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Large-Scale Epitope Identification Screen and Its Potential Application to the Study of Alopecia Areata

2017; Elsevier BV; Volume: 19; Issue: 1 Linguagem: Inglês

10.1016/j.jisp.2017.10.001

1529-1774

Cecilia S. Lindestam Arlehamn, Sinu Paul, Eddy Hsi Chun Wang, Annemieke de Jong, Angela M. Christiano, Alessandro Sette,

Immunotherapy and Immune Responses

The discovery of antigen-specific responses in human diseases, such as infectious and autoimmune diseases, predominantly utilizes a candidate approach. This approach tests the hypothesis whether a panel of proteins or molecules with known roles in the developmental or pathology network can incite disease phenotype such as T-cell response via different molecular biology assays. Although this approach can provide valuable knowledge of the disease through direct functional assays, it can be biased due to the initial candidate selection process. In an autoimmune setting, identification of autoantigen epitopes usually began with validation anecdotal observations that potentially limit the scope of the candidate antigen panel that could initiate disease onset. As such, an unbiased, large-scale screening and identification method is needed to complement the candidate approach to ensure the inclusion of all possible antigens for biological validation. The advancement of technologies in genomics (next generation DNA sequencing), proteomics, and bioinformatics in conjunction with large population-based studies in recent years now allows us to perform high-throughput antigen and epitope identification that could potentially develop into therapeutic strategies that can apply to a wider population. Computational prediction of antigen epitopes that can be presented by HLA class I and II to T cells is an example of combining bioinformatics with proteomics to determine disease-inciting epitope peptide sequences. Publicly available computer algorithms such as BIMAS (Parker et al., 1994Parker K.C. Bednarek M.A. Coligan J.E. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains.J Immunol. 1994; 152: 163-175PubMed Google Scholar) and SYFPEITHI (Rammensee et al., 1995Rammensee H.G. Friede T. Stevanoviic S. MHC ligands and peptide motifs: first listing.Immunogenetics. 1995; 41: 178-228Crossref PubMed Scopus (1511) Google Scholar) have been used to predict peptides based on full protein sequences for type I diabetes (Ouyang et al., 2006Ouyang Q. Standifer N.E. Qin H. Gottlieb P. Verchere C.B. Nepom G.T. et al.Recognition of HLA class I-restricted beta-cell epitopes in type 1 diabetes.Diabetes. 2006; 55: 3068-3074Crossref PubMed Scopus (86) Google Scholar, Panagiotopoulos et al., 2003Panagiotopoulos C. Qin H. Tan R. Verchere C.B. Identification of a beta-cell-specific HLA class I restricted epitope in type 1 diabetes.Diabetes. 2003; 52: 2647-2651Crossref PubMed Scopus (78) Google Scholar). These algorithms can provide high ranking peptides that can be bound to the HLA molecules based on the binding motif. The Immune Epitope Database and Analysis Resource (IEDB; www.iedb.org) is a National Institute of Allergy and Infectious Diseases sponsored freely available resource that serves as a repository of published antibody and T-cell antigen epitope data (Sette et al., 2015Sette A. Paul S. Vaughan K. Peters B. The use of the Immune Epitope Database to study autoimmune epitope data related to alopecia areata.J Investig Dermatol Symp Proc. 2015; 17: 36-41Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar). In addition to the database that curates published antigen epitopes for infectious diseases, allergy, autoimmune diseases, and transplantation, IEDB also houses a number of analysis tools with the capability as diverse as epitope prediction, such as the MHC binding tool version 2.15.1. This robust tool allowed for the large-scale epitope identification of potential new vaccine targets for Mycobacterium tuberculosis described below (Lindestam Arlehamn et al., 2013Lindestam Arlehamn C.S. Gerasimova A. Mele F. Henderson R. Swann J. Greenbaum J.A. et al.Memory T cells in latent Mycobacterium tuberculosis infection are directed against three antigenic islands and largely contained in a CXCR3+CCR6+ Th1 subset.PLoS Pathog. 2013; 9: e1003130Crossref PubMed Scopus (172) Google Scholar). Large-scale genomic and immunoproteomic screening strategies combine pools of peptides likely to bind HLA molecules widely expressed in patient populations, with screening of patient T cells for the presence of T cells reactive against these peptides, categorized by antigen of origin. For example, an unbiased genome-wide screen of the antigens recognized by the M. tuberculosis-specific CD4 T cell in humans (Lindestam Arlehamn et al., 2013Lindestam Arlehamn C.S. Gerasimova A. Mele F. Henderson R. Swann J. Greenbaum J.A. et al.Memory T cells in latent Mycobacterium tuberculosis infection are directed against three antigenic islands and largely contained in a CXCR3+CCR6+ Th1 subset.PLoS Pathog. 2013; 9: e1003130Crossref PubMed Scopus (172) Google Scholar) identified three broadly immunodominant gene sets, all related to bacterial secretion systems. As a result, several previously undescribed antigens were identified and the combined epitope predictions and high-throughput ELISPOT was validated in the context of a screen targeting more than 4,000 different open reading frames. T helper type 2 cells play a key role in unwanted T-cell responses related to human allergies. In the context of timothy grass allergies, 2D gel electrophoresis of pollen extract and transcriptome analyses identified almost a hundred previously undescribed allergen candidates (Schulten et al., 2013Schulten V. Greenbaum J.A. Hauser M. McKinney D.M. Sidney J. Kolla R. et al.Previously undescribed grass pollen antigens are the major inducers of T helper 2 cytokine-producing T cells in allergic individuals.Proc Natl Acad Sci USA. 2013; 110: 3459-3464Crossref PubMed Scopus (56) Google Scholar). Peptides predicted from these proteins were screened for reactivity with T cells from allergic donors, resulting in the identification of several allergens. A further study of house dust mite allergic individuals using the same approach (Oseroff et al., 2017Oseroff C. Christensen L.H. Westernberg L. Pham J. Lane J. Paul S. et al.Immunoproteomic analysis of house dust mite antigens reveals distinct classes of dominant T cell antigens according to function and serological reactivity.Clin Exp Allergy. 2017; 47: 577-592Crossref Scopus (15) Google Scholar) identified a total of more than 60 proteins and several previously undescribed dominant targets of T-cell responses. These two studies highlighted that the proposed strategy is applicable not only to infectious diseases, but also in allergy, where T cells are associated with pathogenesis, and present in much lower frequencies. Alopecia areata (AA) is a T-cell-mediated, nonscarring, inflammatory form of hair loss. Histological presentations, gene expression profiling, and cell-depletion assays provided a strong rationale that the development of AA is associated with an autoimmune mechanism (Betz et al., 2015Betz R.C. Petukhova L. Ripke S. Huang H. Menelaou A. Redler S. et al.Genome-wide meta-analysis in alopecia areata resolves HLA associations and reveals two new susceptibility loci.Nat Commun. 2015; 6: 5966Crossref PubMed Scopus (159) Google Scholar, Chen et al., 2015Chen J.C. Cerise J.E. Jabbari A. Clynes R. Christiano A.M. 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Coda A.B. Sinha A.A. Transcriptional profiling in alopecia areata defines immune and cell cycle control related genes within disease-specific signatures.Genomics. 2010; 96: 146-153Crossref PubMed Scopus (53) Google Scholar). The collapse of immune privilege with the upregulation of antigen-presenting molecules (HLA class I and II) around hair follicles (HFs) during AA onset suggests a strong role in HF antigens (Gilhar et al., 2007Gilhar A. Paus R. Kalish R.S. Lymphocytes, neuropeptides, and genes involved in alopecia areata.J Clin Invest. 2007; 117: 2019-2027Crossref PubMed Scopus (227) Google Scholar, Paus et al., 2005Paus R. Nickoloff B.J. Ito T. A 'hairy' privilege.Trends Immunol. 2005; 26: 32-40Abstract Full Text Full Text PDF PubMed Scopus (248) Google Scholar). Many attempts have been made to identify autoantigens and epitopes in AA; although several antigens and epitopes have been reported and tested, the exact antigens remain elusive (Erb et al., 2013Erb U. 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Shi F.T. et al.Identification of autoantigen epitopes in alopecia areata.J Invest Dermatol. 2016; 136: 1617-1626Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar). Previous studies were hindered by the paucity of appropriate bioinformatic tools such as the ability to predict large panels of HLA-binding, overlapping peptides from a list of full protein sequences. We are now able to leverage the resource from IEDB as well as the associated analytical tools to identify epitopes and antigens in AA. The approach for AA will involve epitope prediction for HLA class I and II from HF proteins that were derived from published proteomic studies, gene expression data, and online resources, followed by functional validation of these peptides in peripheral blood mononuclear cells from AA donors for IFN-γ and IL-5 responses. This type of large-scale T-cell epitope screen utilizing comprehensive bioinformatic analytical tools has not been done in the context of AA, or even in autoimmune diseases in general. By defining and including unique HF proteins as potential sources of autoantigen epitopes, we are addressing the organ-specific (HF-specific) property of AA while remaining unbiased in terms of cellular sources of these epitopes (i.e., derived from keratinocyte or melanocytes). Epitope prediction via the MHC binding tools provided by the IEDB (Sette et al., 2015Sette A. Paul S. Vaughan K. Peters B. The use of the Immune Epitope Database to study autoimmune epitope data related to alopecia areata.J Investig Dermatol Symp Proc. 2015; 17: 36-41Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar) predicted peptides that bind to HLA-A*02:01, chosen as an HLA class I molecule frequently expressed in both AA donors and healthy controls, and also peptides predicted to bind to several common HLA-DR, to take into account that AA is driven by CD8 T-cell infiltration, but also with HLA class II association genetically. This pipeline of peptide prediction is a robust and highly versatile method that increases the likelihood of identifying true, disease-associated autoantigen epitopes. Caveats to this approach include focus on proteins for which sequences are available and thus may miss potential neoantigens associated with the disease. Additionally and as previously shown, peptides based on HLA class II allele binding predictions account for approximately 50% of the total magnitude of response (Lindestam Arlehamn et al., 2016Lindestam Arlehamn C.S. McKinney D.M. Carpenter C. Paul S. Rozot V. Makgotlho E. et al.A quantitative analysis of complexity of human pathogen-specific CD4 T cell responses in healthy M. tuberculosis infected South Africans.PLoS Pathog. 2016; 12: e1005760Crossref PubMed Scopus (82) Google Scholar, Oseroff et al., 2010Oseroff C. Sidney J. Kotturi M.F. Kolla R. Alam R. Broide D.H. et al.Molecular determinants of T cell epitope recognition to the common Timothy grass allergen.J Immunol. 2010; 185: 943-955Crossref PubMed Scopus (113) Google Scholar), and therefore this approach does not capture every single disease-associated epitope. Finally, depending on the readout, there is a bias toward certain cytokines and T-cell subsets. With the availability of the free resources and analytical tools from IEDB, the identification of autoantigen epitopes in AA and their subsequent validation can be streamlined and performed without bias. Cecilia S. Lindestam Arlehamn: http://orcid.org/0000-0001-7302-8002 AMC is a consultant to Aclaris Therapeutics. The rest of the authors state no conflict of interest. Funding for the Summit and the publication of this article was provided by the National Alopecia Areata Foundation. Funding for this Summit was also made possible (in part) by a grant (1 R13AR071266) from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.