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Cited by 250Diabetes and associated disordersT-cell and B-cell ImmunologyCancer Immunotherapy and Biomarkers

Partial exhaustion of CD8 T cells and clinical response to teplizumab in new-onset type 1 diabetes

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Long, S. Alice, Thorpe, Jerill, DeBerg, Hannah A., Gersuk, Vivian, Eddy, James A., Harris, Kristina M., Ehlers, Mario, Herold, Kevan C. +2 more·2016·Science Immunology
DOI 10.1126/sciimmunol.aai7793PMC PMC5486405
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Abstract

Biologic treatment of T1D typically results in transient stabilization of C-peptide levels (a surrogate for endogenous insulin secretion) in some patients, followed by progression at the same rate as in untreated control groups. Here, we used integrated systems biology and flow cytometry approaches with clinical trial blood samples to elucidate pathways associated with C-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody teplizumab. We identified a population of CD8 T cells that accumulated in subjects with the best response to treatment (responders) and showed that these cells phenotypically resembled exhausted T cells by expressing high levels of the transcription factor EOMES, effector molecules, and multiple inhibitory receptors (IRs), including TIGIT and KLRG1. These cells expanded after treatment, with levels peaking after 3-6 months. To functionally characterize these exhausted-like T cells, we isolated memory CD8 TIGIT+KLRG1+ T cells from responders and showed that they exhibited expanded TCR clonotypes, indicative of prior in vivo expansion; recognized a broad-based spectrum expressed of environmental and auto-antigens; and were hypo-proliferative during polyclonal stimulation, increasing expression of IR genes and decreasing cell cycle genes. Triggering these cells with a recombinant ligand for TIGIT during polyclonal stimulation further downregulated their activation, demonstrating their exhausted phenotype was not terminal. These findings identify and functionally characterize a partially exhausted cell type associated with response to teplizumab therapy and suggest that pathways regulating T cell exhaustion may play a role in successful immune interventions for T1D.

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  • In people who respond to teplizumab therapy, increases in the frequency of exhausted CD8+ T cells (PD1+ KLRG1+ CD57-) and anergic CD8+ and CD4+ T cells are observed.

    “In people who respond to teplizumab therapy, increases in the frequency of exhausted CD8 + T cells (PD1 + KLRG1 + CD57 -) and anergic CD8 + and CD4 + T cells are observed.16,17”

    From The future of type 1 diabetes therapy (2025) · cites this paper as [17]

    The Results section ('R subjects display increased levels of CD8 T cells expressing EOMES-associated genes'; page not provided) reports that TIGIT+KLRG1+ CD8 T cells increased after teplizumab treatment, most prominently in responders. The paper describes these cells as partially exhausted-like, but the cited passages do not establish the specified PD1+KLRG1+CD57− phenotype or increases in anergic CD8 and CD4 T cells. The Discussion also says PDCD1 levels did not differ between the compared CD8 cell populations. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • A CD8+ T cell transcriptional profile of exhaustion correlates with beneficial outcome in AID and response to immunotherapies.

    “Consistently, a CD8 + Tcell transcriptional profile of exhaustion correlates with beneficial outcome in AID 35,59 and response to immunotherapies. 60”

    From Autoantigen-specific CD4+ T cells acquire an exhausted phenotype and persist in human antigen-specific autoimmune diseases (2024) · cites this paper as [60]

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Research works printed but not linked: 46

  1. [1]Rigby MR Ehlers MR Targeted immune interventions for type 1 diabetes: not as easy as it looks! Curr Opin Endocrinol Diabetes Obes 21 271 278 2014 24983393 PMC4133939
  2. [2]Greenbaum CJ Schatz DA Haller MJ Sanda S Through the fog: recent clinical trials to preserve β-cell function in type 1 diabetes Diabetes 61 1323 1330 2012 22618767 PMC3357268
  3. [3]Wållberg M Cooke A Immune mechanisms in type 1 diabetes Trends Immunol 34 583 591 2013 24054837
  4. [5]Schietinger A Greenberg PD Tolerance and exhaustion: defining mechanisms of T cell dysfunction Trends Immunol 35 51 60 2014 24210163 PMC3946600
  5. [7]Crawford A Angelosanto JM Kao C Doering TA Odorizzi PM Barnett BE Wherry EJ Molecular and transcriptional basis of CD4+ T cell dysfunction during chronic infection Immunity 40 289 302 2014 24530057 PMC3990591
  6. [8]Doering TA Crawford A Angelosanto JM Paley MA Ziegler CG Wherry EJ Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory Immunity 37 1130 1144 2012 23159438 PMC3749234
  7. [11]Cancer Genome Atlas Network Comprehensive molecular portraits of human breast tumours Nature 490 61 70 2012 23000897 PMC3465532
  8. [12]Reynier F Pachot A Paye M Xu Q Turrel-Davin F Petit F Hot A Auffray C Bendelac N Nicolino M Mougin B Thivolet C Specific gene expression signature associated with development of autoimmune type-I diabetes using whole-blood microarray analysis Genes Immun 11 269 278 2010 20090770
  9. [13]Herold KC Gitelman SE Ehlers MR Gottlieb PA Greenbaum CJ Hagopian W Boyle KD Keyes-Elstein L Aggarwal S Phippard D Sayre PH McNamara J Bluestone JA the AbATE Study Team, Teplizumab (anti-CD3 mAb) treatment preserves C-peptide responses in patients with new-onset type 1 diabetes in a randomized controlled trial: Metabolic and immunologic features at baseline identify a subgroup of responders Diabetes 2013 10.2337/db13-0345 PMC3806618 23835333 · DOI
  10. [14]Linsley PS Speake C Whalen E Chaussabel D Copy number loss of the interferon gene cluster in melanomas is linked to reduced T cell infiltrate and poor patient prognosis PloS One 9 e109760 2014 25314013 PMC4196925
  11. [18]Franceschini A Szklarczyk D Frankild S Kuhn M Simonovic M Roth A Lin J Minguez P Bork P von Mering C Jensen LJ STRING v9.1: protein-protein interaction networks, with increased coverage and integration Nucleic Acids Res 41 D808 815 2013 23203871 PMC3531103
  12. [19]Tooley JE Vudattu N Choi J Cotsapas C Devine L Raddassi K Ehlers MR McNamara JG Harris KM Kanaparthi S Phippard D Herold KC Changes in T-cell subsets identify responders to FcR-nonbinding anti-CD3 mAb (teplizumab) in patients with type 1 diabetes Eur J Immunol 46 230 241 2016 26518356 PMC4882099
  13. [20]Turner SJ Doherty PC McCluskey J Rossjohn J Structural determinants of T-cell receptor bias in immunity Nat Rev Immunol 6 883 894 2006 17110956
  14. [21]Stewart-Jones GB Simpson P van der Merwe PA Easterbrook P McMichael AJ Rowland-Jones SL Jones EY Gillespie GM Structural features underlying T-cell receptor sensitivity to concealed MHC class I micropolymorphisms Proc Natl Acad Sci U S A 109 E3483 3492 2012 23161907 PMC3528510
  15. [22]Schneider-Hohendorf T Mohan H Bien CG Breuer J Becker A Görlich D Kuhlmann T Widman G Herich S Elpers C Melzer N Dornmair K Kurlemann G Wiendl H Schwab N CD8(+) T-cell pathogenicity in Rasmussen encephalitis elucidated by large-scale T-cell receptor sequencing Nat Commun 7 11153 2016 27040081 PMC4822013
  16. [23]Eckle SBG Birkinshaw RW Kostenko L Corbett AJ McWilliam HEG Reantragoon R Chen Z Gherardin NA Beddoe T Liu L Patel O Meehan B Fairlie DP Villadangos JA Godfrey DI Kjer-Nielsen L McCluskey J Rossjohn J A molecular basis underpinning the T cell receptor heterogeneity of mucosal-associated invariant T cells J Exp Med 211 1585 1600 2014 25049336 PMC4113946
  17. [24]Porcelli S Yockey CE Brenner MB Balk SP Analysis of T cell antigen receptor (TCR) expression by human peripheral blood CD4-8- alpha/beta T cells demonstrates preferential use of several V beta genes and an invariant TCR alpha chain J Exp Med 178 1 16 1993 8391057 PMC2191070
  18. [25]Gherardin NA Keller AN Woolley RE Le Nours J Ritchie DS Neeson PJ Birkinshaw RW Eckle SBG Waddington JN Liu L Fairlie DP Uldrich AP Pellicci DG McCluskey J Godfrey DI Rossjohn J Diversity of T Cells Restricted by the MHC Class I-Related Molecule MR1 Facilitates Differential Antigen Recognition Immunity 44 32 45 2016 26795251
  19. [26]Joachims ML Leehan KM Lawrence C Pelikan RC Moore JS Pan Z Rasmussen A Radfar L Lewis DM Grundahl KM Kelly JA Wiley GB Shugay M Chudakov DM Lessard CJ Stone DU Scofield RH Montgomery CG Sivils KL Thompson LF Farris AD Single-cell analysis of glandular T cell receptors in Sjögren’s syndrome JCI Insight 1 2016 10.1172/jci.insight.85609 PMC4922426 27358913 · DOI
  20. [27]Du JW Gu JY Liu J Cen XN Zhang Y Ou Y Chu B Zhu P TCR spectratyping revealed T lymphocytes associated with graft-versus-host disease after allogeneic hematopoietic stem cell transplantation Leuk Lymphoma 48 1618 1627 2007 17701594
  21. [28]Greenough TC Straubhaar JR Kamga L Weiss ER Brody RM McManus MM Lambrecht LK Somasundaran M Luzuriaga KF A Gene Expression Signature That Correlates with CD8+ T Cell Expansion in Acute EBV Infection J Immunol Baltim Md 1950 195 4185 4197 2015 PMC4610856 26416268
  22. [29]Doering TA Crawford A Angelosanto JM Paley MA Ziegler CG Wherry EJ Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory Immunity 37 1130 1144 2012 23159438 PMC3749234
  23. [30]Fuertes Marraco SA Neubert NJ Verdeil G Speiser DE Inhibitory Receptors Beyond T Cell Exhaustion Front Immunol 6 2015 10.3389/fimmu.2015.00310 PMC4481276 26167163 · DOI
  24. [31]Ablamunits V Herold KC Generation and function of human regulatory CD8+ T cells induced by a humanized OKT3 monoclonal antibody hOKT3gamma1 (Ala-Ala) Hum Immunol 69 732 736 2008 18817833
  25. [32]Kim HJ Wang X Radfar S Sproule TJ Roopenian DC Cantor H CD8+ T regulatory cells express the Ly49 Class I MHC receptor and are defective in autoimmune prone B6-Yaa mice Proc Natl Acad Sci U S A 108 2010 2015 2011 21233417 PMC3033298
  26. [33]Jiang H Canfield SM Gallagher MP Jiang HH Jiang Y Zheng Z Chess L HLA-E-restricted regulatory CD8(+) T cells are involved in development and control of human autoimmune type 1 diabetes J Clin Invest 120 3641 3650 2010 20877010 PMC2947239
  27. [34]Guarda G Hons M Soriano SF Huang AY Polley R Martín-Fontecha A Stein JV Germain RN Lanzavecchia A Sallusto F L-selectin-negative CCR7- effector and memory CD8+ T cells enter reactive lymph nodes and kill dendritic cells Nat Immunol 8 743 752 2007 17529983
  28. [35]Laffont S Coudert JD Garidou L Delpy L Wiedemann A Demur C Coureau C Guéry J-C CD8+ T-cell-mediated killing of donor dendritic cells prevents alloreactive T helper type-2 responses in vivo Blood 108 2257 2264 2006 16449531
  29. [36]Bisikirska B Colgan J Luban J Bluestone JA Herold KC TCR stimulation with modified anti-CD3 mAb expands CD8+ T cell population and induces CD8+CD25+ Tregs J Clin Invest 115 2904 2913 2005 16167085 PMC1201661
  30. [37]Yuling H Ruijing X Xiang J Li L Lang C Jie X Wei X Yujuan W Lijun Z Rui Z Xinti T Yongyi B Yan-Ping J Youxin J Jinquan T EBV Promotes Human CD8+ NKT Cell Development PLoS Pathog 6 2010 10.1371/journal.ppat.1000915 PMC2997102 21151944 · DOI
  31. [38]Barber DL Wherry EJ Masopust D Zhu B Allison JP Sharpe AH Freeman GJ Ahmed R Restoring function in exhausted CD8 T cells during chronic viral infection Nature 439 682 687 2006 16382236
  32. [40]Chen DS Mellman I Oncology meets immunology: the cancer-immunity cycle Immunity 39 1 10 2013 23890059
  33. [41]Hughes J Vudattu N Sznol M Gettinger S Kluger H Lupsa B Herold KC Precipitation of autoimmune diabetes with anti-PD-1 immunotherapy Diabetes Care 38 e55 57 2015 25805871 PMC4370325
  34. [42]Ritchie ME Phipson B Wu D Hu Y Law CW Shi W Smyth GK limma powers differential expression analyses for RNA-sequencing and microarray studies Nucleic Acids Res 43 e47 2015 25605792 PMC4402510
  35. [43]Du P Kibbe WA Lin SM lumi: a pipeline for processing Illumina microarray Bioinforma Oxf Engl 24 1547 1548 2008 18467348
  36. [44]Du P Kibbe WA Lin SM nuID: a universal naming scheme of oligonucleotides for illumina, affymetrix, and other microarrays Biol Direct 2 16 2007 17540033 PMC1891274
  37. [45]Langfelder P Horvath S WGCNA: an R package for weighted correlation network analysis BMC Bioinformatics 9 559 2008 19114008 PMC2631488
  38. [47]Giardine B Riemer C Hardison RC Burhans R Elnitski L Shah P Zhang Y Blankenberg D Albert I Taylor J Miller W Kent WJ Nekrutenko A Galaxy: a platform for interactive large-scale genome analysis Genome Res 15 1451 1455 2005 16169926 PMC1240089
  39. [48]Afgan E Baker D van den Beek M Blankenberg D Bouvier D Čech M Chilton J Clements D Coraor N Eberhard C Grüning B Guerler A Hillman-Jackson J Kuster G Von Rasche E Soranzo N Turaga N Taylor J Nekrutenko A Goecks J The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update Nucleic Acids Res 2016 10.1093/nar/gkw343 PMC4987906 27137889 · DOI
  40. [49]Trapnell C Pachter L Salzberg SL TopHat: discovering splice junctions with RNA-Seq Bioinforma Oxf Engl 25 1105 1111 2009 PMC2672628 19289445
  41. [50]Anders S Pyl PT Huber W HTSeq–a Python framework to work with high-throughput sequencing data Bioinforma Oxf Engl 31 166 169 2015 PMC4287950 25260700
  42. [51]Robinson MD Oshlack A A scaling normalization method for differential expression analysis of RNA-seq data Genome Biol 11 R25 2010 20196867 PMC2864565
  43. [52]Robinson MD McCarthy DJ Smyth GK edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinforma Oxf Engl 26 139 140 2010 PMC2796818 19910308
  44. [53]Law CW Chen Y Shi W Smyth GK voom: Precision weights unlock linear model analysis tools for RNA-seq read counts Genome Biol 15 R29 2014 24485249 PMC4053721
  45. [54]Grabherr MG Haas BJ Yassour M Levin JZ Thompson DA Amit I Adiconis X Fan L Raychowdhury R Zeng Q Chen Z Mauceli E Hacohen N Gnirke A Rhind N di Palma F Birren BW Nusbaum C Lindblad-Toh K Friedman N Regev A Full-length transcriptome assembly from RNA-Seq data without a reference genome Nat Biotechnol 29 644 652 2011 21572440 PMC3571712
  46. [55]Brochet X Lefranc MP Giudicelli V IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis Nucleic Acids Res 36 W503 508 2008 18503082 PMC2447746

Cited by

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Authors
Long, S. A., Thorpe, J., DeBerg, H. A., Gersuk, V., Eddy, J. A., Harris, K. M., Ehlers, M., Herold, K. C., Nepom, G. T., & Linsley, P. S.
Affiliations
  • Benaroya Research Institute (US)
  • Immune Tolerance Network (US)
  • Yale University (US)
Corresponding author
Linsley, Peter S.
ORCID
Venue
Science Immunology
Publisher
American Association for the Advancement of Science (AAAS)
Published
2016-11-18
Volume
1
Issue
5
ISSN
2470-9468 (ISSN-L)
DOI
10.1126/sciimmunol.aai7793
PMC
PMC5486405
OpenAlex
W2554850815
Cited by
250
References
10
Citations by year
2026: 19 · 2025: 36 · 2024: 38 · 2023: 40 · 2022: 23 · 2021: 30 · 2020: 17 · 2019: 19 · 2018: 18 · 2017: 10
FWCI
12.66
Type
journal article
Language
en
Open access
Locations
  • Science Immunology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute of Allergy and Infectious Diseases (UM1AI109565)
  • National Institute of Diabetes and Digestive and Kidney Diseases (DP3DK104465, R01DK057846)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Keywords
type 1 diabetes, teplizumab, CD8+ T cells, T cell exhaustion, anti-CD3 monoclonal antibody, TIGIT, KLRG1, Eomes, inhibitory receptors, TCR clonotypes, T cell proliferation, immune checkpoint blockade, treatment response

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