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Effect of multiple genetic polymorphisms on antigen presentation and susceptibility to Mycobacterium tuberculosis infection.

Chang ST, Linderman JJ, Kirschner DE

Program in Bioinformatics, Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.

Several molecules related to antigen presentation, including gamma interferon (IFN-gamma) and the major histocompatibility complex (MHC), are encoded by polymorphic genes. Some polymorphisms were found to affect susceptibility to tuberculosis (TB) when they were considered singly in epidemiological studies, but how multiple polymorphisms interact to determine susceptibility to TB in an individual remains an open question. We hypothesized that polymorphisms in some genes may counteract or intensify the effects of polymorphisms in other genes. For example, an increase in IFN-gamma expression may counteract the weak binding that a particular MHC variant displays for a peptide from Mycobacterium tuberculosis to establish the same T-cell response as another, more strongly binding MHC variant. To test this hypothesis, we developed a mathematical model of antigen presentation based on experimental data for the known effects of genetic polymorphisms and simulated time courses when multiple polymorphisms were present. We found that polymorphisms in different genes could affect antigen presentation to the same extent and therefore compensate for each other. Furthermore, we defined the conditions under which such relationships could exist. For example, increased IFN-gamma expression compensated for decreased peptide-MHC affinity in the model only above a certain threshold of expression. Below this threshold, changes in IFN-gamma expression were ineffectual compared to changes in peptide-MHC affinity. The finding that polymorphisms exhibit such relationships could explain discrepancies in the epidemiological literature, where some polymorphisms have been inconsistently associated with susceptibility to TB. Furthermore, the model allows polymorphisms to be ranked by effect, providing a new tool for designing association studies.

Published 20 June 2008 in Infect Immun, 76(7): 3221-32.
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