Population-based sequencing of <i>Mycobacterium tuberculosis</i> reveals how current population dynamics are shaped by past epidemics

Abstract Background Transmission has been proposed as a driver of tuberculosis (TB) epidemics in high-burden regions, with negligible impact in low-burden areas. Genomic epidemiology can greatly help to quantify transmission in different settings but the lack of whole genome sequencing population-based studies has hampered its use to compare transmission dynamics and contribution across settings. Methods We generated an additional population-based sequencing dataset from Valencia Region, a low burden setting, and compared it with available datasets from different TB settings to reveal heterogeneity of transmission dynamics and its public health implications. We sequenced the whole genome of 785 M. tuberculosis strains and linked genomes to patient epidemiological data. We applied a pairwise distance clustering approach and phylodynamics methods to characterize transmission events over the last 150 years, in Valencia, Spain (low burden), Oxfordshire, United Kingdom (low burden) and a high-burden (Karonga, Malawi). Results Our results revealed high local transmission in the Valencia Region (47.4% clustering), in contrast to Oxfordshire (27% clustering), and similar to a high-burden setting like Malawi (49.8% clustering). By modelling times of the transmission events, we observed that settings with high transmission are associated with uninterrupted transmission of strains over decades, irrespective of burden. Conclusions Our results underscore significant differences in transmission between TB settings even with similar burdens, reveal the role of past epidemic in on-going TB epidemic and highlight the need for in-depth characterization of transmission dynamics and specifically-tailored TB control strategies. Funding European Research Council under the European Union’s Horizon 2020 research and innovation program (Grants 638553-TB-ACCELERATE, 101001038-TB-RECONNECT), and Ministerio de Ciencia e Innovación (Spanish Government, SAF2016-77346-R and PID2019-104477RB-I00)