The main goal of the Carrington laboratory is to understand the genetic basis for resistance or susceptibility to human disease conferred by polymorphic immune response loci, including human leukocyte antigen (HLA), killer immunoglobulin-like receptor (KIR) genes and others. We study a large variety of disorders including cancer, transplantation outcome, autoimmune and infectious diseases.  Our multidisciplinary team consists of experimental biologists and computational scientists.

Extensive genetic polymorphism is a primary characteristic of the HLA class I and class II loci, which encode cell surface molecules that present antigenic peptides to T cells, initiating an adaptive immune response.  Variation within these loci is concentrated at positions that determine specificity for peptides, which can affect efficiency of immune responses.  Thus, allelic composition of a given genotype determines the diversity of the peptide repertoire presented by a carrier of that genotype.  Using mass-spectrometry data, we have been developing analytical tools to estimate the breadth of peptide repertoire based on HLA genotype.

Apart from determining antigenic peptide specificity, polymorphism at the HLA loci can differentially impact various functional properties of HLA molecules.  These include expression levels, dependence on the intracellular chaperone tapasin for peptide loading, and interactions with immune receptors, such as KIR and leukocyte immunoglobulin-like receptors (LILR) expressed on the surface of innate immune cells.  Using experimental data, we have developed models to test these allele-specific properties for their influence on disease outcomes.  Our lab has shown that these genetically defined HLA features associate with HIV-1 pathogenesis, outcome to hematopoietic transplantion, and other diseases.

Variation at the HLA loci has been a major focus in genetic studies of cancer immunotherapy outcomes. We performed the largest epidemiological study of HLA class I alleles in cancer immunotherapy patients to date, and discovered that HLA-A*03 is associated with poor response to immunotherapy, which can be now used as a predictive marker.

Recent emphasis in our lab has focused on the non-classical HLA class I molecule HLA-E, which regulates immune responses through interactions with the inhibitory CD94-NKG2A and activating CD94-NKG2C receptors expressed by subsets of NK and T cells. HLA-E binds peptides derived from the signal sequences of the classical HLA-A, HLA-B, and HLA-C, and polymorphism in the HLA class I signal sequences differentially influence both HLA-E expression levels and interactions with CD94-NKG2A/C receptors.  We are systematically investigating the impact of signal sequence polymorphism on HLA-E function using in vitro models and ex vivo analysis of NK cells.  Our aim is to build a prediction algorithm to estimate the strength of HLA-E-mediated regulation of immune responses based on HLA class I genotype.  Given the ability of HLA-E to regulate anti-viral and anti-tumor immune responses, these studies may inform vaccine and therapy design.

In the era where massive datasets are publicly available, and our own immunogenetic database is extensive, we are particularly interested in developing novel computational approaches that will take advantage of these resources. These tools will allow us to estimate the effects of variation in immune response genes on human disease by combining the knowledge of genetic association data with biological function, and to this end we are involved in analyses of several large biobank-based studies.