: In plasma, individual lysophosphatidylcholines (positively) and the metabolite classes carnitines (positively), diacylglycerols and triacylglycerols (inversely) were associated with restraint stress (adjusted-p's<0.2). In contrast, diacylglycerols and triacylglycerols were increased while carnitines were decreased in ovarian tissue from stressed mice (adjusted-p's<0.2). However, several metabolites (cholesteryl esters, phosphatidylcholines/ phosphatidylethanolamines plasmalogens and multiple amino acids) were consistently inversely associated with restraint stress in plasma and ovarian tissue (adjusted-p's<0.2). Conclusion: We identified differences in multiple lipid and amino acid metabolites in plasma and ovarian tissue of female mice after exposure to chronic stress. Some affected metabolites (primarily triacylglycerols and diacylglycerols) exhibited opposite associations with chronic stress in plasma (a marker of systemic influences) versus in ovarian tissue (representing local changes), suggesting research to understand the biological impact of chronic stress needs to consider both systemic and tissue-specific alterations.

Plasma Metabolomics Profiling of Metabolic Pathways Affected by Major Depressive Disorder

Background: Major depressive disorder (MDD) is a common disease which is complicated by metabolic disorder. Although MDD has been studied relatively intensively, its metabolism is yet to be elucidated.Methods: To profile the global pathophysiological processes of MDD patients, we used metabolomics to identify differential metabolites and applied a new database Metabolite set enrichment analysis (MSEA) to discover dysfunctions of metabolic pathways of this disease. Hydrophilic metabolomics were applied to identify metabolites by profiling the plasma from 55 MDD patients and 100 sex-, gender-, BMI-matched healthy controls. The metabolites were then analyzed in MSEA in an attempt to discover different metabolic pathways. To investigate dysregulated pathways, we further divided MDD patients into two cohorts: (1) MDD patients with anxiety symptoms and (2) MDD patients without anxiety symptoms.Results: Metabolites which were hit in those pathways correlated with depressive and anxiety symptoms. Altogether, 17 metabolic pathways were enriched in MDD patients, and 23 metabolites were hit in those pathways. Three metabolic pathways were enriched in MDD patients without anxiety, including glycine and serine metabolism, arginine and proline metabolism, and phenylalanine and tyrosine metabolism. In addition, L-glutamic acid was positively correlated with the severity of depression and retardation if hit in MDD patients without anxiety symptoms.Conclusions: Different kinds of metabolic pathophysiological processes were found in MDD patients. Disorder of glycine and serine metabolism was observed in both MDD patients with anxiety and those without.

Pre-diagnostic plasma metabolomics and the risk of exfoliation glaucoma

Objective: To identify pre-diagnostic plasma metabolomic biomarkers associated with risk of exfoliation glaucoma (XFG). Methods: We conducted a metabolomic study using a 1:1 matched nested case-control study design within the Nurses' Health Study (NHS) and Health Professionals Follow-up Study (HPFS). Participants provided blood samples in 1989-'90 (NHS) and 1993-'95 (HPFS); we identified 205 participants who newly developed XFG during follow-up to 2018 (average time to diagnosis from blood draw=11.8 years); XFG was confirmed with medical record review. We profiled plasma metabolites using liquid chromatography-mass spectrometry and identified 379 known metabolites that passed quality control checks. Metabolites were transformed using probit scores for normality. We used multivariable-adjusted logistic regression adjusting for matching factors (such as age, residential latitude, season and time of blood draw), glaucoma family history and other covariates. Metabolite Set Enrichment Analysis was used to identify metabolite classes associated with risk of XFG. Number of effective tests (NEF) and False Discovery Rate (FDR) were used to adjust for multiple comparisons. Results: Mean age of cases (n=205) at diagnosis was 71 years; 84% were women and >99% were Caucasian; matched controls (n=205) all reported eye exams as of the matched cases' index date. A total of 33 metabolites were nominally significantly associated with XFG risk (p<0.05) and 4 metabolite classes were significantly associated (FDR<0.05). Overall, adverse associations were observed for the classes of lysophosphatidylcholines (FDR=0.02) and phosphatidylethanolamine plasmalogens (FDR=0.004). Inverse associations were observed for triglycerides (FDR<0.001) and steroid and steroid derivatives (FDR=0.03); in particular, the multivariable-adjusted odds ratio for XFG risk associated with each 1 standard deviation increase in plasma cortisone levels was 0.49 (95% CI=0.32-0.74; NEF=0.05). Results did not differ materially by time between blood draw and diagnosis, latitude of residence (< or ≥41°N latitude), age (< or ≥60 years), sex or glaucoma family history. Conclusions: Four broad classes of metabolites (including steroids such as cortisone and 3 lipid classes) in pre-diagnostic plasma collected almost a decade before diagnosis were associated with XFG risk; these results should be confirmed in future studies.

Identification of Metabolic Alterations in Breast Cancer Using Mass Spectrometry-Based Metabolomic Analysis

Breast cancer (BC) is a major global health issue and remains the second leading cause of cancer-related death in women, contributing to approximately 41,760 deaths annually. BC is caused by a combination of genetic and environmental factors. Although various molecular diagnostic tools have been developed to improve diagnosis of BC in the clinical setting, better detection tools for earlier diagnosis can improve survival rates. Given that altered metabolism is a characteristic feature of BC, we aimed to understand the comparative metabolic differences between BC and healthy controls. Metabolomics, the study of metabolism, can provide incredible insight and create useful tools for identifying potential BC biomarkers. In this study, we applied two analytical mass spectrometry (MS) platforms, including hydrophilic interaction chromatography (HILIC) and gas chromatography (GC), to generate BC-associated metabolic profiles using breast tissue from BC patients. These metabolites were further analyzed to identify differentially expressed metabolites in BC and their associated metabolic networks. Additionally, Chemical Similarity Enrichment Analysis (ChemRICH), MetaMapp, and Metabolite Set Enrichment Analysis (MSEA) identified significantly enriched clusters and networks in BC tissues. Since metabolomic signatures hold significant promise in the clinical setting, more effort should be placed on validating potential BC biomarkers based on identifying altered metabolomes.

Mass Spectrometry-Based Metabolomics Analysis of Obese Patients’ Blood Plasma

Scientists currently use only a small portion of the information contained in the blood metabolome. The identification of metabolites is a huge challenge because only highly abundant and well-separated compounds can be easily identified in complex samples. However, new approaches that enhance the identification of compounds have emerged; among them, the identification of compounds based on their involvement in a particular biological context is a recent development. In this work, this approach was first applied to identify metabolites in complex samples and, together with metabolite set enrichment analysis, was used for the evaluation of blood plasma from obese patients. The proposed approach was found to provide a statistically sound overview of the biochemical pathways, thus presenting additional information on obesity. Obesity progression was demonstrated to be accompanied by marked alterations in steroidogenesis, androstenedione metabolism, and androgen and estrogen metabolism. The findings of this study suggest that the workflow used for blood analysis is sufficient to demonstrate obesity at the biochemical pathway level as well as to monitor the response to treatment. This workflow is also expected to be suitable for studying other metabolic diseases.

Metabolomic Urine Profile: Searching for New Biomarkers of SDHx-Associated Pheochromocytomas and Paragangliomas

Context Metabolomic studies of pheochromocytoma and paraganglioma tissue showed a correlation between metabolomic profile and presence of SDHx mutations, especially a pronounced increase of succinate. Objective To compare the metabolomic profile of 24-hour urine samples of SDHx mutation carriers with tumors (affected mutation carriers), without tumors (asymptomatic mutation carriers), and patients with sporadic pheochromocytomas and paragangliomas. Methods Proton nuclear magnetic resonance spectroscopic profiling of urine samples and metabolomic analysis using pairwise comparisons were complemented by metabolite set enrichment analysis to identify meaningful patterns. Results The urine of the affected SDHx carriers showed substantially lower levels of seven metabolites than the urine of asymptomatic mutation carriers (including, succinate and N-acetylaspartate). The urine of patients with SDHx-associated tumors presented substantially higher levels of three metabolites compared with the urine of patients without mutation; the metabolite set enrichment analysis identified gluconeogenesis, pyruvate, and aspartate metabolism as the pathways that most probably explained the differences found. N-acetylaspartate was the only metabolite the urinary levels of which were significantly different between the three groups. Conclusions The metabolomic urine profile of the SDHx mutation carriers with tumors is different from that of asymptomatic carriers and from that of patients with sporadic neoplasms. Differences are likely to reflect the altered mitochondria energy production and pseudohypoxia signature of these tumors. The urinary levels of N-acetylaspartate and succinate contrast with those reported in tumor tissue, suggesting a defective washout process of oncometabolites in association with tumorigenesis. The role of N-acetylaspartate as a tumor marker for these tumors merits further investigation.

A prospective analysis of circulating plasma metabolomics and ovarian cancer risk

We assessed the association of pre-diagnostic plasma metabolites (N=420) with ovarian cancer risk. We included 252 cases and 252 matched controls from the Nurses’ Health Studies. Multivariable logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) comparing the 90th-10thpercentile in metabolite levels, using permutation tests to account for testing multiple correlated hypotheses. Weighted gene co-expression network analysis (WGCNA) modules (n=10) and metabolite set enrichment analysis (MSEA; n=23) were also evaluated. Pseudouridine had the strongest statistical association with ovarian cancer risk overall (OR=2.56, 95%CI=1.48-4.45; p=0.001/adjusted-p=0.15). C36:2 phosphatidylcholine (PC) plasmalogen had the strongest statistical association with lower risk (OR=0.11, 95%CI=0.03-0.35; p<0.001/adjusted-p=0.06) and pseudouridine with higher risk (OR=9.84, 95%CI=2.89-37.82; p<0.001/adjusted-p=0.07) of non-serous tumors. Seven WGCNA modules and 15 classes were associated with risk at FDR≤0.20. Triacylglycerols (TAGs) showed heterogeneity by tumor aggressiveness (case-only heterogeneity-p<0.0001). TAG association with risk overall and serous tumors differed by acyl carbon content and saturation. Pseudouridine may be a novel risk factor for ovarian cancer. TAGs may also be important, particularly for rapidly fatal tumors, with associations differing by structural features. Validation in independent prospective studies and complementary experimental work to understand biological mechanisms is needed.