scholarly journals Pre-detection history of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

2019 ◽  
Vol 116 (46) ◽  
pp. 23284-23291 ◽  
Author(s):  
Tyler S. Brown ◽  
Lavanya Challagundla ◽  
Evan H. Baugh ◽  
Shaheed Vally Omar ◽  
Arkady Mustaev ◽  
...  

Antimicrobial-resistant (AMR) infections pose a major threat to global public health. Similar to other AMR pathogens, both historical and ongoing drug-resistant tuberculosis (TB) epidemics are characterized by transmission of a limited number of predominant Mycobacterium tuberculosis (Mtb) strains. Understanding how these predominant strains achieve sustained transmission, particularly during the critical period before they are detected via clinical or public health surveillance, can inform strategies for prevention and containment. In this study, we employ whole-genome sequence (WGS) data from TB clinical isolates collected in KwaZulu-Natal, South Africa to examine the pre-detection history of a successful strain of extensively drug-resistant (XDR) TB known as LAM4/KZN, first identified in a widely reported cluster of cases in 2005. We identify marked expansion of this strain concurrent with the onset of the generalized HIV epidemic 12 y prior to 2005, localize its geographic origin to a location in northeastern KwaZulu-Natal ∼400 km away from the site of the 2005 outbreak, and use protein structural modeling to propose a mechanism for how strain-specific rpoB mutations offset fitness costs associated with rifampin resistance in LAM4/KZN. Our findings highlight the importance of HIV coinfection, high preexisting rates of drug-resistant TB, human migration, and pathoadaptive evolution in the emergence and dispersal of this critical public health threat. We propose that integrating whole-genome sequencing into routine public health surveillance can enable the early detection and local containment of AMR pathogens before they achieve widespread dispersal.

2019 ◽  
Vol 8 (7) ◽  
Author(s):  
Syed Beenish Rufai ◽  
Sarman Singh

The emergence of extensively drug-resistant tuberculosis (XDR-TB) presents a considerable challenge and a public health concern due to the high mortality rate of this disease. Whole-genome sequencing (WGS) of XDR-TB isolates is thus essential for understanding the mechanism of drug resistance.


PLoS ONE ◽  
2009 ◽  
Vol 4 (11) ◽  
pp. e7778 ◽  
Author(s):  
Thomas R. Ioerger ◽  
Sunwoo Koo ◽  
Eun-Gyu No ◽  
Xiaohua Chen ◽  
Michelle H. Larsen ◽  
...  

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0181797 ◽  
Author(s):  
Thandi Kapwata ◽  
Natashia Morris ◽  
Angela Campbell ◽  
Thuli Mthiyane ◽  
Primrose Mpangase ◽  
...  

2011 ◽  
Vol 17 (10) ◽  
pp. 1942-1945 ◽  
Author(s):  
Max R. O’Donnell ◽  
Jennifer Zelnick ◽  
Lise Werner ◽  
Iqbal Master ◽  
Marian Loveday ◽  
...  

2020 ◽  
Vol 189 (7) ◽  
pp. 735-745
Author(s):  
Kristin N Nelson ◽  
Neel R Gandhi ◽  
Barun Mathema ◽  
Benjamin A Lopman ◽  
James C M Brust ◽  
...  

Abstract Patterns of transmission of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases worldwide in 2017. Modeling TB transmission networks can provide insight into drivers of transmission, but incomplete sampling of TB cases can pose challenges for inference from individual epidemiologic and molecular data. We assessed the effect of missing cases on a transmission network inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases in KwaZulu-Natal, South Africa, diagnosed in 2011–2014. We tested scenarios in which cases were missing at random, missing differentially by clinical characteristics, or missing differentially by transmission (i.e., cases with many links were under- or oversampled). Under the assumption that cases were missing randomly, the mean number of transmissions per case in the complete network needed to be larger than 20, far higher than expected, to reproduce the observed network. Instead, the most likely scenario involved undersampling of high-transmitting cases, and models provided evidence for super-spreading. To our knowledge, this is the first analysis to have assessed support for different mechanisms of missingness in a TB transmission study, but our results are subject to the distributional assumptions of the network models we used. Transmission studies should consider the potential biases introduced by incomplete sampling and identify host, pathogen, or environmental factors driving super-spreading.


Sign in / Sign up

Export Citation Format

Share Document