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Identifying miRNA synergism using multiple-intervention causal inference.

ABSTRACT: BACKGROUND:Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test. RESULTS:n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network. CONCLUSIONS:Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.

SUBMITTER: Zhang J

PROVIDER: S-EPMC6933624 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Identifying miRNA synergism using multiple-intervention causal inference.

Zhang Junpeng J Pham Vu Viet Hoang VVH Liu Lin L Xu Taosheng T Truong Buu B Li Jiuyong J Rao Nini N Le Thuc Duy TD

BMC bioinformatics 20191227 Suppl 23

<h4>Background</h4>Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regula ...[more]

PMID: 31881825

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Project description:BackgroundThe chromosomal region 6q23 has been found to be associated with multiple sclerosis (MS) predisposition through genome wide association studies (GWAS). There are four independent single nucleotide polymorphisms (SNPs) associated with MS in this region, which spans around 2.5 Mb. Most GWAS variants associated with complex traits, including these four MS associated SNPs, are non-coding and their function is currently unknown. However, GWAS variants have been found to be enriched in enhancers and there is evidence that they may be involved in transcriptional regulation of their distant target genes through long range chromatin looping.AimThe aim of this work is to identify causal disease genes in the 6q23 locus by studying long range chromatin interactions, using the recently developed Capture Hi-C method in human T and B-cell lines. Interactions involving four independent associations unique to MS, tagged by rs11154801, rs17066096, rs7769192 and rs67297943 were analysed using Capture Hi-C Analysis of Genomic Organisation (CHiCAGO).ResultsWe found that the pattern of chromatin looping interactions in the MS 6q23 associated region is complex. Interactions cluster in two regions, the first involving the rs11154801 region and a second containing the rs17066096, rs7769192 and rs67297943 SNPs. Firstly, SNPs located within the AHI1 gene, tagged by rs11154801, are correlated with expression of AHI1 and interact with its promoter. These SNPs also interact with other potential candidate genes such as SGK1 and BCLAF1. Secondly, the rs17066096, rs7769192 and rs67297943 SNPs interact with each other and with immune-related genes such as IL20RA, IL22RA2, IFNGR1 and TNFAIP3. Finally, the above-mentioned regions interact with each other and therefore, may co-regulate these target genes.ConclusionThese results suggest that the four 6q23 variants, independently associated with MS, are involved in the regulation of several genes, including immune genes. These findings could help understand mechanisms of disease and suggest potential novel therapeutic targets.

Project description:BackgroundmicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments.ResultsIn this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method.ConclusionsThis research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.

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Identifying miRNA synergism using multiple-intervention causal inference.

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Identifying miRNA synergism using multiple-intervention causal inference.

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