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Mawla, A. M.

Publications and source records attributed to Mawla, A. M..

2 recordsLinked to original sources

Chromatin accessibility differences between alpha, beta, and delta cells identifies common and cell type-specific enhancers

High throughput sequencing has enabled the interrogation of the transcriptomic landscape of glucagon-secreting alpha cells, insulin-secreting beta cells, and somatostatin-secreting delta cells. These approaches have furthered our understanding of expression patterns that define healthy or diseased islet cell types and helped explicate some of the intricacies between major islet cell crosstalk and glucose regulation. All three endocrine cell types derive from a common pancreatic progenitor, yet alpha and beta cells have partially opposing functions, and delta cells modulate and control insulin and glucagon release. While gene signatures that define and maintain cellular identity have been widely explored, the underlying epigenetic components are incompletely characterized and understood. Chromatin accessibility and remodeling is a dynamic attribute that plays a critical role to determine and maintain cellular identity. Here, we compare and contrast the chromatin landscape between mouse alpha, beta, and delta cells using ATAC-Seq to evaluate the significant differences in chromatin accessibility. The similarities and differences in chromatin accessibility between these related islet endocrine cells help define their fate in support of their distinct functional roles. We identify patterns that suggest that both alpha and delta cells are poised, but repressed, from becoming beta-like. We also identify patterns in differentially enriched chromatin that have transcription factor motifs preferentially associated with different regions of the genome. Finally, we identify and visualize both novel and previously discovered common endocrine- and cell specific- enhancer regions across differentially enriched chromatin.

genomics↗

epiRomics: a multi-omics R package to identify and visualize enhancers

SummaryepiRomics is an R package designed to integrate multi-omics data in order to identify and visualize enhancer regions alongside gene expression and other epigenomic modifications. Regulatory network analysis can be done using combinatory approaches to infer regions of significance such as enhancers, when combining ChIP and histone data. Downstream analysis can identify co-occurrence of these regions of interest with other user-supplied data, such as chromatin availability or gene expression. Finally, this package allows for results to be visualized at high resolution in a stand-alone browser. Availability and ImplementationepiRomics is released under Artistic-2.0 License. The source code and documents are freely available through Github (https://github.com/Huising-Lab/epiRomics). Contactammawla@ucdavis.edu or mhuising@ucdavis.edu Supplementary informationSupplementary data, and methods are available online on biorxiv or Github.

bioinformatics↗