Gut Microbiota-Derived Inflammation-Related Serum Metabolites as Potential Biomarkers for Major Depressive Disorder

Abstract

Shunjie Bai,1,* Jing Xie,2,* Huili Bai,3 Tian Tian,4 Tao Zou,5,6 Jian-Jun Chen7 1Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Endocrinology, the Fourth People’s Hospital of Chongqing, Chongqing University Central Hospital, Chongqing, People’s Republic of China; 3The Center for Clinical Molecular Medical Detection, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 4Department of Neurology, the Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, People’s Republic of China; 5Department of Psychiatry, the Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, People’s Republic of China; 6Shanghai Key Laboratory of Forensic Medicine, Academy of Forensic Science, Shanghai, People’s Republic of China; 7Institute of Life Sciences, Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jian-Jun ChenInstitute of Life Sciences, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, People’s Republic of ChinaEmail [email protected] ZouDepartment of Psychiatry, The Affiliated Hospital of Guizhou Medical University, Yunyan District, Guiyang, Guizhou Province, 550004, People’s Republic of ChinaEmail [email protected]: Although many works have been conducted to explore the biomarkers for diagnosing major depressive disorder (MDD), the widely accepted biomarkers are still not identified. Thus, the combined application of serum metabolomics and fecal microbial communities was used to identify gut microbiota-derived inflammation-related serum metabolites as potential biomarkers for MDD.Methods: MDD patients and healthy controls (HCs) were included in this study. Both serum samples and fecal samples were collected. The liquid chromatography mass spectrometry (LC-MS) was used to detect the metabolites in serum samples, and the 16S rRNA gene sequencing was used to analyze the gut microbiota compositions in fecal samples.Results: Totally, 60 MDD patients and 60 HCs were recruited. The 24 differential serum metabolites were identified, and 10 of these were inflammation-related metabolites. Three significantly affected inflammation-related pathways were identified using differential metabolites. The 17 differential genera were identified, and 14 of these genera belonged to phyla Firmicutes. Four significantly affected inflammation-related pathways were identified using differential genera. Five inflammation-related metabolites (LysoPC(16:0), deoxycholic acid, docosahexaenoic acid, taurocholic acid and LysoPC(20:0)) were identified as potential biomarkers. These potential biomarkers had significant correlations with genera belonged to phyla Firmicutes. The panel consisting of these biomarkers could effectively distinguish MDD patients from HCs with an area under the curve (AUC) of 0.95 in training set and 0.92 in testing set.Conclusion: These findings suggested that the disturbance of phyla Firmicutes might be involved in the onset of depression by regulating host’s inflammatory response, and these potential biomarkers could be useful for future investigating the objective methods for diagnosing MDD.Keywords: major depressive disorder, gut microbiota, biomarkers, inflammation, metabolite

Keywords

• major depressive disorder
• gut microbiota
• biomarkers
• inflammation
• metabolite