Enhancers regulate spatiotemporal gene expression and impart cell-specific transcriptional outputs that drive cell identity1. Super-enhancers (SEs), also known as stretch-enhancers, are a subset of enhancers especially important for genes associated with cell identity and genetic risk of disease2,3,4,5,6. CD4+ T cells are critical for host defence and autoimmunity. Here we analysed maps of mouse T-cell SEs as a non-biased means of identifying key regulatory nodes involved in cell specification. We found that cytokines and cytokine receptors were the dominant class of genes exhibiting SE architecture in T cells. Nonetheless, the locus encoding Bach2, a key negative regulator of effector differentiation, emerged as the most prominent T-cell SE, revealing a network in which SE-associated genes critical for T-cell biology are repressed by BACH2. Disease-associated single-nucleotide polymorphisms for immune-mediated disorders, including rheumatoid arthritis, were highly enriched for T-cell SEs versus typical enhancers or SEs in other cell lineages7. Intriguingly, treatment of T cells with the Janus kinase (JAK) inhibitor tofacitinib disproportionately altered the expression of rheumatoid arthritis risk genes with SE structures. Together, these results indicate that genes with SE architecture in T cells encompass a variety of cytokines and cytokine receptors but are controlled by a ‘guardian’ transcription factor, itself endowed with an SE. Thus, enumeration of SEs allows the unbiased determination of key regulatory nodes in T cells, which are preferentially modulated by pharmacological intervention.
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Gene Expression Omnibus
All ChIP- and RNA-sequencing data sets have been deposited in the Gene Expression Omnibus under accession number GSE60482.
The authors thank B. Afzali, A. Nussenzweig, A. Poholek, S. Canna, A. Richard and E. Mathe for critically reading this manuscript. We are grateful to R. Faryabi, H.-Y. Shih, W. Resch, M. Ombrello, Z. Deng and E. Remmers for their contributions to experimental and analytical components of this study. We also thank H. Sun, G. Gutierrez-Cruz, J. Simone, J. Lay and K. Tinsley for their excellent technical support. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health. R.R. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 105663/Z/14/Z). This work was supported by the Intramural Research Program of NIAMS and by NCI grant R01 CA186714.
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Scientific Reports (2019)