Exploring the potential role of microbiota and metabolites in acute exacerbation of chronic obstructive pulmonary disease

Abstract

The acute exacerbation of chronic obstructive pulmonary disease seriously affects the respiratory system function and quality of life of patients. This study employed 16S rRNA sequencing and metabolomics techniques to analyze the respiratory microbiota and serum metabolites of COPD and AECOPD patients. The results showed that the microbial diversity in the respiratory tract of AECOPD patients was significantly lower than that of COPD patients, and the relative abundance of Bacteroidetes, Prevotella and Neisseria in the respiratory tract of AECOPD patients was significantly lower than that of COPD patients. However, the relative abundance of Haemophilus_D, Veillonella_A and Pseudomonas_E, in AECOPD patients was significantly higher than that of COPD patients, and the ability of respiratory microbiota in AECOPD patients to participate in alanine metabolism was significantly lower than that of COPD patients. Metabolome results further revealed that the serum alanine levels in AECOPD patients were significantly lower than those in COPD patients, and these differential metabolites were mainly involved in linoleic acid metabolism, protein digestion and absorption and regulation of lipolysis in adipocytes. In summary, the structural characteristics of respiratory microbiota in COPD and AECOPD patients are different from those in healthy populations, and their microbiota diversity decreases and microbial community structure and function will also undergo changes when acute exacerbations occur. In addition, the predicted microbial community function and metabolomics results indicate that the onset of AECOPD is mainly related to energy and amino acid metabolism disorders, especially alanine metabolism.

Keywords: AECOPD; COPD; maker; metabolite; respiratory microbiota.

Figure 1

Alpha and beta diversity results. (A) Alpha diversity indexes (Simpson, Chao1, Pielou-e, Faith_pd, Shannon and Observed_specific indexes). (B) Species cumulative curve. (C) Principal co-ordinates analysis.

Figure 2

Composition and distribution of microbial communities at the phylum and genus levels. (A,B) Composition and distribution of microbial communities at the phylum level. (C,D) Composition and distribution of microbial communities at the genus level.

Figure 3

Species differences and marker species. (A) Venn diagram based on ASV level. (B) Species composition heatmap (Genus level). (C) LefSe analysis.

Figure 4

PICRUSt2 Functional potential prediction. (A) Microbial communities participate in the metabolic pathways of KEGG. (B–D) Significant differences in metabolic pathways among the three groups.

Figure 5

Metabolome data quality control and overall metabolite identification. (A) PCA analysis of the overall samples in positive ion mode. (B) PCA analysis of the overall samples in negative ion mode. (C) Identification results of metabolites in positive ion mode. (D) Identification results of metabolites in negative ion mode.

Figure 6

Expression abundance analysis. (A) Statistical results of expression abundance of metabolites in each sample under positive ion mode. (B) Statistical results of expression abundance of metabolites in each sample under negative ion mode. (C) Overall material clustering heatmap in positive ion mode. (D) Overall material clustering heatmap in negative ion mode.

Figure 7

Differential metabolite analysis. (A) Differential substance screening in positive and negative ion merging mode. (B) Differential substance Venn analysis in positive and negative ion merging mode. (C–K) Metabolites with significant differences in AECOPD compared to HC and COPD in positive and negative ion combination mode. (L) KEGG enrichment analysis of differential substances in positive and negative ion merging mode.

Figure 8

The Correlation analysis between microbiota and metabolites. The legend displays the correlation coefficient values, where red represents positive correlation, blue represents negative correlation, and the depth of color indicates the strength of correlation. * or ** indicates significant correlation between microorganism and metabolite (p < 0.05 or p < 0.01).