The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection
Open accessAbstract
The clinical course of autoimmune and infectious disease varies greatly, even between individuals with the same condition. An understanding of the molecular basis for this heterogeneity could lead to significant improvements in both monitoring and treatment. During chronic infection the process of T-cell exhaustion inhibits the immune response, facilitating viral persistence. Here we show that a transcriptional signature reflecting CD8 T-cell exhaustion is associated with poor clearance of chronic viral infection, but conversely predicts better prognosis in multiple autoimmune diseases. The development of CD8 T-cell exhaustion during chronic infection is driven both by persistence of antigen and by a lack of accessory 'help' signals. In autoimmunity, we find that where evidence of CD4 T-cell co-stimulation is pronounced, that of CD8 T-cell exhaustion is reduced. We can reproduce the exhaustion signature by modifying the balance of persistent stimulation of T-cell antigen receptors and specific CD2-induced co-stimulation provided to human CD8 T cells in vitro, suggesting that each process plays a role in dictating outcome in autoimmune disease. The 'non-exhausted' T-cell state driven by CD2-induced co-stimulation is reduced by signals through the exhaustion-associated inhibitory receptor PD-1, suggesting that induction of exhaustion may be a therapeutic strategy in autoimmune and inflammatory disease. Using expression of optimal surrogate markers of co-stimulation/exhaustion signatures in independent data sets, we confirm an association with good clinical outcome or response to therapy in infection (hepatitis C virus) and vaccination (yellow fever, malaria, influenza), but poor outcome in autoimmune and inflammatory disease (type 1 diabetes, anti-neutrophil cytoplasmic antibody-associated vasculitis, systemic lupus erythematosus, idiopathic pulmonary fibrosis and dengue haemorrhagic fever). Thus, T-cell exhaustion plays a central role in determining outcome in autoimmune disease and targeted manipulation of this process could lead to new therapeutic opportunities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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 [59]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
The 1982 revised criteria for the classification of systemic lupus erythematosus
Tan, E. M., Cohen, A. S., Fries, J. F., Masi, A. T., Mcshane, D. J., Rothfield, N. F., Schaller, J. G., Talal, N., & Winchester, R. J. (1982) · Arthritis & Rheumatism · doi:10.1002/art.1780251101 · Cited by 14,519
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Wherry, E. J. (2011) · Nature Immunology · doi:10.1038/ni.2035 · Cited by 4,062
Cited by
Sorted by citations
CD8 T Cell Exhaustion During Chronic Viral Infection and Cancer
McLane, L. M., Abdel-Hakeem, M. S., & Wherry, E. J. (2019) · Annual Review of Immunology · doi:10.1146/annurev-immunol-041015-055318 · Cited by 2,054
Fraietta, J. A., Lacey, S. F., Orlando, E. J., Pruteanu-Malinici, I., Gohil, M., Lundh, S., Boesteanu, A. C., Wang, Y., O’Connor, R. S., Hwang, W.-T., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0010-1 · Cited by 1,785
Blank, C. U., Haining, W. N., Held, W., Hogan, P. G., Kallies, A., Lugli, E., Lynn, R. C., Philip, M., Rao, A., Restifo, N. P., et al. (2019) · Nature Reviews Immunology · doi:10.1038/s41577-019-0221-9 · Cited by 1,603
An anti-CD3 antibody, teplizumab, in relatives at risk for type 1 diabetes
Herold, K. C., Bundy, B. N., Long, S. A., Bluestone, J. A., DiMeglio, L. A., Dufort, M. J., Gitelman, S. E., Gottlieb, P. A., Krischer, J. P., Linsley, P. S., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1902226 · Cited by 1,189
Beltra, J., Manne, S., Abdel-Hakeem, M. S., Kurachi, M., Giles, J. R., Chen, Z., Casella, V., Ngiow, S. F., Khan, O., Huang, Y., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.04.014 · Cited by 1,139
“Conversely, as exhaustion gets more severe, clinical symptoms of autoimmunity decrease (McKinney et al., 2015).”
Background
Metadata
- Authors
- McKinney, E. F., Lee, J. C., Jayne, D. R. W., Lyons, P. A., & Smith, K. G. C.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- McKinney, Eoin F.
- Smith, Kenneth G. C.
- Venue
- Nature
- Published
- 2015-06-29
- Volume
- 523
- Issue
- 7562
- Pages
- 612-616
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4623162
- OpenAlex
- W2103056503
- Cited by
- 685
- References
- 56
- Citations by year
- 2026: 45 · 2025: 54 · 2024: 82 · 2023: 45 · 2022: 81 · 2021: 65 · 2020: 73 · 2019: 74 · 2018: 65 · 2017: 48 · 2016: 40 · 2015: 10
- FWCI
- 26.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (083650/Z/07/Z, 104064, 104064/Z/14/Z, 100140, 079895/Z/06/Z, 083650, 079895, 100140/Z/12/Z, 094227, 094227/Z/10/Z, 079895/Z/06/B)
- Medical Research Council (MR/L019027/1)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Keywords
- T cell exhaustion, CD8+ T cells, PD-1, chronic viral infection, autoimmune disease, inflammatory diseases, prognosis, hepatitis C virus, vaccine response, type 1 diabetes, systemic lupus erythematosus, antineutrophil cytoplasmic antibody-associated vasculitis, idiopathic pulmonary fibrosis, dengue hemorrhagic fever, gene expression signatures, autoreactivity
- MeSH terms
- Animals, Autoimmune Diseases, Humans, Infections, Inflammation, Lupus Erythematosus, Systemic, Phenotype, Receptors, Antigen, T-Cell, Inflammatory Bowel Diseases, CD4-Positive T-Lymphocytes, Autoimmunity, CD8-Positive T-Lymphocytes, CD2 Antigens, Receptors, Interleukin-7, Mice, Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis, Transcriptome, Programmed Cell Death 1 Receptor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
