1
|
Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets. Funct Integr Genomics 2023; 23:33. [PMID: 36625940 PMCID: PMC9838419 DOI: 10.1007/s10142-022-00947-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023]
Abstract
Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.
Collapse
|
2
|
Ivanova Z, Minkov G, Gisel A, Yahubyan G, Minkov I, Toneva V, Baev V. The Multiverse of Plant Small RNAs: How Can We Explore It?
. Int J Mol Sci 2022; 23:ijms23073979. [PMID: 35409340 PMCID: PMC8999349 DOI: 10.3390/ijms23073979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/28/2022] [Accepted: 03/31/2022] [Indexed: 12/22/2022] Open
Abstract
Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20–24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes—microRNAs (miRNAs) and small interfering RNAs (siRNAs)—which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.
Collapse
Affiliation(s)
- Zdravka Ivanova
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
| | - Georgi Minkov
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Andreas Gisel
- Institute of Biomedical Technologies (ITB), CNR, 70126 Bari, Italy;
| | - Galina Yahubyan
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Ivan Minkov
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Center of Plant System Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Valentina Toneva
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
| | - Vesselin Baev
- Institute of Molecular Biology and Biotechnologies, 4108 Markovo, Bulgaria; (Z.I.); (G.M.); (I.M.); (V.T.)
- Department of Plant Physiology and Molecular Biology, University of Plovdiv, 4000 Plovdiv, Bulgaria;
- Correspondence:
| |
Collapse
|
3
|
Patil S, Joshi S, Jamla M, Zhou X, Taherzadeh MJ, Suprasanna P, Kumar V. MicroRNA-mediated bioengineering for climate-resilience in crops. Bioengineered 2021; 12:10430-10456. [PMID: 34747296 PMCID: PMC8815627 DOI: 10.1080/21655979.2021.1997244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/19/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022] Open
Abstract
Global projections on the climate change and the dynamic environmental perturbations indicate severe impacts on food security in general, and crop yield, vigor and the quality of produce in particular. Sessile plants respond to environmental challenges such as salt, drought, temperature, heavy metals at transcriptional and/or post-transcriptional levels through the stress-regulated network of pathways including transcription factors, proteins and the small non-coding endogenous RNAs. Amongs these, the miRNAs have gained unprecedented attention in recent years as key regulators for modulating gene expression in plants under stress. Hence, tailoring of miRNAs and their target pathways presents a promising strategy for developing multiple stress-tolerant crops. Plant stress tolerance has been successfully achieved through the over expression of microRNAs such as Os-miR408, Hv-miR82 for drought tolerance; OsmiR535A and artificial DST miRNA for salinity tolerance; and OsmiR535 and miR156 for combined drought and salt stress. Examples of miR408 overexpression also showed improved efficiency of irradiation utilization and carbon dioxide fixation in crop plants. Through this review, we present the current understanding about plant miRNAs, their roles in plant growth and stress-responses, the modern toolbox for identification, characterization and validation of miRNAs and their target genes including in silico tools, machine learning and artificial intelligence. Various approaches for up-regulation or knock-out of miRNAs have been discussed. The main emphasis has been given to the exploration of miRNAs for development of bioengineered climate-smart crops that can withstand changing climates and stressful environments, including combination of stresses, with very less or no yield penalties.
Collapse
Affiliation(s)
- Suraj Patil
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Pune, India
| | - Shrushti Joshi
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Pune, India
| | - Monica Jamla
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Pune, India
| | - Xianrong Zhou
- School of Life Science and Biotechnology, Yangtze Normal University, Ch-ongqing, China
| | | | - Penna Suprasanna
- Bhabha Atomic Research Centre, Homi Bhabha National Institute, Mumbai, India
| | - Vinay Kumar
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Pune, India
| |
Collapse
|
4
|
MicroRNAs Regulating Autophagy in Neurodegeneration. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1208:191-264. [PMID: 34260028 DOI: 10.1007/978-981-16-2830-6_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Social and economic impacts of neurodegenerative diseases (NDs) become more prominent in our constantly aging population. Currently, due to the lack of knowledge about the aetiology of most NDs, only symptomatic treatment is available for patients. Hence, researchers and clinicians are in need of solid studies on pathological mechanisms of NDs. Autophagy promotes degradation of pathogenic proteins in NDs, while microRNAs post-transcriptionally regulate multiple signalling networks including autophagy. This chapter will critically discuss current research advancements in the area of microRNAs regulating autophagy in NDs. Moreover, we will introduce basic strategies and techniques used in microRNA research. Delineation of the mechanisms contributing to NDs will result in development of better approaches for their early diagnosis and effective treatment.
Collapse
|
5
|
Liu Q, Ding C, Lang X, Guo G, Chen J, Su X. Small noncoding RNA discovery and profiling with sRNAtools based on high-throughput sequencing. Brief Bioinform 2019; 22:463-473. [PMID: 31885040 PMCID: PMC7820841 DOI: 10.1093/bib/bbz151] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 09/24/2019] [Accepted: 11/01/2019] [Indexed: 02/05/2023] Open
Abstract
Small noncoding RNAs (sRNA/sncRNAs) are generated from different genomic loci and play important roles in biological processes, such as cell proliferation and the regulation of gene expression. Next-generation sequencing (NGS) has provided an unprecedented opportunity to discover and quantify diverse kinds of sncRNA, such as tRFs (tRNA-derived small RNA fragments), phasiRNAs (phased, secondary, small-interfering RNAs), Piwi-interacting RNA (piRNAs) and plant-specific 24-nt short interfering RNAs (siRNAs). However, currently available web-based tools do not provide approaches to comprehensively analyze all of these diverse sncRNAs. This study presents a novel integrated platform, sRNAtools (https://bioinformatics.caf.ac.cn/sRNAtools), that can be used in conjunction with high-throughput sequencing to identify and functionally annotate sncRNAs, including profiling microRNAss, piRNAs, tRNAs, small nuclear RNAs, small nucleolar RNAs and rRNAs and discovering isomiRs, tRFs, phasiRNAs and plant-specific 24-nt siRNAs for up to 21 model organisms. Different modules, including single case, batch case, group case and target case, are developed to provide users with flexible ways of studying sncRNA. In addition, sRNAtools supports different ways of uploading small RNA sequencing data in a very interactive queue system, while local versions based on the program package/Docker/virtureBox are also available. We believe that sRNAtools will greatly benefit the scientific community as an integrated tool for studying sncRNAs.
Collapse
Affiliation(s)
- Qi Liu
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing 10091, China
| | - Changjun Ding
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing 10091, China
| | - Xiaoqiang Lang
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
| | - Ganggang Guo
- Precision Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China 610041
| | - Jiafei Chen
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Beijing 10091, China
| | - Xiaohua Su
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| |
Collapse
|
6
|
Chen L, Heikkinen L, Wang C, Yang Y, Sun H, Wong G. Trends in the development of miRNA bioinformatics tools. Brief Bioinform 2019; 20:1836-1852. [PMID: 29982332 PMCID: PMC7414524 DOI: 10.1093/bib/bby054] [Citation(s) in RCA: 344] [Impact Index Per Article: 68.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 05/18/2018] [Indexed: 12/13/2022] Open
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.
Collapse
Affiliation(s)
- Liang Chen
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Liisa Heikkinen
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Changliang Wang
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Yang Yang
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| | - Huiyan Sun
- Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Garry Wong
- Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
| |
Collapse
|
7
|
Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome. Methods Mol Biol 2019; 1912:215-250. [PMID: 30635896 DOI: 10.1007/978-1-4939-8982-9_9] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.
Collapse
|
8
|
Yang K, Wen X, Mudunuri S, Varma GPS, Sablok G. Diff isomiRs: Large-scale detection of differential isomiRs for understanding non-coding regulated stress omics in plants. Sci Rep 2019; 9:1406. [PMID: 30723229 PMCID: PMC6363768 DOI: 10.1038/s41598-019-38932-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 01/14/2019] [Indexed: 11/11/2022] Open
Abstract
Plants have an amazing ability to cope with wide variety of stresses by regulating the expression of genes and thus by altering the physiological status. In the past few years, canonical microRNA variants (isomiRs) have been shown to play pivotal roles by acting as regulators of the transcriptional machinery. In the present research, we present Diff isomiRs, a web-based exploratory repository of differential isomiRs across 16 sequenced plant species representing a total of 433 datasets across 21 different stresses and 158 experimental states. Diff isomiRs provides the high-throughput detection of differential isomiRs using mapping-based and model-based differential analysis revealing a total of 16,157 and 2,028 differential isomiRs, respectively. Easy-to-use and web-based exploration of differential isomiRs provides several features such as browsing of the differential isomiRs according to stress or species, as well as association of the differential isomiRs to targets and plant endogenous target mimics (PeTMs). Diff isomiRs also provides the relationship between the canonical miRNAs, isomiRs and the miRNA-target interactions. This is the first web-based large-scale repository for browsing differential isomiRs and will facilitate better understanding of the regulatory role of the isomiRs with respect to the canonical microRNAs. Diff isomiRs can be accessed at: www.mcr.org.in/diffisomirs.
Collapse
Affiliation(s)
- Kun Yang
- Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region (Guizhou University), Ministry of Education, Institute of Agro-bioengineering/College of Life Sciences, Guizhou University, Guiyang, 550025, Guizhou Province, P. R. China
| | - Xiaopeng Wen
- Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region (Guizhou University), Ministry of Education, Institute of Agro-bioengineering/College of Life Sciences, Guizhou University, Guiyang, 550025, Guizhou Province, P. R. China.
| | - Suresh Mudunuri
- Centre for Bioinformatics Research, SRKR Engineering College, Chinna Amiram, Bhimavaram, West Godavari District, Andhra Pradesh, 534204, India
| | - G P Saradhi Varma
- Centre for Bioinformatics Research, SRKR Engineering College, Chinna Amiram, Bhimavaram, West Godavari District, Andhra Pradesh, 534204, India
| | - Gaurav Sablok
- Finnish Museum of Natural History, Helsinki, Finland. .,Organismal and Evolutionary Biology (OEB) Research Programme, Department of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
| |
Collapse
|
9
|
Identifying and characterizing functional 3' nucleotide addition in the miRNA pathway. Methods 2018; 152:23-30. [PMID: 30138674 DOI: 10.1016/j.ymeth.2018.08.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 08/02/2018] [Accepted: 08/14/2018] [Indexed: 02/07/2023] Open
Abstract
Over the past decade, modifications to microRNAs (miRNAs) via 3' end nucleotide addition have gone from a deep-sequencing curiosity to experimentally confirmed drivers of a range of regulatory activities. Here we overview the methods that have been deployed by researchers seeking to untangle these diverse functional roles and include characterizing not only the nucleotidyl transferases catalyzing the additions but also the nucleotides being added, and the timing of their addition during the miRNA pathway. These methods and their further development are key to clarifying the diverse and sometimes contradictory functional findings presently attributed to these nucleotide additions.
Collapse
|
10
|
Yang K, Wen X, Mudunuri SB, Sablok G. Plant IsomiR Atlas: Large Scale Detection, Profiling, and Target Repertoire of IsomiRs in Plants. FRONTIERS IN PLANT SCIENCE 2018; 9:1881. [PMID: 30723486 PMCID: PMC6349829 DOI: 10.3389/fpls.2018.01881] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 12/05/2018] [Indexed: 05/15/2023]
Abstract
microRNAs (miRNAs) play an important role as key regulators controlling the post-transcriptional events in plants across development, abiotic and biotic stress, tissue polarity and also in defining the evolutionary basis of the origin of the post-transcriptional machinery. Identifying patterns of regulated and co-regulated small RNAs, in particular miRNAs and their sequence variants with the availability of next generation sequencing approaches has widely demonstrated the role of miRNAs and their temporal regulation in maintaining plant development and their response to stress conditions. Although the role of canonical miRNAs has been widely explored and functional diversity is revealed, those works for isomiRs are still limited and urgent to be carried out across plants. This relative lack of information with respect to isomiRs might be attributed to the non-availability of large-scale detection of isomiRs across wide plant species. In the present research, we addressed this by developing Plant isomiR Atlas, which provides large-scale detection of isomiRs across 23 plant species utilizing 677 smallRNAs datasets and reveals a total of 98,374 templated and non-templated isomiRs from 6,167 precursors. Plant isomiR Atlas provides several visualization features such as species specific isomiRs, isomiRs and canonical miRNAs overlap, terminal modification classifications, target identification using psRNATarget and TargetFinder and also canonical miRNAs:target interactions. Plant isomiR Atlas will play a key role in understanding the regulatory nature of miRNAome and will accelerate to understand the functional role of isomiRs. Plant isomiR Atlas is available at www.mcr.org.in/isomir. One Sentence Summary Plant isomiR Atlas will play a key role in understanding the regulatory nature of miRNAome and will accelerate the understanding and diversity of functional targets of plants isomiRs.
Collapse
Affiliation(s)
- Kun Yang
- Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region (Guizhou University), Ministry of Education, Institute of Agro-Bioengineering, College of Life Sciences, Guizhou University, Guiyang, China
| | - Xiaopeng Wen
- Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region (Guizhou University), Ministry of Education, Institute of Agro-Bioengineering, College of Life Sciences, Guizhou University, Guiyang, China
- *Correspondence: Xiaopeng Wen
| | - Suresh B. Mudunuri
- Centre for Bioinformatics Research, SRKR Engineering College, Bhimavaram, India
| | - Gaurav Sablok
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
- Organismal and Evolutionary Biology (OEB) Research Programme, Department of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
- Gaurav Sablok
| |
Collapse
|