The DOI matches its Crossref record; 163 of the 163 references it cites were found in a registry or an index.
Transcriptional and Epigenetic Control of T Helper Cell Specification: Molecular Mechanisms Underlying Commitment and Plasticity
Open accessAbstract
T helper cell differentiation occurs in the context of the extracellular cytokine milieu evoked by diverse microbes and other pathogenic stimuli along with T cell receptor stimulation. The culmination of these signals results in specification of T helper lineages, which occurs through the combinatorial action of multiple transcription factors that establish distinctive transcriptomes. In this manner, inducible, but constitutively active, master regulators work in conjunction with factors such as the signal transducer and activator of transcriptions (STATs) that sense the extracellular environment. The acquisition of a distinctive transcriptome also depends on chromatin modifications that impact key cis elements as well as the changes in global genomic organization. Thus, signal transduction and epigenetics are linked in these processes of differentiation. In this review, recent advances in understanding T helper lineage specification and deciphering the action of transcription factors are summarized with emphasis on comprehensive views of the dynamic T cell epigenome.
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“Differentiation of T cells to specific TH lineages is driven by specific transcription factors but reinforced by epigenetic modifications on histone tails (H3K4 and H3K27 trimethylation) and DNA methylation of CpG dinucleotides (103).”
From Fueling Immunity: Insights into Metabolism and Lymphocyte Function (2013) · cites this paper as [103]
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Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
Human DNA methylomes at base resolution show widespread epigenomic differences
Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., et al. (2009) · Nature · doi:10.1038/nature08514 · Cited by 4,673
Histone H3K27ac separates active from poised enhancers and predicts developmental state
Creyghton, M. P., Cheng, A. W., Welstead, G. G., Kooistra, T., Carey, B. W., Steine, E. J., Hanna, J., Lodato, M. A., Frampton, G. M., Sharp, P. A., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016071107 · Cited by 4,302
Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals
Guttman, M., Amit, I., Garber, M., French, C., Lin, M. F., Feldser, D., Huarte, M., Zuk, O., Carey, B. W., Cassady, J. P., et al. (2009) · Nature · doi:10.1038/nature07672 · Cited by 4,098
A Mammalian microRNA Expression Atlas Based on Small RNA Library Sequencing
Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., Landthaler, M., et al. (2007) · Cell · doi:10.1016/j.cell.2007.04.040 · Cited by 3,626
The Transcriptional Landscape of the Mammalian Genome
Carninci, P., Kasukawa, T., Katayama, S., Gough, J., Frith, M. C., Maeda, N., Oyama, R., Ravasi, T., Lenhard, B., Wells, C. A., et al. (2005) · Science · doi:10.1126/science.1112014 · Cited by 3,492
Differentiation of Effector CD4 T Cell Populations
Zhu, J., Yamane, H., & Paul, W. E. (2010) · Annual Review of Immunology · doi:10.1146/annurev-immunol-030409-101212 · Cited by 3,452
Tet proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine
Ito, S., Shen, L., Dai, Q., Wu, S. C., Collins, L. B., Swenberg, J. A., He, C., & Zhang, Y. (2011) · Science · doi:10.1126/science.1210597 · Cited by 3,451
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Fueling Immunity: Insights into Metabolism and Lymphocyte Function
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JAK and STAT Signaling Molecules in Immunoregulation and Immune-Mediated Disease
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Self-citationThe JAK-STAT pathway at 30: Much learned, much more to do
Philips, R. L., Wang, Y., Cheon, H., Kanno, Y., Gadina, M., Sartorelli, V., Horvath, C. M., Darnell, J. E., Jr., Stark, G. R., & O’Shea, J. J. (2022) · Cell · doi:10.1016/j.cell.2022.09.023 · Cited by 755
Self-citationHillmer, E. J., Zhang, H., Li, H. S., & Watowich, S. S. (2016) · Cytokine & Growth Factor Reviews · doi:10.1016/j.cytogfr.2016.05.001 · Cited by 740
Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity
Brown, C. C., Gudjonson, H., Pritykin, Y., Deep, D., Lavallée, V., Mendoza, A., Fromme, R. M., Mažutis, L., Ariyan, C. E., Leslie, C. S., et al. (2019) · Cell · doi:10.1016/j.cell.2019.09.035 · Cited by 601
Metadata
- Authors
- Kanno, Y., Vahedi, G., Hirahara, K., Singleton, K., & O'Shea, J. J.
- Affiliations
- National Institutes of Health (US)
- Molecular Immunology and Inflammation Branch, National Institutes of Arthritis, and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland 20892;
- Venue
- Annual Review of Immunology
- Publisher
- Annual Reviews
- Published
- 2012-04-23
- Volume
- 30
- Issue
- 1
- Pages
- 707-731
- ISSN
- 0732-0582 (ISSN-L)
- 1545-3278
- PMC
- PMC3314163
- OpenAlex
- W2095649879
- Cited by
- 342
- References
- 163
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 12 · 2023: 10 · 2022: 16 · 2021: 19 · 2020: 13 · 2019: 21 · 2018: 22 · 2017: 33 · 2016: 34 · 2015: 49 · 2014: 45 · 2013: 40 · 2012: 9
- FWCI
- 4.22
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Immunology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99‐AA999999, Z99 AR999999, Z99 DA999999)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
- Keywords
- T helper cell differentiation, transcription factors, chromatin modification, epigenetics, STAT signaling, T cell receptor signaling, cytokine signaling, T cell transcriptome, T cell plasticity, signal transduction
- MeSH terms
- Animals, Autoimmune Diseases, Cell Differentiation, Gene Expression Regulation, T-Lymphocytes, Helper-Inducer, Humans, Transcription Factors, Transcription, Genetic, Cell Lineage, RNA, Untranslated, Epigenesis, Genetic, Regulatory Elements, Transcriptional, Epigenomics
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