Metaproteogenomic analysis of saliva samples from Parkinson’s disease patients with cognitive impairment

• Published in: NPJ biofilms and microbiomes Vol. 9; no. 1; p. 86
• Main Authors: Arıkan, Muzaffer, Demir, Tuğçe Kahraman, Yıldız, Zeynep, Nalbantoğlu, Özkan Ufuk, Korkmaz, Nur Damla, Yılmaz, Nesrin H., Şen, Aysu, Özcan, Mutlu, Muth, Thilo, Hanoğlu, Lütfü, Yıldırım, Süleyman
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18-11-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647/514/2254; 631/326/107; 631/326/2565; Biomedical and Life Sciences; Cognitive ability; Dementia; Dementia disorders; Disease; Factor analysis; Hygiene; Life Sciences; Medical Microbiology; Microbial Ecology; Microbial Genetics and Genomics; Microbiology; Microbiota; Microorganisms; Movement disorders; Neurodegenerative diseases; Oral hygiene; Parkinson's disease; Risk assessment; Saliva
• ISSN: 2055-5008; 2055-5008
• DOI: 10.1038/s41522-023-00452-x

Cognitive impairment (CI) is very common in patients with Parkinson’s Disease (PD) and progressively develops on a spectrum from mild cognitive impairment (PD-MCI) to full dementia (PDD). Identification of PD patients at risk of developing cognitive decline, therefore, is unmet need in the clinic to manage the disease. Previous studies reported that oral microbiota of PD patients was altered even at early stages and poor oral hygiene is associated with dementia. However, data from single modalities are often unable to explain complex chronic diseases in the brain and cannot reliably predict the risk of disease progression. Here, we performed integrative metaproteogenomic characterization of salivary microbiota and tested the hypothesis that biological molecules of saliva and saliva microbiota dynamically shift in association with the progression of cognitive decline and harbor discriminatory key signatures across the spectrum of CI in PD. We recruited a cohort of 115 participants in a multi-center study and employed multi-omics factor analysis (MOFA) to integrate amplicon sequencing and metaproteomic analysis to identify signature taxa and proteins in saliva. Our baseline analyses revealed contrasting interplay between the genus Neisseria and Lactobacillus and Ligilactobacillus genera across the spectrum of CI. The group specific signature profiles enabled us to identify bacterial genera and protein groups associated with CI stages in PD. Our study describes compositional dynamics of saliva across the spectrum of CI in PD and paves the way for developing non-invasive biomarker strategies to predict the risk of CI progression in PD.