Evaluation of the Oral Bacterial Genome and Metabolites in Patients with Wolfram Syndrome

Abstract

In Wolfram syndrome (WFS), due to the loss of wolframin function, there is increased ER stress and, as a result, progressive neurodegenerative disorders, accompanied by insulin-dependent diabetes. The aim of the study was to evaluate the oral microbiome and metabolome in WFS patients compared with patients with type 1 diabetes mellitus (T1DM) and controls. The buccal and gingival samples were collected from 12 WFS patients, 29 HbA1c-matched T1DM patients (p = 0.23), and 17 healthy individuals matched by age (p = 0.09) and gender (p = 0.91). The abundance of oral microbiota components was obtained by Illumina sequencing the 16S rRNA gene, and metabolite levels were measured by gas chromatography-mass spectrometry. Streptococcus (22.2%), Veillonella (12.1%), and Haemophilus (10.8%) were the most common bacteria in the WFS patients, while comparisons between groups showed significantly higher abundance of Olsenella, Dialister, Staphylococcus, Campylobacter, and Actinomyces in the WFS group (p < 0.001). An ROC curve (AUC = 0.861) was constructed for the three metabolites that best discriminated WFS from T1DM and controls (acetic acid, benzoic acid, and lactic acid). Selected oral microorganisms and metabolites that distinguish WFS patients from T1DM patients and healthy individuals may suggest their possible role in modulating neurodegeneration and serve as potential biomarkers and indicators of future therapeutic strategies.

Keywords: Wolfram syndrome; gingival samples; metabolomics; neurodegeneration; oral microbiome.

Figure 1

Taxonomic composition of community at bacteria genus level. WFS—Wolfram syndrome; HS—healthy subjects; T1DM—Type 1 diabetes mellitus.

Figure 2

The result of the hierarchical clustering analysis presented as a heatmap. WFS—Wolfram syndrome; HS—healthy subjects; T1DM—Type 1 diabetes mellitus.

Figure 3

Boxplot showing the overall measure of alpha-diversity in the groups studied using the Shannon method at the bacterial genus level. WFS—Wolfram syndrome; HS—healthy subjects; T1DM—Type 1 diabetes mellitus. The black dot indicates the average value. Statistical significance was evaluated by ANOVA F-value: 12.038; p-value < 0.0001.

Figure 4

Two-Dimensional Principal coordinates analysis plot using bray distance. Statistical significance was evaluated by PERMANOVA F-value: 6.3631; p-value < 0.001.

Figure 5

Boxplots comparing abundance for the groups studied for selected bacterial genera: (A). Olsenella; (B). Dialister; (C). Staphylococcus; (D). Campylobacter; and (E). Actinomyces. WFS—Wolfram syndrome; HS—healthy subjects; T1DM—Type 1 diabetes mellitus. Significant differences in abundance of bacteria genera among the three groups were identified using ANOVA; p < 0.001 in all presented bacteria.

Figure 6

Levels of metabolites discriminating GCF samples of patients with WFS, T1DM, and healthy controls. Significant differences in metabolite intensity among the three groups were identified using a non-parametric Kruskal–Wallis ANOVA (p < 0.05), followed by a Conover–Iman post-hoc test (p < 0.05 *, <0.01 **). WFS—Wolfram syndrome; HS—healthy subjects; T1DM—Type 1 diabetes mellitus.

Figure 7

ROC curves and AUC values for all statistically significant metabolites (A), multivariate analysis of the ROC curve for the 3 metabolites (B), and individual ROC curves for the 3 metabolites (C), which can distinguish WFS patients from both T1DM patients and healthy subjects.