Profiling of Microbial Landscape in Lung of Chronic Obstructive Pulmonary Disease Patients Using RNA Sequencing

Abstract

Purpose

The aim of the study was to use RNA sequencing (RNA-seq) data of lung from chronic obstructive pulmonary disease (COPD) patients to identify the bacteria that are most commonly detected. Additionally, the study sought to investigate the differences in these infections between normal lung tissues and those affected by COPD.

Patients and Methods

We re-analyzed RNA-seq data of lung from 99 COPD patients and 93 non-COPD smokers to determine the extent to which the metagenomes differed between the two groups and to assess the reliability of the metagenomes. We used unmapped reads in the RNA-seq data that were not aligned to the human reference genome to identify more common infections in COPD patients.

Results

We identified 18 bacteria that exhibited significant differences between the COPD and non-COPD smoker groups. Among these, Yersinia enterocolitica was found to be more than 30% more abundant in COPD. Additionally, we observed difference in detection rate based on smoking history. To ensure the accuracy of our findings and distinguish them from false positives, we double-check the metagenomic profile using Basic Local Alignment Search Tool (BLAST). We were able to identify and remove specific species that might have been misclassified as other species in Kraken2 but were actually Staphylococcus aureus, as identified by BLAST analysis.

Conclusion

This study highlighted the method of using unmapped reads, which were not typically used in sequencing data, to identify microorganisms present in patients with lung diseases such as COPD. This method expanded our understanding of the microbial landscape in COPD and provided insights into the potential role of microorganisms in disease development and progression.

Keywords:

• chronic obstructive pulmonary disease
• next-generation sequencing
• microbiome
• metagenomics

Abbreviations

COPD, chronic obstructive pulmonary disease; NGS, next-generation sequencing; RNA, ribonucleic acid; DNA, deoxyribonucleic acid; RNA-seq, RNA sequencing; SAM, Sequence Alignment Map; BAM, Binary Alignment Map; IL, interleukin; pCMF, probabilistic Count Matrix Factorization; BLAST, Basic Local Alignment Search Tool; BLASTN, Nucleotide BLAST; FEV1, forced expiratory volume in 1 second.

Data Sharing Statement

The data used in this study are all publicly available in the Gene Expression Omnibus (GEO). The accession number is GSE57148. The additional three samples (1 COPD sample, 2 normal samples) are available from the respective authors upon reasonable request.

Ethics Approval and Informed Consent

The study protocol was approved by the Institutional Review Board of the Asan Medical Center (IRB no. 2021-1337). The board waived the requirement for obtaining patient informed consent because of the nature of the analysis. All provided data from the GlaxoSmithKline were anonymized, and this study did not present any identifiable and private information.

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03047972), as well as the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education (2021R1A6C101A445).

Disclosure

The authors report no conflicts of interest in this work.