Multi-omics data compendium: Data package 8 (Pck008)

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Authors: Soumyadeep Sarkar, Javier E. Flores, Navaj Nune, MIREIA RAMOS-RODRÍGUEZ, Abhishek Kulkarni, Ashley N. Ives, Batoul Hammoud, Wenting Wu, Xiaoyan Yi, Arturo Roca-Rivada, Marlie M. Maestas, Austin Gluth
+21 more Emily C. Elliott, Youngki You, Preethi Krishnan, Farooq Syed, Young-Mo Kim, Jennifer Kyle, Tatsuyoshi Kono, James M. Fulcher, Elizabeth Sims, Sarah A. Tersey, Lori Sussel, Bobbie‐Jo Webb‐Robertson, Weijun Qian, Tong Zhang, Thomas O. Metz, Jeffrey R. Millman, Carmella Evans‐Molina, Lorenzo Pasquali, Raghavendra G. Mirmira, Decio Laks Eizirik, Ernesto Satoshi Nakayasu
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Abstract

In type 1 diabetes (T1D), autoimmune response and inflammation cause the death of pancreatic ß cells, leading to the body’s inability to produce insulin and maintain glucose homeostasis. This process is at least in part mediated by pro-inflammatory cytokines, such as interferon (IFN)a, IFN?, interleukin (IL)-1ß, and tumor necrosis factor (TNF)a, which induce ß-cell dysfunction and apoptosis. A deep understanding of the ß-cell signaling and regulatory networks induced by these cytokines could lead to the identification of therapeutic targets to prevent T1D development. To study cytokine-mediated islets/ß-cell signaling and regulatory networks, a variety of omics experiments have been conducted, including transcriptomics, epigenomics (DNA methylation, UMI-4C, ATAC-seq & ChIP-seq), proteomics (bottom-up, top-down, post-translational modification analysis), lipidomics, and metabolomics. The combination of these datasets can be instrumental in identifying signaling components and regulatory factors involved in ß-cell stress/death. Here, we aggregated these multiple omics datasets into a centralized location, providing a quality-controlled and statistically rigorous resource for investigators seeking to holistically study ß-cell regulation by pro-inflammatory cytokines.

Publication details

Journal / source
DOE Pacific Northwest National Laboratory (PNNL) Repository
Publication date
7 January 2026
Document type
Dataset