scholarly journals It's an RNA world: a focus on recent advances in RNA biochemistry

2016 ◽  
Vol 38 (2) ◽  
pp. 4-7
Author(s):  
Sheila Graham
Keyword(s):  
Molecular Biology ◽  
Protein Interactions ◽  
Rna World ◽  
Enzymatic Catalysis ◽  
Protein Translation ◽  
Rna Molecules ◽  
The Core ◽  
Recent Advances ◽  
Non Coding Rna ◽  
Encoding Protein

RNA is a fascinating molecule. Its array of different properties is highlighted by our knowledge of the ribosome. RNA can have structural properties; for example, rRNA is the core of the ribosome. RNA can bind proteins; for example, rRNA–ribosomal protein interactions are used to build the protein translation machinery. Finally, RNA can display enzymatic catalysis. In the ribosome during translation, non-coding RNA carries out decoding (tRNA) and amino acid polymerization (rRNA). If this is not fascinating enough, the last decade or so has seen a considerable reassessment of the core of Francis Crick's ‘central dogma of molecular biology’ that states that RNA molecules (rRNAs, tRNAs and mRNAs) serve to drive protein synthesis, decode mRNAs or act as a templates encoding protein. Much of the upheaval in our understanding of RNA biology has come from deep mining of the human transcriptome by RNA sequencing (RNAseq) by next generation sequencing techniques. One of the most startling revelations from the wealth of new data provided by the ‘-omics’ revolution is that over 80% of the human genome encodes RNA, whereas only up to 2% encodes proteins. In other words, our genomes are largely RNA-coding. The discovery of the plethora of non-coding RNAs in our genomes has revolutionized molecular biology. These RNAs do not encode protein and, unlike rRNAs or tRNAs, most are not intimately linked to protein translation. In this edition of The Biochemist, we revisit recent advances in RNA research to reveal the broad scope of this hot topic in today's biochemistry and to spotlight some new areas of RNA research.

scholarly journals RNA 2’-O-Methylation (Nm) Modification in Human Diseases

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 117 ◽  
Author(s):  
Dilyana G. Dimitrova ◽  
Laure Teysset ◽  
Clément Carré
Keyword(s):  
Rna World ◽  
Human Diseases ◽  
Structure Stability ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Self Recognition ◽  
Non Coding Rna ◽  
Long Time ◽  
Epigenetic Gene Regulation ◽  
Biological Defects

Nm (2’-O-methylation) is one of the most common modifications in the RNA world. It has the potential to influence the RNA molecules in multiple ways, such as structure, stability, and interactions, and to play a role in various cellular processes from epigenetic gene regulation, through translation to self versus non-self recognition. Yet, building scientific knowledge on the Nm matter has been hampered for a long time by the challenges in detecting and mapping this modification. Today, with the latest advancements in the area, more and more Nm sites are discovered on RNAs (tRNA, rRNA, mRNA, and small non-coding RNA) and linked to normal or pathological conditions. This review aims to synthesize the Nm-associated human diseases known to date and to tackle potential indirect links to some other biological defects.

scholarly journals CROSSalive: a web server for predicting the in vivo structure of RNA molecules

2019 ◽  
Author(s):  
Riccardo Delli Ponti ◽  
Alexandros Armaos ◽  
Andrea Vandelli ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Cross Validation ◽  
Supplementary Information ◽  
Supplementary Data ◽  
High Confidence ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Motivation RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions. Results Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single- and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions. Availability and implementation CROSSalive webserver is freely accessible at http://service.tartaglialab.com/new_submission/crossalive Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ribozymes that use redox cofactors

2004 ◽  
Vol 76 (7-8) ◽  
pp. 1525-1536 ◽  
Author(s):  
S. Tsukiji ◽  
K. Ramaswamy ◽  
H. Suga
Keyword(s):  
Chemical Transformation ◽  
Redox Reactions ◽  
Rna World ◽  
Fundamental Question ◽  
Biological Molecules ◽  
Rna Molecules ◽  
Recent Advances ◽  
Living Organisms ◽  
Shed Light

This review summarizes the history and most recent advances in aptamers and ribozymes that bind and use redox (reduction–oxidation) cofactors. Redox reactions, catalyzed by protein enzymes in an extant world, play a central role in the metabolism of numerous biological molecules in the living organisms. The burden of catalyzing these reactions in a pre-protein-based world (i.e., the hypothesized RNA World) could have been borne by RNA molecules. To this end, we raise a fundamental question: can RNA accelerate the redox chemical transformation? We hope that this article will be able to shed light on this intriguing question.

scholarly journals Special Issue: MicroRNA Regulation in Health and Disease

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 457 ◽  
Author(s):  
Subbaya Subramanian ◽  
Clifford J. Steer
Keyword(s):  
Molecular Biology ◽  
Special Issue ◽  
Microrna Regulation ◽  
Recent Advances ◽  
Non Coding Rna ◽  
Health And Disease

Our understanding of non-coding RNA has significantly changed based on recent advances in genomics and molecular biology, and their role is recognized to include far more than a link between the sequence of DNA and synthesized proteins [...]

scholarly journals Non-Coding RNAs in Adrenocortical Cancer: From Pathogenesis to Diagnosis

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 461 ◽  
Author(s):  
Abel Decmann ◽  
Pál Perge ◽  
Peter Istvan Turai ◽  
Attila Patócs ◽  
Peter Igaz
Keyword(s):  
Adrenocortical Cancer ◽  
Rna Molecules ◽  
Recent Advances ◽  
Data Support ◽  
Non Coding Rna ◽  
Potential Applicability ◽  
Numerous Data ◽  
Non Coding Rnas

Non-coding RNA molecules including microRNAs and long non-coding RNAs (lncRNA) have been implicated in the pathogenesis of several tumors and numerous data support their applicability in diagnosis as well. Despite recent advances, the pathogenesis of adrenocortical cancer still remains elusive and there are no reliable blood-borne markers of adrenocortical malignancy, either. Several findings show the potential applicability of microRNAs as biomarkers of malignancy and prognosis, and there are some data on lncRNA as well. In this review, we present a synopsis on the potential relevance of non-coding RNA molecules in adrenocortical pathogenesis and their applicability in diagnosis from tissue and blood.

scholarly journals CROSSalive: a web server for predicting the in vivo structure of RNA molecules

2019 ◽  
Author(s):  
Riccardo Delli Ponti ◽  
Alexandros Armaos ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Protein Interactions ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Sequence Length ◽  
Test Set ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Map Data ◽  
Long Non Coding Rna

ABSTRACTHere we introduce CROSSalive, an algorithm to predict the RNA secondary structure profile (double and single stranded regions) in vivo and without sequence length limitations. Using predictions of protein interactions CROSSalive predicts the effect of N6 adenosine methylation (m6a) on RNA structure. Trained on icSHAPE data in presence (m6a+) and absence (m6a-) of methylation CROSSalive achieves an accuracy of 0.88 on the test set. The algorithm was also applied to the murine long non-coding RNA Xist (17900 nt, not present in the training) and shows a Pearson’s correlation of 0.45 with SHAPE-map data. CROSSalive webserver is freely accessible at the following page: http://service.tartaglialab.com/new_submission/crossalive

scholarly journals Using Bioinformatics to predict potential targets of Microrna-144 in osteoarthritis

2020 ◽  
Vol 8 (1) ◽  
pp. 43-52
Author(s):  
Ho Thi Bich Phuong ◽  
Vien Ngoc Thach ◽  
Luong Hoang Ngan ◽  
Le Thi Truc Linh
Keyword(s):  
Gene Expression ◽  
Molecular Mechanism ◽  
Early Phase ◽  
Protein Translation ◽  
Mrna Degradation ◽  
Regulate Gene Expression ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
End Stage ◽  
Regulate Gene

MicroRNAs are short endogenous non-coding RNA molecules, typically 19-25 nucleotides in length, which negatively regulate gene expression through binding to 3’UTR of target mRNAs, leading to repression of protein translation or target mRNA degradation. MicroRNA-144 (miR-144) was found as an abnormal expression in various diseases, including osteoarthritis (OA). We have identified increased microRNA-144 expression in early phase and end stage of OA. However, the molecular mechanism of this increase has not been yet to be determined yet. Using bioinformatics tools, we found more than 4,000 mRNAs that are predicted to be potential direct targets of miR-144, including mRNAs involved in the critical signaling pathways in OA e.g. TGFβ/Smad2/3 and WNT/β-catenin. Results from this research provide information for future ex periments to validate miR-144 potential targets.

scholarly journals Relevance of long non-coding RNAs in tumour biology

2012 ◽  
Vol 153 (38) ◽  
pp. 1494-1501 ◽  
Author(s):  
Zoltán Nagy ◽  
Diána Rita Szabó ◽  
Adrienn Zsippai ◽  
András Falus ◽  
Károly Rácz ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Molecular Biology ◽  
Molecular Diagnostics ◽  
Small Rnas ◽  
Molecular Processes ◽  
Major Fraction ◽  
Rna Molecules ◽  
Mediated Effects ◽  
Non Coding Rna ◽  
Non Coding Rnas

The discovery of the biological relevance of non-coding RNA molecules represents one of the most significant advances in contemporary molecular biology. It has turned out that a major fraction of the non-coding part of the genome is transcribed. Beside small RNAs (including microRNAs) more and more data are disclosed concerning long non-coding RNAs of 200 nucleotides to 100 kb length that are implicated in the regulation of several basic molecular processes (cell proliferation, chromatin functioning, microRNA-mediated effects, etc.). Some of these long non-coding RNAs have been associated with human tumours, including H19, HOTAIR, MALAT1, etc., the different expression of which has been noted in various neoplasms relative to healthy tissues. Long non-coding RNAs may represent novel markers of molecular diagnostics and they might even turn out to be targets of therapeutic intervention. Orv. Hetil., 2012, 153, 1494–1501.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

scholarly journals The Role of Extracellular Vesicles as Shuttles of RNA and Their Clinical Significance as Biomarkers in Hepatocellular Carcinoma

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 902
Author(s):  
Eva Costanzi ◽  
Carolina Simioni ◽  
Gabriele Varano ◽  
Cinzia Brenna ◽  
Ilaria Conti ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Extracellular Vesicles ◽  
Tumor Metastasis ◽  
Biological Processes ◽  
Rna Molecules ◽  
Expression Of Genes ◽  
Non Coding Rna ◽  
Donor Cells ◽  
Relevant Role

Extracellular vesicles (EVs) have attracted interest as mediators of intercellular communication following the discovery that EVs contain RNA molecules, including non-coding RNA (ncRNA). Growing evidence for the enrichment of peculiar RNA species in specific EV subtypes has been demonstrated. ncRNAs, transferred from donor cells to recipient cells, confer to EVs the feature to regulate the expression of genes involved in differentiation, proliferation, apoptosis, and other biological processes. These multiple actions require accuracy in the isolation of RNA content from EVs and the methodologies used play a relevant role. In liver, EVs play a crucial role in regulating cell–cell communications and several pathophysiological events in the heterogeneous liver class of cells via horizontal transfer of their cargo. This review aims to discuss the rising role of EVs and their ncRNAs content in regulating specific aspects of hepatocellular carcinoma development, including tumorigenesis, angiogenesis, and tumor metastasis. We analyze the progress in EV-ncRNAs’ potential clinical applications as important diagnostic and prognostic biomarkers for liver conditions.

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