Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs

Lai, Xin; Schmitz, Ulf; Gupta, Satish; Bhattacharya, Animesh; Kunz, Manfred; Vera, Julio

doi:10.1093/nar/gks657

Cited by 82 publications

(66 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…MiRNAs have a significant impact on the regulation of the key molecular signatures in integrative regulatory networks [30]. In many cases, miRNAs regulate TFs and target genes through different regulatory circuits including feedback and feed-forward loops.…”

Section: Resultsmentioning

confidence: 99%

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

et al. 2016

Self Cite

View full text Add to dashboard Cite

Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.

show abstract

Section: Resultsmentioning

confidence: 99%

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…This phenomenon can foster, at the post-transcriptional level, an effective and fine-tuned regulation of gene expression and can buffer efficiently noise coming from external stimuli (3). …”

Section: Discussionmentioning

confidence: 99%

Cooperative gene regulation by microRNA pairs and their identification using a computational workflow

Schmitz¹,

Lai²,

Winter³

et al. 2014

Nucleic Acids Res

Self Cite

View full text Add to dashboard Cite

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/.

show abstract

“…In the model parameterization analyzed, the feedback loop between E2F1 and miR-205 has minor effects in the dynamics of the network. Our previous results of modeling miRNA regulation of cancer-related genes suggest that the strength of miRNA regulation is context-dependent and may be affected by the coregulation of other miRNAs or proteins (42). Thus, we hypothesize that this feedback loop could be negligible in the scenarios investigated in this article, but still crucial in other biologic contexts which have not been investigated in this paper.…”

Section: Discussionmentioning

confidence: 80%

Kinetic Modeling–Based Detection of Genetic Signatures That Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network

et al. 2013

Self Cite

View full text Add to dashboard Cite

Drug resistance is a major cause of deaths from cancer. E2F1 is a transcription factor involved in cell proliferation, apoptosis. and metastasis through an intricate regulatory network, which includes other transcription factors like p73 and cancer-related microRNAs like miR-205. To investigate the emergence of drug resistance, we developed a methodology that integrates experimental data with a network biology and kinetic modeling. Using a regulatory map developed to summarize knowledge on E2F1 and its interplay with p73/ DNp73 and miR-205 in cancer drug responses, we derived a kinetic model that represents the network response to certain genotoxic and cytostatic anticancer drugs. By perturbing the model parameters, we simulated heterogeneous cell configurations referred to as in silico cell lines. These were used to detect genetic signatures characteristic for single or double drug resistance. We identified a signature composed of high E2F1 and low miR-205 expression that promotes resistance to genotoxic drugs. In this signature, downregulation of miR-205, can be mediated by an imbalance in the p73/DNp73 ratio or by dysregulation of other cancer-related regulators of miR-205 expression such as TGFb-1 or TWIST1. In addition, we found that a genetic signature composed of high E2F1, low miR-205, and high ERBB3 can render tumor cells insensitive to both cytostatic and genotoxic drugs. Our model simulations also suggested that conventional genotoxic drug treatment favors selection of chemoresistant cells in genetically heterogeneous tumors, in a manner requiring dysregulation of incoherent feedforward loops that involve E2F1, p73/DNp73, and miR-205. Cancer Res; 73(12); 3511-24. Ó2013 AACR.

show abstract

Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs

Cited by 82 publications

References 69 publications

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

Cooperative gene regulation by microRNA pairs and their identification using a computational workflow

Kinetic Modeling–Based Detection of Genetic Signatures That Provide Chemoresistance via the E2F1-p73/DNp73-miR-205 Network

Contact Info

Product

Resources

About