Cell GenomicsGenomicsPeer-reviewed journal article
Implications of noncoding regulatory functions in the development of insulinomas
Mireia Ramos-RodrígueziD, Marc Subirana-GranésiD, Richard NorrisiD, Valeria SordiiD, Ángel Fernández, Georgina Fuentes-Páez, Beatriz Pérez-GonzáleziD, Clara Berenguer Balaguer, Helena Raurell‐VilaiD, Murad ChowdhuryiD, Raquel CorripioiD, Stefano PartelliiD, Núria López-BigasiD, Silvia PellegriniiD, Eduard MontanyaiD, Montserrat NacheriD, Massimo FalconiiD, Ryan M. LayeriD, Meritxell RoviraiD, Abel González-PéreziD, Lorenzo PiemontiiD, Lorenzo PasqualiiD
Abstract
Insulinomas are rare neuroendocrine tumours arising from the pancreatic {beta}-cells. While retaining the ability to produce insulin, insulinomas feature aberrant proliferation and altered hormone secretion resulting in failure to maintain glucose homeostasis. With the aim of uncovering the role of noncoding regulatory regions and their aberrations to the development of these tumors, we coupled epigenetic and gene expression profiling with whole-genome sequencing. As a result, we mapped H3K27ac sites in the tumoral tissue and unraveled overlapping somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development and epigenetic modifying genes, including key components of the polycomb complex. Chromatin remodeling is apparent as insulinoma-selective regions are mostly clustered in regulatory domains, shared across patients and containing a specific set of regulatory sequences dominated by the binding motif of the transcription factor SOX17. Moreover, a large fraction of these regions are H3K27me3-repressed in unaffected {beta}-cells, suggesting that tumoral transition is coupled with derepression of {beta}-cell polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements and transcription factors that alter {beta}-cell function and fate in their progression to pancreatic neuroendocrine tumors and a framework to identify coding and noncoding driver mutations.
- organism: human
- assay: ChIP-seq
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
- FWCI
- 0.91
- Field percentile
- top 26%
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