Identifying the immune interactions underlying HLA class I disease associations
Abstract
Variation in the risk and severity of many autoimmune diseases, malignancies and infections is strongly associated with polymorphisms in the HLA class I loci. These genetic associations provide a powerful opportunity for understanding the etiology of human disease. HLA class I associations are often interpreted in the light of 'protective' or 'detrimental' CD8+ T cell responses which are restricted by the host HLA class I allotype. However, given the diverse receptors which are bound by HLA class I molecules, alternative interpretations are possible. As well as binding T cell receptors on CD8+ T cells, HLA class I molecules are important ligands for inhibitory and activating killer immunoglobulin-like receptors (KIRs) which are found on natural killer cells and some T cells; for the CD94:NKG2 family of receptors also expressed mainly by NK cells and for leukocyte immunoglobulin-like receptors (LILRs) on myeloid cells. The aim of this study is to develop an immunogenetic approach for identifying and quantifying the relative contribution of different receptor-ligand interactions to a given HLA class I disease association and then to use this approach to investigate the immune interactions underlying HLA class I disease associations in three viral infections: Human T cell Leukemia Virus type 1, Human Immunodeficiency Virus type 1 and Hepatitis C Virus as well as in the inflammatory condition Crohn's disease.
Data availability
Upon acceptance we will upload, to a public database, all the data analysis ie the data underlying Figure 1, Figure 2, Figure 3, Supplementary Figure S1, Supplementary Figure S2, Supplementary Figure S3 and Supplementary Figure S4. We are unable to provide the raw patient data as this has been released to us under MTAs and uploading of data would violate the terms of these MTAs.The PIs we contacted for the various cohorts are: Pat Fast, IAVI, New York (IAVI); Charles Bangham, Imperial College London, UK (Kagoshima cohort); Greg Kirk, Johns Hopkins, USA (ALIVE cohort); James Goedert, NIH (MHCS cohort); Sharyne Donfield, Rho, USA (HGDS cohort); Salim Khakoo, University of Southampton, UK (UK HCV cohort) and James Lee, University of Cambridge, UK (Crohn's disease cohort). Requests for data access and usage are reviewed by the relevant boards at each institution.
Article and author information
Author details
Funding
Wellcome (103865Z/14/Z)
- Becca Asquith
National Institutes of Health (R01-DA-12568)
- Gregory Kirk
National Institutes of Health (K24-AI118591)
- Gregory Kirk
Medical Research Council (J007439)
- Becca Asquith
Medical Research Council (G1001052)
- Becca Asquith
European Commission (317040)
- Becca Asquith
Bloodwise (15012)
- Becca Asquith
Wellcome (105920/Z/14/Z)
- James C Lee
National Institutes of Health (DA13324)
- Chloe L Thio
National Institutes of Health (R01-HD-41224)
- Sharyne M Donfield
National Institutes of Health (U01-DA-036297)
- Gregory Kirk
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: This study was approved by the NHS Research Ethics Committee (13/WS/0064) and the Imperial College Research Ethics Committee (ICREC_11_1_2). Informed consent was obtained at the study sites from all individuals. The study was conducted according to the principles of the Declaration of Helsinki.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Metrics
-
- 5,180
- views
-
- 558
- downloads
-
- 23
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study bias affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.
-
- Computational and Systems Biology
- Microbiology and Infectious Disease
Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that Streptococcus agalactiae MprF synthesizes lysyl-phosphatidylglycerol (Lys-PG) from anionic PG, and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), from neutral glycolipid Glc-DAG. This unexpected result prompted us to investigate whether Lys-Glc-DAG occurs in other MprF-containing bacteria, and whether other novel MprF products exist. Here, we studied protein sequence features determining MprF substrate specificity. First, pairwise analyses identified several streptococcal MprFs synthesizing Lys-Glc-DAG. Second, a restricted Boltzmann machine-guided approach led us to discover an entirely new substrate for MprF in Enterococcus, diglucosyl-diacylglycerol (Glc2-DAG), and an expanded set of organisms that modify glycolipid substrates using MprF. Overall, we combined the wealth of available sequence data with machine learning to model evolutionary constraints on MprF sequences across the bacterial domain, thereby identifying a novel cationic lipid.