Evaluation of the Oral Bacterial Genome and Metabolites in Patients with Wolfram Syndrome.
Ewa Zmysłowska-PolakowskaT PłoszajSebastian SkoczylasP MojsakMichal CiborowskiAdam Jacek KretowskiMonika Lukomska-SzymańskaA SzadkowskaWojciech MlynarskiAgnieszka ZmyslowskaPublished in: International journal of molecular sciences (2023)
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.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- type diabetes
- peritoneal dialysis
- prognostic factors
- gene expression
- metabolic syndrome
- ms ms
- adipose tissue
- multiple sclerosis
- mass spectrometry
- cystic fibrosis
- pseudomonas aeruginosa
- escherichia coli
- high resolution
- antibiotic resistance genes