Primer sequences(96K, docx) Acknowledgements We thank Isabelle Millard, Nathalie Pachera, Michael Pangerl, Ying Cai and Anyisha? Musuaya from the ULB Center for Diabetes Research for excellent technical and experimental support

Primer sequences(96K, docx) Acknowledgements We thank Isabelle Millard, Nathalie Pachera, Michael Pangerl, Ying Cai and Anyisha? Musuaya from the ULB Center for Diabetes Research for excellent technical and experimental support. Abbreviations CPACyclopiazonic acidDAVIDDatabase for Annotation, Visualization and Integrated DiscoveryEREndoplasmic reticulumFFAFree fatty acidsIPAIngenuity Pathway AnalysisRNA-seqRNA-sequencingROSReactive oxygen species Authors contributions ML1, KG, ML2, VR, AP, XY, HJ, JL, MIE, DAC, LM, HO, and MC generated and analyzed experimental data; LM and PM contributed materials and samples, PM CP671305 and DLE contributed to the study design and provided expert advice; ML1, ML2, HO and MC wrote the manuscript. mRNA expression measured by qPCR. (E-G) INS-1E cells were transfected with control siRNA or two Creb3l2 siRNAs. (E) Creb3l2 mRNA expression measured by qPCR. (F) Insulin secretion after incubation with 1.7?mM and 16.7?mM glucose and (G) insulin content following Creb3l2 knockdown. Insulin secretion and content were measured by ELISA and corrected by total protein CP671305 content. Data are from 4 to 7 independent experiments. *was used (criteria for selection non-adjusted em p /em ? ?0.001). 53 regulators were obtained and added to the set of differentially expressed genes/proteins (2 of them were already present – the added 51 regulators are ATF2, MEF2C, NFE2L1, NF1, USF1, RFX1, BACH1, CUX1, POU2F1, CREB1, NFYA, HNF1A, TCF3, ARNT, STAT3, FOXO1, PML, ACLY, HNF4A, LSS, LAMC1, APP, CDKN1A, MTA3, PTEN, E2F4, SCAP, PCM1, HDAC10, LPIN1, WT1, KRAS, SIRT1, RRP1B, MLXIPL, SLC2A1, CP671305 ATM, PPP3CA, ITGAV, PNPLA2, VEGFA, TOPBP1, E2F3, IDH2, ABCA1, ALG2, IQCB1, MBNL2, EIF2B3, ACOT8, and SLC25A10). A prior regulatory network was obtained by associating the enriched transcription factors to the respective targets, and including regulations obtained in the TRANSFAC [85] and RegNetwork [86] databases, involving the novel set of 258 genes/proteins. In the end, a prior network of 3082 regulations between 258 genes/proteins was obtained (1877 regulations from DAVID, 232 regulations from IPA, 938 regulations from TRANSFAC, 551 regulations from RegNetwork). Network inference from expression dataA regulatory network was inferred in the RNA-seq and proteomic datasets separately. In the RNA-seq data, fold change values were used (the minimum RPKM was set to 0.1). Inference was done on 6 samples (of fold change values). On both datasets, the data was log2 transformed and the expression of each gene/protein was divided by its standard deviation. In both datasets, network inference was done on a Rabbit Polyclonal to OR6P1 variable scoring manner. For each gene/protein, that gene/protein is considered a target variable, and all other genes/proteins are scored with respect to their predictive value towards it. In the proteomics dataset, the inference was directed, making use of the fact that different time points were used. In this case, the target variable takes the form 4h#1, 4h#2, 16h#1, 16h#2, 24h#1, 24h#2. The predictor variables take the form CP671305 0h#1, 0h#2, 4h#1, 4h#2, 16h#1, 16h#2. In the RNA-seq dataset, the inference was undirected, and the regulation score between two genes was the maximum of the two scores obtained when each of the genes was considered as target. A random forest algorithm was used to score predictors of a target variable. A similar approach has been proposed in GENIE3 [87]. This was implemented in R using the package randomForest RF [88]. The number of trees was set to 20,000 and the number of variables randomly sampled as candidates at each split was set to 244/3. The adopted score (variable importance) is the total decrease in node impurities from splitting on the variable, averaged over all trees (node impurity measured by the residual sum of squares). A null distribution of random scores was obtained by shuffling the data and repeating the network inference procedure. Using this distribution, original regulation scores were associated to a em p /em -value. Regulations (edges) were selected if em p /em ? ?0.001 or alternatively if em p /em ? ?0.05 and the regulation was present in the prior network. This analysis was performed for the 2 datasets (RNA-seq and proteomics) separately. The two obtained networks were then merged and a final network of 416 regulations involving 190 genes/proteins was obtained. Treatments For validation and functional studies, INS-1E cells and dispersed human islets were exposed in independent experiments to 0.5?mM palmitate precomplexed to 0.67% CP671305 FFA-free BSA for 24?h. For these experiments, human.