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. 2021 Oct 28;7(1):51.
doi: 10.1038/s41537-021-00180-1.

Salivary microbiome profiling reveals a dysbiotic schizophrenia-associated microbiota

Ying Qing #  1 , Lihua Xu #  2 , Gaoping Cui #  1 , Liya Sun  1 , Xiaowen Hu  1 , Xuhan Yang  1 , Jie Jiang  1 , Juan Zhang  1 , Tianhong Zhang  2 , Tao Wang  3   4 , Lin He  1 , Jijun Wang  5 , Chunling Wan  6
Affiliations

Salivary microbiome profiling reveals a dysbiotic schizophrenia-associated microbiota

Ying Qing et al. NPJ Schizophr. .

Abstract

Schizophrenia is a debilitating mental disorder and often has a prodromal period, referred to as clinical high risk (CHR) for psychosis, prior to the first episode. The etiology and pathogenesis of schizophrenia remain unclear. Despite the human gut microbiome being associated with schizophrenia, the role of the oral microbiome, which is a vital player in the mouth-body connection, is not well understood. To address this, we performed 16S rRNA gene sequencing to investigate the salivary microbiome in 85 patients with drug-naïve first-episode schizophrenia (FES), 43 individuals at CHR, and 80 healthy controls (HCs). The salivary microbiome of FES patients was characterized by higher α-diversity and lower β-diversity heterogeneity than those of CHR subjects and HCs. Proteobacteria, the predominant phylum, was depleted, while Firmicutes and the Firmicutes/Proteobacteria ratio was enriched, in a stepwise manner from HC to CHR to FES. H2S-producing bacteria exhibited disease-stage-specific enrichment and could be potential diagnostic biomarkers for FES and CHR. Certain salivary microbiota exhibited disease-specific correlation patterns with symptomatic severities, peripheral pro-inflammatory cytokines, thioredoxin, and S100B in FES. Furthermore, the metabolic functions from inferred metagenomes of the salivary microbiome were disrupted in FES, especially amino acid metabolism, carbohydrate metabolism, and xenobiotic degradation. This study has established a link between salivary microbiome alterations and disease initiation and provided the hypothesis of how the oral microbiota could influence schizophrenia.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. The overall salivary microbiome composition differs according to different clinical stages of schizophrenia (FES, CHR and HC) in 208 subjects.
The principal coordinate analysis was conducted based on the weighted UniFrac distances, with 95% confidence ellipses drawn and centroids representing the coordinate mean of the first and second axes. Each sample is colored either by the disease phenotype (a) or the Shannon diversity index (c). b Comparison of within-group distances among the three groups. The bar plots show median values for each group and error bars show interquartile range. d Comparison of three α-diversity indices among the three groups. Center lines of box plots show median values, box hinges indicate first and third quartiles, and whisker represent the furthest data points within 1.5 interquartile ranges of the hinges. The comparisons among the three groups were performed by the Kruskal–Wallis test and the comparisons between the two groups were conducted by the quantile regression, adjusting for age, gender, and education level. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. Pink lines represent comparisons among the three groups.
Fig. 2
Fig. 2. Differential abundances of salivary bacterial communities during initiation of schizophrenia.
a Differentially abundant taxa between the FES, CHR, and HC groups are colored. The largest circles represent the phylum level, and the inner circles represent class, order, family, and genus. b The leading five abundant phyla differed in abundances among the three groups. Center lines of bean plots represent median values. c The ratios of Firmicutes/Proteobacteria, Actinobacteria/Proteobacteria, and Bacteroidetes/Proteobacteria were higher in FES and CHR patients than in HCs. Center lines of box plots show median values, box hinges indicate first and third quartiles, and whisker represent the furthest data points within 1.5 interquartile ranges of the hinges. d H2S-producing bacteria were enriched in either the FES or the CHR group than in HCs. e Receiver operating characteristic (ROC) curves for the logistic regression models. The area under the curve (AUC) values for distinguishing FES from HCs in the training and test sets were 0.824 (sensitivity: 0.691; specificity: 0.891) and 0.813 (sensitivity: 0.941; specificity: 0.813), respectively. AUCs for distinguishing CHR from HCs in the training and test sets were 0.813 (sensitivity: 0.853; specificity: 0.609) and 0.833 (sensitivity: 0.667; specificity: 0.875), respectively. The comparisons among the three groups were performed by the Kruskal–Wallis test and the q values were corrected with FDR; the comparisons between the two groups were conducted by the quantile regression, adjusting for age, gender and education level and correcting with FDR. *q < 0.05; **q < 0.01; ***q < 0.001; ****q < 0.0001. Pink lines represent comparisons among the three groups.
Fig. 3
Fig. 3. Salivary taxa are correlated with symptomatic severities and blood markers relevant to inflammation in schizophrenia.
a The correlation network of salivary microbiota with symptoms of two disease statuses. Red lines denote positive correlations, while green lines denote negative correlations. Yellow diamonds represent symptoms of schizophrenia, while red diamonds represent CHR symptoms. Ellipses denote taxa relevant to a single symptom of either schizophrenia or CHR, and hollow inverted triangles indicate taxa associated with more than one symptom of either schizophrenia or CHR, while solid inverted triangles represent taxa related to symptoms of both schizophrenia and CHR. b The Circos plot showed distinct relationships of salivary taxa with blood markers (CRP, IFNγ, TNFα, IL-8, IL-1β, thioredoxin, and S100B) in the FES group relative to HCs. Orange curves denote positive correlations, while blue curves denote negative correlations. GS general symptom, DS disorganized symptom, NS negative symptoms, PS positive symptoms, SIPS structured interview of prodrome syndromes, BPRS brief psychiatric rating scale, CGI-S clinical global impressions severity scale, SANS scale for the assessment of negative symptoms, p phylum, c class, o order, f family, g genus.
Fig. 4
Fig. 4. The functions of the salivary microbiota were dysregulated in the FES and CHR groups, especially those of metabolism related pathways.
a A volcano plot shows the differentially abundant KEGG pathways in FES versus HCs. b Selected metabolic pathways associated with amino acid catabolism and anabolism, oxygen-independent pathway and xenobiotic biodegradation pathway were differentially abundant in the FES group relative to HCs. False discovery rate adjusted q values were calculated based on p values estimated by DESeq2, adjusting for age, gender, and education level. *q < 0.05; **q < 0.01; ***q < 0.001; ****q < 0.0001; n.s. indicates no significance. c Correlation network of KEGG pathways classified as the term metabolism based on their upstream/downstream relationships. Parallelograms indicate trigger pathways or main terminal pathways. Red dots/parallelograms represent pathways enriched in the FES group compared to HCs, while green represents depleted pathways, and gray denotes pathways with no significance. d Levels of plasma l-aspartate were increased in the FES group relative to HCs, controlling for confounders. Center lines of box plots show median values, box hinges indicate first and third quartiles, and whisker represent the furthest data points within 1.5 interquartile ranges of the hinges. P values were calculated by quantile regression, adjusting for age, gender, and education level. *p < 0.05. e The heatmap shows correlations of the selected genera of interest with certain KEGG pathways. Only statistically significant correlations (p < 0.05) are shown. Red asterisk indicates H2S-producing bacteria. KEGG Kyoto Encyclopedia of Gene and Genomes, FC fold change.
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