Biology subjects

Light, D.

Publications and source records attributed to Light, D..

4 recordsLinked to original sources

bioRxiv2025

RNA binding by ADARs prevents RNA interference from attacking self-produced dsRNA

The ability of an organism to identify self and foreign RNA is central to eliciting an immune response in times of need while avoiding autoimmunity. As viral pathogens typically employ double-stranded RNA (dsRNA), host identification, modulation, and response to dsRNA is key. However, dsRNA is also abundant in host transcriptomes, raising the question of how these molecules can be differentiated. Two host pathways that regulate dsRNA are A-to-I RNA editing by adenosine deaminases (ADARs), and RNA interference (RNAi). Both mechanisms are important for normal organism development and function by regulating gene expression. Herein, we studied the structure and amount of siRNAs at editing sites and the ability of ADARs to prevent exogenous RNAi using the model organism, Caenorhabditis elegans. We found that the number of siRNAs targeting edited genes is significantly upregulated in ADAR mutant animals. We also found that despite an almost complete depletion of primary siRNAs generated from editing sites in wildtype animals, secondary siRNAs are generated from edited transcripts, suggesting ADARs antagonize only the first step of RNAi processing. We show that ADARs interfere with the efficacy of exogenous RNAi in vivo, probably to prevent trans-silencing, and have indications that ADR-2 binding to the dsRNA is needed for the efficient prevention of RNAi. This work sheds light on how the RNA editing process protects self-produced dsRNAs from aberrant recognition by the immune processes in the cell and from by-product degradation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/672520v2_ufig1.gif" ALT="Figure 1"> View larger version (72K): org.highwire.dtl.DTLVardef@b34f2corg.highwire.dtl.DTLVardef@130c88org.highwire.dtl.DTLVardef@9be2eborg.highwire.dtl.DTLVardef@4ebeec_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

RESIC: A tool for comprehensive adenosine to inosine RNA Editing Site Identification and Classification

Adenosine to inosine (A-to-I) RNA editing, the most prevalent type of RNA editing in metazoans, is carried out by adenosine deaminases (ADARs) in double-stranded RNA regions. Several computational approaches have been recently developed to identify A-to-I RNA editing sites from sequencing data, each addressing a particular issue. Here we present RESIC, an efficient pipeline that combines several approaches for the detection and classification of RNA editing sites. The pipeline can be used for all organisms and can use any number of RNA-sequencing datasets as input. RESIC provides 1. The detection of editing sites in both repetitive and non-repetitive genomic regions; 2. The identification of hyper-edited regions; 3. Optional exclusion of polymorphism sites to increase reliability, based on DNA, and ADAR-mutant RNA sequencing datasets, or SNP databases. We demonstrate the utility of RESIC by applying it to human, successfully overlapping and extending the list of known putative editing sites. We further tested changes in the patterns of A-to-I RNA editing, and RNA abundance of ADAR enzymes, following SARS-CoV-2 infection in human cell lines. Our results suggest that upon SARS-CoV-2 infection, compared to mock, the number of hyper editing sites is increased, and in agreement, the activity of ADAR1, which catalyzes hyper-editing, is enhanced. These results imply the involvement of A-to-I RNA editing in conceiving the unpredicted phenotype of COVID-19 disease. RESIC code is open-source and is easily extendable.

bioinformatics↗

bioRxiv2021

BiSEK: a platform for a reliable differential expression analysis

Differential Expression Analysis (DEA) of RNA-sequencing data is frequently performed for detecting key genes, affected across different conditions. Although DEA-workflows are well established, preceding reliability-testing of the input material, which is crucial for consistent and strong results, is challenging and less straightforward. Here we present Biological Sequence Expression Kit (BiSEK), a graphical user interface-based platform for DEA, dedicated to a reliable inquiry. BiSEK is based on a novel algorithm to track discrepancies between the data and the statistical model design. Moreover, BiSEK enables differential-expression analysis of groups of genes, to identify affected pathways, without relying on the significance of genes comprising them. Using BiSEK, we were able to improve previously conducted analysis, aimed to detect genes affected by FUBP1 depletion in chronic myeloid leukemia cells of mice bone-marrow. We found affected genes that are related to the regulation of apoptosis, supporting in-vivo experimental findings. We further tested the host response following SARS-CoV-2 infection. We identified a substantial interferon-I reaction and low expression levels of TLR3, an inducer of interferon-III (IFN-III) production, upon infection with SARS-CoV-2 compared to other respiratory viruses. This finding may explain the low IFN-III response upon SARS-CoV-2 infection. BiSEK is open-sourced, available as a web-interface.

bioinformatics↗

bioRxiv2018

QsRNA-seq: a method for high-throughput profiling and quantifying small RNAs

The finding that small non-coding RNAs (sRNAs) can affect cellular processes by regulating gene expression had a significant impact on biological research and clinical diagnosis. Yet, the ability to quantify and profile sRNAs, specifically miRNAs, using high-throughput sequencing is especially challenging because of their small size and repetitive nature. We developed QsRNA-seq, a method for preparation of sRNA libraries for high-throughput sequencing that overcomes this difficulty by enabling separation of fragments shorter than 100nt long that differ only by 20nt in length. The method supports using unique molecular identifiers for quantification. We show that QsRNA-seq gives very accurate, comprehensive and reproducible results. Using QsRNA-seq to study the miRNA repertoire in C. elegans embryo and L4 larval developmental stages, enabled extending the list of miRNAs that are expressed in a developmental-specific manner. Interestingly, we found that miRNAs 23nt long are predominantly expressed in developmental stage L4, suggesting a possible connection between the length of miRNA and its developmental role.

genomics↗