scholarly journals RNA editing in mesothelioma: a look forward

Open Biology ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 200112
Author(s):  
Ananya Hariharan ◽  
Suna Sun ◽  
Martin Wipplinger ◽  
Emanuela Felley-Bosco
Keyword(s):  
Rna Editing ◽  
Chronic Exposure ◽  
Cancer Growth ◽  
Biological Processes ◽  
Epigenetic Alterations ◽  
Rare Cancer ◽  
Public Data ◽  
Transcript Diversity ◽  
Transcriptional Process ◽  
Hydrolytic Deamination

RNA editing is a post-transcriptional process increasing transcript diversity, thereby regulating different biological processes. We recently observed that mutations resulting from RNA editing due to hydrolytic deamination of adenosine increase during the development of mesothelioma, a rare cancer linked to chronic exposure to asbestos. This review gathers information from the published literature and public data mining to explore several aspects of RNA editing and their possible implications for cancer growth and therapy. We address possible links between RNA editing and particular types of mesothelioma genetic and epigenetic alterations and discuss the relevance of an edited substrate in the context of current chemotherapy or immunotherapy.

Artificial RNA Editing with ADAR for Gene Therapy

2020 ◽  
Vol 20 (1) ◽  
pp. 44-54 ◽  
Author(s):  
Sonali Bhakta ◽  
Toshifumi Tsukahara
Keyword(s):  
Gene Therapy ◽  
Genetic Code ◽  
Molecular Mechanism ◽  
Rna Editing ◽  
Comparative Studies ◽  
Genetic Diseases ◽  
Adenosine Deaminases ◽  
Family Protein ◽  
Hydrolytic Deamination

Editing mutated genes is a potential way for the treatment of genetic diseases. G-to-A mutations are common in mammals and can be treated by adenosine-to-inosine (A-to-I) editing, a type of substitutional RNA editing. The molecular mechanism of A-to-I editing involves the hydrolytic deamination of adenosine to an inosine base; this reaction is mediated by RNA-specific deaminases, adenosine deaminases acting on RNA (ADARs), family protein. Here, we review recent findings regarding the application of ADARs to restoring the genetic code along with different approaches involved in the process of artificial RNA editing by ADAR. We have also addressed comparative studies of various isoforms of ADARs. Therefore, we will try to provide a detailed overview of the artificial RNA editing and the role of ADAR with a focus on the enzymatic site directed A-to-I editing.

scholarly journals Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

2020 ◽  
Author(s):  
Peng Yu ◽  
Jin Li ◽  
Su-Ping Deng ◽  
Feiran Zhang ◽  
Petar N. Grozdanov ◽  
...  
Keyword(s):  
Rett Syndrome ◽  
Neuronal Morphology ◽  
Biological Research ◽  
Integrated Analysis ◽  
Splicing Factors ◽  
Biological Processes ◽  
Rna Seq ◽  
Key Factors ◽  
Mouse Tissues ◽  
Public Data

AbstractA vast amount of public RNA-sequencing datasets have been generated and used widely to study transcriptome mechanisms. These data offer precious opportunity for advancing biological research in transcriptome studies such as alternative splicing. We report the first large-scale integrated analysis of RNA-Seq data of splicing factors for systematically identifying key factors in diseases and biological processes. We analyzed 1,321 RNA-Seq libraries of various mouse tissues and cell lines, comprising more than 6.6 TB sequences from 75 independent studies that experimentally manipulated 56 splicing factors. Using these data, RNA splicing signatures and gene expression signatures were computed, and signature comparison analysis identified a list of key splicing factors in Rett syndrome and cold-induced thermogenesis. We show that cold-induced RNA-binding proteins rescue the neurite outgrowth defects in Rett syndrome using neuronal morphology analysis, and we also reveal that SRSF1 and PTBP1 are required for energy expenditure in adipocytes using metabolic flux analysis. Our study provides an integrated analysis for identifying key factors in diseases and biological processes and highlights the importance of public data resources for identifying hypotheses for experimental testing.

scholarly journals ADAGE signature analysis: differential expression analysis with data-defined gene sets

2017 ◽  
Author(s):  
Jie Tan ◽  
Matthew Huyck ◽  
Dongbo Hu ◽  
René A. Zelaya ◽  
Deborah A. Hogan ◽  
...  
Keyword(s):  
Differential Expression ◽  
Web Server ◽  
R Package ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Signature Analysis ◽  
Biological Processes ◽  
Gene Set ◽  
Gene Sets ◽  
Public Data

AbstractBackgroundGene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.ResultsHere we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr.ConclusionsWe designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

scholarly journals Differentiating Genomic SNPs Using Allele Depth and Predicted Genotype

2018 ◽  
Author(s):  
Saam Hasan
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Rna Editing ◽  
Editing Site ◽  
Biological Processes ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide Variants ◽  
Accuracy Rate ◽  
Single Nucleotide

AbstractDifferentiating between genomic SNPs and other types of single nucleotide variants becomes a key issue in research aimed at studying the importance of these variants of a particular type in biological processes. Here we present an R based method for differentiating between genomic single nucleotide polymorphisms (SNPs) and RNA editing sites. We use data from an earlier study of ours and target only the known dbsnp SNPs that we found in our study. Our method involves calculating the ratio of allele depth for ref and alt alleles and comparing that to the predicted genotype. We use the concept that editing levels should be different for each allele and thus should not reflect the ratio predicted by the genotype. The study yielded an accuracy rate ranging from 86 to over 90 percent at successfully predicted dbsnp entries as SNPs. Albeit this is in the absence of known RNA editing site vcf data to compare as a reference.

scholarly journals Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Peng Yu ◽  
Jin Li ◽  
Su-Ping Deng ◽  
Feiran Zhang ◽  
Petar N. Grozdanov ◽  
...  
Keyword(s):  
Rett Syndrome ◽  
Neuronal Morphology ◽  
Biological Research ◽  
Integrated Analysis ◽  
Splicing Factors ◽  
Biological Processes ◽  
Rna Seq ◽  
Key Factors ◽  
Mouse Tissues ◽  
Public Data

Abstract A vast amount of public RNA-sequencing datasets have been generated and used widely to study transcriptome mechanisms. These data offer precious opportunity for advancing biological research in transcriptome studies such as alternative splicing. We report the first large-scale integrated analysis of RNA-Seq data of splicing factors for systematically identifying key factors in diseases and biological processes. We analyzed 1,321 RNA-Seq libraries of various mouse tissues and cell lines, comprising more than 6.6 TB sequences from 75 independent studies that experimentally manipulated 56 splicing factors. Using these data, RNA splicing signatures and gene expression signatures were computed, and signature comparison analysis identified a list of key splicing factors in Rett syndrome and cold-induced thermogenesis. We show that cold-induced RNA-binding proteins rescue the neurite outgrowth defects in Rett syndrome using neuronal morphology analysis, and we also reveal that SRSF1 and PTBP1 are required for energy expenditure in adipocytes using metabolic flux analysis. Our study provides an integrated analysis for identifying key factors in diseases and biological processes and highlights the importance of public data resources for identifying hypotheses for experimental testing.

The emerging roles for histone demethylases in the modulation of signaling pathways

2013 ◽  
Vol 4 (1) ◽  
pp. 13-27 ◽  
Author(s):  
Luisa Di Stefano ◽  
Nicholas J. Dyson
Keyword(s):  
Cell Proliferation ◽  
Signaling Pathways ◽  
Biological Processes ◽  
Epigenetic Alterations ◽  
Histone Demethylases ◽  
Epigenetic Landscape ◽  
Environmental Signals ◽  
Cellular Processes ◽  
A Cell ◽  
Uncontrolled Cell Proliferation

AbstractSince their discovery in 2004, histone demethylases have emerged as key regulators of chromatin. Recent studies have started to reveal the interconnections between histone demethylases and signaling pathways, suggesting that this interplay drives fundamental biological processes. Here, we summarize the different families and subfamilies of histone demethylases and the insights into the biological roles of these enzymes that have been provided by the analysis of mutant animals. We then review recent work linking demethylases and signaling pathways. These studies suggest that demethylase activities are a component of the critical connections that enable environmental signals to modulate the epigenetic landscape of a cell. A greater mechanistic understanding of the network of signals that control chromatin states during normal cellular processes, together with a better understanding of the ways that epigenetic alterations lead to uncontrolled cell proliferation, might help in the design of effective tools for cancer therapy.

scholarly journals RNA m6A methylation orchestrates cancer growth and metastasis via macrophage reprogramming

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Huilong Yin ◽  
Xiang Zhang ◽  
Pengyuan Yang ◽  
Xiaofang Zhang ◽  
Yingran Peng ◽  
...  
Keyword(s):  
Regulatory T Cell ◽  
Tumour Growth ◽  
Cancer Growth ◽  
Erk Pathway ◽  
Biological Processes ◽  
Wild Type ◽  
Potential Therapeutic Target ◽  
T Cell Infiltration ◽  
Deficient Mice

AbstractN6-methyladenosine (m6A) is a reversible mRNA modification that has been shown to play important roles in various biological processes. However, the roles of m6A modification in macrophages are still unknown. Here, we discover that ablation of Mettl3 in myeloid cells promotes tumour growth and metastasis in vivo. In contrast to wild-type mice, Mettl3-deficient mice show increased M1/M2-like tumour-associated macrophage and regulatory T cell infiltration into tumours. m6A sequencing reveals that loss of METTL3 impairs the YTHDF1-mediated translation of SPRED2, which enhances the activation of NF-kB and STAT3 through the ERK pathway, leading to increased tumour growth and metastasis. Furthermore, the therapeutic efficacy of PD-1 checkpoint blockade is attenuated in Mettl3-deficient mice, identifying METTL3 as a potential therapeutic target for tumour immunotherapy.

scholarly journals Conservation of A-to-I RNA editing in bowhead whale and pig

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260081
Author(s):  
Knud Larsen ◽  
Mads Peter Heide-Jørgensen
Keyword(s):  
Rna Editing ◽  
Editing Site ◽  
Bowhead Whale ◽  
Regulatory Sequence ◽  
Shark Species ◽  
Protein Coding ◽  
Bowhead Whales ◽  
Editing Enzyme ◽  
Adar Editing ◽  
Transcriptional Process

RNA editing is a post-transcriptional process in which nucleotide changes are introduced into an RNA sequence, many of which can contribute to proteomic sequence variation. The most common type of RNA editing, contributing to nearly 99% of all editing events in RNA, is A-to-I (adenosine-to-inosine) editing mediated by double-stranded RNA-specific adenosine deaminase (ADAR) enzymes. A-to-I editing at ‘recoding’ sites results in non-synonymous substitutions in protein-coding sequences. Here, we present studies of the conservation of A-to-I editing in selected mRNAs between pigs, bowhead whales, humans and two shark species. All examined mRNAs–NEIL1, COG3, GRIA2, FLNA, FLNB, IGFBP7, AZIN1, BLCAP, GLI1, SON, HTR2C and ADAR2 –showed conservation of A-to-I editing of recoding sites. In addition, novel editing sites were identified in NEIL1 and GLI1 in bowhead whales. The A-to-I editing site of human NEIL1 in position 242 was conserved in the bowhead and porcine homologues. A novel editing site was discovered in Tyr244. Differential editing was detected at the two adenosines in the NEIL1 242 codon in both pig and bowhead NEIL1 mRNAs in various tissues and organs. No conservation of editing of KCNB1 and EEF1A mRNAs was seen in bowhead whales. In silico analyses revealed conservation of five adenosines in ADAR2, some of which are subject to A-to-I editing in bowheads and pigs, and conservation of a regulatory sequence in GRIA2 mRNA that is responsible for recognition of the ADAR editing enzyme.

scholarly journals MultiPLIER: a transfer learning framework for transcriptomics reveals systemic features of rare disease

2018 ◽  
Author(s):  
Jaclyn N. Taroni ◽  
Peter C. Grayson ◽  
Qiwen Hu ◽  
Sean Eddy ◽  
Matthias Kretzler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rare Disease ◽  
Large Datasets ◽  
Biological Processes ◽  
Biological Factors ◽  
Learning Framework ◽  
Machine Learning Methods ◽  
Public Data ◽  
Level Information ◽  
Pathway Level

SUMMARYUnsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most gene expression datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We trained a Pathway Level Information ExtractoR (PLIER) model using on a large public data compendium comprised of multiple experiments, tissues, and biological conditions. We then transferred the model to small rare disease datasets in an approach we term MultiPLIER. Models constructed from large, diverse public data i) included features that aligned well to important biological factors; ii) were more comprehensive than those constructed from individual datasets or conditions; iii) transferred to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained on specifically those datasets.

RNA editing and alternative splicing generate mRNA transcript diversity from the Drosophila 4f-rnp locus

Gene ◽  
1997 ◽  
Vol 204 (1-2) ◽  
pp. 267-276 ◽  
Author(s):  
Jane P. Petschek ◽  
Mark R. Scheckelhoff ◽  
Matthew J. Mermer ◽  
Jack C. Vaughn
Keyword(s):  
Alternative Splicing ◽  
Rna Editing ◽  
Mrna Transcript ◽  
Transcript Diversity
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