Temporal profiling of cytokine-induced genes in pancreatic β-cells by meta-analysis and network inference.

Lopes M, Kutlu B, Miani M, Bang-Berthelsen CH, Størling J, Pociot F, Goodman N, Hood L, Welsh N, Bontempi G, Eizirik DL

Genomics 103 (4) 264-275 [2014-04-00; online 2014-01-28]

Type 1 Diabetes (T1D) is an autoimmune disease where local release of cytokines such as IL-1β and IFN-γ contributes to β-cell apoptosis. To identify relevant genes regulating this process we performed a meta-analysis of 8 datasets of β-cell gene expression after exposure to IL-1β and IFN-γ. Two of these datasets are novel and contain time-series expressions in human islet cells and rat INS-1E cells. Genes were ranked according to their differential expression within and after 24 h from exposure, and characterized by function and prior knowledge in the literature. A regulatory network was then inferred from the human time expression datasets, using a time-series extension of a network inference method. The two most differentially expressed genes previously unknown in T1D literature (RIPK2 and ELF3) were found to modulate cytokine-induced apoptosis. The inferred regulatory network is thus supported by the experimental validation, providing a proof-of-concept for the proposed statistical inference approach.

NGI Stockholm (Genomics Applications)

NGI Stockholm (Genomics Production)

QC bibliography QC xrefs

PubMed 24462878

DOI 10.1016/j.ygeno.2013.12.007

Crossref 10.1016/j.ygeno.2013.12.007

GEO GSE53453

GEO GSE53454