Background: Adenosine-to-inosine (A-to-I) editing is a site-selective post-transcriptional alteration of double-stranded RNA by ADAR deaminases that is crucial for homeostasis and development. Recently the Mouse Genomes Project generated genome sequences for 17 laboratory mouse strains and rich catalogues of variants. We also generated RNA-seq data from whole brain RNA from 15 of the sequenced strains.
Results: Here we present a computational approach that takes an initial set of transcriptome/genome mismatch sites and filters these calls taking into account systematic biases in alignment, single nucleotide variant calling, and sequencing depth to identify RNA editing sites with high accuracy. We applied this approach to our panel of mouse strain transcriptomes identifying 7,389 editing sites with an estimated false-discovery rate of between 2.9 and 10.5%. The overwhelming majority of these edits were of the A-to-I type, with less than 2.4% not of this class, and only three of these edits could not be explained as alignment artifacts. We validated 24 novel RNA editing sites in coding sequence, including two non-synonymous edits in the Cacna1d gene that fell into the IQ domain portion of the Cav1.2 voltage-gated calcium channel, indicating a potential role for editing in the generation of transcript diversity.
Conclusions: We show that despite over two million years of evolutionary divergence, the sites edited and the level of editing at each site is remarkably consistent across the 15 strains. In the Cds2 gene we find evidence for RNA editing acting to preserve the ancestral transcript sequence despite genomic sequence divergence.
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http://dx.doi.org/10.1186/gb-2012-13-4-r26 | DOI Listing |
Mol Neurodegener
January 2025
Guangdong Key Laboratory of Non-Human Primate Research, Key Laboratory of CNS Regeneration (Ministry of Education), School of Medicine, GHM Institute of CNS Regeneration, Jinan University, Guangzhou, 510632, China.
Background: HD is a devastating neurodegenerative disorder caused by the expansion of CAG repeats in the HTT. Silencing the expression of mutated proteins is a therapeutic direction to rescue HD patients, and recent advances in gene editing technology such as CRISPR/CasRx have opened up new avenues for therapeutic intervention.
Methods: The CRISPR/CasRx system was employed to target human HTT exon 1, resulting in an efficient knockdown of HTT mRNA.
BMC Plant Biol
January 2025
Chengdu Botanical Garden, Chengdu Park Urban Plant Science Research Institute, Chengdu, 610083, Sichuan, China.
Background: Ginkgo biloba L., an iconic living fossil, challenges traditional views of evolutionary stasis. While nuclear genomic studies have revealed population structure across China, the evolutionary patterns reflected in maternally inherited plastomes remain unclear, particularly in the Sichuan Basin - a potential glacial refugium that may have played a crucial role in Ginkgo's persistence.
View Article and Find Full Text PDFGenetic medicines, including CRISPR/Cas technologies, extend tremendous promise for addressing unmet medical need in inherited retinal disorders and other indications; however, there remain challenges for the development of therapeutics. Herein, we evaluate genome editing by engineered Cas9 ribonucleoproteins (eRNP) in vivo via subretinal administration using mouse and pig animal models. Subretinal administration of adenine base editor and double strand break-inducing Cas9 nuclease eRNPs mediate genome editing in both species.
View Article and Find Full Text PDFGenome organization recapitulates function, yet ciliates like possess highly-specialized germline genomes, which are largely transcriptionally silent. During post-zygotic development, 's germline undergoes large-scale genome editing, rearranging precursor genome elements into a transcriptionally-active genome with thousands of gene-sized nanochromosomes. Transgenerationally-inherited RNAs, derived from the parental somatic genome, program the retention and reordering of germline fragments.
View Article and Find Full Text PDFVariant calling using long-read RNA sequencing (lrRNA-seq) can be applied to diverse tasks, such as capturing full-length isoforms and gene expression profiling. It poses challenges, however, due to higher error rates than DNA data, the complexities of transcript diversity, RNA editing events, etc. In this paper, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data.
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