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Hassan M, Iqbal MS, Naqvi S, Alashwal H, Moustafa AA, Kloczkowski A. Prediction of Site Directed miRNAs as Key Players of Transcriptional Regulators Against Influenza C Virus Infection Through Computational Approaches. Front Mol Biosci 2022; 9:866072. [PMID: 35463952 PMCID: PMC9023806 DOI: 10.3389/fmolb.2022.866072] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in gene expression, cell differentiation, and immunity against viral infections. In this study, we have used the computational tools, RNA22, RNAhybrid, and miRanda, to predict the microRNA-mRNA binding sites to find the putative microRNAs playing role in the host response to influenza C virus infection. This computational research screened the following four miRNAs: hsa-mir-3155a, hsa-mir-6796-5p, hsa-mir-3194-3p and hsa-mir-4673, which were further investigated for binding site prediction to the influenza C genome. Moreover, multiple sites in protein-coding region (HEF, CM2, M1-M2, NP, NS1- NS2, NSF, P3, PB1 and PB2) were predicted by RNA22, RNAhybrid and miRanda. Furthermore, 3D structures of all miRNAs and HEF were predicted and checked for their binding potential through molecular docking analysis. The comparative results showed that among all proteins, HEF is higher in prevalence throughout the analysis as a potential (human-derived) microRNAs target. The target-site conservation results showed that core nucleotide sequence in three different strains is responsible for potential miRNA binding to different viral strains. Further steps to use these microRNAs may lead to new therapeutic insights on fighting influenza virus infection.
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Affiliation(s)
- Mubashir Hassan
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children Hospital, Columbus, OH, United States
- *Correspondence: Mubashir Hassan, ; Hany Alashwal, ; Andrzej Kloczkowski,
| | - Muhammad Shahzad Iqbal
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | - Sawaira Naqvi
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan
| | - Hany Alashwal
- College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates
- *Correspondence: Mubashir Hassan, ; Hany Alashwal, ; Andrzej Kloczkowski,
| | - Ahmed A. Moustafa
- Department of Human Anatomy and Physiology, The Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD, Australia
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children Hospital, Columbus, OH, United States
- Department of Pediatrics, The Ohio State University, Columbus, OH, United States
- *Correspondence: Mubashir Hassan, ; Hany Alashwal, ; Andrzej Kloczkowski,
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Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:109-131. [DOI: 10.1007/978-3-031-08356-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Arif KT, Okolicsanyi RK, Haupt LM, Griffiths LR. A combinatorial in silico approach for microRNA-target identification: Order out of chaos. Biochimie 2021; 187:121-130. [PMID: 34019954 DOI: 10.1016/j.biochi.2021.05.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 04/17/2021] [Accepted: 05/11/2021] [Indexed: 02/07/2023]
Abstract
Contemporary computational microRNA(miRNA)-target prediction tools have been playing a vital role in pursuing putative targets for a solitary miRNA or a group of miRNAs. These tools utilise a set of probabilistic algorithms, machine learning techniques and analyse experimentally validated miRNA targets to identify the potential miRNA-target pairs. Unfortunately, current tools generate a huge number of false-positive predictions. A standardized approach with a single tool or a combination of tools is still lacking. Moreover, sensitivity, specificity and overall efficiency of any single tool are yet to be satisfactory. Therefore, a systematic combination of selective online tools combining the factors regarding miRNA-target identification would be valuable as an miRNA-target prediction scheme. The focus of this study was to develop a theoretical framework by combining six available online tools to facilitate the current understanding of miRNA-target identification.
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Affiliation(s)
- Km Taufiqul Arif
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, 60 Musk Ave., Kelvin Grove, Queensland, 4059, Australia.
| | - Rachel K Okolicsanyi
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, 60 Musk Ave., Kelvin Grove, Queensland, 4059, Australia.
| | - Larisa M Haupt
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, 60 Musk Ave., Kelvin Grove, Queensland, 4059, Australia.
| | - Lyn R Griffiths
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, 60 Musk Ave., Kelvin Grove, Queensland, 4059, Australia.
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Syed SN, Brüne B. MicroRNAs as Emerging Regulators of Signaling in the Tumor Microenvironment. Cancers (Basel) 2020; 12:E911. [PMID: 32276464 PMCID: PMC7225969 DOI: 10.3390/cancers12040911] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 04/07/2020] [Accepted: 04/07/2020] [Indexed: 12/13/2022] Open
Abstract
A myriad of signaling molecules in a heuristic network of the tumor microenvironment (TME) pose a challenge and an opportunity for novel therapeutic target identification in human cancers. MicroRNAs (miRs), due to their ability to affect signaling pathways at various levels, take a prominent space in the quest of novel cancer therapeutics. The role of miRs in cancer initiation, progression, as well as in chemoresistance, is being increasingly investigated. The canonical function of miRs is to target mRNAs for post-transcriptional gene silencing, which has a great implication in first-order regulation of signaling pathways. However, several reports suggest that miRs also perform non-canonical functions, partly due to their characteristic non-coding small RNA nature. Examples emerge when they act as ligands for toll-like receptors or perform second-order functions, e.g., to regulate protein translation and interactions. This review is a compendium of recent advancements in understanding the role of miRs in cancer signaling and focuses on the role of miRs as novel regulators of the signaling pathway in the TME.
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Affiliation(s)
- Shahzad Nawaz Syed
- Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, 60590 Frankfurt, Germany
| | - Bernhard Brüne
- Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, 60590 Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt, 60590 Frankfurt, Germany
- Frankfurt Cancer Institute, Goethe-University Frankfurt, 60596 Frankfurt, Germany
- Project Group Translational Medicine and Pharmacology TMP, Fraunhofer Institute for Molecular Biology and Applied Ecology, 60596 Frankfurt, Germany
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Sagar A, Xue B. Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions. Protein Pept Lett 2019; 26:601-619. [PMID: 31215361 DOI: 10.2174/0929866526666190619103853] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 04/04/2019] [Accepted: 06/01/2019] [Indexed: 12/18/2022]
Abstract
The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.
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Affiliation(s)
- Amit Sagar
- Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, Florida 33620, United States
| | - Bin Xue
- Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, Florida 33620, United States
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