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Cui S, Yu S, Huang HY, Lin YCD, Huang Y, Zhang B, Xiao J, Zuo H, Wang J, Li Z, Li G, Ma J, Chen B, Zhang H, Fu J, Wang L, Huang HD. miRTarBase 2025: updates to the collection of experimentally validated microRNA-target interactions. Nucleic Acids Res 2025; 53:D147-D156. [PMID: 39578692 PMCID: PMC11701613 DOI: 10.1093/nar/gkae1072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2024] [Revised: 10/02/2024] [Accepted: 11/14/2024] [Indexed: 11/24/2024] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs (18-26 nucleotides) that regulate gene expression by interacting with target mRNAs, affecting various physiological and pathological processes. miRTarBase, a database of experimentally validated miRNA-target interactions (MTIs), now features over 3 817 550 validated MTIs from 13 690 articles, significantly expanding its previous version. The updated database includes miRNA interactions with therapeutic agents, revealing roles in drug resistance and therapeutic strategies. It also highlights miRNAs as predictive, safety and monitoring biomarkers for toxicity assessment, clinical treatment guidance and therapeutic optimization. The expansion of miRNA-mRNA and miRNA-miRNA networks allows the identification of key regulatory genes and co-regulatory miRNAs, providing deeper insights into miRNA functions and critical target genes. Information on oxidized miRNA sequences has been added, shedding light on how oxidative modifications influence miRNA targeting and regulation. The integration of the LLAMA3 model into the NLP pipeline, alongside prompt engineering, enables the efficient identification of MTIs and miRNA-disease associations without large training datasets. An updated data integration and a redesigned user interface enhance accessibility, reinforcing miRTarBase as an essential resource for molecular oncology, drug development and related fields. The updated miRTarBase is available at https://mirtarbase.cuhk.edu.cn/∼miRTarBase/miRTarBase_2025.
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Affiliation(s)
- Shidong Cui
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Sicong Yu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Yang-Chi-Dung Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Bojian Zhang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jihan Xiao
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Huali Zuo
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jiayi Wang
- Stem Cell Clinical Research Center, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P.R. China
| | - Zhuoran Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Guanghao Li
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jiajun Ma
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Baiming Chen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Haoxuan Zhang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Jiehui Fu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
| | - Liang Wang
- Stem Cell Clinical Research Center, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P.R. China
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, P.R. China
- Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P.R. China
- Department of Endocrinology, Key Laboratory of Endocrinology of National Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
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Marceca GP, Romano G, Acunzo M, Nigita G. ncRNA Editing: Functional Characterization and Computational Resources. Methods Mol Biol 2025; 2883:455-495. [PMID: 39702721 DOI: 10.1007/978-1-0716-4290-0_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2024]
Abstract
Non-coding RNAs (ncRNAs) play crucial roles in gene expression regulation, translation, and disease development, including cancer. They are classified by size in short and long non-coding RNAs. This chapter focuses on the functional implications of adenosine-to-inosine (A-to-I) RNA editing in both short (e.g., miRNAs) and long ncRNAs. RNA editing dynamically alters the sequence and structure of primary transcripts, impacting ncRNA biogenesis and function. Notable findings include the role of miRNA editing in promoting glioblastoma invasiveness, characterizing RNA editing hotspots across cancers, and its implications in thyroid cancer and ischemia. This chapter also highlights bioinformatics resources and next-generation sequencing (NGS) technologies that enable comprehensive ncRNAome studies and genome-wide RNA editing detection. Dysregulation of RNA editing machinery has been linked to various human diseases, emphasizing the potential of RNA editing as a biomarker and therapeutic target. This overview integrates current knowledge and computational tools for studying ncRNA editing, providing insights into its biological significance and clinical applications.
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Affiliation(s)
| | - Giulia Romano
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Mario Acunzo
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Giovanni Nigita
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
- Center for RNA Biology, The Ohio State University, Columbus, OH, USA.
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Sarı-Tunel F, Demirkan A, Vural B, Yıldız CE, Komurcu-Bayrak E. Omics Data Integration Uncovers mRNA-miRNA Interaction Regions in Genes Associated with Chronic Venous Insufficiency. Genes (Basel) 2024; 16:40. [PMID: 39858587 PMCID: PMC11765502 DOI: 10.3390/genes16010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2024] [Revised: 12/20/2024] [Accepted: 12/24/2024] [Indexed: 01/27/2025] Open
Abstract
Background/Objectives: Chronic venous insufficiency (CVI), a chronic vascular dysfunction, is a common health problem that causes serious complications such as painful varicose veins and even skin ulcers. Identifying the underlying genetic and epigenetic factors is important for improving the quality of life of individuals with CVI. In the literature, many genes, variants, and miRNAs associated with CVI have been identified through genomic and transcriptomic studies. Despite molecular pathogenesis studies, how the genes associated with CVI are regulated by miRNAs and the effect of variants in binding regions on expression levels are still not fully understood. In this study, previously identified genes, variants, and miRNAs associated with CVI, common variants in the mRNA-miRNA binding regions, were investigated using in silico analyses. Methods: For this purpose, miRNA research tools, MBS (miRNA binding site) database, genome browsers, and the eQTL Calculator in the GTEx portal were used. Results: We identified SNVs associated with CVI that may play a direct role in the miRNA-mediated regulation of the ZNF664, COL1A2, HFE, MDN, MTHFR, SRPX, TDRD5, TSPYL4, VEGFA, and APOE genes. In addition, when the common SNVs in the mRNA binding region of 75 unique CVI related-miRNAs in five candidate genes associated with CVI were examined, seven miRNAs associated with the expression profiles of ABCA1, PIEZO1, and CASZ1 genes were identified. Conclusions: In conclusion, the relationship between genetic markers identified in the literature that play a role in the pathogenesis of the CVI and the expression profiles was evaluated for the first time in the mRNA-miRNA interaction axis.
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Affiliation(s)
- Fatma Sarı-Tunel
- Department of Genetics, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; (F.S.-T.); (B.V.)
- Graduate School Institute of Health Sciences, Istanbul University, 34093 Istanbul, Turkey
| | - Ayse Demirkan
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine and People-Centred AI Institute, University of Surrey, Guildford GU2 7XH, UK
| | - Burcak Vural
- Department of Genetics, Aziz Sancar Institute of Experimental Medicine, Istanbul University, 34093 Istanbul, Turkey; (F.S.-T.); (B.V.)
| | - Cenk Eray Yıldız
- Department of Cardiovascular Surgery, Institute of Cardiology, Istanbul University-Cerrahpasa, 34098 Istanbul, Turkey;
| | - Evrim Komurcu-Bayrak
- Department of Medical Genetics, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Turkey;
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4
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Hemedan AA, Satagopam V, Schneider R, Ostaszewski M. Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression. iScience 2024; 27:110956. [PMID: 39429779 PMCID: PMC11489052 DOI: 10.1016/j.isci.2024.110956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 07/02/2024] [Accepted: 09/10/2024] [Indexed: 10/22/2024] Open
Abstract
Parkinson's disease (PD) involves complex molecular interactions and diverse comorbidities. To better understand its molecular mechanisms, we employed systems medicine approaches using the PD map, a detailed repository of PD-related interactions and applied Probabilistic Boolean Networks (PBNs) to capture the stochastic nature of molecular dynamics. By integrating cohort-level and real-world patient data, we modeled PD's subtype-specific pathway deregulations, providing a refined representation of its molecular landscape. Our study identifies key regulatory biomolecules and pathways that vary across PD subtypes, offering insights into the disease's progression and patient stratification. These findings have significant implications for the development of targeted therapeutic interventions.
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Affiliation(s)
- Ahmed Abdelmonem Hemedan
- Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Bioinformatics Core Unit, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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5
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Caporali A, Anwar M, Devaux Y, Katare R, Martelli F, Srivastava PK, Pedrazzini T, Emanueli C. Non-coding RNAs as therapeutic targets and biomarkers in ischaemic heart disease. Nat Rev Cardiol 2024; 21:556-573. [PMID: 38499868 DOI: 10.1038/s41569-024-01001-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/19/2024] [Indexed: 03/20/2024]
Abstract
The adult heart is a complex, multicellular organ that is subjected to a series of regulatory stimuli and circuits and has poor reparative potential. Despite progress in our understanding of disease mechanisms and in the quality of health care, ischaemic heart disease remains the leading cause of death globally, owing to adverse cardiac remodelling, leading to ischaemic cardiomyopathy and heart failure. Therapeutic targets are urgently required for the protection and repair of the ischaemic heart. Moreover, personalized clinical biomarkers are necessary for clinical diagnosis, medical management and to inform the individual response to treatment. Non-coding RNAs (ncRNAs) deeply influence cardiovascular functions and contribute to communication between cells in the cardiac microenvironment and between the heart and other organs. As such, ncRNAs are candidates for translation into clinical practice. However, ncRNA biology has not yet been completely deciphered, given that classes and modes of action have emerged only in the past 5 years. In this Review, we discuss the latest discoveries from basic research on ncRNAs and highlight both the clinical value and the challenges underscoring the translation of these molecules as biomarkers and therapeutic regulators of the processes contributing to the initiation, progression and potentially the prevention or resolution of ischaemic heart disease and heart failure.
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Affiliation(s)
- Andrea Caporali
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Maryam Anwar
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Luxembourg, Luxemburg
| | - Rajesh Katare
- Department of Physiology, HeartOtago, University of Otago, Dunedin, New Zealand
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, Milan, Italy
| | | | - Thierry Pedrazzini
- Experimental Cardiology Unit, Division of Cardiology, Department of Cardiovascular Medicine, University of Lausanne Medical School, Lausanne, Switzerland
- School of Cardiovascular and Metabolic Medicine & Sciences, King's College London, London, UK
- British Heart Foundation Centre of Research Excellence, King's College London, London, UK
| | - Costanza Emanueli
- National Heart and Lung Institute, Imperial College London, London, UK.
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6
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Chaudhary U, Banerjee S. Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration. ACS Pharmacol Transl Sci 2024; 7:1901-1915. [PMID: 39022352 PMCID: PMC11249652 DOI: 10.1021/acsptsci.3c00388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 05/15/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024]
Abstract
Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.
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Affiliation(s)
- Uma Chaudhary
- Department of Biotechnology,
School of Biosciences and Technology, Vellore
Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Satarupa Banerjee
- Department of Biotechnology,
School of Biosciences and Technology, Vellore
Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
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7
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Ma Y, Ma Y. Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations. PLoS Comput Biol 2024; 20:e1012287. [PMID: 38976761 PMCID: PMC11257412 DOI: 10.1371/journal.pcbi.1012287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 07/18/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024] Open
Abstract
Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.
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Affiliation(s)
- Yingjun Ma
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China
| | - Yuanyuan Ma
- School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, China
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8
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Mok ETY, Chitty JL, Cox TR. miRNAs in pancreatic cancer progression and metastasis. Clin Exp Metastasis 2024; 41:163-186. [PMID: 38240887 PMCID: PMC11213741 DOI: 10.1007/s10585-023-10256-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 12/06/2023] [Indexed: 06/30/2024]
Abstract
Small non-coding RNA or microRNA (miRNA) are critical regulators of eukaryotic cells. Dysregulation of miRNA expression and function has been linked to a variety of diseases including cancer. They play a complex role in cancers, having both tumour suppressor and promoter properties. In addition, a single miRNA can be involved in regulating several mRNAs or many miRNAs can regulate a single mRNA, therefore assessing these roles is essential to a better understanding in cancer initiation and development. Pancreatic cancer is a leading cause of cancer death worldwide, in part due to the lack of diagnostic tools and limited treatment options. The most common form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), is characterised by major genetic mutations that drive cancer initiation and progression. The regulation or interaction of miRNAs with these cancer driving mutations suggests a strong link between the two. Understanding this link between miRNA and PDAC progression may give rise to novel treatments or diagnostic tools. This review summarises the role of miRNAs in PDAC, the downstream signalling pathways that they play a role in, how these are being used and studied as therapeutic targets as well as prognostic/diagnostic tools to improve the clinical outcome of PDAC.
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Affiliation(s)
- Ellie T Y Mok
- Matrix & Metastasis Lab, Cancer Ecosystems Program, The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
- School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, UNSW Medicine and Health, UNSW Sydney, Sydney, NSW, Australia
| | - Jessica L Chitty
- Matrix & Metastasis Lab, Cancer Ecosystems Program, The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia.
- School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, UNSW Medicine and Health, UNSW Sydney, Sydney, NSW, Australia.
| | - Thomas R Cox
- Matrix & Metastasis Lab, Cancer Ecosystems Program, The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia.
- School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, UNSW Medicine and Health, UNSW Sydney, Sydney, NSW, Australia.
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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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10
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Lv J, Yang F, Li Y, Gao N, Zeng Q, Ma H, He J, Zhang Y. Characterization and Function Analysis of miRNA Editing during Fat Deposition in Chinese Indigenous Ningxiang Pigs. Vet Sci 2024; 11:183. [PMID: 38668450 PMCID: PMC11054885 DOI: 10.3390/vetsci11040183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/29/2024] Open
Abstract
This study aimed to identify active miRNA editing sites during adipose development in Ningxiang pigs and analyze their characteristics and functions. Based on small RNA-seq data from the subcutaneous adipose tissues of Ningxiang pigs at four stages-30 days (piglet), 90 days (nursery), 150 days (early fattening), and 210 days (late fattening)-we constructed a developmental map of miRNA editing in the adipose tissues of Ningxiang pigs. A total of 505 miRNA editing sites were identified using the revised pipeline, with C-to-U editing types being the most prevalent, followed by U-to-C, A-to-G, and G-to-U. Importantly, these four types of miRNA editing exhibited base preferences. The number of editing sites showed obvious differences among age groups, with the highest occurrence of miRNA editing events observed at 90 days of age and the lowest at 150 days of age. A total of nine miRNA editing sites were identified in the miRNA seed region, with significant differences in editing levels (p < 0.05) located in ssc-miR-23a, ssc-miR-27a, ssc-miR-30b-5p, ssc-miR-15a, ssc-miR-497, ssc-miR-15b, and ssc-miR-425-5p, respectively. Target gene prediction and KEGG enrichment analyses indicated that the editing of miR-497 might potentially regulate fat deposition by inhibiting adipose synthesis via influencing target binding. These results provide new insights into the regulatory mechanism of pig fat deposition.
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Affiliation(s)
- Jiayu Lv
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Fang Yang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Yiyang Li
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Ning Gao
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Qinghua Zeng
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Haiming Ma
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Jun He
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
| | - Yuebo Zhang
- College of Animal Science and Technology, Hunan Agricultural University, Changsha 410128, China; (J.L.); (F.Y.); (Y.L.); (N.G.); (Q.Z.); (H.M.)
- Key Laboratory of Livestock and Poultry Resources (Pig) Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Changsha 410000, China
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11
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Feng X, Liu S, Li K, Bu F, Yuan H. NCAD v1.0: a database for non-coding variant annotation and interpretation. J Genet Genomics 2024; 51:230-242. [PMID: 38142743 DOI: 10.1016/j.jgg.2023.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 12/26/2023]
Abstract
The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.
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Affiliation(s)
- Xiaoshu Feng
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Sihan Liu
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Ke Li
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China
| | - Fengxiao Bu
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China.
| | - Huijun Yuan
- Institute of Rare Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610044, China.
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12
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Jiao Y, Xu Y, Liu C, Miao R, Liu C, Wang Y, Liu J. The role of ADAR1 through and beyond its editing activity in cancer. Cell Commun Signal 2024; 22:42. [PMID: 38233935 PMCID: PMC10795376 DOI: 10.1186/s12964-023-01465-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/27/2023] [Indexed: 01/19/2024] Open
Abstract
Adenosine-to-inosine (A-to-I) editing of RNA, catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes, is a prevalent RNA modification in mammals. It has been shown that A-to-I editing plays a critical role in multiple diseases, such as cardiovascular disease, neurological disorder, and particularly cancer. ADARs are the family of enzymes, including ADAR1, ADAR2, and ADAR3, that catalyze the occurrence of A-to-I editing. Notably, A-to-I editing is mainly catalyzed by ADAR1. Given the significance of A-to-I editing in disease development, it is important to unravel the complex roles of ADAR1 in cancer for the development of novel therapeutic interventions.In this review, we briefly describe the progress of research on A-to-I editing and ADARs in cancer, mainly focusing on the role of ADAR1 in cancer from both editing-dependent and independent perspectives. In addition, we also summarized the factors affecting the expression and editing activity of ADAR1 in cancer.
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Affiliation(s)
- Yue Jiao
- School of Basic Medicine Sciences, Weifang Medical University, Weifang, 261053, China
| | - Yuqin Xu
- School of Basic Medicine Sciences, Weifang Medical University, Weifang, 261053, China
| | - Chengbin Liu
- School of Basic Medicine Sciences, Weifang Medical University, Weifang, 261053, China
| | - Rui Miao
- School of Basic Medicine Sciences, Weifang Medical University, Weifang, 261053, China
| | - Chunyan Liu
- School of Basic Medicine Sciences, Weifang Medical University, Weifang, 261053, China
| | - Yilong Wang
- School of Basic Medicine Sciences, Weifang Medical University, Weifang, 261053, China
| | - Jiao Liu
- School of Basic Medicine Sciences, Weifang Medical University, Weifang, 261053, China.
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13
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Yang L, Feng H. Cross-kingdom regulation by plant-derived miRNAs in mammalian systems. Animal Model Exp Med 2023; 6:518-525. [PMID: 38064180 PMCID: PMC10757204 DOI: 10.1002/ame2.12358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 10/15/2023] [Indexed: 12/31/2023] Open
Abstract
MicroRNAs (miRNAs) are small noncoding RNA molecules ubiquitously distributed across diverse organisms, serving as pivotal regulators of genetic expression. Notably, plant-derived miRNAs have been demonstrated to have unique bioactivity and certain stability in mammalian systems, thereby facilitating their capacity for cross-kingdom modulation of gene expression. While there is substantial evidence supporting the regulation of mammalian cells by plant-derived miRNAs, several questions remain unanswered. Specifically, a comprehensive investigation of the mechanisms underlying the stability and transport of plant miRNAs and their cross-kingdom regulation of gene expression in mammals remains to be done. In this review, we summarized the origin, processing, and functional mechanisms of plant miRNAs in mammalian tissues and circulation, emphasizing their greater resistance to mammalian digestion and circulation systems compared to animal miRNAs. Additionally, we introduce four well-known plant miRNAs that have been extensively studied for their functions and mechanisms in mammalian systems. By delving into these aspects, we aim to offer a fundamental understanding of this intriguing field and shed light on the complex interactions between plant miRNAs and mammalian biology.
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Affiliation(s)
- Linpu Yang
- National Laboratory of Biomacromolecules, CAS Center for Excellence in BiomacromoleculesInstitute of Biophysics, Chinese Academy of SciencesBeijingChina
| | - Han Feng
- National Laboratory of Biomacromolecules, CAS Center for Excellence in BiomacromoleculesInstitute of Biophysics, Chinese Academy of SciencesBeijingChina
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14
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Frezza V, Chellini L, Del Verme A, Paronetto MP. RNA Editing in Cancer Progression. Cancers (Basel) 2023; 15:5277. [PMID: 37958449 PMCID: PMC10648226 DOI: 10.3390/cancers15215277] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
Coding and noncoding RNA molecules play their roles in ensuring cell function and tissue homeostasis in an ordered and systematic fashion. RNA chemical modifications can occur both at bases and ribose sugar, and, similarly to DNA and histone modifications, can be written, erased, and recognized by the corresponding enzymes, thus modulating RNA activities and fine-tuning gene expression programs. RNA editing is one of the most prevalent and abundant forms of post-transcriptional RNA modification in normal physiological processes. By altering the sequences of mRNAs, it makes them different from the corresponding genomic template. Hence, edited mRNAs can produce protein isoforms that are functionally different from the corresponding genome-encoded variants. Abnormalities in regulatory enzymes and changes in RNA-modification patterns are closely associated with the occurrence and development of various human diseases, including cancer. To date, the roles played by RNA modifications in cancer are gathering increasing interest. In this review, we focus on the role of RNA editing in cancer transformation and provide a new perspective on its impact on tumorigenesis, by regulating cell proliferation, differentiation, invasion, migration, stemness, metabolism, and drug resistance.
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Affiliation(s)
- Valentina Frezza
- Laboratory of Molecular and Cellular Neurobiology, Fondazione Santa Lucia, CERC, Via del Fosso di Fiorano, 64, 00143 Rome, Italy; (V.F.); (L.C.); (A.D.V.)
| | - Lidia Chellini
- Laboratory of Molecular and Cellular Neurobiology, Fondazione Santa Lucia, CERC, Via del Fosso di Fiorano, 64, 00143 Rome, Italy; (V.F.); (L.C.); (A.D.V.)
| | - Arianna Del Verme
- Laboratory of Molecular and Cellular Neurobiology, Fondazione Santa Lucia, CERC, Via del Fosso di Fiorano, 64, 00143 Rome, Italy; (V.F.); (L.C.); (A.D.V.)
| | - Maria Paola Paronetto
- Laboratory of Molecular and Cellular Neurobiology, Fondazione Santa Lucia, CERC, Via del Fosso di Fiorano, 64, 00143 Rome, Italy; (V.F.); (L.C.); (A.D.V.)
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza Lauro de Bosis, 15, 00135 Rome, Italy
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15
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Ribeiro DR, Nunes A, Ribeiro D, Soares AR. The hidden RNA code: implications of the RNA epitranscriptome in the context of viral infections. Front Genet 2023; 14:1245683. [PMID: 37614818 PMCID: PMC10443596 DOI: 10.3389/fgene.2023.1245683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 07/19/2023] [Indexed: 08/25/2023] Open
Abstract
Emerging evidence highlights the multifaceted roles of the RNA epitranscriptome during viral infections. By modulating the modification landscape of viral and host RNAs, viruses enhance their propagation and elude host surveillance mechanisms. Here, we discuss how specific RNA modifications, in either host or viral RNA molecules, impact the virus-life cycle and host antiviral responses, highlighting the potential of targeting the RNA epitranscriptome for novel antiviral therapies.
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16
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Abstract
Cardiovascular disease still remains the leading cause of morbidity and mortality worldwide. Current pharmacological or interventional treatments help to tackle symptoms and even reduce mortality, but cardiovascular disease cases continue to rise. The emergence of novel therapeutic strategies that precisely and efficiently combat cardiovascular disease is therefore deemed more essential than ever. RNA editing, the cell-intrinsic deamination of adenosine or cytidine RNA residues, changes the molecular identity of edited nucleotides, severely altering the fate of RNA molecules involved in key biological processes. The most common type of RNA editing is the deamination of adenosine residue to inosine (A-to-I), which is catalysed by adenosine deaminases acting on RNA (ADARs). Recent efforts have convincingly liaised RNA editing-based mechanisms to the pathophysiology of the cardiovascular system. In this review, we will briefly introduce the basic concepts of the RNA editing field of research. We will particularly focus our discussion on the therapeutic exploitation of RNA editing as a novel therapeutic tool as well as the future perspectives for its use in cardiovascular disease treatment.
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17
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Luna Buitrago D, Lovering RC, Caporali A. Insights into Online microRNA Bioinformatics Tools. Noncoding RNA 2023; 9:18. [PMID: 36960963 PMCID: PMC10037614 DOI: 10.3390/ncrna9020018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/24/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
MicroRNAs (miRNAs) are members of the small non-coding RNA family regulating gene expression at the post-transcriptional level. MiRNAs have been found to have critical roles in various biological and pathological processes. Research in this field has significantly progressed, with increased recognition of the importance of miRNA regulation. As a result of the vast data and information available regarding miRNAs, numerous online tools have emerged to address various biological questions related to their function and influence across essential cellular processes. This review includes a brief introduction to available resources for an investigation covering aspects such as miRNA sequences, target prediction/validation, miRNAs associated with disease, pathway analysis and genetic variants within miRNAs.
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Affiliation(s)
- Diana Luna Buitrago
- BHF Centre for Cardiovascular Science, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh EH164TJ, UK
| | - Ruth C. Lovering
- Functional Gene Annotation, Institute of Cardiovascular Science, University College London, London WC1E 6BT, UK
| | - Andrea Caporali
- BHF Centre for Cardiovascular Science, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh EH164TJ, UK
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18
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Distefano R, Tomasello L, Rampioni Vinciguerra GL, Gasparini P, Xiang Y, Bagnoli M, Marceca GP, Fadda P, Laganà A, Acunzo M, Ma Q, Nigita G, Croce CM. Pan-Cancer Analysis of Canonical and Modified miRNAs Enhances the Resolution of the Functional miRNAome in Cancer. Cancer Res 2022; 82:3687-3700. [PMID: 36040379 PMCID: PMC9574374 DOI: 10.1158/0008-5472.can-22-0240] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 06/24/2022] [Accepted: 08/18/2022] [Indexed: 12/14/2022]
Abstract
Epitranscriptomic studies of miRNAs have added a new layer of complexity to the cancer field. Although there is fast-growing interest in adenosine-to-inosine (A-to-I) miRNA editing and alternative cleavage that shifts miRNA isoforms, simultaneous evaluation of both modifications in cancer is still missing. Here, we concurrently profiled multiple miRNA modification types, including A-to-I miRNA editing and shifted miRNA isoforms, in >13,000 adult and pediatric tumor samples across 38 distinct cancer cohorts from The Cancer Genome Atlas and The Therapeutically Applicable Research to Generate Effective Treatments data sets. The differences between canonical miRNAs and the wider miRNAome in terms of expression, clustering, dysregulation, and prognostic standpoint were investigated. The combination of canonical miRNAs and modified miRNAs boosted the quality of clustering results, outlining unique clinicopathologic features among cohorts. Certain modified miRNAs showed opposite expression from their canonical counterparts in cancer, potentially impacting their targets and function. Finally, a shifted and edited miRNA isoform was experimentally validated to directly bind and suppress a unique target. These findings outline the importance of going beyond the well-established paradigm of one mature miRNA per miRNA arm to elucidate novel mechanisms related to cancer progression. SIGNIFICANCE Modified miRNAs may act as cancer biomarkers and function as allies or antagonists of their canonical counterparts in gene regulation, suggesting the concurrent consideration of canonical and modified miRNAs can boost patient stratification.
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Affiliation(s)
- Rosario Distefano
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Luisa Tomasello
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Gian Luca Rampioni Vinciguerra
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
- Faculty of Medicine and Psychology, Department of Clinical and Molecular Medicine, University of Rome “Sapienza,” Santo Andrea Hospital, Rome, Italy
| | - Pierluigi Gasparini
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Yujia Xiang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Marina Bagnoli
- Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Gioacchino P. Marceca
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Paolo Fadda
- Genomics Shared Resource, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Alessandro Laganà
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Mario Acunzo
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, Virginia
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Giovanni Nigita
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Carlo M. Croce
- Department of Cancer Biology and Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
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19
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Dutta N, Deb I, Sarzynska J, Lahiri A. Inosine and its methyl derivatives: Occurrence, biogenesis, and function in RNA. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2022; 169-170:21-52. [PMID: 35065168 DOI: 10.1016/j.pbiomolbio.2022.01.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/11/2021] [Accepted: 01/11/2022] [Indexed: 05/21/2023]
Abstract
Inosine is one of the most common post-transcriptional modifications. Since its discovery, it has been noted for its ability to contribute to non-Watson-Crick interactions within RNA. Rapidly accumulating evidence points to the widespread generation of inosine through hydrolytic deamination of adenosine to inosine by different classes of adenosine deaminases. Three naturally occurring methyl derivatives of inosine, i.e., 1-methylinosine, 2'-O-methylinosine and 1,2'-O-dimethylinosine are currently reported in RNA modification databases. These modifications are expected to lead to changes in the structure, folding, dynamics, stability and functions of RNA. The importance of the modifications is indicated by the strong conservation of the modifying enzymes across organisms. The structure, binding and catalytic mechanism of the adenosine deaminases have been well-studied, but the underlying mechanism of the catalytic reaction is not very clear yet. Here we extensively review the existing data on the occurrence, biogenesis and functions of inosine and its methyl derivatives in RNA. We also included the structural and thermodynamic aspects of these modifications in our review to provide a detailed and integrated discussion on the consequences of A-to-I editing in RNA and the contribution of different structural and thermodynamic studies in understanding its role in RNA. We also highlight the importance of further studies for a better understanding of the mechanisms of the different classes of deamination reactions. Further investigation of the structural and thermodynamic consequences and functions of these modifications in RNA should provide more useful information about their role in different diseases.
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Affiliation(s)
- Nivedita Dutta
- Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata, 700009, West Bengal, India
| | - Indrajit Deb
- Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata, 700009, West Bengal, India
| | - Joanna Sarzynska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704, Poznan, Poland
| | - Ansuman Lahiri
- Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, 92, Acharya Prafulla Chandra Road, Kolkata, 700009, West Bengal, India.
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20
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Distefano R, Nigita G, Le P, Romano G, Acunzo M, Nana-Sinkam P. Disparities in Lung Cancer: miRNA Isoform Characterization in Lung Adenocarcinoma. Cancers (Basel) 2022; 14:773. [PMID: 35159038 PMCID: PMC8833952 DOI: 10.3390/cancers14030773] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/28/2022] [Accepted: 01/30/2022] [Indexed: 02/04/2023] Open
Abstract
Despite the development of targeted therapeutics, immunotherapy, and strategies for early detection, lung cancer carries a high mortality. Further, significant racial disparities in outcomes exist for which the molecular drivers have yet to be fully elucidated. The growing field of Epitranscriptomics has introduced a new layer of complexity to the molecular pathogenesis of cancer. RNA modifications can occur in coding and non-coding RNAs, such as miRNAs, possibly altering their gene regulatory function. The potential role for such modifications as clinically informative biomarkers remains largely unknown. Here, we concurrently profiled canonical miRNAs, shifted isomiRs (templated and non-templated), and miRNAs with single-point modification events (RNA and DNA) in White American (W) and Black or African American (B/AA) lung adenocarcinoma (LUAD) patients. We found that while most deregulated miRNA isoforms were similar in W and B/AA LUAD tissues compared to normal adjacent tissues, there was a subgroup of isoforms with deregulation according to race. We specifically investigated an edited miRNA, miR-151a-3p with an A-to-I editing event at position 3, to determine how its altered expression may be associated with activation of divergent biological pathways between W and B/AA LUAD patients. Finally, we identified distinct race-specific miRNA isoforms that correlated with prognosis for both Ws and B/AAs. Our results suggested that concurrently profiling canonical and non-canonical miRNAs may have potential as a strategy for identifying additional distinct biological pathways and biomarkers in lung cancer.
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Affiliation(s)
- Rosario Distefano
- Department of Cancer Biology and Genetics and Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA; (R.D.); (G.N.)
| | - Giovanni Nigita
- Department of Cancer Biology and Genetics and Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA; (R.D.); (G.N.)
| | - Patricia Le
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA; (P.L.); (G.R.)
| | - Giulia Romano
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA; (P.L.); (G.R.)
| | - Mario Acunzo
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA; (P.L.); (G.R.)
| | - Patrick Nana-Sinkam
- Division of Pulmonary Diseases and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA 23298, USA; (P.L.); (G.R.)
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21
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Huang HY, Lin YCD, Cui S, Huang Y, Tang Y, Xu J, Bao J, Li Y, Wen J, Zuo H, Wang W, Li J, Ni J, Ruan Y, Li L, Chen Y, Xie Y, Zhu Z, Cai X, Chen X, Yao L, Chen Y, Luo Y, LuXu S, Luo M, Chiu CM, Ma K, Zhu L, Cheng GJ, Bai C, Chiang YC, Wang L, Wei F, Lee TY, Huang HD. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 2021; 50:D222-D230. [PMID: 34850920 PMCID: PMC8728135 DOI: 10.1093/nar/gkab1079] [Citation(s) in RCA: 472] [Impact Index Per Article: 118.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/09/2021] [Accepted: 10/25/2021] [Indexed: 12/19/2022] Open
Abstract
MicroRNAs (miRNAs) are noncoding RNAs with 18–26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA–target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update’s critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3′ untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.
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Affiliation(s)
- Hsi-Yuan Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yang-Chi-Dung Lin
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Shidong Cui
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yixian Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yun Tang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jiatong Xu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jiayang Bao
- Division of Biological Sciences, Section of Bioinformatics, University of California, San Diego, San Diego, CA 92093, USA
| | - Yulin Li
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jia Wen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Huali Zuo
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
| | - Weijuan Wang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jing Li
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Jie Ni
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yini Ruan
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Liping Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yidan Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yueyang Xie
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Zihao Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Xiaoxuan Cai
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Xinyi Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China
| | - Yigang Chen
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Yijun Luo
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Shupeng LuXu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Chih-Min Chiu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Kun Ma
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Lizhe Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Gui-Juan Cheng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Chen Bai
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China
| | - Liping Wang
- Department of Reproductive Medicine Centre, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong 518035, China
| | - Fengxiang Wei
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,Department of Cell Biology, Jiamusi University, Jiamusi, Heilongjiang 154007, China.,Shenzhen Children's Hospital of China Medical University, Shenzhen, Guangdong518172, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
| | - Hsien-Da Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong518172, China
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22
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Micheel J, Safrastyan A, Wollny D. Advances in Non-Coding RNA Sequencing. Noncoding RNA 2021; 7:70. [PMID: 34842804 PMCID: PMC8628893 DOI: 10.3390/ncrna7040070] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022] Open
Abstract
Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.
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Affiliation(s)
| | | | - Damian Wollny
- RNA Bioinformatics/High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University, 07743 Jena, Germany; (J.M.); (A.S.)
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