Fecal Microbiota Underlying the Coexistence of Schizophrenia and Multiple Sclerosis in Chinese Patients

Abstract

Both schizophrenia (SZ) and multiple sclerosis (MS) affect millions of people worldwide and impose a great burden on society. Recent studies indicated that MS elevated the risk of SZ and vice versa, whereas the underlying pathological mechanisms are still obscure. Considering that fecal microbiota played a vital role in regulating brain functions, the fecal microbiota and serum cytokines from 90 SZ patients and 71 age-, gender-, and BMI-matched cognitively normal subjects (referred as SZC), 22 MS patients and 33 age-, gender-, and BMI-matched healthy subjects (referred as MSC) were analyzed. We found that both diseases demonstrated similar microbial diversity and shared three differential genera, including the down-regulated Faecalibacterium, Roseburia, and the up-regulated Streptococcus. Functional analysis indicated that the three genera were involved in pathways such as "carbohydrate metabolism" and "amino acid metabolism." Moreover, the variation patterns of serum cytokines associated with MS and SZ patients were a bit different. Among the six cytokines perturbed in both diseases, TNF-α increased, while IL-8 and MIP-1α decreased in both diseases. IL-1ra, PDGF-bb, and RANTES were downregulated in MS patients but upregulated in SZ patients. Association analyses showed that Faecalibacterium demonstrated extensive correlations with cytokines in both diseases. Most notably, Faecalibacterium correlated negatively with TNF-α. In other words, fecal microbiota such as Faecalibacterium may contribute to the coexistence of MS and SZ by regulating serum cytokines. Our study revealed the potential roles of fecal microbiota in linking MS and SZ, which paves the way for developing gut microbiota-targeted therapies that can manage two diseases with a single treat.

Figure 1

(a) A schema of the study design. (b) PCA score plot of ASV abundance tables. (c) Shannon index for MS patients, SZ patients, and corresponding controls. (d) Simpson index for MS patients, SZ patients, and corresponding controls.

Figure 2

(a) Boxplot illustrating the relative abundances of the three common genera significantly perturbed in MS and SZ patients. (b) LDA scores of the three common genera in datasets MS and SZ. (c, d) Functional pathways associated with the three common genera in different levels.

Figure 3

(a) Volcano plot showing the differential serum cytokines between MS patients and corresponding controls. (b) Volcano plot showing the differential serum cytokines between SZ patients and corresponding controls. (c) Venn diagram illustrating the differential serum cytokines shared by both diseases and unique in each disease. (d) Heatmap illustrating the log2 (fold change) values for the six common cytokines in MS and SZ patients versus corresponding controls.

Figure 4

(a) Heatmap illustrating the relative abundances of serum cytokines perturbed in MS patients. (b) Heatmap showing the relative abundances of serum cytokines perturbed in SZ patients.

Figure 5

(a) The association network between three common genera and serum cytokines perturbed in MS patients. (b) The association network between three common genera and serum cytokines perturbed in SZ patients.