Temporal profiling of cytokine-induced genes in pancreatic beta-cells by meta-analysis and network inference
2014 (English)In: Genomics, ISSN 0888-7543, E-ISSN 1089-8646, Vol. 103, no 4, 264-275 p.Article in journal (Refereed) Published
Type I Diabetes (T1D) is an autoimmune disease where local release of cytokines such as IL-1 beta and IFN-gamma contributes to beta-cell apoptosis. To identify relevant genes regulating this process we performed a meta-analysis of 8 datasets of beta-cell gene expression after exposure to IL-1 beta and IFN-gamma. 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.
Place, publisher, year, edition, pages
2014. Vol. 103, no 4, 264-275 p.
Diabetes, Pancreatic beta cells, Cytokines, Gene expression, Meta-analysis, Time series, Network inference
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy) Medical Genetics
IdentifiersURN: urn:nbn:se:uu:diva-228495DOI: 10.1016/j.ygeno.2013.12.007ISI: 000336884900003OAI: oai:DiVA.org:uu-228495DiVA: diva2:734189