Gen-Cheng Gong1, Sheng-Ren Song2, Jin Su3. 1. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510030, PR China. 2. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510030, PR China; Department of Respiratory Medicine Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou 550001, PR China. 3. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510030, PR China. Electronic address: sujin@gird.cn.
Abstract
AIMS: The "gut-lung axis" reflects intimate connection and bidirectional effect between gut and lung, involving numerous lung diseases. Pulmonary fibrosis is a progressive interstitial lung disease with high fatality rate, so far, its association with gut remains unexplored. We investigated the correlation between pulmonary fibrosis and gut microbiota. MATERIALS AND METHODS: We collected feces from two pulmonary fibrotic models respectively, and performed a combinatory study using 16S rDNA sequencing and non-targeted metabonomics. Correlation matrix was used to indicate the correlation between microbiome, metabolites and fibrotic indicators, and the possibility of gut microbiota in identifying pulmonary fibrosis was assessed by ROC analysis. KEY FINDINGS: 412 genera of microflora and 26 kinds of metabolites were synchronously altered with same trend in two models but differed observably with control. Among these, 7 microorganisms and 9 metabolites were the typical representatives, which were correlated significantly and highly correlated with fibrotic indicators shown by correlation matrix. ROC analysis indicated that it was dependable to identify pulmonary fibrosis by using gut microorganisms and metabolites in both models (AUC > 0.85, p < 0.01). SIGNIFICANCE: In summary, our findings first revealed a previously unknown correlation between gut and pulmonary fibrosis in mouse models, which creates novel insights of the interaction between pulmonary fibrosis and gut microbiota.
AIMS: The "gut-lung axis" reflects intimate connection and bidirectional effect between gut and lung, involving numerous lung diseases. Pulmonary fibrosis is a progressive interstitial lung disease with high fatality rate, so far, its association with gut remains unexplored. We investigated the correlation between pulmonary fibrosis and gut microbiota. MATERIALS AND METHODS: We collected feces from two pulmonary fibrotic models respectively, and performed a combinatory study using 16S rDNA sequencing and non-targeted metabonomics. Correlation matrix was used to indicate the correlation between microbiome, metabolites and fibrotic indicators, and the possibility of gut microbiota in identifying pulmonary fibrosis was assessed by ROC analysis. KEY FINDINGS: 412 genera of microflora and 26 kinds of metabolites were synchronously altered with same trend in two models but differed observably with control. Among these, 7 microorganisms and 9 metabolites were the typical representatives, which were correlated significantly and highly correlated with fibrotic indicators shown by correlation matrix. ROC analysis indicated that it was dependable to identify pulmonary fibrosis by using gut microorganisms and metabolites in both models (AUC > 0.85, p < 0.01). SIGNIFICANCE: In summary, our findings first revealed a previously unknown correlation between gut and pulmonary fibrosis in mouse models, which creates novel insights of the interaction between pulmonary fibrosis and gut microbiota.
Authors: Carolina F F A Costa; Benedita Sampaio-Maia; Ricardo Araujo; Diana S Nascimento; Joana Ferreira-Gomes; Manuel Pestana; Maria J Azevedo; Ines S Alencastre Journal: Nutrients Date: 2022-01-14 Impact factor: 5.717