bioRxivMolecular BiologyPreprint
Tead1 reciprocally regulates adult β-cell proliferation and function to maintain glucose homeostasis
Jeongkyung LeeiD, Ruya LiuiD, Byung S. KimiD, Yiqun ZhangiD, Feng LiiD, Rajaganapti Jagannathan, Ping YangiD, Vinny NegiiD, Joseph Danvers, Eliana Melissa Perez-GarciaiD, Pradip SahaiD, Omaima M. SabekiD, Chad J. CreightoniD, Cristian CoarfaiD, Mark O. HuisingiD, Hung-Ping ShihiD, Rita BottinoiD, Ke MaiD, Mousumi MoulikiD, Vijay K. YechooriD
Abstract
Proliferative quiescence in {beta}-cells is required to maintain functional competence. While this presents a significant hurdle in regenerative therapy for diabetes, the molecular underpinnings of this reciprocal relationship remain unclear. Here, we demonstrate that TEAD1, the transcription effector of the mammalian-Hippo pathway, drives developmental stage-specific {beta}-cell proliferative capacity in conjunction with its functional maturation. TEAD1 promotes adult {beta}-cell mature identity by direct transcriptional control of a network of critical {beta}-cell transcription factors, including, Pdx1, Nkx6.1, and MafA, while its regulation of Cdkn2a maintains proliferative quiescence. Consequently, mice with either constitutive or inducible genetic deletion of TEAD1 in {beta}-cells developed overt diabetes due to a severe loss of secretory function despite induction of proliferation. Furthermore, we show that TEAD1 has a similar regulatory role in human {beta}-cells. Consistent with this function in {beta}-cells, variants in TEAD1 have been associated with c-HOMA-B in American Indians. We propose that TEAD1 is an essential intrinsic molecular switch coordinating adult {beta}-cell proliferative quiescence with mature identity and its differential modulation may be necessary to overcome the challenge of inducing proliferation with functional competence in human beta cells.
- organism: mouse
- organism: human
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- approach: experimental assay
- Citations
- 4
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