Identification of neuron-related genes for cell therapy of neurological disorders by network analysis#

Identification of neuron-related genes for cell therapy of neurological disorders by network analysis # # 基于芯片数据的神经分化基因网络互作分析为神经系统疾病的细胞疗法提供基础 first-author corresp ‡Corresponding Author ‡Corresponding Author †E-mail:whp123456@sina.com †E-mail:whp123456@sina.com whp123456@sina.com Department of Biology, Hebei North University, Zhangjiakou 075029, China Department of Biology, Hebei North University, Zhangjiakou 075029, China Bone mesenchymal stem cells (BMSCs) differentiated into neurons have been widely proposed for use in cell therapy of many neurological disorders. It is therefore important to understand the molecular mechanisms underlying this differentiation. We screened differentially expressed genes between immature neural tissues and untreated BMSCs to identify the genes responsible for neuronal differentiation from BMSCs. GSE68243 gene microarray data of rat BMSCs and GSE18860 gene microarray data of rat neurons were received from the Gene Expression Omnibus database. Transcriptome Analysis Console software showed that 1248 genes were up-regulated and 1273 were down-regulated in neurons compared with BMSCs. Gene Ontology functional enrichment, protein-protein interaction networks, functional modules, and hub genes were analyzed using DAVID, STRING 10, BiNGO tool, and Network Analyzer software, revealing that nine hub genes, Nrcam , Sema3a , Mapk8 , Dlg4 , Slit1 , Creb1 , Ntrk2 , Cntn2 , and Pax6 , may play a pivotal role in neuronal differentiation from BMSCs. Seven genes, Dcx , Nrcam , Sema3a , Cntn2 , Slit1 , Ephb1 , and Pax6 , were shown to be hub nodes within the neuronal development network, while six genes, Fgf2 , Tgfβ1 , Vegfa , Serpine1 , Il6 , and Stat1 , appeared to play an important role in suppressing neuronal differentiation. However, additional studies are required to confirm these results. 目的 2 通过筛选差异基因，获得控制骨髓间充质干细胞向神经细胞分化及神经发育的中心基因，为治疗神经系统疾病提供参考。 方法 2 从基因表达综合数据库（Gene Expression Omnibus database）中获得芯片数据，利用生物信息学软件筛选差异基因，并对差异基因进行GO功能富集、蛋白互作网络分析和中心基因分析。 结论 2 通过分析，初步推测 Nrcam 、 Sema3a 、 Mapk8 、 Dlg4 、 Slit1 、 Creb1 、 Ntrk2 、 Cntn2 和 Pax6 等中心基因在调控骨髓间充质干细胞向神经细胞的分化中发挥重要作用； Dcx 、 Nrcam 、 Sema3a 、 Cntn2 、 Slit1 、 Ephb1 和 Pax6 等中心基因在神经发育过程中发挥作用； Fgf2 、 Tgfβ1 、 Vegfa 、 Serpine1 、 Il6 和 Stat1 等中心基因在抑制神经分化过程中发挥作用。 Neuronal differentiation Bone mesenchymal stem cells (BMSCs) Protein-protein interaction network Differentially expressed genes 神经分化 骨髓间充质干细胞 蛋白质相互作用网络 差异基因 Introduction 1 Neurological disorders are problems of the nervous system, which may be possible to treat by the transplantation and differentiation of stem cells to enhance nerve regeneration. Several studies have shown that bone mesenchymal stem cells (BMSCs) can be induced and differentiated into neural cells under specific, controlled microenvironments (Jiang et al., 2005 2005 ; Çapkın et al., 2012 2012 ). Thus, BMSCs are known as “seed cells” in the cell therapy of many neurological disorders and have the advantage of being easy to isolate. Many factors have been reported to induce neuronal cell differentiation from BMSCs, including basic fibroblast growth factor, epidermal growth factor, neurotrophic factors, vascular endothelial growth factor, hepatocyte growth factor, β-mercaptoethanol, dimethylsulfoxide, and butylated hydroxyanisole (Bae et al., 2011 2011 ; Edamura et al., 2014 2014 ; Parivar et al., 2015 2015 ). Bertani et al. ( 2005 2005 ) previously showed that gene expression was altered in BMSCs exposed to butylated hydroxyanisole and dimethylsulfoxide neural induction medium, but did not match the set of genes differentially expressed in immature neural tissues compared with untreated BMSCs. However, because of the many factors involved with neuronal cell differentiation, including risk, survival time, and instability, the use of BMSCs has limited application in the clinic. In particular, the process of BMSC differentiation into nerve cells is unstable, and so it is important to define more stable differentiation conditions. In this study, microarray data were examined to identify genes expressed differentially between immature neural tissues and BMSCs. Bioinformatic methods were used to analyze Gene Ontology (GO) functional enrichment and to construct protein-protein interaction (PPI) networks. We also identified high connectivity (hub genes) associated with neuronal differentiation and neuronal development. Materials and methods 1 Materials 2 The gene expression profiles of GSE68243 and GSE18860 were obtained from the Gene Expression Omnibus database ( http://www.ncbi.nlm.nih.gov/ http://www.ncbi.nlm.nih.gov/ geo). Dataset GSE68243 includes three untreated Sprague-Dawley rat BMSC samples (GSM1666210, GSM1666211, and GSM1666212), while dataset GSE18860 includes three cortical neuron samples from d18 Sprague-Dawley rat embryos (GSM4674922, GSM4674923, and GSM4674924). Both datasets are based on GPL6247 platform information. Data preprocessing 2 The data downloaded as CEL files were preprocessed using Expression Console™ software and underwent background correction and quartile data normalization by the robust multiarray average algorithm (Irizarry et al., 2003 2003 ) with default parameters. Non-annotated probes were filtered out. Screening of differentially expressed genes and hierarchical cluster analysis 2 Differentially expressed genes (DEGs) between neurons and BMSCs were identified by one-way between-subjects analysis of variance based on Transcriptome Analysis Console software (Torres et al., 2015 2015 ). Genes with fold-changes |log 2 | > 4 were selected as DEGs, and P of < 0.05 and false discovery rate (FDR) of < 0.05 were used as thresholds. Functional enrichment analysis 2 GO is widely used in functional studies and the enrichment analysis of gene sets. The DAVID (Sherman et al., 2007 2007 ) gene annotation tool was used to analyze GO enrichment in DEG functions. Values of P < 0.05 and FDR < 0.05 were again used as cutoff criteria. GO terms were displayed as a significant network using the BiNGO Plug-in of Cytoscape v. 3.2.1 software (Shannon et al., 2003 2003 ). DEGs associated with neural differentiation were clustered in neuronal differentiation and regulation of cell proliferation modules. Construction of GO:0030182, GO:0048666, and GO:0042127 PPI networks, and the identification of hub genes 2 The STRING 10 database (Snel et al., 2000 2000 ) provides known and predicted interactions based on confidence. The interactions contain direct (physical) and indirect (functional) associations, which are derived from genomic context, experiments, coexpression, and previous knowledge. Genes in the neuronal differentiation and regulation of cell proliferation modules were considered to be seed nodes and were mapped onto the STRING database to construct extended PPI networks with medium confidence scores of 0.4. All PPI networks were visualized by Cytoscape v. 3.2.1 software and calculated using the Network Analyzer tool (Assenov et al., 2008 2008 ) based on parameters of betweenness centrality (BC) and degree. BC reflects the number of shortest paths that pass one node, and is essential in the analysis of nodal importance. The degree indicates the number of interactions of a particular protein. In the final networks, nodes with a high degree were displayed within a large circle while shades of green and yellow represented high to low BC values for the node (Assenov et al., 2008 2008 ). In this study, nodes with thresholds of BC > 0.05 and degrees above the average value of each network were considered to be hub genes. Results 1 DEG screening 2 The standardization of expression data revealed a good degree based on all the black lines in Fig. 1 1 on the same straight line. A total of 2521 genes were identified as being differentially expressed between neurons and BMSCs, 1248 of which were up-regulated and 1273 down-regulated in neurons compared with BMSCs (Table S1 S1 ). Hierarchical cluster analysis showed that the three neuronal samples were distributed within the neuronal sample cluster and that the three BMSC samples were within the BMSC sample cluster (Fig. 2 2 ). This showed that grouping was rational and that the data could be used directly for further analysis. The horizontal axis represents sample names while the vertical axis means the expression value The horizontal axis shows sample names. GSM1666210, GSM1666211, and GSM1666212 are BMSC samples. GSM4674922, GSM4674923, and GSM4674924 are neuron samples. The right vertical axis represents clusters of DEGs, and the above horizontal axis means clusters of samples. Red means up-regulated genes and green signifies down-regulated genes Functional enrichment analysis 2 GO enrichment analyzed using DAVID software revealed that the up-regulated genes were significantly enriched in the biological processes of neuronal differentiation (GO:0030182), neuronal development (GO:0048666), transmission of nerve impulses (GO:0019226), synaptic transmission (GO: 0007268), and neuron projection development (GO: 0031175) (Table S2 S2 , Fig. 3a 3a ), while down-regulated genes were significantly enriched in regulation of cell proliferation (GO:0042127), response to organic substances (GO:0010033), wound healing (GO: 0042060), response to wounding (GO:0009611), and blood vessel development (GO:0001568) (Table S2 S2 , Fig. 3b 3b ). GO terms for up-regulated and down-regulated genes were calculated using the BiNGO Plug-in of Cytoscape software, and significant terms shown using yellow nodes (Fig. 4 4 ). (a) The top 20 GO enrichment terms of up-regulated genes of neurons compared with BMSCs; (b) The top 20 GO enrichment terms of down-regulated genes of neurons compared with BMSCs. The horizontal axis represents the number of DEGs. The vertical axis represents the enriched GO terms. P values increase from bottom to top (a) Up-regulated genes GO terms; (b) Down-regulated genes GO terms. Yellow nodes: nodes with P -value < 0.05 and FDR < 0.05 PPI network analysis of GO:0030182, GO:0048666, and GO:0042127 2 To investigate the molecular mechanism of neural differentiation from BMSCs, we selected the up-regulated genes involved in neuronal differentiation (GO:0030182) and neuronal development (GO:0048666), and the down-regulated genes involved in regulation of cell proliferation (GO: 0042127) because these are closely associated with neuronal cell differentiation. Genes of the three GO terms (Table 1 1 ) were used to construct PPI networks with the STRING database. Networks were visualized and analyzed using Cytoscape v. 3.2.1 software. Analysis of nodal properties in each network revealed average degrees of 6.39 for GO:0030182 nodes, 4.88 for GO:0048666 nodes, and 11.94 for GO:0042127 nodes. A total of nine genes were identified as hub genes for neuronal differentiation: neuronal cell adhesion molecule ( Nrcam ), semaphoring-3A ( Sema3a ), mitogen-activated protein kinase 8 ( Mapk8 ), discs large MAGUK scaffold protein 4 ( Dlg4 ), slit homolog 1 ( Slit1 ), cyclic adenosine monophosphate (cAMP)-responsive element binding protein 1 ( Creb1 ), neurotrophic tyrosine kinase receptor 2 ( Ntrk2 ), contactin 2 ( Cntn2 ), and paired box gene 6 ( Pax6 ); seven genes were identified as hub genes for neuronal development: doublecortin ( Dcx ), Nrcam , Sema3a , Cntn2 , Slit1 , ephrin type-B receptor 1 ( Ephb1 ), and Pax6 ; and six genes were identified as hub genes for the regulation of cell proliferation: basic fibroblast growth factor 2 ( Fgf2 ), transforming growth factor β-1 ( Tgfβ1 ), vascular endothelial growth factor A ( Vegfa ), plasminogen activator inhibitor-1 ( Serpine1 ), interleukin 6 ( Il6 ), and signal transducer and activator of transcription ( Stat1 ) (Fig. 5 5 and Table 2 2 ). GO terms Biological process Genes GO:0030182 Neuron differentiation Gprin1 , Plxna3 , Efna2 , Rorb , I1cam , Cspg5 , Klhl1 , Nrcam , Atp2b2 , Ank3 , Unc5a , Robo1 , Ss18l1 , Dscam , Stmn3 , Emx1 , Efnb3 , Mdga1 , Mdga2 , Kif5c , Dll1 , Tbr1 , Ctnna2 , Slitrk2 , Ascl1 , Slitrk1 , Ncam2 , Slitrk3 , Foxg1 , Reln , Mapk8 , Stmn1 , Igsf9 , Slitrk5 , Cdk5r1 , Lppr4 , Sox2 , Sox5 , Rgd1311558 , Ephb1 , Ephb2 , Arx , Crmp1 , Lhx2 , Lhx6 , Olfm3 , Bhlhe22 , Celsr3 , Evl , Celsr2 , Gas7 , Dlx2 , Fezf2 , Epha4 , Epha7 , Nrep , Dlx1 , Sema6c , Ntrk2 , Map2 , Chrnb2 , Syngap1 , Atl1 , Agtpbp1 , Uchl1 , Pax6 , Vgf , Gpc2 , Casp3 , Dynll2 , Mapt , Dlg4 , Sema3a , Dlg2 , Tubb3 , Kcnma1 , Slit1 , Psd , Sema4f , Cntn2 , Cntn4 , Gap43 , Dcc , Rab3a , Cck , Ndn , Nnat , Brsk1 , Bcl11b , Pou3f4 , Pou3f2 , Cd24 , Dcx , Snap25 , Nefl , Dclk1 , Dfna5 , Gnao1 , Ptprz1 , Creb1 , Nfasc , Dpysl5 , Nlgn1 , Ntng2 , Dpysl4 , Isl1 , Wnt7b , Ppp1r9a , Neurod2 , Wnt7a , Apbb1 , Fez1 GO:0048666 Neuron development Gprin1 , Plxna3 , Atl1 , Efna2 , Uchl1 , Pax6 , I1cam , Rorb , Cspg5 , Klhl1 , Nrcam , Atp2b2 , Ank3 , Unc5a , Dynll2 , Robo1 , Dlg4 , Sema3a , Ss18l1 , Dlg2 , Dscam , Stmn3 , Efnb3 , Kif5c , Tbr1 , Slit1 , Ctnna2 , Slitrk2 , Ncam2 , Ascl1 , Slitrk1 , Slitrk3 , Sema4f , Foxg1 , Cntn2 , Cntn4 , Mapk8 , Reln , Stmn1 , Igsf9 , Slitrk5 , Gap43 , Dcc , Rab3a , Cdk5r1 , Lppr4 , Cck , Ndn , Rgd1311558 , Ephb1 , Ephb2 , Arx , Crmp1 , Bcl11b , Lhx2 , Lhx6 , Cd24 , Dcx , Olfm3 , Snap25 , Nefl , Dclk1 , Gnao1 , Ptprz1 , Creb1 , Nfasc , Nlgn1 , Ntng2 , Celsr3 , Dpysl4 , Celsr2 , Evl , Isl1 , Gas7 , Fezf2 , Epha4 , Epha7 , Nrep , Wnt7b , Ppp1r9a , Sema6c , Ntrk2 , Map2 , Neurod2 , Chrnb2 , Syngap1 , Apbb1 , Fez1 GO:0042127 Regulation of cell proliferation Dlc1 , Apobec1 , Thrb , Ptgs2 , Il6st , Osmr , Pgf , Pdgfa , Ptgs1 , Fgfrl1 , Tgfβ3 , Fgf10 , Tlr4 , Jag1 , Pmaip1 , Il15 , Cxcl12 , Tgfβ1 , Tgfβ2 , H19 , Pgr , Myd88 , S1pr1 , Cdkn2b , Serpine1 , Pdgfc , Rarg , Mfge8 , Thy1 , Vegfc , Cd38 , Serpinf1 , Ptprv , F3 , Vegfa , Pdgfra , Wfdc1 , Tgif1 , Ripk2 , Pdgfrb , Pmp22 , Ngf , Cav2 , Cav1 , Ccl2 , Ifitm3 , Ifi30 , Itgb1 , Irak4 , Lif , Vdr , Itgav , Runx2 , Runx3 , Bmp4 , B4galt1 , Bmp2 , Sphk1 , Tgfbr2 , Hgf , Shox2 , Notch2 , Pla2g4a , Cdkn1a , Atf3 , Cd274 , Avpr1a , Kctd11 , Klf4 , Plau , Sat1 , Ppard , Fgf7 , Acvrl1 , Pparg , Prrx1 , Gja1 , Pawr , Prrx2 , Tenc1 , Ddr2 , Wisp2 , Ang , Hmox1 , Hey2 , Shc1 , Fgf1 , Il13ra1 , Fgf2 , Fosl1 , Lyn , Cdk6 , Mbd2 , Mmp12 , Pura , Cth , Ccnd1 , Adam17 , Tbx18 , Eif5a2 , Csf1 , Nr3c1 , Stat6 , Camk2d , Plcd1 , Cd28 , Nox4 , Ptprc , Il6 , Tbx3 , Tbx2 , Anxa1 , Sparc , Stat1 , Clec11a , Cyba , Nupr1 , Id3 BC: betweenness centrality; AD: average degree of all nodes Network name Gene Degree BC AD 9 Neuron differentiation Nrcam 11 0.052 742 72 9 6.39 Sema3a 15 0.053 825 15 Mapk8 12 0.090 814 13 Dlg4 16 0.103 825 38 Slit1 19 0.108 426 54 Cntn2 12 0.110 418 82 Creb1 10 0.113 116 98 Ntrk2 13 0.122 851 31 Pax6 24 0.163 991 00 7 Neuron development Dcx 6 0.058 705 83 7 4.88 Nrcam 7 0.066 951 81 Sema3a 10 0.145 349 80 Cntn2 10 0.268 125 51 Slit1 11 0.101 701 35 Ephb1 11 0.138 484 58 Pax6 13 0.181 375 54 6 Regulation of cell proliferation Fgf2 38 0.059 946 33 6 11.94 Tgfβ1 34 0.063 818 16 Vegfa 40 0.074 373 06 Serpine1 26 0.074 690 11 Il6 39 0.116 884 44 Stat1 40 0.126 546 91 Node color: shades of green to yellow color depict node with highest to lowest values of betweenness centrality (BC); Node size: sizes from biggest to smallest circle mean the node degrees. Bigger and dark colored nodes represent genes with more links Discussion 1 Neuronal differentiation from BMSCs offers a number of advantages for cell therapy of neurological disorders, but an understanding of the underlying cellular and genetic mechanisms is required. Through the comparative analyses of gene expression data in neuronal cells and BMSCs, we identified a total of 2521 DEGs between the two cell types, of which 1248 were up-regulated and 1273 were down-regulated in neuronal cells compared with BMSCs (Table S1 S1 ). GO functional enrichment analysis showed that of the up-regulated genes, 10.46% were associated with the neuronal differentiation GO term, 8.22% with the neuronal development GO term, and 7.28% with the transmission of nerve impulses. Among the down-regulated genes, GO functions were associated with the regulation of cell proliferation, which is related to cell differentiation, response to organic substances, and wound healing (Table S2 S2 ). Neuronal differentiation, neuronal development, and cell proliferation are closely associated with neuronal cell differentiation, indicating that the findings could be viewed with high confidence. In the present study, specific DEGs were up-regulated in neural tissue compared with untreated BMSCs. These include Sema3a , Mapk8 , Nrcam , Dlg4 , Slit1 , Creb1 , Ntrk2 , Cntn2 , and Pax6 , which play important roles in neuronal differentiation, while Dcx , Nrcam , Sema3a , Cntn2 , Slit1 , Ephb1 , and Pax6 may have a critical role in neuronal development. Fgf2 , Tgfβ1 , Vegfa , Serpine1 , Il6 , and Stat1 were selected as key genes in the regulation of cell proliferation network, and were down-regulated in neural tissue compared with untreated BMSCs; this implies a role in suppressing neuronal differentiation. Slit1 has previously been reported to be involved in the regeneration and functional recovery of the trigeminal ganglion and inferior alveolar nerve (Ceber et al., 2015 2015 ), while a role for Slit1 was identified in the development of dopaminergic neurons, which is mediated by NCK adaptor protein 2 to trigger changes in cortical neuron morphology (Round and Sun, 2011 2011 ). PAX6 is a highly conserved multifunctional transcription factor, which has been demonstrated to be a key protein in neurogenesis and neuronal plasticity by influencing cascades of gene expression (Mishra et al., 2015 2015 ). In retinoic acid-induced neuronal differentiation, PAX6 was found to be repressed by the recruitment of histone modifier lysine-specific demethylase 1 (Wu et al., 2016 2016 ). Our results showed that Pax6 is a key gene in neuronal differentiation and neuronal development. CNTN2 was previously suggested to control axonal growth both in vitro and in vivo. Ratie et al. ( 2014 2014 ) showed that Cntn2 expression was restricted to the early axon scaffold populations, cranial ganglia, and spinal motor neurons, as well as mature neurons. However, we observed a likely role for Cntn2 in neuronal differentiation and development. NRCAM, a neuronal cell adhesion molecule, is important in aspects of neural development including cell proliferation and differentiation, axonal growth and guidance, synapse formation, and formation of the myelinated nerve structure (Sakurai, 2012 2012 ), which is involved in neuron-neuron and neuron-glial adhesions. NRCAM also modulates axonal growth and guidance by a mechanism involving the neuronal receptor axonin-1 (Lustig et al., 1999 1999 ). NRCAM deletion in mice previously led to elevated spine densities on the apical dendrites of star pyramidal cells during both postnatal and adult stages. NRCAM was also found to form a complex with semaphorin 3F (Sema3F) in the brain to regulate the spine density of star pyramidal neurons (Demyanenko et al., 2014 2014 ). Dcx expression was followed in migrating and differentiating neurons in the brains of embryonic mouse (Francis et al., 1999 1999 ). In postnatal mice, reducing the levels of DCX caused abnormal neuronal migration, which affected neuronal development in the postnatal forebrain (Belvindrah et al., 2011 2011 ). Within the two main adult neurogenic regions, Dcx was shown to be transiently expressed in dividing neural progenitor cells until neuronal maturation (Brown et al., 2003 2003 ). We observed a likely role for DCX and NRCAM in neuronal development. Sema3a is a member of the class 3 secreted semaphorin and has been shown to induce growth cone collapse or repel sensory axons of the dorsal root ganglia in vitro (Wu et al., 2014 2014 ) and regulate adult neurogenesis by controlling stem cells proliferation and differentiation in the subventricular zone (Sun et al., 2016 2016 ). A recent study has shown that distinct cytoplasmic domains in Plexin-A4 mediated diverse responses to Sema3a in developing mammalian neurons (Mlechkovich et al., 2014 2014 ). EPHB1 has been indicated to play important roles in synaptic plasticity of the nervous system (Liu et al., 2009 2009 ), with Ephb1 null mice exhibiting neuronal loss in the substantia nigra pars reticulata and spontaneous locomotor hyperactivity (Richards et al., 2007 2007 ). Little is known about the role of Dlg4 , Ntrk2 , Creb1 , or Mapk8 in neuronal differentiation and development. In the present study, Stat1 was represented as the hub gene of regulation of the cell proliferation network, showing the highest degree and BC. The knockdown of Stat1 previously demonstrated that interferon γ (IFN-γ) inhibits the differentiation of neuronal precursor cells by negatively regulating the expression of Neurog2 via the Janus kinase/Stat pathway (Ahn et al., 2015 2015 ). Pereira et al. ( 2015 2015 ) reported that IFN-γ induced neuronal differentiation and acted as an antiproliferative factor via STAT1 in the adult subventricular zone, thereby regulating neurogenesis in normal adult brains. IL-6 has been shown to suppress neurogenesis within human retinal cell suspensions in the presence of N2 culture medium containing FGF2, which enhances neurosphere generation (Balasubramaniam et al., 2009 2009 ). FGF2 was reported to be a potent neurotrophic factor that promotes the differentiation of BMSCs into dopaminergic neurons and enhances their survival both in vitro and in vivo (Nandy et al., 2014 2014 ). FGF2 has also been widely used to induce neuronal differentiation in mice and primates, including humans, but was shown to promote self-renewal in a macaque embryonic stem cell line; its effects on the differentiation of stem cells appear to be influenced by the presence or absence of supplemental retinoic acid (Hatori et al., 2014 2014 ). Additionally, FGF2 has been found to delay neurogenesis in embryonic chicks (McGowan et al., 2013 2013 ). Moreover, low concentrations of FGF2 increased neurogenesis in human neural progenitor cells while high levels maintained progenitor cell proliferation and blocked neurogenesis (Nelson and Svendsen, 2006 2006 ). This result is consistent with our own findings. VEGF is known to modulate learning and memory, the plasticity of mature neurons, synaptic transmission, and neurogenesis (Yang et al., 2016 2016 ). However, evidence for its role in the regulation of excitatory synaptic activity is conflicting (Yang et al., 2016 2016 ). In our analysis, Vegfa appeared to be an inhibitory factor in neuronal differentiation. A previous study suggested that TGF-β1 enhances neuronal excitability (Zhang et al., 2016 2016 ) and protects midbrain dopaminergic neurons from IFN-γ-induced neurotoxicity (Zhou et al., 2015 2015 ), but there is no evidence to indicate that TGF-β1 suppresses neuronal differentiation. Finally, the role of Serpine1 in neuronal cells is currently unclear. Conclusions 1 According to the centrality-lethality rule, hubs are more likely to be functionally relevant than other genes (Nair et al., 2014 2014 ). 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This article does not contain any studies with human or animal subjects performed by any of the authors. 10.1631/jzus.B1600109 Project supported by the Key Project of Hebei North University (No. 120177) and the Science and Technology Research Project of Hebei Province Department Institutions of Higher Learning (No. Z2015047), China 2016-03-09 2017-01-05 2016-07-13 2022-01-05 Copyright © Zhejiang University and Springer-Verlag Berlin Heidelberg 2017 2017 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 02 18