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An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury.

ABSTRACT: The regenerative response of Schwann cells after peripheral nerve injury is a critical process directly related to the pathophysiology of a number of neurodegenerative diseases. This SC injury response is dependent on an intricate gene regulatory program coordinated by a number of transcription factors and microRNAs, but the interactions among them remain largely unknown. Uncovering the transcriptional and post-transcriptional regulatory networks governing the Schwann cell injury response is a key step towards a better understanding of Schwann cell biology and may help develop novel therapies for related diseases. Performing such comprehensive network analysis requires systematic bioinformatics methods to integrate multiple genomic datasets.In this study we present a computational pipeline to infer transcription factor and microRNA regulatory networks. Our approach combined mRNA and microRNA expression profiling data, ChIP-Seq data of transcription factors, and computational transcription factor and microRNA target prediction. Using mRNA and microRNA expression data collected in a Schwann cell injury model, we constructed a regulatory network and studied regulatory pathways involved in Schwann cell response to injury. Furthermore, we analyzed network motifs and obtained insights on cooperative regulation of transcription factors and microRNAs in Schwann cell injury recovery.This work demonstrates a systematic method for gene regulatory network inference that may be used to gain new information on gene regulation by transcription factors and microRNAs.

SUBMITTER: Chang LW

PROVIDER: S-EPMC3599357 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury.

Chang Li-Wei LW Viader Andreu A Varghese Nobish N Payton Jacqueline E JE Milbrandt Jeffrey J Nagarajan Rakesh R

BMC genomics 20130206

<h4>Background</h4>The regenerative response of Schwann cells after peripheral nerve injury is a critical process directly related to the pathophysiology of a number of neurodegenerative diseases. This SC injury response is dependent on an intricate gene regulatory program coordinated by a number of transcription factors and microRNAs, but the interactions among them remain largely unknown. Uncovering the transcriptional and post-transcriptional regulatory networks governing the Schwann cell inj ...[more]

PMID: 23387820

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Project description:Rationale: Peripheral nerves are unique in their remarkable elasticity. Schwann cells (SCs), important components of the peripheral nervous system (PNS), are constantly subjected to physiological and mechanical stresses from dynamic stretching and compression forces during movement. So far, it is not clear if SCs sense and respond to mechanical signals. It is also unknown whether mechanical stimuli can interfere with the intercellular communications between neurons and SCs, and what role extracellular vesicles (EVs) play in this process. The present study aimed to examine the effect of mechanical stimuli on the EV-mediated intercellular communication between neurons and SCs, explore their effect on axonal regeneration, and investigate the underlying mechanism. Methods: Purified SCs were stimulated using a magnetic force-based mechanical stimulation (MS) system and EVs were purified from mechanically stimulated SCs (MS-SCs-EVs) and non-stimulated SCs (SCs-EVs). The effect of MS-SCs-EVs on axonal elongation was examined in vitro and in vivo. High throughput miRNA sequencing was performed to compare the differential miRNA profiles between MS-SCs-EVs and SCs-EVs. The functional role of differentially expressed miRNAs on neurite extension in MS-SCs-EVs was examined. Also, the putative target genes of differentially expressed miRNAs in MS-SCs-EVs were predicted by bioinformatics tools, and the regulatory effect of those miRNAs on putative target genes was validated both in vitro and in vivo. Results: The MS-SCs-EVs showed an average size of 137.52±1.77 nm, and could be internalized by dorsal root ganglion (DRG) neurons. Compared to SCs-EVs, MS-SCs-EVs showed a stronger ability to enhance neurite outgrowth in vitro and nerve regeneration in vivo. High throughput miRNA sequencing identified a number of differentially expressed miRNAs in MS-SCs-EVs. Further analysis of those EV-miRNAs demonstrated that miR-23b-3p played a predominant role in MS-SCs-EVs since its deprivation abolished their enhanced axonal elongation. Furthermore, we identified neuropilin 1 (Nrp1) in neurons as the target gene of miR-23b-3p in MS-SCs-EVs. This observation was supported by the evidence that miR-23b-3p could decrease Nrp1-3'-UTR-WT luciferase activity in vitro and down-regulate Nrp1 expression in neurons. Conclusion: Our findings suggested that mechanical stimuli are capable of modulating the intercellular communication between neurons and SCs by altering miRNA composition in MS-SCs-EVs. Transfer of miR-23b-3p by MS-SCs-EVs from mechanically stimulated SCs to neurons decreased neuronal Nrp1 expression, which was responsible, at least in part, for the beneficial effect of MS-SCs-EVs on axonal regeneration. Our results highlighted the potential therapeutic value of MS-SCs-EVs and miR-23b-3p-enriched EVs in peripheral nerve injury repair.

Project description:OBJECTIVE:Colorectal cancer (CRC) is an extremely common gastrointestinal malignancy. The present study aimed to identify microRNAs (miRNAs) and transcription factors (TFs) associated with tumor development. METHODS:Three miRNA profile datasets were integrated and analyzed to elucidate the potential key candidate miRNAs in CRC. The starBase database was used to identify the potential targets of common differentially expressed miRNAs (DEMs). Transcriptional Regulatory Element Database and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text databases were used to identify cancer-related TFs and the TF-regulated target genes. Functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integration Discovery (DAVID) database, and the miRNA-TF-gene networks were constructed by Cytoscape. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression of genes and miRNAs. RESULTS:In total, 14 DEMs were found in CRC. By bioinformatics analysis, 5 DEMs (miR-145, miR-497, miR-30a, miR-31, and miR-20a) and 8 TFs (ELK4 (ETS-family transcription factor), myeloblastosis proto-oncogene like (MYBL)1, MYBL2, CEBPA, PPARA, PPARD, PPARG, and endothelial PAS domain protein (EPAS1)) appeared to be associated with CRC and were therefore used to construct miRNA-TF-gene networks. From the networks, we found that miR-20a might play the most important role as an miRNA in the networks. By qRT-PCR, we demonstrated that miR-20a was significantly upregulated in CRC tissues. We also performed qRT-PCR to identify the expression of miR-20a-related TFs (PPARA, PPARD, PPARG, EPAS1). Three of them, PPARA, PPARG, and EPAS1, were downregulated in CRC tissues, with statistically significant differences, while the downregulation of PPARD in CRC tissues was not significantly different. Pathway enrichment analyses indicated that the phosphoinositide 3-kinase (PI3K)-Akt signaling pathway was the most significantly enriched pathway. Two main elements of the PI3K-Akt signaling pathway, phosphatase and tensin homolog deleted on chromosome 10 and B-cell lymphoma 2-associated agonist of cell death, were demonstrated to be downregulated in CRC. CONCLUSION:The present study identified hub miRNAs and miRNA-related TF regulatory networks in CRC, which might be potential targets for the diagnosis and treatment of CRC.

Project description:BACKGROUND: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. RESULTS: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network.We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. CONCLUSIONS: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.

Project description:Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. Recent studies revealed that patterns of microRNA (miRNA) expression in GBM tissue samples are different from those in normal brain tissues, suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM. However, little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action. To address this issue, in this study, we systematically explored the main regulation format (feed-forward loops, FFLs) consisting of miRNAs, transcription factors (TFs) and their impacting GBM-related genes, and developed a computational approach to construct a miRNA-TF regulatory network. First, we compiled GBM-related miRNAs, GBM-related genes, and known human TFs. We then identified 1,128 3-node FFLs and 805 4-node FFLs with statistical significance. By merging these FFLs together, we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network. Then, from the network, we extracted a composite GBM-specific regulatory network. To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components, we specifically examined a Notch signaling pathway subnetwork. Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs (miR-124, miR-137, miR-219-5p, miR-34a, miR-9, and miR-92b) might play important roles in GBM, including some results that are supported by previous studies. In this study, we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM, providing a valuable resource for further understanding the complex regulatory mechanisms in GBM. The observation of critical miRNAs in the Notch signaling pathway, with partial verification from previous studies, demonstrates that our network-based approach is promising for the identification of new and important miRNAs in GBM and, potentially, other cancers.

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An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury.

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An integrated approach to characterize transcription factor and microRNA regulatory networks involved in Schwann cell response to peripheral nerve injury.

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