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High-throughput validation of ceRNA regulatory networks.

ABSTRACT: MicroRNAs (miRNAs) play multiple roles in tumor biology. Interestingly, reports from multiple groups suggest that miRNA targets may be coupled through competitive stoichiometric sequestration. Specifically, computational models predicted and experimental assays confirmed that miRNA activity is dependent on miRNA target abundance, and consequently, changes in the abundance of some miRNA targets lead to changes to the regulation and abundance of their other targets. The resulting indirect regulatory influence between miRNA targets resembles competition and has been dubbed competitive endogenous RNA (ceRNA). Recent studies have questioned the physiological relevance of ceRNA interactions, our ability to accurately predict these interactions, and the number of genes that are impacted by ceRNA interactions in specific cellular contexts.To address these concerns, we reverse engineered ceRNA networks (ceRNETs) in breast and prostate adenocarcinomas using context-specific TCGA profiles, and tested whether ceRNA interactions can predict the effects of RNAi-mediated gene silencing perturbations in PC3 and MCF7 cells._ENREF_22 Our results, based on tests of thousands of inferred ceRNA interactions that are predicted to alter hundreds of cancer genes in each of the two tumor contexts, confirmed statistically significant effects for half of the predicted targets.Our results suggest that the expression of a significant fraction of cancer genes may be regulated by ceRNA interactions in each of the two tumor contexts.

SUBMITTER: Chiu HS

PROVIDER: S-EPMC5450082 | biostudies-other | 2017 May

REPOSITORIES: biostudies-other

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High-throughput validation of ceRNA regulatory networks.

Chiu Hua-Sheng HS Martínez María Rodríguez MR Bansal Mukesh M Subramanian Aravind A Golub Todd R TR Yang Xuerui X Sumazin Pavel P Califano Andrea A

BMC genomics 20170530 1

<h4>Background</h4>MicroRNAs (miRNAs) play multiple roles in tumor biology. Interestingly, reports from multiple groups suggest that miRNA targets may be coupled through competitive stoichiometric sequestration. Specifically, computational models predicted and experimental assays confirmed that miRNA activity is dependent on miRNA target abundance, and consequently, changes in the abundance of some miRNA targets lead to changes to the regulation and abundance of their other targets. The resultin ...[more]

PMID: 28558729

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Project description:BackgroundWith the advancement of sequencing technologies, a plethora of noncoding RNA (ncRNA) species have been widely discovered, including microRNAs (miRNAs), circular RNAs (circRNAs), and long ncRNAs (lncRNAs). However, the mechanism of these non-coding RNAs in diseases caused by enterovirus d68 (EV-D68) remains unclear. The goal of this research was to identify significantly altered circRNAs, lncRNAs, miRNAs, and mRNAs pathways in RD cells infected with EV-D68, analyze their target relationships, demonstrate the competing endogenous RNA (ceRNA) regulatory network, and evaluate their biological functions.MethodsThe total RNAs were sequenced by high-throughput sequencing technology, and differentially expressed genes between control and infection groups were screened using bioinformatics method. We discovered the targeting relationship between three ncRNAs and mRNA using bioinformatics methods, and then built a ceRNA regulatory network centered on miRNA. The biological functions of differentially expressed mRNAs (DEmRNAs) were discovered through GO and KEGG enrichment analysis. Create a protein interaction network (PPI) to seek for hub mRNAs and learn more about protein-protein interactions. The relative expression was verified using RT-qPCR. The effects of Fos and ARRDC3 on virus replication were confirmed using RT-qPCR, virus titer (TCID50/ml), Western blotting.Results375 lncRNAs (154 upregulated and 221 downregulated), 33 circRNAs (32 upregulated and 1 downregulated), 96 miRNAs (49 upregulated and 47 downregulated), and 239 mRNAs (135 upregulated and 104 downregulated) were identified as differently in infected group compare to no-infected group. A single lncRNA or circRNA can be connected with numerous miRNAs, which subsequently coregulate additional mRNAs, according to the ceRNA regulatory network. The majority of DEmRNAs were shown to be connected to DNA binding, transcription regulation by RNA polymerase II, transcription factor, MAPK signaling pathways, Hippo signal pathway, and apoptosis pathway, according to GO and KEGG pathway enrichment analysis. The hub mRNAs with EGR1, Fos and Jun as the core were screened through PPI interaction network. We preliminarily demonstrated that the Fos and ARRDC3 genes can suppress EV-D68 viral replication in order to further verify the results of full transcriptome sequencing.ConclusionThe results of whole transcriptome analysis after EV-D68 infection of RD cells were first reported in this study, and for the first time, a ceRNA regulation network containing miRNA at its center was established for the first time. The Fos and ARRDC3 genes were found to hinder viral in RD cells. This study establishes a novel insight host response during EV-D68 infection and further investigated potential drug targets.

Project description:MicroRNAs (miRNAs) simultaneously target many transcripts through partial complementarity binding, and have emerged as a key type of post-transcriptional regulator for gene expression. How miRNA accomplishes its pleiotropic effects largely depends on its expression and its target repertoire. Previous studies discovered thousands of miRNAs and numerous miRNA target genes mainly through computation and prediction methods which produced high rates of false positive prediction. The development of Argonaute cross-linked immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) provides a system to effectively determine miRNA target genes. Likewise, the accuracy of dissecting the transcriptional regulation of miRNA genes has been greatly improved by chromatin immunoprecipitation of the transcription factors coupled with sequencing (ChIP-Seq). Elucidation of the miRNA target repertoire will provide an in-depth understanding of the functional roles of microRNA pathways. To reliably reconstruct a miRNA-mediated regulatory network, we established a computational framework using publicly available, sequence-based transcription factor-miRNA databases, including ChIPBase and TransmiR for the TF-miRNA interactions, along with miRNA-target databases, including miRTarBase, TarBase and starBase, for the miRNA-target interactions. We applied the computational framework to elucidate the miRNA-mediated regulatory network in the Mir122a?/? mouse model, which has an altered transcriptome and progressive liver disease.We applied our computational framework to the expression profiles of miRNA/mRNA of Mir122a?/? mutant mice and wild-type mice. The miRNA-mediated network involves 40 curated TFs contributing to the aberrant expression of 65 miRNAs and 723 curated miRNA target genes, of which 56% was found in the differentially-expressed genes of Mir122a--mice. Hence, the regulatory network disclosed previously-known and also many previously-unidentified miRNA-mediated regulations in mutant mice. Moreover, we demonstrate that loss of imprinting at the chromosome 12qF1 region is associated with miRNA overexpression in human hepatocellular carcinoma and stem cells, suggesting initiation of precancerous changes in young mice deficient in miR-122. A group of 9 miRNAs was found to share miR-122 target genes, indicating synergy between miRNAs and target genes by way of multiplicity and cooperativity.The study provides significant insight into miRNA-mediated regulatory networks. Based on experimentally verified data, this network is highly reliable and effective in revealing previously-undetermined disease-associated molecular mechanisms. This computational framework can be applied to explore the significant TF-miRNA-miRNA target interactions in any complex biological systems with high degrees of confidence.

Project description:BackgroundProbability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative biological data. In this work we introduce a new statistical learning strategy, MI3 that addresses three common issues in previous methods simultaneously: (1) handling of continuous variables, (2) detection of more complex three-way relationships and (3) better differentiation of causal versus confounding relationships. With these improvements, we provide a more realistic representation of the underlying biological system.ResultsWe test the MI3 algorithm using both synthetic and experimental data. In the synthetic data experiment, MI3 achieved an absolute sensitivity/precision of 0.77/0.83 and a relative sensitivity/precision both of 0.99. In addition, MI3 significantly outperformed the control methods, including Bayesian networks, classical two-way mutual information and a discrete version of MI3. We then used MI3 and control methods to infer a regulatory network centered at the MYC transcription factor from a published microarray dataset. Models selected by MI3 were numerically and biologically distinct from those selected by control methods. Unlike control methods, MI3 effectively differentiated true causal models from confounding models. MI3 recovered major MYC cofactors, and revealed major mechanisms involved in MYC dependent transcriptional regulation, which are strongly supported by literature. The MI3 network showed that limited sets of regulatory mechanisms are employed repeatedly to control the expression of large number of genes.ConclusionOverall, our work demonstrates that MI3 outperforms the frequently used control methods, and provides a powerful method for inferring mechanistic relationships underlying biological and other complex systems. The MI3 method is implemented in R in the "mi3" package, available under the GNU GPL from http://sysbio.engin.umich.edu/~luow/downloads.php and from the R package archive CRAN.

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High-throughput validation of ceRNA regulatory networks.

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High-throughput validation of ceRNA regulatory networks.

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