Understanding the bacteria in Mycobacterium avium complex (MAC) from a bioinformatic perspective – a review

Abstract

Mycobacterium avium complex (MAC) houses a group of non-tuberculous mycobacteria causing pulmonary and disseminated infections. They are accountable for nodular bronchiectatic and fibrocavitary lung diseases in humans, Johne’s disease in ruminants, and respiratory diseases in birds. MAC infections pose challenges, owing to antibiotic resistance, prolonged therapy with antibiotic combinations, side effects, and risk of reinfections. Our objective was to summarize the outcome of computational research on the bacteria in MAC. This aimed to advance our understanding of characteristics, pathogenicity, and transmission dynamics to control infections. We incorporated information from the research on genomes, microbiomes, phylogeny, transcriptomes, proteomes, antibiotic resistance, and vaccine/drug target development to enhance our knowledge. It illuminated the significance of computational studies in distinguishing MAC species/subspecies and recognizing: virulence factors, lineage-specific markers, and transmission clusters. Moreover, it assisted in understanding: genomic diversity, resistance patterns, impact of polymorphisms in disease susceptibility, and taxa-induced dysbiosis in microbiomes. Additionally, this work highlighted the outcome of bioinformatic studies in predicting suitable vaccine epitopes, and novel drug targets to combat MAC infections. Bioinformatic research on bacteria within MAC has contributed to a deeper insight into the pathogens. These would facilitate better diagnosis, improved: therapeutic strategies, patient-specific surveillance, and community-level awareness.