The effect of multiple genetic polymorphisms on antigen presentation and susceptibility to Mycobacterium tuberculosis infection

Program in Bioinformatics, Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan 48109 USA; Department of Chemical Engineering, Department of Bioengineering, University of Michigan, Ann Arbor, Michigan 48109 USA

^* To whom correspondence should be addressed. Email: kirschne{at}umich.edu.

	Abstract

Several molecules related to antigen presentation including IFN- and MHC are encoded by polymorphic genes. Some polymorphisms have been found to affect susceptibility to tuberculosis (TB) when considered singly in epidemiological studies, but how multiple polymorphisms interact to determine susceptibility to TB in an individual remains an open question. We hypothesize that polymorphisms in some genes may counteract or intensify the effects of polymorphisms in other genes. For example, an increase in IFN- expression may counteract the weak binding that a particular MHC variant displays for a peptide from Mycobacterium tuberculosis (Mtb) 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 of 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- expression compensated for decreased peptide-MHC affinity in the model only above a certain threshold of expression. Below this threshold, changes in IFN- 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.