Temporal profiling of cytokine-induced genes in pancreatic β-cells by meta-analysis and network inference.
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.
Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels, Belgium; Interuniversity Institute of Bioinformatics in Brussels (IB)(2), Brussels, Belgium. Electronic address: mlopes@ulb.ac.be.
Miguel
M
Lopes
Institute for Systems Biology, Seattle, USA. Electronic address: burak.kutlu@systemsbiology.org.
Burak
B
Kutlu
Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium. Electronic address: mmiani@ulb.ac.be.
Michela
M
Miani
Diagnostic Unit and Center for Non-Coding RNA in Technology and Health, Glostrup Research Institute, Glostrup Hospital, University of Copenhagen, Glostrup, Denmark. Electronic address: claus.heiner.bang-berthelsen@regionh.dk.
Claus H
CH
Bang-Berthelsen
Diagnostic Unit and Center for Non-Coding RNA in Technology and Health, Glostrup Research Institute, Glostrup Hospital, University of Copenhagen, Glostrup, Denmark. Electronic address: joachim.stoerling@regionh.dk.
Joachim
J
Størling
Diagnostic Unit and Center for Non-Coding RNA in Technology and Health, Glostrup Research Institute, Glostrup Hospital, University of Copenhagen, Glostrup, Denmark. Electronic address: flemming.pociot@regionh.dk.
Flemming
F
Pociot
Institute for Systems Biology, Seattle, USA. Electronic address: natg@shore.net.
Nathan
N
Goodman
Institute for Systems Biology, Seattle, USA. Electronic address: lhood@systemsbiology.org.
Lee
L
Hood
Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden. Electronic address: Nils.Welsh@medcellbiol.uu.se.
Nils
N
Welsh
Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels, Belgium; Interuniversity Institute of Bioinformatics in Brussels (IB)(2), Brussels, Belgium. Electronic address: gbonte@ulb.ac.be.
Gianluca
G
Bontempi
Laboratory of Experimental Medicine, Faculty of Medicine, Université Libre de Bruxelles, Brussels, Belgium. Electronic address: deizirik@ulb.ac.be.
Decio L
DL
Eizirik
D000818:Animals; D016207:Cytokines / Q000378:metabolism* / Q000494:pharmacology; D004268:DNA-Binding Proteins / Q000235:genetics; D003922:Diabetes Mellitus, Type 1; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D050417:Insulin-Secreting Cells / Q000187:drug effects / Q000502:physiology*; D007371:Interferon-gamma / Q000378:metabolism / Q000494:pharmacology; D007515:Islets of Langerhans / Q000502:physiology; D050783:Proto-Oncogene Proteins c-ets / Q000235:genetics; D051381:Rats; D053475:Receptor-Interacting Protein Serine-Threonine Kinase 2 / Q000235:genetics; D015203:Reproducibility of Results; D014157:Transcription Factors / Q000235:genetics
D016207:Cytokines; D004268:DNA-Binding Proteins; C105529:ELF3 protein, human; D050783:Proto-Oncogene Proteins c-ets; D014157:Transcription Factors; D007371:Interferon-gamma; C506253:RIPK2 protein, human; D053475:Receptor-Interacting Protein Serine-Threonine Kinase 2
Cytokines; Diabetes; Gene expression; Meta-analysis; Network inference; Pancreatic beta cells; Time series