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Intestinal Microbes in Patients With Schizophrenia Undergoing Short-Term Treatment: Core Species Identification Based on Co-Occurrence Networks and Regression Analysis.

ABSTRACT: Schizophrenia, a common mental disorder, has a tremendous impact on the health and economy of people worldwide. Evidence suggests that the microbial-gut-brain axis is an important pathway for the interaction between the gut microbiome and the development of schizophrenia. What is not clear is how changes in the gut microbiota composition and structure during antipsychotic treatment improve the symptoms of schizophrenia. In this study, 25 patients with schizophrenia were recruited. Their fecal samples were collected before and after hospital treatment for 14-19 days. The composition and structure of the intestinal microbiota were evaluated by 16S rRNA sequencing analysis, and the results showed significant differences in fecal microbiota before and after treatment. Firmicutes (relative abundances of 82.60 and 86.64%) and Gemminger (relative abundances of 14.17 and 13.57%) were the first dominant species at the phylum and genus levels, respectively. The random forest algorithm and co-occurrence network analysis demonstrated that intestinal flora (especially the core species ASV57) could be used as biomarkers to distinguish different clinical states and match treatment regimens accordingly. In addition, after fecal microbiota transplantation, antibiotic-treated recipient mice showed multiple behavioral improvements. These included decreased psychomotor hyperactivity, increased social interaction, and memory. In conclusion, this study suggests that differences in the composition and structure of gut microbiota after treatment are associated with the development and severity of schizophrenia. Results may provide a potential target for the treatment of this disorder.

SUBMITTER: Xiang M

PROVIDER: S-EPMC9247572 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Co-occurrence analysis is a technique often applied in text mining, comparative genomics, and promoter analysis. The methodologies and statistical models used to evaluate the significance of association between co-occurring entities are quite diverse, however. METHODOLOGY/PRINCIPAL FINDINGS: We present a general framework for co-occurrence analysis based on a bipartite graph representation of the data, a novel co-occurrence statistic, and software performing co-occurrence analysis as well as generation and analysis of co-occurrence networks. We show that the overall stringency of co-occurrence analysis depends critically on the choice of the null-model used to evaluate the significance of co-occurrence and find that random sampling from a complete permutation set of the bipartite graph permits co-occurrence analysis with optimal stringency. We show that the Poisson-binomial distribution is the most natural co-occurrence probability distribution when vertex degrees of the bipartite graph are variable, which is usually the case. Calculation of Poisson-binomial P-values is difficult, however. Therefore, we propose a fast bi-binomial approximation for calculation of P-values and show that this statistic is superior to other measures of association such as the Jaccard coefficient and the uncertainty coefficient. Furthermore, co-occurrence analysis of more than two entities can be performed using the same statistical model, which leads to increased signal-to-noise ratios, robustness towards noise, and the identification of implicit relationships between co-occurring entities. Using NetCutter, we identify a novel protein biosynthesis related set of genes that are frequently coordinately deregulated in human cancer related gene expression studies. NetCutter is available at http://bio.ifom-ieo-campus.it/NetCutter/). CONCLUSION: Our approach can be applied to any set of categorical data where co-occurrence analysis might reveal functional relationships such as clinical parameters associated with cancer subtypes or SNPs associated with disease phenotypes. The stringency of our approach is expected to offer an advantage in a variety of applications.

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Intestinal Microbes in Patients With Schizophrenia Undergoing Short-Term Treatment: Core Species Identification Based on Co-Occurrence Networks and Regression Analysis.

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