High-resolution mapping of tuberculosis transmission: Whole genome sequencing and phylogenetic modelling of a cohort from Valencia Region, Spain

Abstract

BackgroundWhole genome sequencing provides better delineation of transmission clusters in Mycobacterium tuberculosis than traditional methods. However, its ability to reveal individual transmission links within clusters is limited. Here, we used a 2-step approach based on Bayesian transmission reconstruction to (1) identify likely index and missing cases, (2) determine risk factors associated with transmitters, and (3) estimate when transmission happened.Methods and findingsWe developed our transmission reconstruction method using genomic and epidemiological data from a population-based study from Valencia Region, Spain. Tuberculosis (TB) incidence during the study period was 8.4 cases per 100,000 people. While the study is ongoing, the sampling frame for this work includes notified TB cases between 1 January 2014 and 31 December 2016. We identified a total of 21 transmission clusters that fulfilled the criteria for analysis. These contained a total of 117 individuals diagnosed with active TB (109 with epidemiological data). Demographic characteristics of the study population were as follows: 80/109 (73%) individuals were Spanish-born, 76/109 (70%) individuals were men, and the mean age was 42.51 years (SD 18.46). We found that 66/109 (61%) TB patients were sputum positive at diagnosis, and 10/109 (9%) were HIV positive. We used the data to reveal individual transmission links, and to identify index cases, missing cases, likely transmitters, and associated transmission risk factors. Our Bayesian inference approach suggests that at least 60% of index cases are likely misidentified by local public health. Our data also suggest that factors associated with likely transmitters are different to those of simply being in a transmission cluster, highlighting the importance of differentiating between these 2 phenomena. Our data suggest that type 2 diabetes mellitus is a risk factor associated with being a transmitter (odds ratio 0.19 [95% CI 0.02–1.10], p < 0.003). Finally, we used the most likely timing for transmission events to study when TB transmission occurred; we identified that 5/14 (35.7%) cases likely transmitted TB well before symptom onset, and these were largely sputum negative at diagnosis. Limited within-cluster diversity does not allow us to extrapolate our findings to the whole TB population in Valencia Region.ConclusionsIn this study, we found that index cases are often misidentified, with downstream consequences for epidemiological investigations because likely transmitters can be missed. Our findings regarding inferred transmission timing suggest that TB transmission can occur before patient symptom onset, suggesting also that TB transmits during sub-clinical disease. This result has direct implications for diagnosing TB and reducing transmission. Overall, we show that a transition to individual-based genomic epidemiology will likely close some of the knowledge gaps in TB transmission and may redirect efforts towards cost-effective contact investigations for improved TB control.

Transmission and drug-resistance of tuberculosis in Luodian revealed by whole genome sequencing based molecular epidemiology study

“…We found that 13.1% (14/107) of the pulmonary TB cases had genomic-clustered strains in Luodian county during a period less than one year. The cluster rate was lower than the results of other studies, including one conducted in Shenzhen (25.2% , 105/417) during ve years 17 , and another one from Spain (33.5%, 109/325) in a 3-year population-based study 23 . The cluster rate in current study likely be underestimated by following reasons.…”

Possible Transmission Mechanisms of Mixed Mycobacterium tuberculosis Infection in High HIV Prevalence Country, Botswana

“…However, our finding reconfirmed that TB transmission was ongoing in the community. A comprehensive molecular characterization of withinhost M. tuberculosis diversity, as well as an attempt to temporally identify the primary source or index of transmission by comparing diagnosis times and the times of symptom onset (20), might be needed to fully capture TB transmission chains and accurately infer TB transmission (21,22).…”

Mycobacterium tuberculosis clinical isolates carry mutational signatures of host immune environments

“…2). Because the in vivo mutation rate of Mtb is approximately 0.24 to 0.5 SNPs per genome per year (17,(19)(20)(21), this disparity suggested that the mutation rate in vivo might not be uniform. To test this, we simulated Mtb population growth in silico using a growth model with a constant mutation rate (Wright-Fisher model) (22).…”