Interaction of JAK with steroid receptor function

Gupta, Nandini; Mayer, Doris

doi:10.4161/jkst.24911

Cited by 19 publications

(17 citation statements)

References 86 publications

(89 reference statements)

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“…Other information as Figure 7. as previously reported (Gupta and Mayer 2013). The current observations provided convincing evidence that scalp hair follicles are strikingly capable of reflecting such HIIT adaptations.…”

Section: Resultssupporting

confidence: 90%

“…). Moreover, the distinctly downregulated KEGG pathway Steroid hormone biosynthesis might be the linked to the observed inhibition of JAK‐STAT associated hormone expression, as previously reported (Gupta and Mayer ). The current observations provided convincing evidence that scalp hair follicles are strikingly capable of reflecting such HIIT adaptations.…”

Section: Resultssupporting

confidence: 77%

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Human hair follicle transcriptome profiling: a minimally invasive tool to assess molecular adaptations upon low‐volume, high‐intensity interval training

et al. 2017

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High‐intensity interval training (HIIT) has become a popular fitness training approach under both civilian and military settings. Consisting of brief and intense exercise intervals, HIIT requires less time commitment yet is able to produce the consistent targeted physical adaptations as conventional endurance training. To effectively characterize and monitor HIIT‐induced cellular and molecular responses, a highly accessible yet comprehensive biomarker discovery source is desirable. Both gene differential expression (DE) and gene set (GS) analyses were conducted using hair follicle transcriptome established from pre and postexercise subjects upon a 10‐day HIIT program by RNA‐Seq, Comparing between pre and posttraining groups, differentially expressed protein coding genes were identified. To interpret the functional significance of the DE results, a comprehensive GS analysis approach featuring multiple algorithms was used to enrich gene ontology (GO) terms and KEGG pathways. The GS analysis revealed enriched themes such as energy metabolism, cell proliferation/growth/survival, muscle adaptations, and cytokine–cytokine interaction, all of which have been previously proposed as HIIT responses. Moreover, related cell signaling pathways were also measured. Specifically, G‐protein‐mediated signal transduction, phosphatidylinositide 3‐kinases (PI3K) – protein kinase B (PKB) and Janus kinase (JAK) – Signal Transducer and Activator of Transcription (STAT) signaling cascades were over‐represented. Additionally, the RNA‐Seq analysis also identified several HIIT‐responsive microRNAs (miRNAs) that were involved in regulating hair follicle‐specific processes, such as miR‐99a. For the first time, this study demonstrated that both existing and new biomarkers like miRNA can be explored for HIIT using the transcriptomic responses exhibited by the hair follicle.

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Section: Resultssupporting

confidence: 90%

Section: Resultssupporting

confidence: 77%

Human hair follicle transcriptome profiling: a minimally invasive tool to assess molecular adaptations upon low‐volume, high‐intensity interval training

et al. 2017

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“…This suggests that ganetespib’s ability to decrease both AR and GR expression may be associated with increased SUM159PT cytotoxic sensitivity compared to MDA-MB-231 cells. The role of JAK/STAT signaling in TNBC tumorigenesis and chemoresistance is well established [43, 44]; interestingly, GR and JAK/STAT pathways crosstalk to inhibit cytokines and promote apoptosis of immune cells, leading to immunosuppression and suppressing inflammation [45]. The interplay between GR signaling and Hsp90 client proteins suggest that proteins encoded by the GR transcriptional network can also be inactivated by Hsp90 inhibition.…”

Section: Discussionmentioning

confidence: 99%

Hsp90 Inhibition Results in Glucocorticoid Receptor Degradation in Association with Increased Sensitivity to Paclitaxel in Triple-Negative Breast Cancer

et al. 2016

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Targetable molecular drivers for triple-negative breast cancer (TNBC) have been difficult to identify; therefore standard treatment remains limited to conventional chemotherapy. Recently, new-generation small molecule Hsp90 inhibitors (e.g. ganetespib and NVP-AUY922) have demonstrated improved safety and activity profiles over the first generation ansamycin class. In breast cancer, clinical responses have been observed in a subset of TNBC patients following ganetespib monotherapy, however the underlying biology of Hsp90 inhibitor treatment and tumor response is not well understood. Glucocorticoid receptor (GR) activity in TNBC is associated with chemotherapy resistance. Here we find that treatment of TNBC cell lines with ganetespib resulted in GR degradation and decreased GR- mediated gene expression. Ganetespib-associated GR degradation also sensitized TNBC cells to paclitaxel-induced cell death both in vitro and in vivo. The beneficial effect of the Hsp90 inhibitor on paclitaxel-induced cytotoxicity was reduced when GR was depleted in TNBC cells, but could be recovered with GR overexpression. These findings suggest that GR-regulated anti-apoptotic and pro- proliferative signaling networks in TNBC are disrupted by Hsp90 inhibitors, thereby sensitizing TNBC to paclitaxel-induced cell death. Thus, GR+ TNBC patients may be a subgroup of breast cancer patients who are most likely to benefit from adding an Hsp90 inhibitor to taxane therapy.

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“…We found several categories related to haematopoiesis (selected with green color in Table S5). Among the enriched pathways were 'JakSTAT signalling pathway', 'estrogen signalling pathway' 43 and 'GPVImediated activation cascade' (the latter plays a role in activation and aggregation of platelets). Furthermore, Gene Ontology analysis showed enrichment for the 'Regulation of cytokine production' term.…”

Section: Sc3 Characterises Subclones In Myeloproliferative Neoplasmmentioning

confidence: 99%

SC3 - consensus clustering of single-cell RNA-Seq data

Kiselev

Kirschner²,

Schaub

et al. 2016

Preprint

249

358

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Using single-cell RNA-seq (scRNA-seq), the full transcriptome of individual cells can be acquired, enabling a quantitative cell-type characterisation based on expression profiles. However, due to the large variability in gene expression, identifying cell types based on the transcriptome remains challenging. We present Single-Cell Consensus Clustering (SC3), a tool for unsupervised clustering of scRNA-seq data. SC3 achieves high accuracy and robustness by consistently integrating different clustering solutions through a consensus approach. Tests on twelve published datasets show that SC3 outperforms five existing methods while remaining scalable, as shown by the analysis of a large dataset containing 44,808 cells. Moreover, an interactive graphical implementation makes SC3 accessible to a wide audience of users, and SC3 aids biological interpretation by identifying marker genes, differentially expressed genes and outlier cells. We illustrate the capabilities of SC3 by characterising newly obtained transcriptomes from subclones of neoplastic cells collected from patients.

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Interaction of JAK with steroid receptor function

Cited by 19 publications

References 86 publications

Human hair follicle transcriptome profiling: a minimally invasive tool to assess molecular adaptations upon low‐volume, high‐intensity interval training

Human hair follicle transcriptome profiling: a minimally invasive tool to assess molecular adaptations upon low‐volume, high‐intensity interval training

Hsp90 Inhibition Results in Glucocorticoid Receptor Degradation in Association with Increased Sensitivity to Paclitaxel in Triple-Negative Breast Cancer

SC3 - consensus clustering of single-cell RNA-Seq data

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