IL-12 inhibits glucocorticoid-induced T cell apoptosis by inducing GMEB1 and activating PI3K/Akt pathway

Kawabe, Kiyokazu; Lindsay, Daniel; Braitch, Manjit; Fahey, Angela J.; Showe, Louise C.; Constantinescu, Cris S.

doi:10.1016/j.imbio.2011.07.018

Cited by 16 publications

(13 citation statements)

References 38 publications

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“…For example, among the top 10 positive and negative coefficients (Figure 9B) we found the factor Gmeb1 . This factor was shown to inhibit T-cell apoptosis (67). Another TF with a positive coefficient is Ets1 , which was shown to be critical for T-cell development (68).…”

Section: Resultsmentioning

confidence: 99%

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

et al. 2016

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The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

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Section: Resultsmentioning

confidence: 99%

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

et al. 2016

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“…Signaling molecules activated by the TCR, such as AP-1 and NF-κB, inhibit glucocorticoid-induced apoptosis and conversely glucocorticoids prevent TCR activation-induced cell death [58]. Cytokines such as, IL-2, IL-4, IL-10, and IL-12 also protect T cells from glucocorticoid-induced apoptosis [56, 59, 60]. …”

Section: Glucocorticoid-induced Apoptosis Of T Cellsmentioning

confidence: 99%

A gradient of glucocorticoid sensitivity among helper T cell cytokines

Banuelos

2016

Cytokine & Growth Factor Reviews

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Helper T (Th) cells secret specific cytokines that promote immune responses whereas glucocorticoids limit the extent of immune responses by inhibiting cytokine secretion and other functions of Th cells. However, glucocorticoid resistance develops in subgroups of patients with Th cell-driven diseases such as asthma and Crohn’s disease. Recent evidence supports that Th1, Th2, and Th17 cells have distinct glucocorticoid sensitivity. Th1 cells are sensitive to glucocorticoid-induced apoptosis and cytokine suppression while Th2 cells are sensitive to the latter but not the former and Th17 cells are resistant to both. This gradient of glucocorticoid sensitivity of Th cells corresponds to the glucocorticoid sensitivity of the diseases they underlie. We identify the mechanisms contributing to distinct glucocorticoid sensitivity of Th cells and their cytokines in the literature, as this information is useful to improve treatment strategies for glucocorticoid resistant immunological disorders.

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“…27 Several general approaches have been proposed to identify TFs acting as key players 28 in gene regulation depending on the available data: Coexpression analysis combined 29 with computational predictions of TF sequence binding can be used to identify key 30 TFs [18]. Genome-wide TF binding data, as produced by TF ChIP-seq, is widely used 31 to identify important TFs: ChIP-seq data was incorporated into coexpression 32 analysis [43], was combined with transcriptome data [49,66], used for the construction 33 of Gene Regulatory Networks (GRNs) [8], and used together with Hi-C data [40].…”

mentioning

confidence: 99%

“…and negative coefficients (Figure 9b) we found the factor Gmeb1. This factor was shown 435 to inhibit T-cell apoptosis [33]. Another TF with a positive coefficient is Ets1, which 436 was shown to be critical for T-cell development [17].…”

mentioning

confidence: 99%

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Schmidt

Gasparoni

et al. 2016

Preprint

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The binding and contribution of transcription factors (TF) to cell specific gene Using machine learning, we find low affinity binding sites to improve our ability to 10 explain gene expression variability compared to the standard presence/absence 11 classification of binding sites. Further, we show that both footprints and peaks capture 12 essential TF binding events and lead to a good prediction performance. In our nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly 15

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IL-12 inhibits glucocorticoid-induced T cell apoptosis by inducing GMEB1 and activating PI3K/Akt pathway

Cited by 16 publications

References 38 publications

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

A gradient of glucocorticoid sensitivity among helper T cell cytokines

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

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