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Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs.

ABSTRACT: BACKGROUND:In silico investigations on the integration of multiple datasets are in need of higher statistical power methods to unveil secondary findings that were hidden from the initial analyses. We present here a novel method for the network analysis of messenger RNA post-translational regulation by microRNA molecules. The method integrates expression data and sequence binding predictions through a set of sound machine learning techniques, forwarding all results to an ensemble graph of regulations. RESULTS:Bayesian network classifiers are induced based on a pool of ensemble graphs with ascending order of complexity. Individual goodness-of-fit and classification performances are evaluated for each learned model. As a testbed, four Alzheimer's disease datasets are integrated using the new approach, achieving top values of 0.9794 ± 0.01 for the area under the receiver operating characteristic curve and 0.9439 ± 0.0234 for the prediction accuracy. CONCLUSIONS:Post-transcriptional regulations found by the optimal network classifier concur with previous literature findings. Furthermore, additional network structures suggest previously unreported regulations in the state of the art of Alzheimer's research. The quantitative performance as well as sound biological findings provide confidence in the ensemble approach and encourage similar integrative analyses for other conditions.

SUBMITTER: Armananzas R

PROVIDER: S-EPMC6157163 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs.

Armañanzas Rubén R

BMC genomics 20180924 Suppl 7

<h4>Background</h4>In silico investigations on the integration of multiple datasets are in need of higher statistical power methods to unveil secondary findings that were hidden from the initial analyses. We present here a novel method for the network analysis of messenger RNA post-translational regulation by microRNA molecules. The method integrates expression data and sequence binding predictions through a set of sound machine learning techniques, forwarding all results to an ensemble graph of ...[more]

PMID: 30255799

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Project description:The pluripotent transcription factor NANOG is essential for maintaining embryonic stem cells and driving tumorigenesis. We previously showed that PKC activity is involved in the regulation of NANOG expression. To explore the possible involvement of microRNAs in regulating the expression of key pluripotency factors, we performed a genome-wide analysis of microRNA expression in the embryonal carcinoma cell line NT2/D1 in the presence of the PKC activator, PMA. We found that MIR630 was significantly upregulated in PMA-treated cells. Experimentally, we showed that transfection of MIR630 mimic into embryonal carcinoma cell lines directly targeted the 3'UTR of OCT4, SOX2, and NANOG and markedly suppressed their expression. RNAhybrid and RNA22 algorithms were used to predict miRNA target sites in the NANOG 3'UTR, four possible target sites of MIR630 were identified. To examine the functional interaction between MIR630 and NANOG mRNA, the predicted MIR630 target sites in the NANOG 3'UTR were deleted and the activity of the reporters were compared. After targeted mutation of the predicted MIR630 target sites, the MIR630 mimic inhibited NANOG significantly less than the wild-type reporters. It is worth noting that mutation of a single putative binding site in the 3'UTR of NANOG did not completely abolish MIR630-mediated suppression, suggesting that MIR630 in the NANOG 3'UTR may have multiple binding sites and act together to maximally repress NANOG expression. Interestingly, MIR630 mimics significantly downregulated NANOG gene transcription. Exogenous expression of OCT4, SOX2, and NANOG lacking the 3'UTR almost completely rescued the reduced transcriptional activity of MIR630. MIR630 mediated the expression of differentiation markers in NT2/D1 cells, suggesting that MIR630 leads to the differentiation of NT2/D1 cell. Our findings show that MIR630 represses NANOG through transcriptional and post-transcriptional regulation, suggesting a direct link between core pluripotency factors and MIR630.

Project description:BackgroundRespiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks.ResultsHere we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs.ConclusionCompared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection.

Dataset Information

Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs.

Publications

Revealing post-transcriptional microRNA-mRNA regulations in Alzheimer's disease through ensemble graphs.

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