Journal of oral microbiology · 2024
Alterations of oral microbiome and metabolic signatures and their interaction in oral lichen planus.
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Yan Li, Xu Jingyi, Lou Fangzhi, Dong Yunmei, Lv Shiping, Kang Ning, Luo Zhuoyan, Liu Yiyun, Pu Juncai, Zhong Xiaogang, Ji Ping, Xie Peng, Jin Xin
Аннотация
BACKGROUND: Oral lichen planus (OLP) is a chronic oral mucosal inflammatory disease with a risk of becoming malignant. Emerging evidence suggests that microbial imbalance plays an important role in the development of OLP. However, the association between the oral microbiota and the metabolic features in OLP is still unclear.
METHODS: We conducted 16S rRNA sequencing and metabolomics profiling on 95 OLP patients and 105 healthy controls (HC).To study oral microbes and metabolic changes in OLP, we applied differential analysis, Spearman correlation analysis and four machine learning algoeithms.
RESULTS: The alpha and beta diversity both differed between OLP and HC. After adjustment for gender and age, we found an increase in the relative abundance of Pseudomonas, Aggregatibacter, Campylobacter, and Lautropia in OLP, while 18 genera decreased in OLP. A total of 153 saliva metabolites distinguishing OLP from HC were identified. Notably, correlations were found between Oribacterium, specific lipid and amino acid metabolites, and OLP's clinical phenotype. Additionally, the combination of Pseudomonas, Rhodococcus and (±)10-HDoHE effectively distinguished OLP from HC.
CONCLUSIONS: Based on multi-omics data, this study provides comprehensive evidence of a novel interplay between oral microbiome and metabolome in OLP pathogenesis using the oral microbiota and metabolites of OLP patients.
METHODS: We conducted 16S rRNA sequencing and metabolomics profiling on 95 OLP patients and 105 healthy controls (HC).To study oral microbes and metabolic changes in OLP, we applied differential analysis, Spearman correlation analysis and four machine learning algoeithms.
RESULTS: The alpha and beta diversity both differed between OLP and HC. After adjustment for gender and age, we found an increase in the relative abundance of Pseudomonas, Aggregatibacter, Campylobacter, and Lautropia in OLP, while 18 genera decreased in OLP. A total of 153 saliva metabolites distinguishing OLP from HC were identified. Notably, correlations were found between Oribacterium, specific lipid and amino acid metabolites, and OLP's clinical phenotype. Additionally, the combination of Pseudomonas, Rhodococcus and (±)10-HDoHE effectively distinguished OLP from HC.
CONCLUSIONS: Based on multi-omics data, this study provides comprehensive evidence of a novel interplay between oral microbiome and metabolome in OLP pathogenesis using the oral microbiota and metabolites of OLP patients.