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

• We present 2 novel gene expression time series datasets, of human and rat cells.
• We analyzed 8 datasets of beta-cell gene expression after cytokine exposure.
• Genes were ranked by relevance and characterized. A regulatory network was inferred.
• Predicted interactions were experimentally validated.

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.