Adenosine deaminases that act on RNA (ADARs) constitute a family of RNA-editing enzymes that convert adenosine to inosine within double-stranded regions of RNA. We previously developed a method to identify inosine-containing RNAs and used it to identify five ADAR substrates in Caenorhabditis elegans. Here we use the same method to identify five additional C. elegans substrates, including three mRNAs that encode proteins known to affect neuronal functions. All 10 of the C. elegans substrates are edited in long stem-loop structures located in noncoding regions, and thus contrast with previously identified substrates of other organisms, in which ADARs target codons. To determine whether editing in noncoding regions was a conserved ADAR function, we applied our method to poly(A)+ RNA of human brain and identified 19 previously unknown ADAR substrates. The substrates were strikingly similar to those observed in C. elegans, since editing was confined to 3' untranslated regions, introns, and a noncoding RNA. Also similar to what was found in C. elegans, 15 of the 19 substrates were edited in repetitive elements. The identities of the newly identified ADAR substrates suggest that RNA editing may influence many biologically important processes, and that for many metazoa, A-to-I conversion in coding regions may be the exception rather than the rule.
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http://dx.doi.org/10.1073/pnas.112704299 | DOI Listing |
Sci Rep
January 2025
Department of Traditional Chinese Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, 6 Taoyuan Road, Qingxiu District, Nanning City, Guangxi Zhuang Autonomous Region, People's Republic of China.
Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index.
View Article and Find Full Text PDFViroids, small circular non-coding RNAs, act as infectious pathogens in higher plants, demonstrating high stability despite consisting solely of naked RNA. Their dependence of replication on host machinery poses the question of whether RNA modifications play a role in viroid biology. Here, we explore RNA modifications in the avocado sunblotch viroid (ASBVd) and the citrus exocortis viroid (CEVd), representative members of viroids replicating in chloroplasts and the nucleus, respectively, using LC - MS and Oxford Nanopore Technology (ONT) direct RNA sequencing.
View Article and Find Full Text PDFJ Insect Sci
January 2025
ZooLab, Department of Biodiversity and Ecology, Plant Science and Biodiversity Centre, Slovak Academy of Sciences, Bratislava, Slovakia.
Mitochondrial genomes are a rich source of data for various downstream analyses such as population genetics, phylogeny, and systematics. Today it is possible to assemble rapidly large numbers of mitogenomes, mainly employing next-generation sequencing and third-generation sequencing. However, verification of the correctness of the generated sequences is often lacking, especially for noncoding, length-variable parts.
View Article and Find Full Text PDFBiochem Biophys Rep
March 2025
Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Vitiligo is a common skin depigmentation condition caused by selective destruction of melanocytes. It is regarded as a polygenic disorder. In addition to protein-coding loci, non-coding regions of the genome contribute to the pathogenesis of vitiligo.
View Article and Find Full Text PDFGenet Epidemiol
March 2025
Department of Social and Preventive Medicine, Laval University, Quebec City, Quebec, Canada.
A large proportion of genetic variations involved in complex diseases are rare and located within noncoding regions, making the interpretation of underlying biological mechanisms a daunting task. Although technical and methodological progress has been made to annotate the genome, current disease-rare-variant association tests incorporating such annotations suffer from two major limitations. First, they are generally restricted to case-control designs of unrelated individuals, which often require tens or hundreds of thousands of individuals to achieve sufficient power.
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