Stress transgenerationally programs metabolic pathways linked to altered mental health

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• 作者：Douglas Kiss ; Mirela Ambeskovic ; Tony Montina…
• 关键词：Multigenerational stress ; Transgenerational stress ; Disease etiology ; Metabolic profile ; 1H nuclear magnetic resonance spectroscopy ; Pathway analysis ; Metabolite set enrichment analysis
• 刊名：Cellular and Molecular Life Sciences (CMLS)
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：73
• 期：23
• 页码：4547-4557
• 全文大小：1,536 KB
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Cell Biology
  Biomedicine
  Life Sciences
  Biochemistry
  
• 出版者：Birkh盲user Basel
• ISSN：1420-9071
• 卷排序：73

文摘

Stress is among the primary causes of mental health disorders, which are the most common reason for disability worldwide. The ubiquity of these disorders, and the costs associated with them, lends a sense of urgency to the efforts to improve prediction and prevention. Down-stream metabolic changes are highly feasible and accessible indicators of pathophysiological processes underlying mental health disorders. Here, we show that remote and cumulative ancestral stress programs central metabolic pathways linked to mental health disorders. The studies used a rat model consisting of a multigenerational stress lineage (the great-great-grandmother and each subsequent generation experienced stress during pregnancy) and a transgenerational stress lineage (only the great-great-grandmother was stressed during pregnancy). Urine samples were collected from adult male F4 offspring and analyzed using 1H NMR spectroscopy. The results of variable importance analysis based on random variable combination were used for unsupervised multivariate principal component analysis and hierarchical clustering analysis, as well as metabolite set enrichment analysis (MSEA) and pathway analysis. We identified distinct metabolic profiles associated with the multigenerational and transgenerational stress phenotype, with consistent upregulation of hippurate and downregulation of tyrosine, threonine, and histamine. MSEA and pathway analysis showed that these metabolites are involved in catecholamine biosynthesis, immune responses, and microbial host interactions. The identification of metabolic signatures linked to ancestral programming assists in the discovery of gene targets for future studies of epigenetic regulation in pathogenic processes. Ultimately, this research can lead to biomarker discovery for better prediction and prevention of mental health disorders.