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The Non-coding RNAs: Sequence, Structure and Function
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The Non-coding RNAs: Sequence, Structure and Function

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 10137

Special Issue Editor

Dr. Deepak Koirala

E-Mail Website
Guest Editor
Department of Chemistry and Biochemistry, University of Maryland Baltimore County (UMBC), Baltimore, MD 21250, USA
Interests: RNA structure and dynamics; RNA–protein interactions; chaperone-assisted RNA crystallogrpahy; cap-independent viral translation

Special Issue Information

Dear Colleagues,

Cellular RNA molecules are emerging as one of the most critical biomolecules in modern biology and biomedicine. Recent advances in next-generation sequencing strategies coupled with bioinformatic techniques have uncovered an unprecedented number of non-coding RNAs. These RNAs play a wide variety of roles in almost every aspect of cellular functions, including gene regulation, genome dynamics, inflammation, protein trafficking, developmental programming, and catalysis. Many non-coding RNAs are directly involved in the pathogenesis of both genetic and infectious diseases. Besides naturally occurring RNAs, several artificial RNAs such as aptamers have also been used in biosensing and biotechnology applications. Similar to proteins, these RNAs often fold into three-dimensional structures to perform their functions, but their structural characteristics and evolutionary constraints differ significantly from proteins. Several methodologies and approaches have been developed to facilitate the understanding of non-coding RNA structure–function relationships. However, the structural determination of non-coding RNAs and the integration of high-throughput transcriptomics, epi-transcriptomics, structuromics, and interactomics data with bioinformatic strategies remain a demanding challenge. This Special Issue, “The Non-coding RNAs: Sequence, Structure, and Function”, covers the various aspects of non-coding RNAs. The topics highly welcome for submission as comprehensive reviews and original research manuscripts include but are not limited to: biochemical, biophysical, and computational methods of non-coding RNA structure determination, RNA omics methodologies, RNA modifications, riboregulation, and RNA catalysis.

Dr. Deepak Koirala
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-coding RNAs
  • RNA structure and dynamics
  • RNA modifications
  • biophysical methods of RNA structure determination
  • transcriptomics
  • RNA aptamers
  • ribozymes
  • riboswitches
  • RNA structure modeling and computation
  • RNA bioinformatics

Published Papers (4 papers)

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Research

14 pages, 14958 KiB  
Article
Small Extracellular Vesicles of M1-BV2 Microglia Induce Neuronal PC12 Cells Apoptosis via the Competing Endogenous Mechanism of CircRNAs
by Sheng Gao, Luyue Bai, Shu Jia and Chunyang Meng
Genes 2022, 13(9), 1603; https://doi.org/10.3390/genes13091603 - 8 Sep 2022
Cited by 4 | Viewed by 1883
Abstract
Polarized microglia play a vital role in neurodegenerative diseases. However, the effects of polarized microglia-derived small extracellular vesicles (SEVs) on neuronal cells and the regulatory mechanisms of circular RNAs (circRNAs) in SEVs remain incompletely defined. In the present study, we carried out high-throughput [...] Read more.
Polarized microglia play a vital role in neurodegenerative diseases. However, the effects of polarized microglia-derived small extracellular vesicles (SEVs) on neuronal cells and the regulatory mechanisms of circular RNAs (circRNAs) in SEVs remain incompletely defined. In the present study, we carried out high-throughput sequencing and differential expression analysis of circRNAs in the SEVs of M0-phenotype BV2 microglia (M0-BV2) and polarized M1-phenotype BV2 microglia (M1-BV2). Hub circRNAs in the SEVs and their functions were screened using multiple bioinformatics methods. We further validated the effects of SEVs on neuronal PC12 cells by co-culturing M0-BV2 SEVs and M1-BV2 SEVs with neuronal PC12 cells. Among the differentially expressed circRNAs, the target mRNAs of six hub circRNAs (circ_0000705, circ_0001313, circ_0000229, circ_0001123, circ_0000621, and circ_0000735) were enriched in apoptosis-related biological processes. Furthermore, western blot and flow cytometry analysis demonstrated that M0-BV2 SEVs had no distinct effect on apoptosis of neuronal PC12 cells, while M1-BV2 SEVs remarkably increased the apoptosis of neuronal PC12 cells. We then constructed the competing endogenous RNA (ceRNA) networks of the six hub circRNAs. Taken together, the results suggest that polarized M1-BV2 microglia can induce apoptosis of neuronal PC12 cells through secreted SEVs, and this regulatory effect may be achieved by the circRNAs circ_0000705, circ_0001313, circ_0000229, circ_0001123, circ_0000621, and circ_0000735 through ceRNAs regulatory networks. These findings provide new potential targets for the treatment of neurodegenerative diseases. Full article
(This article belongs to the Special Issue The Non-coding RNAs: Sequence, Structure and Function)
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12 pages, 23070 KiB  
Article
Bioinformatics Analysis of the Interaction of miRNAs and piRNAs with Human mRNA Genes Having di- and Trinucleotide Repeats
by Ayaz Belkozhayev, Raigul Niyazova, Cornelia Wilson, Nurlan Jainakbayev, Anna Pyrkova, Yeldar Ashirbekov, Aigul Akimniyazova, Kamalidin Sharipov and Anatoliy Ivashchenko
Genes 2022, 13(5), 800; https://doi.org/10.3390/genes13050800 - 29 Apr 2022
Cited by 7 | Viewed by 2211
Abstract
The variability of nucleotide repeats is considered one of the causes of diseases, but their biological function is not understood. In recent years, the interaction of miRNAs and piRNAs with the mRNAs of genes responsible for developing neurodegenerative and oncological diseases and diabetes [...] Read more.
The variability of nucleotide repeats is considered one of the causes of diseases, but their biological function is not understood. In recent years, the interaction of miRNAs and piRNAs with the mRNAs of genes responsible for developing neurodegenerative and oncological diseases and diabetes have been actively studied. We explored candidate genes with nucleotide repeats to predict associations with miRNAs and piRNAs. The parameters of miRNAs and piRNA binding sites with mRNAs of human genes having nucleotide repeats were determined using the MirTarget program. This program defines the start of the initiation of miRNA and piRNA binding to mRNAs, the localization of miRNA and piRNA binding sites in the 5′-untranslated region (5′UTR), coding sequence (CDS) and 3′-untranslated region (3′UTR); the free energy of binding; and the schemes of nucleotide interactions of miRNAs and piRNAs with mRNAs. The characteristics of miRNAs and piRNA binding sites with mRNAs of 73 human genes were determined. The 5′UTR, 3′UTR and CDS of the mRNAs of genes are involved in the development of neurodegenerative, oncological and diabetes diseases with GU, AC dinucleotide and CCG, CAG, GCC, CGG, CGC trinucleotide repeats. The associations of miRNAs, piRNAs and candidate target genes could be recommended for developing methods for diagnosing diseases, including neurodegenerative diseases, oncological diseases and diabetes. Full article
(This article belongs to the Special Issue The Non-coding RNAs: Sequence, Structure and Function)
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9 pages, 1804 KiB  
Article
The Identification of MATE Antisense Transcripts in Soybean Using Strand-Specific RNA-Seq Datasets
by Yee-Shan Ku, Xiao Lin, Kejing Fan, Sau-Shan Cheng, Ting-Fung Chan, Gyuhwa Chung and Hon-Ming Lam
Genes 2022, 13(2), 228; https://doi.org/10.3390/genes13020228 - 26 Jan 2022
Cited by 1 | Viewed by 2335
Abstract
Natural antisense transcripts (NATs) have been generally reported as negative regulators of their sense counterparts. Multidrug and toxic compound extrusion (MATE) proteins mediate the transport of various substrates. Although MATEs have been identified genome-wide in various plant species, their transcript regulators remain [...] Read more.
Natural antisense transcripts (NATs) have been generally reported as negative regulators of their sense counterparts. Multidrug and toxic compound extrusion (MATE) proteins mediate the transport of various substrates. Although MATEs have been identified genome-wide in various plant species, their transcript regulators remain unclear. Here, using the publicly available strand-specific RNA-seq datasets of Glycine soja (wild soybean) which have the data from various tissues including developing pods, developing seeds, embryos, cotyledons and hypocotyls, roots, apical buds, stems, and flowers, we identified 35 antisense transcripts of MATEs from 28 gene loci after transcriptome assembly. Spearman correlation coefficients suggested the positive expression correlations of eight MATE antisense and sense transcript pairs. By aligning the identified transcripts with the reference genome of Glycine max (cultivated soybean), the MATE antisense and sense transcript pairs were identified. Using soybean C08 (Glycine max), in developing pods and seeds, the positive correlations between MATE antisense and sense transcript pairs were shown by RT-qPCR. These findings suggest that soybean antisense transcripts are not necessarily negative transcription regulators of their sense counterparts. This study enhances the existing knowledge on the transcription regulation of MATE transporters by uncovering the previously unknown MATE antisense transcripts and their potential synergetic effects on sense transcripts. Full article
(This article belongs to the Special Issue The Non-coding RNAs: Sequence, Structure and Function)
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11 pages, 6503 KiB  
Article
Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA
by Yunhe Liu, Qiqing Fu, Xueqing Peng, Chaoyu Zhu, Gang Liu and Lei Liu
Genes 2021, 12(12), 2018; https://doi.org/10.3390/genes12122018 - 19 Dec 2021
Cited by 5 | Viewed by 2710
Abstract
Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) [...] Read more.
Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture fed with a raw sequence, to learn the sparse features of RNA sequences and to accomplish the circRNAs identification task. The model outperformed the state-of-art models. Moreover, following the validation of the attention mechanism effectiveness by the handwritten digit dataset, the key sequence loci underlying circRNA’s recognition were obtained based on the corresponding attention score. Then, motif enrichment analysis identified some of the key motifs for circRNA formation. In conclusion, we designed deep learning network architecture suitable for learning gene sequences with sparse features and implemented it for the circRNA identification task, and the model has strong representation capability in the indication of some key loci. Full article
(This article belongs to the Special Issue The Non-coding RNAs: Sequence, Structure and Function)
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