Multiomics Study of Gut Bacteria and Host Metabolism in Irritable Bowel Syndrome and Depression Patients

Abstract

Background and Aims: Irritable bowel syndrome (IBS) and depression have high tendencies of comorbidity. In particular, diarrhea-predominant IBS (IBS-D) and depression exhibit similar fecal microbiota signatures, yet little is known about their pathogenic mechanism. Here, we propose that the differences in structure and composition of IBS-D and depression gut microbiota give rise to different downstream functions, which lead to distinct clinical phenotypes via host metabolism and further influence the interaction of brain-gut axis.Methods: We performed multiomics study, including fecal metagenome-wide sequencing and serum metabolomics profiling in 65 individuals with IBS-D (n=22), depression (n=15), comorbid patients (n=13), and healthy controls (n=15). We analyzed functional genes contributed by the primary genus and evaluated their correlations with clinical indices and host metabolites.Results: Metagenomic analysis revealed 26 clusters of orthologous groups of protein (COG) categories consisting of a total of 4,631 functional genes. Trehalose and maltose hydrolase (COG1554) and fucose permease (COG0738) were the most relevant and enriched functional genes in the IBS-D patients; urease accessory proteins UreE (COG2371) was that in the depression patients. Context based genome annotation suggest that an alteration of Escherichia coli and Enterobacter cloacae in IBS-D and depression respectively may be responsible for the enrichment described above. Correlation with host metabolites, such as maltotriose and isomaltose in carbohydrate metabolism and anandamide in neuroactive metabolism, drew further connections between these findings.Conclusions: These changes led us to propose a connection between genomic signatures and clinical differences observed in IBS-D and depression. Our findings provide further insights into the involvement of gut microbiota in diseases related to brain-gut disorder.

More Positive or More Negative? Metagenomic Analysis Reveals Roles of Virome in Human Disease-Related Gut Microbiome

“…The alterations in gut bacteria between IBS and healthy controls have been reported in our previous work ( Liu et al., 2016 ; Wang et al., 2019 ; Xu et al., 2020 ). Herein, we further explore the roles of the viral elements using these sequenced metagenomic samples, including 22 IBS patients (diarrhea-predominant) and 15 healthy controls, and 18 paired host metabolomic samples.…”

“…This study includes five metagenomic datasets, including IBS datasets sequenced from recruited subjects and four other datasets from the public database. The dataset of 22 IBS patients and 15 healthy controls have been described in our previous paper ( Xu et al., 2020 ). Briefly, these subjects were recruited at the Outpatient Department of Gastroenterology of Peking University Third Hospital.…”

Gut Microbiota‐Related Effects of Tanhuo Decoction in Acute Ischemic Stroke

“…The hub bacteria were identified using Cytohubba plugin [ 35 ] based on Maximal Clique Centrality (MCC) algorithms. The positive connections, negative connection, complexity, and degrees of nodes were used to describe the topological characteristics of the cooccurrence network [ 36 , 37 ]. To explore the functional capacity of participants' gut microbiota, we applied the PICRUST2 pipeline [ 38 ] to predict the KEGG Orthology (KO) profile for each sample from the relative abundances of ASVs.…”

Integrated Traditional Chinese Medicine Improves Functional Outcome in Acute Ischemic Stroke: From Clinic to Mechanism Exploration With Gut Microbiota

“…We then annotated the ASVs using a naive Bayesian classifier method with 99% identity Greengenes rRNA database (version 13.8.99) (DeSantis et al, 2006). After that, the abundance matrices at the levels of phylum, class, order, family, and genus were created for each sample (Wang et al, 2019;Xu et al, 2020;Guo et al, 2021). The composition of gut microbiota of patients was typed into different enterotypes using the "DMM" R package (version 1.32.0) (Holmes et al, 2012).…”