A pilot exploration of multi-omics research of gut microbiome in major depressive disorders

Abstract

The pathophysiology of major depressive disorder (MDD) remains obscure. Recently, the microbiota-gut-brain (MGB) axis’s role in MDD has an increasing attention. However, the specific mechanism of the multi-level effects of gut microbiota on host metabolism, immunity, and brain structure is unclear. Multi-omics approaches based on the analysis of different body fluids and tissues using a variety of analytical platforms have the potential to provide a deeper understanding of MGB axis disorders. Therefore, the data of metagenomics, metabolomic, inflammatory factors, and MRI scanning are collected from the two groups including 24 drug-naïve MDD patients and 26 healthy controls (HCs). Then, the correlation analysis is performed in all omics. The results confirmed that there are many markedly altered differences, such as elevated Actinobacteria abundance, plasma IL-1β concentration, lipid, vitamin, and carbohydrate metabolism disorder, and diminished grey matter volume (GMV) of inferior frontal gyrus (IFG) in the MDD patients. Notably, three kinds of discriminative bacteria, Ruminococcus bromii, Lactococcus chungangensis, and Streptococcus gallolyticus have an extensive correlation with metabolome, immunology, GMV, and clinical symptoms. All three microbiota are closely related to IL-1β and lipids (as an example, phosphoethanolamine (PEA)). Besides, Lactococcus chungangensis is negatively related to the GMV of left IFG. Overall, this study demonstrate that the effects of gut microbiome exert in MDD is multifactorial.

Exploration of the gut microbiome in Thai patients with major depressive disorder uncovered a specific bacterial profile with depletion of theRuminococcusgenus as a putative biomarker

“…On the other hand, neither study discovered any similarities in the taxa related to MDD. In another study, multi-omics studies of the gut microbiome in MDD and LEfSe analysis identified numerous microbiome characteristics in MDD, however none of these correlated with our findings [78]. In addition, whereas we observed an increase in the prevalence of Ruminococcus and Clostridium in the control group, Zhao et al [78] observed an increase in the prevalence of some Ruminococcus and Clostridium taxa in MDD.…”

“…In another study, multi-omics studies of the gut microbiome in MDD and LEfSe analysis identified numerous microbiome characteristics in MDD, however none of these correlated with our findings [78]. In addition, whereas we observed an increase in the prevalence of Ruminococcus and Clostridium in the control group, Zhao et al [78] observed an increase in the prevalence of some Ruminococcus and Clostridium taxa in MDD. The LEfSe analysis in the study by Liu et al [75] revealed that Deinococcaceae were enriched in MDD, whereas Clostridiaceae, Bacteroidaceae, Turicibacteraceae, and Barnesiellaceae were enriched in controls.…”

“…Maybe the most consistent finding in the reviewed literature, is the higher relative abundance of Ruminococcus , which is increased in ours and some other studies [38,79, 80, 81], whereas one study reported contradictory results [78]. Additionally, utilizing bidirectional Mendelian randomization, Chen et al [85] discovered that Ruminococcus protects against MDD.…”

Gut Microbiota Profiles in Children and Adolescents with Psychiatric Disorders

“…Comparing the gut microbiota in the groups targeted here, we found some common and some different characteristics in children compared with adults. In depression, Faecalibacterium had decreased abundance in adults [ 107 ], and in children [ 94 ], and Streptococcus genera were increased in both groups [ 94 , 108 ]. Parabacteroides and Alistipes had increased levels in adults [ 109 ], but decreased levels in children [ 94 , 95 ], while Lactobacillus and Eggerthella were elevated in adults but not changed in children [ 109 ].…”

Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study

“… 66 Finding easily accessible predictive biomarkers help early identification of potential benefits to certain drug, reducing cost of treatment and unnecessary drug exposure. Studies have shown the richness of the neurobiological mechanisms of MDD: increased activation of inflammatory system, 67 correlations between epigenetic regulation and stress environment, 68 multiple influencing factors from intestinal microbiota, 69 aberrant neural circuits 70 and so on. In such a complicated mechanism background, data-driven approaches based on multiomics integration rather than hypothesis-driven methods might be the solution.…”