title: Unravelling evolution of Nanog, the key transcription factor involved in self-renewal of undifferentiated embryonic stem cells, by pattern recognition in nucleotide and tandem repeats characteristics.
abstract: Nanog, an important transcription factor in embryonic stem cells (ESC), is the key factor in maintaining pluripotency to establish ESC identity and has the ability to induce embryonic germ layers. Nanog is responsible for self-renewal and pluripotency of stem cells as well as cancer invasiveness, tumor cell proliferation, motility and drug-resistance. Understanding the underlying mechanisms of Nanog evolution and regulation can lead to future advances in treatment of cancers. Recent integration of machine learning models with genetics has provided a powerful tool for knowledge discovery and uncovering evolutionary pathways. Herein, sequences of 47 Nanog genes from various species were extracted and two datasets of features were computationally extracted from these sequences. At the first dataset, 76 nucleotide acid attributes were calculated for each Nanog sequence. The second dataset was prepared based on the 10,480 repeated nucleotide sequences (from 5 to 50bp lengths). Then, various data mining algorithms such as decision tree models were applied on these datasets to find the evolutionary pathways of Nanog diversion. Attribute weighting models were highlighted features such as the frequencies of AA and GC as the most important genomic features in Nanog gene classification and differentiation. Similar findings were obtained by tree induction algorithms. Results from the second database showed that some short sequence strings, such as ACTACT, TCCTGA, CCTGA, GAAGAC, and TATCCC can be effectively used to identify Nanog genes in various species. The outcomes of this study, for the first time, unravels the importance of particular genomic features in Nanog gene evolution paving roads toward better understanding of stem cell development and human targeted disorder therapy.
authors.affiliation: Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Molecular Medicine, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Anatomical Sciences, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran; Women's Reproductive Health Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran. Electronic address: pashaim@tbzmed.ac.ir.
authors.forename: Maryam
authors.initials: M
authors.lastname: Pashaiasl
authors.affiliation: Genetic Research Theme, Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, Australia.
authors.forename: Khodadad
authors.initials: K
authors.lastname: Khodadadi
authors.affiliation: Department of Biology, University of Qom, Qom, Iran; Bonn-Aachen International Center for Information Technology (B-IT ), University of Bonn, Bonn, Germany.
authors.forename: Amir Hossein
authors.initials: AH
authors.lastname: Kayvanjoo
authors.affiliation: Department of Molecular Medicine, School of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Anatomy, Medical School, Iran University of Medical Science, Tehran, Iran.
authors.forename: Roghiyeh
authors.initials: R
authors.lastname: Pashaei-Asl
authors.affiliation: Institute of Biotechnology, Shiraz University, Shiraz, Iran; School of Information Technology and Mathematical Sciences, Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, Australia; Department of Genetics and Evolution, School of Biological Sciences, The University of Adelaide, Adelaide, Australia; School of Biological Sciences, Faculty of Science and Engineering, Flinders University, Adelaide, Australia.
authors.forename: Esmaeil
authors.initials: E
authors.lastname: Ebrahimie
authors.affiliation: Department of Biology, University of Qom, Qom, Iran. Electronic address: mansour@future.org.
authors.forename: Mansour
authors.initials: M
authors.lastname: Ebrahimi
mesh_terms: D000818:Animals; D002454:Cell Differentiation / Q000235:genetics; D049109:Cell Proliferation / Q000235:genetics; D030541:Databases, Genetic; D053595:Embryonic Stem Cells; D019143:Evolution, Molecular; D018398:Homeodomain Proteins / Q000235:genetics*; D006801:Humans; D000071317:Nanog Homeobox Protein; D010802:Phylogeny; D017422:Sequence Analysis, DNA; D020080:Tandem Repeat Sequences / Q000235:genetics*
chemical_list: D018398:Homeodomain Proteins; C486780:NANOG protein, human; D000071317:Nanog Homeobox Protein
keywords: Bioinformatics; Gene Evolution; Machine Learning; Nanog; Tandem Repeats