Abstract
BackgroundNeural stem cells offer potential treatment for neurodegenerative disorders, such like Alzheimer's disease (AD). While much progress has been made in understanding neural stem cell function, a precise description of the molecular mechanisms regulating neural stem cells is not yet established. This lack of knowledge is a major barrier holding back the discovery of therapeutic uses of neural stem cells. In this paper, the regulatory mechanism of mouse neural stem cell (NSC) differentiation by tmem59 is explored on the genome-level.ResultsWe identified regulators of tmem59 during the differentiation of mouse NSCs from a compendium of expression profiles. Based on the microarray experiment, we developed the parallelized SWNI algorithm to reconstruct gene regulatory networks of mouse neural stem cells. From the inferred tmem59 related gene network including 36 genes, pou6f1 was identified to regulate tmem59 significantly and might play an important role in the differentiation of NSCs in mouse brain. There are four pathways shown in the gene network, indicating that tmem59 locates in the downstream of the signalling pathway. The real-time RT-PCR results shown that the over-expression of pou6f1 could significantly up-regulate tmem59 expression in C17.2 NSC line. 16 out of 36 predicted genes in our constructed network have been reported to be AD-related, including Ace, aqp1, arrdc3, cd14, cd59a, cds1, cldn1, cox8b, defb11, folr1, gdi2, mmp3, mgp, myrip, Ripk4, rnd3, and sncg. The localization of tmem59 related genes and functional-related gene groups based on the Gene Ontology (GO) annotation was also identified.ConclusionsOur findings suggest that the expression of tmem59 is an important factor contributing to AD. The parallelized SWNI algorithm increased the efficiency of network reconstruction significantly. This study enables us to highlight novel genes that may be involved in NSC differentiation and provides a shortcut to identifying genes for AD.
Highlights
Neural stem cells offer potential treatment for neurodegenerative disorders, such like Alzheimer’s disease (AD)
neural stem cell (NSC) related microarrays are selected We selected microarrays about NSCs, neurogenesis, glias and central nervous system (CNS), due to that NSCs are the principal source of constitutive neurogenesis and glias in the CNS. 146 microarray datasets were selected from 21 different platforms
In this study, we predicted the mouse NSCs related GRNs by the parallelized Stepwise Network Inference (SWNI) algorithm integrating data from the tmem59 knock out microarray datasets and 62 mouse stem cell related microarray datasets in Gene Expression Omnibus (GEO)
Summary
Neural stem cells offer potential treatment for neurodegenerative disorders, such like Alzheimer’s disease (AD). A major focus on microarray data analysis is the reconstruction of gene regulatory networks, which aims to find new gene functions and provide insights into the transcriptional regulation that underlies biological processes [5]. A wide variety of approaches have been proposed to infer gene regulatory networks from microarray data. Those approaches are based on different theories, including Boolean networks [6], Bayesian networks [7], relevance networks [8], graphical models [9], genetic algorithm [10], neural networks [11], controlled language-generating automata [12], linear differential equations [13], and nonlinear differential equations [14]. A fundamental problem with the sequential algorithms is their limitation to handle large data sets within a reasonable time and memory resources
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