Project description:Background: Parkinson's disease (PD) is a common neurodegenerative disorder characterized by both motor and non-motor symptoms, including olfactory dysfunction, which often precedes motor symptoms by several years. However, the underlying mechanisms linking olfactory dysfunction in PD to biological changes remain unclear. Recent studies suggest a potential connection between PD's olfactory dysfunction and alterations in the nasal microbiome and metabolome, but comprehensive investigations are still lacking.This study aims to explore the interplay between nasal microbiota, metabolites, and olfactory function in PD, identify potential biomarkers for early diagnosis, and investigate therapeutic targets for olfactory dysfunction in PD. Methods: From October 1, 2023, to September 30, 2024, 66 potential participants were enrolled, including PD patients with varying degrees of olfactory dysfunction and healthy controls. Nasal samples were collected for 16S rRNA sequencing and metabolomic analysis using GC-MS. The study also used MPTP-induced PD mouse models to assess the protective effects of cholic acid on olfactory function and dopaminergic neurons. Results: The study found significant differences in nasal microbiota composition and metabolite profiles between PD patients and controls, with correlations to the severity of olfactory dysfunction. Specifically, the relative abundances of Corynebacterium, Dolosigranulum, Muribacter, and Moraxella were elevated in the PD group, while the abundances of Hydrogenophaga, Staphylococcus, Klebsiella, and Bacillus decreased. Metabolomic analysis revealed that cholic acid, octanal, hexadecanol, and phytol were significantly altered in PD patients compared to controls, with cholic acid showing potential as a diagnostic biomarker (AUC > 0.97). In MPTP-induced PD mouse models, cholic acid treatment significantly reduced latencies in detecting buried food pellets, and increased tyrosine hydroxylase (TH)-positive fluorescence intensity in the olfactory bulb and substantia nigra. However, cholic acid did not significantly ameliorate motor symptoms in PD mice, as assessed by the open field test, and did not prevent the loss of TH+ cells in the striatum. Conclusion: The findings highlight the interplay between the nasal microbiome, metabolome, and olfactory dysfunction in PD, suggesting that modulating metabolic pathways could be a promising therapeutic strategy. Future research should focus on larger sample sizes, longitudinal studies, and further validation of biomarkers and therapeutic targets to enhance the understanding and management of PD.

Project description:The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their interactions with the host remain unclear. In this study, we introduced a dual-transcriptomics analysis strategy, which can characterize the host transcriptome and microbiome from nasal samples simultaneously. RNA sequencing reads from human nasal swab samples were pre-processed and aligned to the human genome for host gene expression counting, while unmapped reads were further aligned to microbiota genome. After taxonomic classification, microbial abundance matrix was derived at each taxonomic level for differential and host-microbiota interaction analysis. We applied this workflow to a local SARS-CoV-2 cohort with 76 infected patients, among whom 55 (72.37%) were symptomatic at enrollment. Nasal swabs were collected from all 76 patients at enrollment and from 73 patients at one-week later follow-up. We detected a median of 4.81% reads unmapped from the human genome across all 149 samples, among which around half (median 48.63%) were successfully mapped to microbiome genome. Meta-transcriptomic analysis detected significantly higher SARS-related coronavirus loads in samples from the symptomatic group at enrollment, and both groups showed decreased loads one week later. Compared with benchmarking 16S rRNA sequencing on 53 samples, our computational strategy showed high correlation of relative abundance in all top 20 genus. A total of 685 bacteria species were identified to show a relative abundance >= 0.01% in at least 10% samples. Differential abundance analysis identified 66 species (DASs) from 6 phyla with significantly decreased abundance in samples from the symptomatic group compared to the asymptomatic group at enrollment. Integrating these symptom-associated DASs with host’s gene expression using an expression quantitative trait bacteria (eQTB) model, we found 58 symptom-associated DASs identified at enrollment were significantly associated with one to 16 genes. GSEA showed a series of symptom-associated DASs were significantly correlated with pathways related to the activation of olfactory, keratinocyte differentiation, and DNA methylation. In summary, our dual-transcriptomic analysis strategy effectively characterized host-microbiome interactions, offering insights into microbial contributions to respiratory diseases.

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Swim Nasal MIcrobiome

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