Dataset Information

Associations between human leukocyte antigen class I variants and the Mycobacterium tuberculosis subtypes causing disease.

ABSTRACT:

Background

?The development of active tuberculosis disease has been shown to be multifactorial. Interactions between host and bacterial genotype may influence disease outcome, with some studies indicating the adaptation of M. tuberculosis strains to specific human populations. Here we investigate the role of the human leukocyte antigen (HLA) class I genes in this biological process.

Methods

?Three hundred patients with tuberculosis from South Africa were typed for their HLA class I alleles by direct sequencing. Mycobacterium tuberculosis genotype classification was done by IS6110 restriction fragment length polymorphism genotyping and spoligotyping.

Results

?We showed that Beijing strain occurred more frequently in individuals with multiple disease episodes (P < .001) with the HLA-B27 allele lowering the odds of having an additional episode (odds ratio, 0.21; P = .006). Associations were also identified for specific HLA types and disease caused by the Beijing, LAM, LCC, and Quebec strains. HLA types were also associated with disease caused by strains from the Euro-American or East Asian lineages, and the frequencies of these alleles in their sympatric human populations identified potential coevolutionary events between host and pathogen.

Conclusions

?This is the first report of the association of human HLA types and M. tuberculosis strain genotype, highlighting that both host and pathogen genetics need to be taken into consideration when studying tuberculosis disease development.

SUBMITTER: Salie M

PROVIDER: S-EPMC3873786 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Class II human leukocyte antigens (HLA II) are proteins involved in the human immunological adaptive response by binding and exposing some pre-processed, non-self peptides in the extracellular domain in order to make them recognizable by the CD4+ T lymphocytes. However, the understanding of HLA-peptide binding interaction is a crucial step for designing a peptide-based vaccine because the high rate of polymorphisms in HLA class II molecules creates a big challenge, even though the HLA II proteins can be grouped into supertypes, where members of different class bind a similar pool of peptides. Hence, first we performed the supertype classification of 27 HLA II proteins using their binding affinities and structural-based linear motifs to create a stable group of supertypes. For this purpose, a well-known clustering method was used, and then, a consensus was built to find the stable groups and to show the functional and structural correlation of HLA II proteins. Thus, the overlap of the binding events was measured, confirming a large promiscuity within the HLA II-peptide interactions. Moreover, a very low rate of locus-specific binding events was observed for the HLA-DP genetic locus, suggesting a different binding selectivity of these proteins with respect to HLA-DR and HLA-DQ proteins. Secondly, a predictor based on a support vector machine (SVM) classifier was designed to recognize HLA II-binding peptides. The efficiency of prediction was estimated using precision, recall (sensitivity), specificity, accuracy, F-measure, and area under the ROC curve values of random subsampled dataset in comparison with other supervised classifiers. Also the leave-one-out cross-validation was performed to establish the efficiency of the predictor. The availability of HLA II-peptide interaction dataset, HLA II-binding motifs, high-quality amino acid indices, peptide dataset for SVM training, and MATLAB code of the predictor is available at http://sysbio.icm.edu.pl/HLA .