Moein Yaqubi - National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran These authors contributed equally to this work.
Abdulshakour Mohammadnia - National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran These authors contributed equally to this work.
Sajad Esmaeili - Department of Biology, School of Science, Guilan University, Rasht, Iran
Hossein Fallahi - Medical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran Department of Biology, School of Science, Razi University, Kermanshah, Iran

Direct conversion of somatic cell or differentiation of iPS cells are two ways that can produce induced neural stem cells (iNSC). Here we analyze gene regulatory (GRN) network during direct conversion of mouse fibroblast to iNSC to reveal motifs pattern that have role in this conversion. Also we evaluate differentiation of iPS cells to iNS cells GRN to findtranscription factors (TF) that regulate MAPK signaling cascade. Method: GSE31598 is accession number of the data of this study in GEO server. Flexarray software was used to normalization and detection of differentially expressed (DE) genes. Interactions between TFs and DE genes obtained from ChEA database. Protein interactions identified in BioGRID database. Our constructed graph was used for identify pattern of motifs inFANMOD software. To identify affected processes, ClueGO tools was used. JActiveModules Cytoscape plugin was used todetect core active subnetwork. Result: During direct reprogramming of mouse fibroblast into iNSCs 2167 DE genes identified. We identify 46 TFs regulate 85%of DE genes during this conversion. Our best constructed network contains 1857 nodes and 11054 interactions. We find 26motifs with z score more than 2 and p value less than 0.05. Expression profile comparison of primary neurosphere and iPSCs shows 2521 DE genes. 56 TFs regulates expression of 2235 gene out of 2521 DE genes in network. Network ontology analysis reveals 54 DE genes involve in MAPK signalingpathway. We identify SUZ12, POU5F1, TRP53, NANOG, STAT3, as most important regulators of MAPK signaling respectively. Core active modules analysis show presence of 5 DE genes from MAPK signaling list in 5 top modules

Direct reprogramming, iPSCs, MAPK signaling, Motif, Regulatory network