Schizophrenia-associated alterations in fecal mycobiota and systemic immune dysfunction: a cohort study of elderly Chinese patients

Abstract

Schizophrenia (SZ) is a severe psychiatric disorder with a complex etiology involving both genetic and environmental factors. Emerging evidence highlights the role of gut microbiome dysbiosis in SZ, yet the fungal component (mycobiota) remains largely unexplored. This study aimed to evaluate the gut mycobiota using internal transcribed spacer 1 (ITS1) amplicon sequencing and assess host immune responses via multiplex immunoassays in 87 elderly SZ patients and 64 age- and gender-matched healthy controls (HCs). We observed significant increases in fungal α-diversity and richness, along with altered β-diversity in SZ patients. Specifically, there was an elevated Basidiomycota/Ascomycota ratio, with enrichment of Candida, Aspergillus, and Saccharomyces, coupled with a depletion of Purpureocillium. Enterotype analysis revealed a shift from Purpureocillium-dominant (E1) to Candida-dominant (E2) communities in SZ. Notably, key fungal species, such as S. cerevisiae and P. lilacinum, were correlated with systemic immune dysfunction. Our receiver operating characteristic (ROC) analysis indicated that these fungal species could effectively distinguish SZ patients from HCs, suggesting their potential as non-invasive biomarkers for SZ diagnosis. In conclusion, this study demonstrates significant alterations in the gut mycobiota and immune dysfunction in elderly SZ patients, suggesting that mycobiota dysbiosis may contribute to SZ pathogenesis through immune modulation, offering new avenues for potential biomarkers and therapeutic interventions.

Keywords: Candida; Purpureocillium; gut mycobiota; immune dysfunction; schizophrenia.

Figure 1

Comparison of the fecal fungal diversity and richness between SZ patients and healthy controls. (A–E) α-diversity indices (Shannon, and Simpson) and richness indices (Chao1, ACE, and observed species) were used to assess the overall structure of the fecal mycobiota, with data presented as mean ± standard deviation. Unpaired two-tailed t-tests were used for inter-group comparisons. (F) Venn diagram showing the overlap of amplicon sequence variants (ASVs) in the fecal mycobiota of SZ patients and healthy controls. (G–J) Principal coordinate analysis (PCoA) plots illustrating β-diversity of individual fecal mycobiota based on Bray–Curtis, unweighted UniFrac, and weighted UniFrac distances, with each symbol representing a sample. (K) The rank-abundance curve of fungal ASVs from both groups indicates a higher presence of low-abundance ASVs in the fecal mycobiota of SZ patients compared to healthy controls. *p < 0.05.

Figure 2

Fecal mycobiota compositions of in SZ patients and healthy controls. (A) Phylum; (B) Family; (C) Genus; (D) Species; (E) PCoA plot showing two enterotypes; (F) Relative abundances of representative genera of enterotypes.

Figure 3

Differential fecal mycobiota between the SZ patients and healthy controls. (A) LEfSe cladograms illustrating fungal taxa significantly associated with SZ patients or healthy controls. The size of each circle represents the relative abundance of the fungal taxon, with circles indicating taxonomic levels from inner to outer: phylum, class, order, family, genus, and species. Statistical significance was determined using the Wilcoxon rank-sum test (p < 0.05). (B) Histogram displaying the distribution of Linear Discriminant Analysis (LDA) scores (> 3.5) for fungal taxa with the greatest differences in abundance between SZ patients and healthy controls (p < 0.05).

Figure 4

Differential fecal fungal taxa between SZ patients and healthy controls. (A) Key differential functional phyla; (B) Key differential functional families; (C) Key differential functional genera; (D) Key differential functional species. Data are presented as mean ± standard deviation. Mann–Whitney U-tests were used to assess differences between SZ patients and healthy controls. * p < 0.05 compared to the control group.

Figure 5

Diagnostic potential of differential fungal species in SZ patients. (A) Random Forest analysis with Mean Decrease Gini indicating the importance of different fungal species. (B) Receiver-operating characteristic (ROC) curves for individual fungal species to distinguish SZ patients from healthy controls. AUC represents the area under the ROC curve.

Figure 6

Functional prediction of nutritional modes of fungi in the fecal mycobiota of SZ patents. (A) Animal Pathogen; (B) Soil Saprotroph; (C) Fungal Parasite; (D) Wood Saprotroph; (E) Animal Endosymbiont.

Figure 7

Correlations between fecal differential fungal species and systemic immune dysfunction in SZ patients. The heatmap illustrates Spearman’s rank correlations (r) and associated p-values between key functional differential species of gut mycobiota and circulating inflammatory cytokines, chemokines, and growth factors in SZ patients. Significant correlations (*p < 0.05; **p < 0.01; ***p < 0.001) are indicated.