CellsMolecular BiologyPeer-reviewed journal article
Bromodomain protein inhibition protects β-cells from cytokine-induced death and dysfunction via antagonism of NF-κB pathway.
Vinny NegiiD, Jeongkyung LeeiD, Varun Mandi, Joseph Danvers, Ruya LiuiD, Eliana Melissa Perez-GarciaiD, Feng LiiD, Rajaganapati Jagannathan, Ping YangiD, Domenic FilingeriiD, Amit KumariD, Ke MaiD, Mousumi MoulikiD, Vijay K. YechooriD
Abstract
Cytokine induced {beta}-cell apoptosis is the major pathogenic mechanism in type 1 diabetes (T1D). Despite significant advances in understanding underlying mechanisms, few drugs have been translated to protect {beta}-cells in T1D. Epigenetic modulators such as bromodomain-containing BET (Bromo- and Extra-Terminal) proteins are important regulators of immune responses. Pre-clinical studies have demonstrated a protective effect of BET inhibitors in NOD (non-obese diabetes) mouse model of T1D. However, the role of BET proteins in {beta}-cell function in response to cytokines is unknown. Here we demonstrate that I-BET, a BET protein inhibitor, protected {beta}-cells from cytokine induced dysfunction and death. In vivo administration of I-BET to mice exposed to low-dose STZ (streptozotocin), a model of T1D, significantly reduced {beta}-cell apoptosis and preserved {beta}-cell mass, suggesting a cytoprotective function of I-BET. Furthermore, human islets treated with I-BET displayed better glucose stimulated insulin secretion compared to controls, when exposed to cytokines. Mechanistically, RNA-Seq analysis revealed I-BET treatment suppressed pathways involved in apoptosis, including NF-kB signaling, while maintaining the expression of genes critical for {beta}-cell function, such as Pdx1 and Ins1. Taken together, this study demonstrates that I-BET is effective in protecting {beta}-cells from cytokine-induced dysfunction and apoptosis, and may have potential therapeutic values in T1D.
- organism: mouse
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 4
- FWCI
- 1.06
- Field percentile
- top 26%
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