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Cited by 379

The DOI matches its Crossref record; 127 of the 127 references it cites were found in a registry or an index.

Integrating canonical and metabolic signalling programmes in the regulation of T cell responses

Open access
Pollizzi, Kristen N., Powell, Jonathan D.·2014·Nature Reviews Immunology

Abstract

Over the past decade, our understanding of T cell activation, differentiation and function has markedly expanded, providing a greater appreciation of the signals and pathways that regulate these processes. It has become clear that evolutionarily conserved pathways that regulate stress responses, metabolism, autophagy and survival have crucial and specific roles in regulating T cell responses. Recent studies suggest that the metabolic pathways involving MYC, hypoxia-inducible factor 1α (HIF1α), AMP-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) are activated upon antigen recognition and that they are required for directing the consequences of T cell receptor engagement. The purpose of this Review is to provide an integrated view of the role of these metabolic pathways and of canonical T cell signalling pathways in regulating the outcome of T cell responses.

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Metadata

Authors
Pollizzi, K. N., & Powell, J. D.
Venue
Nature Reviews Immunology
Published
2014-06-25
Volume
14
Issue
7
Pages
435-446
ISSN
  • 1474-1733 (ISSN-L)
  • 1474-1741
OpenAlex
W2046959528
Cited by
379
References
127
FWCI
3.14
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Integrating canonical and metabolic signalling programmes in the regulation of T cell responses. https://app.cochat.ai/papers/10.1038/nri3701
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Pollizzi, K. N., & Powell, J. D. (2014). Integrating canonical and metabolic signalling programmes in the regulation of T cell responses. Nature Reviews Immunology. https://doi.org/10.1038/nri3701

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.