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Biology subjects

Auld, S. C.

Publications and source records attributed to Auld, S. C..

2 recordsLinked to original sources

Rare Variable M. tuberculosis Antigens induce predominant Th17 responses in human infection

CD4 T cells are essential for immunity to M. tuberculosis (Mtb), and emerging evidence indicates that IL-17-producing Th17 cells contribute to immunity to Mtb. While identifying protective T cell effector functions is important for TB vaccine design, T cell antigen specificity is also likely to be important. To identify antigens that induce protective immunity, we reasoned that as in other pathogens, effective immune recognition drives sequence diversity in individual Mtb antigens. We previously identified Mtb genes under evolutionary diversifying selection pressure whose products we term Rare Variable Mtb Antigens (RVMA). Here, in two distinct human cohorts with recent exposure to TB, we found that RVMA preferentially induce CD4 T cells that express RoR{gamma}t and produce IL-17, in contrast to classical Mtb antigens that induce T cells that produce IFN{gamma}. Our results suggest that RVMA can be valuable antigens in vaccines for those already infected with Mtb to amplify existing antigen-specific Th17 responses to prevent TB disease.

immunology↗

Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

The transmission patterns of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases in 2017. Modeling TB transmission networks can provide insight into the nature and drivers of transmission, but incomplete and non-random sampling of TB cases can pose challenges to making inferences from epidemiologic and molecular data. We conducted a quantitative bias analysis to assess the effect of missing cases on a transmission network inferred from Mtb sequencing data on extensively drug-resistant (XDR) TB cases in South Africa. We tested scenarios in which cases were missing at random, differentially by clinical characteristics, or differentially by transmission (i.e., cases with many links were under or over-sampled). Under the assumption cases were missing at random, cases in the complete, modeled network would have had a mean of 20 or more transmission links, which is far higher than expected, in order to reproduce the observed, partial network. Instead, we found that the most likely scenario involved undersampling of high-transmitting cases, and further models provided evidence for superspreading behavior. This is, to our knowledge, the first study to define and assess the support for different mechanisms of missingness in a study of TB transmission. Our findings should caution interpretation of results of future studies of TB transmission in high-incidence settings, given the potential for biased sampling, and should motivate further research aimed at identifying the specific host, pathogen, or environmental factors contributing to superspreading.

epidemiology↗