Bayesian reconstruction of Mycobacterium tuberculosis transmission networks in a high incidence area over two decades in Malawi reveals associated risk factors and genomic variants. Issue 4 (1st April 2020)

• Record Type: Journal Article
• Authors: Sobkowiak, Benjamin; Banda, Louis; Mzembe, Themba; Crampin, Amelia C.; Glynn, Judith R.; Clark, Taane G.
• Is Part Of: Microbial genomics. Volume 6:Issue 4(2020)
• Publication Date: 2020-04-01
• Subjects: Mycobacterium tuberculosis -- tuberculosis -- bioinformatics -- molecular epidemiology -- Bayesian analysis -- pathogen transmission; Microbial genomics -- Periodicals; 572.8629
• DOI: 10.1099/mgen.0.000361
• Languages: English
• ISSNs: 2057-5858

Abstract

Abstract : Understanding host and pathogen factors that influence tuberculosis (TB) transmission can inform strategies to eliminate the spread of Mycobacterium tuberculosis (Mtb ). Determining transmission links between cases of TB is complicated by a long and variable latency period and undiagnosed cases, although methods are improving through the application of probabilistic modelling and whole-genome sequence analysis. Using a large dataset of 1857 whole-genome sequences and comprehensive metadata from Karonga District, Malawi, over 19 years, we reconstructed Mtb transmission networks using a two-step Bayesian approach that identified likely infector and recipient cases, whilst robustly allowing for incomplete case sampling. We investigated demographic and pathogen genomic variation associated with transmission and clustering in our networks. We found that whilst there was a significant decrease in the proportion of infectors over time, we found higher transmissibility and large transmission clusters for lineage 2 (Beijing) strains. By performing evolutionary convergence testing (phyC) and genome-wide association analysis (GWAS) on transmitting versus non-transmitting cases, we identified six loci, PPE54, accD2, PE_PGRS62, rplI, Rv3751 and Rv2077c, that were associated with transmission. This study provides a framework for reconstructing large-scale Mtb transmission networks. We have highlighted potential host and pathogen characteristics that were linked to increased transmission in a high-burden setting and identified genomic variants that, with validation, could inform further studies into transmissibility and TB eradication.