Genome-wide association study reveals putative bacterial risk factors for cavitary Mycobacterium avium complex lung disease

Abstract

Mycobacterium avium complex (MAC) lung disease is a slowly progressive disease, and its increasing incidence has garnered increased research interests. Cavitary MAC lung disease is associated with a higher mortality rate. Though genetic studies have unraveled the human risk factors, the role of microbial factors on pathogenesis behind the disease remains elusive. In this study, M. avium isolates were collected from sputum specimens of 109 distinct Japanese patients with or without a cavity (60 with a cavity and 49 without cavity) in a hospital located in Osaka prefecture. M. avium genomes were sequenced and searched for DNA motifs associated with cavity formation using a bacterial GWAS. Excluding known macrolide resistance mutations; cavity formation was found to be primarily associated with variants of cytochrome P450 of the CYP139 family, type I polyketide synthase Pks13, and the promoter region of an operon encoding membrane-anchored protease FtsH and folate synthesis pathway enzymes. Cavity risk variants at these three loci were frequent in the MahEastAsia2 lineage among the six lineages detected in M. avium global populations. Furthermore, the study demonstrated a correlation between the cavity risk promoter variant and increased sulfamethoxazole/trimethoprim resistance. Together, these findings suggest that natural variation in the biosynthesis and maintenance processes of M. avium membrane components influences the disease type of MAC lung disease. Although further validation is needed, the bacterial genetic markers listed in the present study could contribute to prognosis prediction based on bacterial genotyping and help develop treatment strategies in the future.

Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance

“…Mapping unitigs back to the original genome is also simplified compared to k-mers, as each unitig represents a unique non-redundant region. Previous studies support the efficacy of unitig-based GWAS approaches, offering specific genomic information and facilitating functional annotation of associated loci across various bacterial genomes, including Mycobacterium (Jaillard et al, 2018 ; Hang et al, 2019 ; Yano et al, 2021 ), Staphylococcus (Jaillard et al, 2018 ; Chaguza et al, 2022b ; Raineri et al, 2022 ), and E. coli (Denamur et al, 2022 ; Van Wonterghem et al, 2022 ).…”