Altered fecal microbiome and metabolome in adult patients with non-cystic fibrosis bronchiectasis

Wen-Wen Wang, Bei Mao, Yang Liu, Shu-Yi Gu, Hai-Wen Lu, Jiu-Wu Bai, Shuo Liang, Jia-Wei Yang, Jian-Xiong Li, Xiao Su, Hai-Yang Hu, Chen Wang, Jin-Fu Xu, Wen-Wen Wang, Bei Mao, Yang Liu, Shu-Yi Gu, Hai-Wen Lu, Jiu-Wu Bai, Shuo Liang, Jia-Wei Yang, Jian-Xiong Li, Xiao Su, Hai-Yang Hu, Chen Wang, Jin-Fu Xu

Abstract

Background: Emerging experimental and epidemiological evidence highlights a crucial cross-talk between the intestinal flora and the lungs, termed the "gut-lung axis". However, the function of the gut microbiota in bronchiectasis remains undefined. In this study, we aimed to perform a multi-omics-based approach to identify the gut microbiome and metabolic profiles in patients with bronchiectasis.

Methods: Fecal samples collected from non-CF bronchiectasis patients (BE group, n = 61) and healthy volunteers (HC group, n = 37) were analyzed by 16 S ribosomal RNA (rRNA) sequencing. The BE group was divided into two groups based on their clinical status: acute exacerbation (AE group, n = 31) and stable phase (SP group, n = 30). Further, metabolome (lipid chromatography-mass spectrometry, LC-MS) analyses were conducted in randomly selected patients (n = 29) and healthy volunteers (n = 31).

Results: Decreased fecal microbial diversity and differential microbial and metabolic compositions were observed in bronchiectasis patients. Correlation analyses indicated associations between the differential genera and clinical parameters such as bronchiectasis severity index (BSI). Disease-associated gut microbiota was screened out, with eight genera exhibited high accuracy in distinguishing SP patients from HCs in the discovery cohort and validation cohort using a random forest model. Further correlation networks were applied to illustrate the relations connecting disease-associated genera and metabolites.

Conclusion: The study uncovered the relationships among the decreased fecal microbial diversity, differential microbial and metabolic compositions in bronchiectasis patients by performing a multi-omics-based approach. It is the first study to characterize the gut microbiome and metabolome in bronchiectasis, and to uncover the gut microbiota's potentiality as biomarkers for bronchiectasis.

Trial registration: This study is registered with ClinicalTrials.gov, number NCT04490447.

Keywords: Biomarkers; Bronchiectasis; Gut microbiome; Metabolomics.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Flowchart. One hundred forty-five fecal samples were collected from Shanghai Pulmonary Hospital. Samples were randomly allocated to the discovery cohort and validation cohort. In the discovery cohort, 30, 31, and 37 fecal samples collected from the SP, AE, and HC groups, respectively, were assigned to 16 S rRNA gene sequencing. Thirty-one and 29 samples from HC and SP groups were assigned to LC-MS analysis
Fig. 2
Fig. 2
Decreased fecal microbial diversity in patients with bronchiectasis. a Venn diagram displaying the identified OTUs in three groups. α diversity estimated by Chao1 index (b) and observed species index (c). d β diversity calculated among the three groups using the unweighted UniFrac by PCoA. *P < 0.05, **P < 0.01, ***P < 0.001
Fig. 3
Fig. 3
Distinct phylogenetic profiling of gut microbiota among the three groups. The relative proportions of dominant taxa at the phylum level (a) and genus level (b). The top 20 variant genera between HC and SP (c), HC and AE (d). e Correlations between variant genera and clinical parameters were evaluated by Spearman’s correlation analyses. +P < 0.05, *P < 0.01
Fig. 4
Fig. 4
Identification and validation of bronchiectasis patients based on gut microbial genera. a LDA analyses identify differential taxa between HC and SP with LDA scores (log10) > 2. b Taxonomic cladogram indicating the phylogenetic distribution of gut microbiota in HC and SP. c MDA values of candidate genera are listed in descending order. The ROC curve was generated by the RF model using eight genera in the discovery cohort (d) and validation cohort (e)
Fig. 5
Fig. 5
Differential fecal metabolomic features in HC and SP groups. a OPLS-DA score plot. b OPLS-DA validation plot intercepts: R2Y = (0.0, 0.7233), Q2 = (0.0, – 0.1979). c Bubble plot displaying the significantly enriched pathways. d Correlation network of the 8 genera and metabolites with P < 0.01. The size of each node is proportional to the quantity of related metabolites and the thickness of the line is proportional to the correlation coefficient. Lines between nodes and triangles indicate positive correlations (red) or negative correlations (blue)

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