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. 2024 Nov 21;14(1):475.
doi: 10.1038/s41398-024-03183-5.

Oral fungal dysbiosis and systemic immune dysfunction in Chinese patients with schizophrenia

Xia Liu #  1 , Zongxin Ling #  2 , Yiwen Cheng #  3 , Lingbin Wu  4 , Li Shao  5 , Jie Gao  3 , Wenhui Lei  6 , Zhangcheng Zhu  7 , Wenwen Ding  8 , Qinghai Song  4 , Longyou Zhao  9 , Guolin Jin  10
Affiliations

Oral fungal dysbiosis and systemic immune dysfunction in Chinese patients with schizophrenia

Xia Liu et al. Transl Psychiatry. .

Abstract

Oral microbial dysbiosis contributes to the development of schizophrenia (SZ). While numerous studies have investigated alterations in the oral bacterial microbiota among SZ patients, investigations into the fungal microbiota, another integral component of the oral microbiota, are scarce. In this cross-sectional study, we enrolled 118 Chinese patients with SZ and 97 age-matched healthy controls (HCs) to evaluate the oral fungal microbiota from tongue coating samples using internal transcribed spacer 1 amplicon sequencing and assess host immunity via multiplex immunoassays. Our findings revealed that SZ patients exhibited reduced fungal richness and significant differences in β-diversity compared to HCs. Within the oral fungal communities, we identified two distinct fungal clusters (mycotypes): Candida and Malassezia, with SZ patients showing increased Malassezia and decreased Candida levels. These key functional oral fungi may serve as potential diagnostic biomarkers for SZ. Furthermore, SZ patients displayed signs of immunological dysfunction, characterized by elevated levels of pro-inflammatory cytokines such as IL-6 and TNF-α, and chemokines including MIP-1α and MCP-1. Importantly, Malassezia mycotype correlated positively with peripheral pro-inflammatory cytokines, while Candida mycotype exhibited a negative correlation with these cytokines. In conclusion, we have demonstrated, for the first time, the presence of altered oral fungal communities and systemic immune dysfunction in Chinese SZ patients compared to HCs, providing novel insights into the potential role of oral fungi as biomarkers and the broader implications for understanding SZ pathogenesis.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Comparison of the overall structure of the oral fungal microbiota between SZ patients and healthy controls.
A–F α-diversity indices (Shannon, Simpson and invsimpson) and richness indices (ACE, Chao1, and observed species) were utilized to assess the overall structure of oral fungal microbiota. Data are presented as mean ± standard deviation. Unpaired two-tailed t tests were employed for inter-group comparisons. G–I Principal coordinate analysis (PCoA) plots illustrate β-diversity of individual fungal microbiota based on Bray–Curtis, unweighted UniFrac, and weighted UniFrac distances. Each symbol represents a sample. J The rank-abundance curve of fungal OTUs derived from the two groups indicates that there were more OTUs present at low abundance in the oral fungal microbiota of healthy controls compared to SZ patients. K Venn diagram depicts overlap of Operational Taxonomic Units (OTUs) in microbiota associated with SZ and healthy controls.
Fig. 2
Fig. 2. Oral fungal microbiota compositions of in SZ patients and healthy controls.
A Mean relative abundance of major phyla. B Mean relative abundance of major families. C Mean relative abundance of major genera. D Mean relative abundance of major species. E Oral mycotypes identification: a Malassezia-dominated mycotype (Cluster 1) and a Candida-dominated mycotype (Cluster 2).
Fig. 3
Fig. 3. Differential oral fungal taxa between the SZ patients and healthy controls.
A LEfSe cladograms illustrating fungal taxa significantly associated with SZ patients (red) or healthy controls (green). The size of each circle in the cladogram corresponds to the relative abundance of the fungal taxon. Circles represent taxonomic levels from inner to outer: phylum, class, order, family, and genus. Statistical significance was determined using the Wilcoxon rank-sum test with p < 0.05. B Histogram depicting the distribution of Linear Discriminant Analysis (LDA) scores (>2) for fungal taxa that show the greatest differences in abundance between SZ patients and healthy controls (p < 0.05).
Fig. 4
Fig. 4. Differential oral fungal taxa between SZ patients and healthy controls.
Comparisons of the relative abundance of the abundant fungal taxa at the level of bacterial phylum (A), family (B), genus (C), and species (D) in the oral microbiota. The data are presented as the mean ± standard deviation. Mann–Whitney U-tests were used to analyze variation between the SZ patients and controls. * p < 0.05 compared with the control group.
Fig. 5
Fig. 5. Strengths of the correlation between abundant oral fungi in the SZ patients and controls.
Co-occurrence network inferred from the SparCC algorithm applied to relative abundance genera data. Cytoscape version 3.6.1 was used for network construction. The red and blue lines represent positive and negative correlations, respectively.
Fig. 6
Fig. 6. The differential oral fungi as SZ diagnostic biomarkers.
Receiver operating characteristic (ROC) curves were used to evaluate the discriminatory ability of oral differential fungi in distinguishing SZ patients from healthy controls. The area under the curve (AUC) represents the predictive accuracy of each ROC curve. A Candida albicans; (B) Malassezia restricta; (C) Trichosporon asahii; (D) Peniophora incarnata.
Fig. 7
Fig. 7. Altered oral fungal-related metabolic pathways in SZ patients compared to healthy controls.
The oral fungal-related MetaCyc pathways predicted by PiCRUSt2 were evaluated using two-sided Welch’s t test. Percentage comparisons between the groups for each MetaCyc pathway are displayed. The Benjamini–Hochberg method was applied for multiple testing correction based on the false discovery rate (FDR) using STAMP.
Fig. 8
Fig. 8. Correlations between oral differential fungi and MetaCyc pathways.
The heatmap depicts Spearman’s correlation between oral differential fungal taxa and MetaCyc pathways in SZ patients. The color was according to the Spearman’s correlation coefficient distribution. Red represented significant positive correlation (p < 0.05); blue represented significantly negative correlation (p < 0.05), and white represented that the correlation was not significant (p > 0.05).
Fig. 9
Fig. 9. Correlations between oral differential fungi and systemic immune dysfunction in SZ patients.
The heatmap illustrates Spearman’s rank correlations (r) and associated probabilities (p values) between key functional differential genera of gut microbiota and circulating inflammatory cytokines, chemokines, and growth factors in SZ patients. Significant correlations (*p < 0.05) are indicated.
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