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

ABSTRACT: 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/.

SUBMITTER: Schmitz U

PROVIDER: S-EPMC4081082 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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

Schmitz Ulf U Lai Xin X Winter Felix F Wolkenhauer Olaf O Vera Julio J Gupta Shailendra K SK

Nucleic acids research 20140529 12

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 co ...[more]

PMID: 24875477

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Project description:BackgroundTranscriptional regulation of gene expression in eukaryotes is usually accomplished by cooperative transcription factors (TFs). Computational identification of cooperative TF pairs has become a hot research topic and many algorithms have been proposed in the literature. A typical algorithm for predicting cooperative TF pairs has two steps. (Step 1) Define the targets of each TF under study. (Step 2) Design a measure for calculating the cooperativity of a TF pair based on the targets of these two TFs. While different algorithms have distinct sophisticated cooperativity measures, the targets of a TF are usually defined using ChIP-chip data. However, there is an inherent weakness in using ChIP-chip data to define the targets of a TF. ChIP-chip analysis can only identify the binding targets of a TF but it cannot distinguish the true regulatory from the binding but non-regulatory targets of a TF.ResultsThis work is the first study which aims to investigate whether the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. For this purpose, we propose four simple algorithms, all of which consist of two steps. (Step 1) Define the targets of a TF using (i) ChIP-chip data in the first algorithm, (ii) TF binding data in the second algorithm, (iii) TF perturbation data in the third algorithm, and (iv) the intersection of TF binding and TF perturbation data in the fourth algorithm. Compared with the first three algorithms, the fourth algorithm uses a more biologically relevant way to define the targets of a TF. (Step 2) Measure the cooperativity of a TF pair by the statistical significance of the overlap of the targets of these two TFs using the hypergeometric test. By adopting four existing performance indices, we show that the fourth proposed algorithm (PA4) significantly out performs the other three proposed algorithms. This suggests that the computational identification of cooperative TF pairs is indeed improved when using a more biologically relevant way to define the targets of a TF. Strikingly, the prediction results of our simple PA4 are more biologically meaningful than those of the 12 existing sophisticated algorithms in the literature, all of which used ChIP-chip data to define the targets of a TF. This suggests that properly defining the targets of a TF may be more important than designing sophisticated cooperativity measures. In addition, our PA4 has the power to predict several experimentally validated cooperative TF pairs, which have not been successfully predicted by any existing algorithms in the literature.ConclusionsThis study shows that the performance of computational identification of cooperative TF pairs could be improved by using a more biologically relevant way to define the targets of a TF. The main contribution of this study is not to propose another new algorithm but to provide a new thinking for the research of computational identification of cooperative TF pairs. Researchers should put more effort on properly defining the targets of a TF (i.e. Step 1) rather than totally focus on designing sophisticated cooperativity measures (i.e. Step 2). The lists of TF target genes, the Matlab codes and the prediction results of the four proposed algorithms could be downloaded from our companion website http://cosbi3.ee.ncku.edu.tw/TFI/.

Project description:BackgroundHazels (Corylus spp.) are economically important nut-producing species in which ovule development determines seed plumpness, one of the key parameters reflecting nut quality. microRNAs (miRNAs) play important roles in RNA silencing and the post-transcriptional regulation of gene expression. However, very little is currently known regarding the miRNAs involved in regulating ovule growth and development.ResultsIn this study, we accordingly sought to determine the important miRNAs involved in ovule development and growth in hazel. We examined ovules at four developmental stages, namely, ovule formation (Ov1), early ovule growth (Ov2), rapid ovule growth (Ov3), and ovule maturity (Ov4). On the basis of small RNA and mRNA sequencing using the Illumina sequencing platform, we identified 970 miRNAs in hazel, of which 766 and 204 were known and novel miRNAs, respectively. In Ov1-vs-Ov2, Ov1-vs-Ov3, Ov1-vs-Ov4, Ov2-vs-Ov3, Ov2-vs-Ov4, and Ov3-vs-Ov4 paired comparisons, 471 differentially expressed microRNAs (DEmiRNAs) and their 3117 target differentially expressed messenger RNAs (DEmRNAs) formed 11,199 DEmiRNA/DEmRNA pairs, with each DEmiRNA changing the expression of an average of 6.62 target mRNAs. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of all DEmRNAs revealed 29 significantly enriched KEGG pathways in the six paired comparisons, including protein export (ko03060), fatty acid elongation (ko00062), starch and sucrose metabolism (ko00500), fatty acid biosynthesis (ko00061), and amino sugar and nucleotide sugar metabolism (ko00520). Our results indicate that DEmiRNA/DEmRNA pairs showing opposite change trends were related to stress tolerance, embryo and seed development, cell proliferation, auxin transduction, and the biosynthesis of proteins, starch, and fats may participate in ovule growth and development.ConclusionsThese findings contribute to a better understanding of ovule development at the level of post-transcriptional regulation, and lay the foundation for further functional analyses of hazelnut ovule growth and development.

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

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

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