­­­Characterization of integrated prophages within diverse species of clinical nontuberculous mycobacteria

Abstract

Background Nontuberculous mycobacterial (NTM) infections are increasing in prevalence, with current estimates suggesting that over 100,000 people in the United States are affected each year. It is unclear how certain species of mycobacteria transition from environmental bacteria to clinical pathogens, or what genetic elements influence the differences in virulence among strains of the same species. A potential mechanism of genetic diversity within mycobacteria is the integration of viruses called prophages. Prophages may act as carriers of bacterial genes, with the potential of altering bacterial fitness through horizontal gene transfer. In this study, we quantify the frequency and composition of prophages within mycobacteria isolated from clinical samples and compare them against the composition of PhageDB, an environmental mycobacteriophage database.Methods Prophages were predicted by agreement between two discovery tools, VirSorter and Phaster, and the frequencies of integrated prophages were compared by growth rate. Prophages were assigned to PhageDB lettered clusters using BLAST-p. Bacterial virulence gene frequency was calculated using a combination of the Virulence Factor Database (VFDB) and the Pathosystems Resource Integration Center virulence database (Patric-VF) within the gene annotation software Prokka. CRISPR elements were discovered using CRT. ARAGORN was used to quantify tRNAs.Results Rapidly growing mycobacteria (RGM) were more likely to contain prophage than slowly growing mycobacteria (SGM). CRISPR elements were not associated with prophage abundance in mycobacteria. The abundance of tRNAs was enriched in SGM compared to RGM.

Philympics 2021: Prophage Predictions Perplex Programs

“…VirSorter and VirSorter2 were primarily developed to identify viral regions in metagenomes rather than prophages in bacterial genomes—although they have been used for that e.g. in Glickman et al (2020). By openly providing the Prophage Prediction Comparison framework, creating a framework to install and test different software, and defining a straightforward approach to labelling prophages in GenBank files, we hope to expand our gold-standard set of genomes and mitigate many of our biases.…”