Literature DB >> 22069251

Immunology of Diabetes Society T-Cell Workshop: HLA class I tetramer-directed epitope validation initiative T-Cell Workshop Report-HLA Class I Tetramer Validation Initiative.

R Mallone1, M Scotto, C N Janicki, E A James, L Fitzgerald-Miller, R Wagner, P Gottlieb, J Thorpe, N Jospe, I Durinovic-Bellò, C Boitard, O Lou, C M Dayan, F S Wong.   

Abstract

BACKGROUND: Identification of T-cell reactivity to β-cell antigen epitopes is an important goal for studying pathogenesis and for designing and monitoring of immunotherapeutic interventions in type 1 diabetes (T1D).
METHODS: We performed a multicentre validation of known human leukocyte antigen (HLA) class I CD8+ T-cell epitopes. To this end, peripheral blood T-cell responses were measured in 35 recently (<2 years) diagnosed HLA-A*02:01+ T1D patients using blind-coded HLA-A2 tetramers (TMrs) and pentamers (PMrs), encompassing two epitopes of preproinsulin (PPI; PPIA12-20 and PPIB10-18) and two epitopes of glutamic acid decarboxylase (GAD; GAD114-122 and GAD536-545). We also compared the readout of TMrs and PMrs with a CD8+ T-cell interferon-γ enzyme-linked immunospot assay.
RESULTS: Despite the minute frequencies of autoreactive cells detected by TMrs/PMrs, most (73-77%) T1D patients had responses to one or more of the epitopes used. All four epitopes were recognized by T1D patients, with a prevalence ranging from 5 to 25%. TMrs and PMrs detected more positive responses to the β-cell epitopes than CD8+ T-cell interferon-γ enzyme-linked immunospot. However, concordance between positive responses to TMrs and PMrs was limited.
CONCLUSIONS: Using a multicentre blind-coded setup and three different T-cell assays, we have validated PPI and GAD epitopes as commonly recognized CD8+ T-cell targets in recently diagnosed T1D patients. Both TMrs and PMrs showed higher detection sensitivity than the CD8+ T-cell interferon-γ enzyme-linked immunospot assay. However, there are some important methodological issues that need to be addressed in using these sensitive techniques for detecting low frequency responses.
Copyright © 2011 John Wiley & Sons, Ltd.

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Year:  2011        PMID: 22069251     DOI: 10.1002/dmrr.1243

Source DB:  PubMed          Journal:  Diabetes Metab Res Rev        ISSN: 1520-7552            Impact factor:   4.876


  10 in total

Review 1.  Endocrinology research-reflecting on the past decade and looking to the next.

Authors:  Kevan C Herold; Joseph A Majzoub; Shlomo Melmed; Merri Pendergrass; Martin Schlumberger
Journal:  Nat Rev Endocrinol       Date:  2015-10-13       Impact factor: 43.330

2.  Standardizing T-Cell Biomarkers in Type 1 Diabetes: Challenges and Recent Advances.

Authors:  Simi Ahmed; Karen Cerosaletti; Eddie James; S Alice Long; Stuart Mannering; Cate Speake; Maki Nakayama; Timothy Tree; Bart O Roep; Kevan C Herold; Todd M Brusko
Journal:  Diabetes       Date:  2019-07       Impact factor: 9.461

3.  A distinct immunogenic region of glutamic acid decarboxylase 65 is naturally processed and presented by human islet cells to cytotoxic CD8 T cells.

Authors:  R R Knight; G Dolton; D Kronenberg-Versteeg; M Eichmann; M Zhao; G C Huang; K Beck; D K Cole; A K Sewell; A Skowera; M Peakman
Journal:  Clin Exp Immunol       Date:  2015-01       Impact factor: 4.330

Review 4.  Vaccine against autoimmune disease: antigen-specific immunotherapy.

Authors:  Robert P Anderson; Bana Jabri
Journal:  Curr Opin Immunol       Date:  2013-03-13       Impact factor: 7.486

5.  β-cell-specific CD8 T cell phenotype in type 1 diabetes reflects chronic autoantigen exposure.

Authors:  Ania Skowera; Kristin Ladell; David A Price; Mark Peakman; James E McLaren; Garry Dolton; Katherine K Matthews; Emma Gostick; Deborah Kronenberg-Versteeg; Martin Eichmann; Robin R Knight; Susanne Heck; Jake Powrie; Polly J Bingley; Colin M Dayan; John J Miles; Andrew K Sewell
Journal:  Diabetes       Date:  2014-09-23       Impact factor: 9.461

Review 6.  SCT for severe autoimmune diseases: consensus guidelines of the European Society for Blood and Marrow Transplantation for immune monitoring and biobanking.

Authors:  T Alexander; A Bondanza; P A Muraro; R Greco; R Saccardi; T Daikeler; M Kazmi; C Hawkey; B P Simoes; K Leblanc; W E Fibbe; J Moore; E Snarski; T Martin; F Hiepe; A Velardi; A Toubert; J A Snowden; D Farge
Journal:  Bone Marrow Transplant       Date:  2014-11-10       Impact factor: 5.483

7.  Systematic Assessment of Immune Marker Variation in Type 1 Diabetes: A Prospective Longitudinal Study.

Authors:  Cate Speake; Henry T Bahnson; Johnna D Wesley; Nikole Perdue; David Friedrich; Minh N Pham; Erinn Lanxon-Cookson; William W Kwok; Birgit Sehested Hansen; Matthias von Herrath; Carla J Greenbaum
Journal:  Front Immunol       Date:  2019-09-13       Impact factor: 7.561

8.  Tissue distribution and clonal diversity of the T and B cell repertoire in type 1 diabetes.

Authors:  Howard R Seay; Erik Yusko; Stephanie J Rothweiler; Lin Zhang; Amanda L Posgai; Martha Campbell-Thompson; Marissa Vignali; Ryan O Emerson; John S Kaddis; Dave Ko; Maki Nakayama; Mia J Smith; John C Cambier; Alberto Pugliese; Mark A Atkinson; Harlan S Robins; Todd M Brusko
Journal:  JCI Insight       Date:  2016-12-08

9.  The development of standard samples with a defined number of antigen-specific T cells to harmonize T cell assays: a proof-of-principle study.

Authors:  Satwinder Kaur Singh; Bart Tummers; Ton N Schumacher; Raquel Gomez; Kees L M C Franken; Els M Verdegaal; Karoline Laske; Cécile Gouttefangeas; Christian Ottensmeier; Marij J P Welters; Cedrik M Britten; Sjoerd H van der Burg
Journal:  Cancer Immunol Immunother       Date:  2012-09-18       Impact factor: 6.968

Review 10.  T Cell Receptor Profiling in Type 1 Diabetes.

Authors:  Laura M Jacobsen; Amanda Posgai; Howard R Seay; Michael J Haller; Todd M Brusko
Journal:  Curr Diab Rep       Date:  2017-10-11       Impact factor: 4.810

  10 in total

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