Eric An, Desiree R. Delgadillo, Jennifer Yang, Rishabh Agarwal, Jennifer S. Labus, Shrey Pawar, Madelaine Leitman, Lisa A. Kilpatrick, Ravi R. Bhatt, Priten Vora, Allison Vaughan, Tien S. Dong, Arpana Gupta
{"title":"Stress-resilience impacts psychological wellbeing as evidenced by brain–gut microbiome interactions","authors":"Eric An, Desiree R. Delgadillo, Jennifer Yang, Rishabh Agarwal, Jennifer S. Labus, Shrey Pawar, Madelaine Leitman, Lisa A. Kilpatrick, Ravi R. Bhatt, Priten Vora, Allison Vaughan, Tien S. Dong, Arpana Gupta","doi":"10.1038/s44220-024-00266-6","DOIUrl":null,"url":null,"abstract":"The brain–gut microbiome (BGM) system plays an influential role on mental health. We characterized BGM patterns related to resilience using fecal samples and multimodal magnetic resonance imaging. Data integration analysis using latent components showed that the high-resilience phenotype was associated with lower depression and anxiety symptoms, higher frequency of bacterial transcriptomes (related to environmental adaptation, genetic propagation, energy metabolism and anti-inflammation), increased metabolites (N-acetylglutamate, dimethylglycine) and cortical signatures (increased resting-state functional connectivity between reward circuits and sensorimotor networks; decreased gray-matter volume and white-matter tracts within the emotion regulation network). Our findings support a multi-omic signature involving the BGM system, suggesting that resilience impacts psychological symptoms, emotion regulation and cognitive function, as reflected by unique neural correlates and microbiome function supporting eubiosis and gut-barrier integrity. Bacterial transcriptomes provided the highest classification accuracy, suggesting that the microbiome is critical in shaping resilience, and highlighting that microbiome modifications can optimize mental health. The authors evaluated and integrated compositional and functional microbiota data using fecal samples taken from healthy individuals and multimodal neuroimaging.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 8","pages":"935-950"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature mental health","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44220-024-00266-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The brain–gut microbiome (BGM) system plays an influential role on mental health. We characterized BGM patterns related to resilience using fecal samples and multimodal magnetic resonance imaging. Data integration analysis using latent components showed that the high-resilience phenotype was associated with lower depression and anxiety symptoms, higher frequency of bacterial transcriptomes (related to environmental adaptation, genetic propagation, energy metabolism and anti-inflammation), increased metabolites (N-acetylglutamate, dimethylglycine) and cortical signatures (increased resting-state functional connectivity between reward circuits and sensorimotor networks; decreased gray-matter volume and white-matter tracts within the emotion regulation network). Our findings support a multi-omic signature involving the BGM system, suggesting that resilience impacts psychological symptoms, emotion regulation and cognitive function, as reflected by unique neural correlates and microbiome function supporting eubiosis and gut-barrier integrity. Bacterial transcriptomes provided the highest classification accuracy, suggesting that the microbiome is critical in shaping resilience, and highlighting that microbiome modifications can optimize mental health. The authors evaluated and integrated compositional and functional microbiota data using fecal samples taken from healthy individuals and multimodal neuroimaging.