{"title":"Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers.","authors":"Sarmistha Das, Shesh N Rai","doi":"10.3390/ncrna10040045","DOIUrl":null,"url":null,"abstract":"<p><p>Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11357235/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Non-Coding RNA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/ncrna10040045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.
Non-Coding RNABiochemistry, Genetics and Molecular Biology-Genetics
CiteScore
6.70
自引率
4.70%
发文量
74
审稿时长
10 weeks
期刊介绍:
Functional studies dealing with identification, structure-function relationships or biological activity of: small regulatory RNAs (miRNAs, siRNAs and piRNAs) associated with the RNA interference pathway small nuclear RNAs, small nucleolar and tRNAs derived small RNAs other types of small RNAs, such as those associated with splice junctions and transcription start sites long non-coding RNAs, including antisense RNAs, long ''intergenic'' RNAs, intronic RNAs and ''enhancer'' RNAs other classes of RNAs such as vault RNAs, scaRNAs, circular RNAs, 7SL RNAs, telomeric and centromeric RNAs regulatory functions of mRNAs and UTR-derived RNAs catalytic and allosteric (riboswitch) RNAs viral, transposon and repeat-derived RNAs bacterial regulatory RNAs, including CRISPR RNAS Analysis of RNA processing, RNA binding proteins, RNA signaling and RNA interaction pathways: DICER AGO, PIWI and PIWI-like proteins other classes of RNA binding and RNA transport proteins RNA interactions with chromatin-modifying complexes RNA interactions with DNA and other RNAs the role of RNA in the formation and function of specialized subnuclear organelles and other aspects of cell biology intercellular and intergenerational RNA signaling RNA processing structure-function relationships in RNA complexes RNA analyses, informatics, tools and technologies: transcriptomic analyses and technologies development of tools and technologies for RNA biology and therapeutics Translational studies involving long and short non-coding RNAs: identification of biomarkers development of new therapies involving microRNAs and other ncRNAs clinical studies involving microRNAs and other ncRNAs.