Year: 2022
Type: article
Abstract: BACKGROUND: Natural killer (NK) cells represent a critical component of the innate immune system's response against cancer and viral infections, among other diseases. To distinguish healthy host cells from infected or tumor cells, killer immunoglobulin receptors (KIR) on NK cells bind and recognize Human Leukocyte Antigen (HLA) complexes on their target cells. However, NK cells exhibit great diversity in their mechanism of activation, and the outcomes of their activation are not yet understood fully. Just like the HLAs they bind, KIR receptors exhibit high allelic diversity in the human population. Here we provide a method to identify KIR allele variants from whole exome sequencing data and uncover novel associations between these variants and various molecular and clinical correlates. RESULTS: In order to better understand KIRs, we have developed KIRCLE, a novel method for genotyping individual KIR genes from whole exome sequencing data, and used it to analyze approximately sixty-thousand patient samples in The Cancer Genome Atlas (TCGA) and UK Biobank. We were able to assess population frequencies for different KIR alleles and demonstrate that, similar to HLA alleles, individuals' KIR alleles correlate strongly with their ethnicities. In addition, we observed associations between different KIR alleles and HLA alleles, including HLA-B*53 with KIR3DL2*013 (Fisher's exact FDR = 7.64e-51). Finally, we showcased statistically significant associations between KIR alleles and various clinical correlates, including peptic ulcer disease (Fisher's exact FDR = 0.0429) and age of onset of atopy (Mann-Whitney U FDR = 0.0751). CONCLUSIONS: We show that KIRCLE is able to infer KIR variants accurately and consistently, and we demonstrate its utility using data from approximately sixty-thousand individuals from TCGA and UK Biobank to discover novel molecular and clinical correlations with KIR germline variants. Peptic ulcer disease and atopy are just two diseases in which NK cells may play a role beyond their "classical" realm of anti-tumor and anti-viral responses. This tool may be used both as a benchmark for future KIR-variant-inference algorithms, and to better understand the immunogenomics of and disease processes involving KIRs.
Source: BMC Biology
Authors: Galen F. Gao, Dajiang Liu, Xiaowei Zhan, Bo Li
Language: English
Subfield: Immunology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,190
Funders: American Federation for Aging Research, Cancer Prevention and Research Institute of Texas, University of Texas Southwestern Medical Center, National Institutes of Health, Medical Research Council, National Institute on Aging, National Human Genome Research Institute, National Cancer Institute, National Institute of General Medical Sciences
Awards: Method to Enhance the Rare Variant Genetic Architecture Analysis in Meta-Analyses, Recruitment of First-Time, Tenure-Track Faculty Members, Antigen-independent prediction and biomarker identification of cancer-specific T cells, Tools for integrative genomics and disease association study for the X chromosome, G4143021784, G5850737185, Digital Pathology Analysis for Lung Cancer Patient Care, G7829168488, Tracking Peripheral T-Cell Repertoire Changes for Preoperative and Early Ovarian Cancer Diagnosis, UT Southwestern Medical Center Simmons Comprehensive Cancer Center
OpenAlex ID: W4293105663