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Title of Journal: Biogerontology

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Abbravation: Biogerontology

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Springer Netherlands

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DOI

10.1002/pen.760181005

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1573-6768

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A-to-I RNA editing does not change with age in the healthy male rat brain

Authors: Andrew P. Holmes, Shona H. Wood, Brian J. Merry, João Pedro de Magalhães,

Publish Date: 2013/05/26
Volume: 14, Issue:4, Pages: 395-400
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Abstract

RNA editing is a post-transcriptional process, which results in base substitution modifications to RNA. It is an important process in generating protein diversity through amino acid substitution and the modulation of splicing events. Previous studies have suggested a link between gene-specific reductions in adenosine to inosine RNA editing and aging in the human brain. Here we demonstrate that changes in RNA editing observed in humans with age are not observed during aging in healthy rats. Furthermore, we identify a conserved editing site in rats, in Cog3. We propose that either age-related changes in RNA editing are specific to primates or humans, or that they are the manifestation of disease pathology. Since rodents are often used as model organisms for studying aging, these findings demonstrate the importance of understanding species-specific differences in RNA biology during aging.Adenosine to inosine (A-to-I) RNA editing is a post-transcriptional process that alters the sequences of RNA molecules. The adenosine deaminases ADAR and ADARB1 convert specific adenosine residues on RNA to inosine bases. During translation, sequencing, and splicing, inosine is recognized as guanosine. Therefore, A-to-I RNA editing has important implications in altering specific amino acids, miRNA targeting, and in the modulation of alternative splicing (Nishikura 2010).Targets of A-to-I RNA editing are often genes involved in neurotransmission in the central nervous system (CNS), although editing is known to occur in other tissues. Changes in A-to-I RNA editing have been implicated in the development of cancer inside and outside the CNS (Cenci et al. 2008; Galeano et al. 2010; Paz et al. 2007; Shah et al. 2009), and in various neurodegenerative diseases, such as dyschromatosis symmetrica hereditaria (DSH), amyotrophic lateral sclerosis (ALS), Alzheimer’s disease, and Huntington’s disease. In addition, the process has implications in epilepsy, depression, schizophrenia and is associated with a greater risk of suicide (Akbarian et al. 1995; Farajollahi and Maas 2010; Maas et al. 2006). More recently it was identified that single nucleotide polymorphisms in ADARB1 and ADARB2 are associated with extreme longevity in humans (Sebastiani et al. 2009). Furthermore, it was recently observed that editing of the p53-inducible Cyfip2 (Cytoplasmic FMR1 interacting protein 2) significantly declines with age in human cerebral cortex. However, editing of Gabra3 (Gamma-aminobutyric acid A receptor subunit alpha 3) was found not to decline with age in humans. This suggests that RNA editing efficiency may be linked to aging in a gene-specific manner (Nicholas et al. 2010).Humans are known to have far more targets of A-to-I RNA editing than rodents, due to the spread of Alu repeats during human evolution (Neeman et al. 2006). However, there are several known targets of A-to-I RNA editing that are conserved from rodents to humans (Levanon et al. 2005). The brown rat, Rattus norvegicus, is a well-established model species for studying aging and has been used in neurological research for many years. The brown rat is held as a model for mammalian behavioral and neurodegenerative studies (Jacob 1999; Wood et al. 2013). Comparative approaches are essential to fully understand aging; hence it is crucial to establish differences between humans and rodents to assess their importance to inform about human aging. Functional alterations, through RNA editing or other means, may also contribute to species differences in aging. Since studies linking RNA editing to aging have only been reported in humans, this study aimed to investigate whether the effect of age on RNA editing efficacy is conserved in the brown rat.Rat tissues used in this study were supplied from a previous experiment (Merry et al. 2008). All animal husbandry procedures undertaken in this study were carried out in accordance with the provisions of the United Kingdom Animals (Scientific Procedures) Act 1986. Male BN rats (SubstrainBN/SsNOlaHSD) were obtained from Harlan UK at 21–28 days of age and maintained under barrier conditions on a 12 h light: 12 h dark cycle (08:00–20:00). The health status of the rats was monitored at regular intervals through the screening of sentinel animals. All rats were fed ad libitum and sacrificed at 6, 12, and 28 months of age. None of the animals exhibited any signs of pathology when sacrificed. Each age group had six rats, from which brain samples were taken, flash frozen, and stored at −80 °C.RNA was extracted from cerebral cortex of rats using the RNeasy lipid tissue kit (Qiagen). The quality of the extracted RNA was assessed using the Agilent 2100 Bioanalyzer; all RNA integrity numbers (RINs) were above 8.0, indicating that the RNA had minimal degradation. For RNA extraction from brain tissue, RIN > 8 represents a high quality threshold (Bettscheider et al. 2011). The samples were pooled in pairs (leaving 3 samples per age group). Ribosomal RNA was removed from the pooled samples using the Eukaryote Ribominus Kit (Invitrogen) and confirmed with the Agilent 2100 Bioanalyzer.RNA-seq data was generated by SOLiD sequencing (Applied Biosystems) from these samples in a previous study (Wood et al. 2013). The RNA-seq results from the SOLiD system were output as color space FASTA and quality files. These were converted into FASTQ format using a Python script from Galaxy (http://main.g2.bx.psu.edu/). The FASTQ files were mapped to the Ensembl release 65 rat reference genome (RGSC 3.4 assembly, May 2010 gene build) using Bowtie (Langmead et al. 2009) and settings appropriate to SOLiD data. For each sample, ~33.6 million reads were generated (range, 29.5–39.8 million reads). On average 16.7 million reads per sample were mapped to the reference genome (range, 13.8–21.4 million reads, ~50 % of reads generated were mapped). All data have been submitted to GEO under the accession GSE34272 (Wood et al. 2013). A mismatch analysis was performed on the aligned reads using Bambino in order to generate candidate editing sites (Edmonson et al. 2011). These candidates were selected using custom Python scripts for A-G mismatches within exons in genes on the positive strand, and T-C mismatches on the negative strand. Results were narrowed by selecting for non-synonymous mismatches and for those with a high number of read counts. Selected candidate targets were reverse transcribed using M-MLV reverse transcriptase (Invitrogen) and amplified by PCR using rTaq (TaKaRa). Editing levels were then analyzed by Sanger sequencing using the 3730 DNA Analyzer (Applied Biosystems) and quantifications were calculated by peak height analysis (Eggington et al. 2011).


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