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• W3186299673 abstract "We present MultiEditR (Multiple Edit Deconvolution by Inference of Traces in R), the first algorithm specifically designed to detect and quantify RNA editing from Sanger sequencing (z.umn.edu/multieditr). Although RNA editing is routinely evaluated by measuring the heights of peaks from Sanger sequencing traces, the accuracy and precision of this approach has yet to be evaluated against gold standard next-generation sequencing methods. Through a comprehensive comparison to RNA sequencing (RNA-seq) and amplicon-based deep sequencing, we show that MultiEditR is accurate, precise, and reliable for detecting endogenous and programmable RNA editing. We present MultiEditR (Multiple Edit Deconvolution by Inference of Traces in R), the first algorithm specifically designed to detect and quantify RNA editing from Sanger sequencing (z.umn.edu/multieditr). Although RNA editing is routinely evaluated by measuring the heights of peaks from Sanger sequencing traces, the accuracy and precision of this approach has yet to be evaluated against gold standard next-generation sequencing methods. Through a comprehensive comparison to RNA sequencing (RNA-seq) and amplicon-based deep sequencing, we show that MultiEditR is accurate, precise, and reliable for detecting endogenous and programmable RNA editing. RNA editing is the most abundant post-transcriptional modification in messenger RNA (mRNA),1Eisenberg E. Levanon E.Y. A-to-I RNA editing—Immune protector and transcriptome diversifier.Nat. Rev. Genet. 2018; 19: 473-490Crossref PubMed Scopus (165) Google Scholar with two predominant types of editing: cytidine-to-uridine editing (C-to-U) by the APOBEC family of enzymes, and adenosine-to-inosine editing (A-to-I) by the ADAR family of enzymes. RNA editing has implications in a variety of biologically processes, particularly among those involved in neural physiology, immunity, and oncogenesis.2Lerner T. Papavasiliou F.N. Pecori R. RNA editors, cofactors, and mRNA targets: An overview of the C-to-U RNA editing machinery and its implication in human disease.Genes (Basel). 2018; 10: 13Crossref Scopus (24) Google Scholar,3Xu L.-D. Öhman M. ADAR1 editing and its role in cancer.Genes (Basel). 2018; 10: 12Crossref Scopus (44) Google Scholar Importantly, the recent development of programmable RNA base-editing technologies presents the possibility to correct pathogenic mutations at the RNA level, opening important therapeutic scenarios.4Reardon S. Step aside CRISPR, RNA editing is taking off.Nature. 2020; 578: 24-27Crossref PubMed Scopus (19) Google Scholar For both endogenous and programmable RNA editing, the accurate and precise detection as well as quantification of editing is essential. Current identification and quantification of endogenous RNA editing relies on RNA-sequencing (RNA-seq) data analyzed by several different algorithmic approaches.5Diroma M.A. Ciaccia L. Pesole G. Picardi E. Elucidating the editome: Bioinformatics approaches for RNA editing detection.Brief. Bioinform. 2019; 20: 436-447Crossref PubMed Scopus (33) Google Scholar,6Ramaswami G. Li J.B. Identification of human RNA editing sites: A historical perspective.Methods. 2016; 107: 42-47Crossref PubMed Scopus (46) Google Scholar Although these approaches are robust, they are often complicated by genomic sequence polymorphisms, sequencing errors, and-or low coverage of certain genomic regions. This leads to the necessity of routine validation and quantification of RNA editing sites by Sanger sequencing, and by bacterial colony sequencing of subcloned polymerase chain reaction (PCR) amplicons.1Eisenberg E. Levanon E.Y. A-to-I RNA editing—Immune protector and transcriptome diversifier.Nat. Rev. Genet. 2018; 19: 473-490Crossref PubMed Scopus (165) Google Scholar,7Toung J.M. Lahens N. Hogenesch J.B. Grant G. Detection theory in identification of RNA-DNA sequence differences using RNA-sequencing.PLoS ONE. 2014; 9: e112040Crossref PubMed Scopus (7) Google Scholar Meanwhile, identification and quantification of programmable RNA editing is mainly accomplished by Sanger sequencing.8Montiel-González M.F. Vallecillo-Viejo I.C. Rosenthal J.J.C. An efficient system for selectively altering genetic information within mRNAs.Nucleic Acids Res. 2016; 44: e157PubMed Google Scholar, 9Merkle T. Merz S. Reautschnig P. Blaha A. Li Q. Vogel P. Wettengel J. Li J.B. Stafforst T. Precise RNA editing by recruiting endogenous ADARs with antisense oligonucleotides.Nat. Biotechnol. 2019; 37: 133-138Crossref PubMed Scopus (92) Google Scholar, 10Vogel P. Moschref M. Li Q. Merkle T. Selvasaravanan K.D. Li J.B. Stafforst T. Efficient and precise editing of endogenous transcripts with SNAP-tagged ADARs.Nat. Methods. 2018; 15: 535-538Crossref PubMed Scopus (68) Google Scholar, 11Qu L. Yi Z. Zhu S. Wang C. Cao Z. Zhou Z. Yuan P. Yu Y. Tian F. Liu Z. et al.Programmable RNA editing by recruiting endogenous ADAR using engineered RNAs.Nat. Biotechnol. 2019; 37: 1059-1069Crossref PubMed Scopus (70) Google Scholar To validate the existence of programmed RNA edits, the first step is to evaluate the editing efficiency of the targeted base (on-target editing), and the second step is to evaluate the presence of possible undesired editing sites along the same transcript (off-target editing). This part of the process, independently from the RNA base-editing method used, represents a time-consuming step, in which a quick and inexpensive evaluation of on-target and off-target editing at the transcript level is needed to define the best experimental settings to use. Despite the widespread use of Sanger sequencing, no program exists specifically for the quantification of RNA editing, leaving some to creatively, yet not optimally, measure the height of peaks from Sanger sequencing traces using image analysis software such as Adobe Illustrator.12Rinkevich F.D. Schweitzer P.A. Scott J.G. Antisense sequencing improves the accuracy and precision of A-to-I editing measurements using the peak height ratio method.BMC Res. Notes. 2012; 5: 63Crossref PubMed Scopus (14) Google Scholar Although some tools exist for the quantification of DNA base editing from Sanger sequencing,13Kluesner M.G. Nedveck D.A. Lahr W.S. Garbe J.R. Abrahante J.E. Webber B.R. Moriarity B.S. EditR: A method to quantify base editing from Sanger sequencing.CRISPR J. 2018; 1: 239-250Crossref PubMed Google Scholar they are not able to detect and quantify multiple sites simultaneously within the same Sanger trace. This feature is essential to evaluate on-target and off-target programmable RNA editing, and it is also crucial in the context of endogenous RNA editing, where editing sites are often in a cluster, leading to hyper-edited transcript regions that can be missed by standard RNA editing analysis of RNA-seq data.1Eisenberg E. Levanon E.Y. A-to-I RNA editing—Immune protector and transcriptome diversifier.Nat. Rev. Genet. 2018; 19: 473-490Crossref PubMed Scopus (165) Google Scholar,14Porath H.T. Carmi S. Levanon E.Y. A genome-wide map of hyper-edited RNA reveals numerous new sites.Nat. Commun. 2014; 5: 4726Crossref PubMed Scopus (123) Google Scholar Finally, the detection and quantification capabilities of a Sanger based approach has yet to be benchmarked against next-generation sequencing (NGS) methods. To meet these needs we developed multiple edit deconvolution by inference of traces in R (MultiEditR) (z.umn.edu/multieditr), a program with a web interface that provides accurate and cost-effective detection and quantification of RNA editing from Sanger sequencing that yields comparable results to NGS methods. To first determine whether successive base edits could be accurately quantified from Sanger sequencing, we titrated two plasmids that differed by six C-to-T mutations (Supplemental materials and methods; Figure S1A) and subjected the titrations to Sanger sequencing (Figure 1A). Analysis of the traces from both the forward and reverse direction showed that the percent height of the mixed peaks of interest yielded well-fit linear regressions compared to the expected titrated percent for both C-to-T and G-to-A titrations (Figures 1B and 1C; Figures S1B–S1E). Encouraged by the results we adapted the EditR algorithm, which we previously developed for analyzing CRISPR-Cas9 DNA base editing,13Kluesner M.G. Nedveck D.A. Lahr W.S. Garbe J.R. Abrahante J.E. Webber B.R. Moriarity B.S. EditR: A method to quantify base editing from Sanger sequencing.CRISPR J. 2018; 1: 239-250Crossref PubMed Google Scholar for the detection and quantification of multiple RNA edits from Sanger sequencing, which we termed MultiEditR (Figure 1D; Figure S2). To compare the performance of MultiEditR with standard methods for RNA editing detection, we first generated two knockout (KO) cell lines for ADAR1 and APOBEC1 (Figure S3). Next, we performed RNA-seq on RNA from both wild-type (WT) and KO cell lines and analyzed the data with REDItools,15Picardi E. Pesole G. REDItools: High-throughput RNA editing detection made easy.Bioinformatics. 2013; 29: 1813-1814Crossref PubMed Scopus (143) Google Scholar,16Picardi E. D’Erchia A.M. Gallo A. Pesole G. Detection of post-transcriptional RNA editing events.Methods Mol. Biol. 2015; 1269: 189-205Crossref PubMed Scopus (4) Google Scholar a well-established tool for detection of RNA editing from RNA-seq. From the same samples several regions within different transcripts (Table S1) were PCR amplified, Sanger sequenced, and analysis of these traces was performed by MultiEditR. An initial comparison of MultiEditR to RNA-seq showed that although the central tendency of MultiEditR measurements were accurate, there was substantial error relative to the RNA-seq benchmark (Figure 1E; Figure S4). Despite the high coverage of our RNA-seq experiments (62–88 million mapped reads per sample), this error appeared to be influenced by the read depth per base of the RNA-seq dataset (Figures 1F and 1G; Figure S4), which is consistent with findings by others.7Toung J.M. Lahens N. Hogenesch J.B. Grant G. Detection theory in identification of RNA-DNA sequence differences using RNA-sequencing.PLoS ONE. 2014; 9: e112040Crossref PubMed Scopus (7) Google Scholar To address the potential issue that the low coverage of the RNA-seq dataset was introducing error in assessing the accuracy of MultiEditR, we performed a three-way, matched comparison of endogenous RNA editing quantification and detection within several transcripts from two cell lines (Table S2) using MultiEditR, RNA-seq, and high coverage amplicon based NGS (termed, Amplicon-seq) (Figure 2A). The direct comparison of MultiEditR to Amplicon-seq demonstrated that MultiEditR is on average accurate relative to Amplicon-seq with small significant inaccuracies among edits measured from C, G, and T bases (range of M: −1.66% to −2.59%, p < 0.001), and non-significantly different for edits measured from A bases (M = −0.25%, p = 0.476) (Figures 2B and 2C). These small inaccuracies may be attributable to peak-ratio bias, which is a well-known aspect of Sanger sequencing.17Parker L.T. Zakeri H. Deng Q. Spurgeon S. Kwok P.Y. Nickerson D.A. AmpliTaq DNA polymerase, FS dye-terminator sequencing: Analysis of peak height patterns.Biotechniques. 1996; 21: 694-699Crossref PubMed Scopus (74) Google Scholar,18Zakeri H. Amparo G. Chen S.M. Spurgeon S. Kwok P.Y. Peak height pattern in dichloro-rhodamine and energy transfer dye terminator sequencing.Biotechniques. 1998; 25: 406-410, 412–414Crossref PubMed Scopus (37) Google Scholar In comparison, RNA-seq benchmarked against Amplicon-seq exhibited no significant inaccuracies across all bases (Figures 2D and 2E; Figure S5). However, RNA-seq exhibited a greater standard deviation than did MultiEditR for all bases. Importantly, we found that MultiEditR was measured as more precise when benchmarked against Amplicon-seq as opposed to RNA-seq (Figures 2B and 2C; Figures S5E–S5I), confirming that the observed error in MultiEditR detection relative to the RNA-seq (Figure S4) was indeed due to low coverage in some regions of the RNA-seq dataset. Collectively, these results indicate that compared to Amplicon-seq, while the quantification of RNA editing by RNA-seq is more accurate, MultiEditR is more precise than RNA-seq, particularly when looking at editing events above 5% editing (Figure 2F). Next, we wanted to assess how MultiEditR and REDItools analysis of RNA-seq15Picardi E. Pesole G. REDItools: High-throughput RNA editing detection made easy.Bioinformatics. 2013; 29: 1813-1814Crossref PubMed Scopus (143) Google Scholar,16Picardi E. D’Erchia A.M. Gallo A. Pesole G. Detection of post-transcriptional RNA editing events.Methods Mol. Biol. 2015; 1269: 189-205Crossref PubMed Scopus (4) Google Scholar perform in the detection of edits (Figure 2G). Using the MultiEditR p value calculated from the zero-adjusted gamma distribution null hypothesis significance test (Figure 1D), as well as the p value from the REDItools Fisher exact test as classifier values, we performed a receiving operating characteristic (ROC) curve analysis. When including edits that were called 1% or greater by Amplicon-seq, RNA-seq performed modestly better at detecting edits than did MultiEditR in terms of sensitivity, specificity, and area under the curve (AUC), among other metrics (Figure 2H; Figure S6). However, when examining edits that were called 5% or greater by Amplicon-seq, MultiEditR performed better than RNA-seq (Figure 2I). Furthermore, running ROC curve analyses across a range of editing detection thresholds suggests that based on the measured sensitivity, specificity, and AUC, the optimal use of MultiEditR is for detecting editing events ≥5% (Figure 2I). Last, we wanted to assess the utility of MultiEditR in application to biologically relevant problems. Roth et al.19Roth S.H. Levanon E.Y. Eisenberg E. Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity.Nat. Methods. 2019; 16: 1131-1138Crossref PubMed Scopus (40) Google Scholar proposed the Alu editing index (AEI, here as EINGS) as an index for the quantification of global RNA editing. Here, we apply a similar approach to develop the MultiEditR editing index (MEI) as a local editing index across the Sanger trace (Figure 3A). Using our three-way, matched dataset we found that the MEI is moderately correlated with the lower read depth per base RNA-seq EINGS (r = 0.558, p = 3.48e−7), and it is well correlated with the higher read depth per base Amplicon-seq EINGS (r = 0.812, p = 1.39e−5) (Figures 3B and 3C), further showing an effect of read depth in RNA editing detection and quantification from RNA-seq data. Using the MEI we wanted to investigate the effect of adding a nuclear localization signal (NLS) on the specificity of the programmable 4λN-ADAR2DD A-to-I editing system as previously published20Vallecillo-Viejo I.C. Liscovitch-Brauer N. Montiel-Gonzalez M.F. Eisenberg E. Rosenthal J.J.C. Abundant off-target edits from site-directed RNA editing can be reduced by nuclear localization of the editing enzyme.RNA Biol. 2018; 15: 104-114Crossref PubMed Scopus (41) Google Scholar (Figure 3D). Using a fluorescent reporter, we were able to directly compare editing rates to a functional readout via flow cytometry, as well as measuring editing across the transcript with the MEI (Figure 3E). We found that MultiEditR measurements of editing agreed well with flow cytometry values (Figure 3F). Additionally, using a normalized metric of percent editing of the target base (on-target editing) divided by the MEI (off-target editing), we recapitulated results that addition of an NLS to the 4λN-ADAR2DD system improves editing specificity20Vallecillo-Viejo I.C. Liscovitch-Brauer N. Montiel-Gonzalez M.F. Eisenberg E. Rosenthal J.J.C. Abundant off-target edits from site-directed RNA editing can be reduced by nuclear localization of the editing enzyme.RNA Biol. 2018; 15: 104-114Crossref PubMed Scopus (41) Google Scholar (Figure 3G; Figure S7). Last, we wanted to determine whether MultiEditR could be used to similarly quantify CRISPR-Cas9 DNA base editing using data previously published by our group from work using base editing to disrupt genes via splice-site targeting21Webber B.R. Lonetree C.L. Kluesner M.G. Johnson M.J. Pomeroy E.J. Diers M.D. Lahr W.S. Draper G.M. Slipek N.J. Smeester B.A. et al.Highly efficient multiplex human T cell engineering without double-strand breaks using Cas9 base editors.Nat. Commun. 2019; 10: 5222Crossref PubMed Scopus (49) Google Scholar (Figure 3H). Base editing efficiency measured by MultiEditR compared to CRISPR-DAV analysis of Amplicon-seq,22Wang X. Tilford C. Neuhaus I. Mintier G. Guo Q. Feder J.N. Kirov S. CRISPR-DAV: CRISPR NGS data analysis and visualization pipeline.Bioinformatics. 2017; 33: 3811-3812Crossref PubMed Scopus (23) Google Scholar as well as flow cytometry, yielded strong coefficients of determination at both the DNA (R2 = 0.97, Figure 3I) and protein level (R2 = 0.849, Figure 3J). For the best use of MultiEditR, we recommend designing primers to amplify a 350- to 700-bp amplicon to allow for a long enough sequence to construct null distributions for edit detection. Additionally, we recommend the use of one-step RT-PCR kits, over standard cDNA synthesis kits, to exclusively generate cDNA from the transcript of interest. Following amplification, a column-based PCR purification step is typically sufficient to ensure clean sequencing results. For detecting edits above 5%, we recommend using p = 0.001 for applications where false positives are strongly disfavored, and p = 0.01 when an increase in sensitivity is valued over a loss in specificity (Figure S6). Finally, for the best accuracy and precision of MultiEditR we recommend measuring the edit from the T or A base (Figure 2C). Last, due to the sensitivity of MultiEditR, we do not recommend using it to detect or measure edits that are below 5% due to the baseline noise in Sanger traces. For applications where high accuracy and a low limit of edit detection is paramount, we recommend using Amplicon-seq. Collectively, we developed MultiEditR, the first algorithm specifically designed to detect and quantify multiple RNA editing sites in a single trace of Sanger sequencing and we performed a comprehensive comparison with NGS methods to evaluate the performance of the tool. MultiEditR showed higher precision in RNA editing detection than did RNA-seq, particularly when looking at editing events above 5% editing, but with the cost of lower accuracy. Furthermore, in the context of RNA programmable editing, the capability of MultiEditR to detect multiple edits simultaneously and the MEI allow for a quick and inexpensive evaluation of on-target and off-target editing at the transcript level (Figure 3), a crucial aspect to define the best experimental conditions for mutation correction at the RNA level (e.g., choosing an optimal guide RNA). Finally, we showed that MultiEditR can be employed for a variety of nucleic acid editing applications, including endogenous RNA editing, targeted programmable RNA editing, off-target RNA editing, and DNA base editing. The flexibility of the MultiEditR algorithm allows our approach to be readily applied to other applications that involve the change of one base species to another, such as that involved in bisulfite sequencing for identifying methylation, or more recently RNA polymerases and reverse transcriptases recoding various RNA modifications with distinct fidelity.23Potapov V. Fu X. Dai N. Corrêa Jr., I.R. Tanner N.A. Ong J.L. Base modifications affecting RNA polymerase and reverse transcriptase fidelity.Nucleic Acids Res. 2018; 46: 5753-5763Crossref PubMed Scopus (42) Google Scholar Overall, we predict that MultiEditR and the comparisons detailed in this study will have immediate use to the RNA editing community, but also more broadly to the many burgeoning fields studying nucleic acid modifications. The mCherry-mApob-EGFP plasmid (CmAG) was obtained by substituting the human APOB with mouse Apob (mApob) in the original plasmid mCherry-APOB-EGFP24Severi F. Conticello S.G. Flow-cytometric visualization of C>U mRNA editing reveals the dynamics of the process in live cells.RNA Biol. 2015; 12: 389-397Crossref PubMed Scopus (10) Google Scholar (kind gifts from Dr. Silvestro Conticello, Florence, Italy). mCherry-APOB-EGFP was digested with HindIII-SmaI and a PCR fragment of mouse Apob (467 bp from RNA of jejunal epithelial cells from the small intestines of C57BL/6 mice,25Rosenberg B.R. Hamilton C.E. Mwangi M.M. Dewell S. Papavasiliou F.N. Transcriptome-wide sequencing reveals numerous APOBEC1 mRNA-editing targets in transcript 3′ UTRs.Nat. Struct. Mol. Biol. 2011; 18: 230-236Crossref PubMed Scopus (159) Google Scholar oligonucleotides [oligos] #1–2) was inserted into the plasmid using NEBuilder HiFi DNA assembly master mix (NEB). The mouse APOBEC1 expression vector (pCMV APOBEC1) was a kind gift from Dr. Dewi Harjanto (Laboratory of Lymphocyte Biology, The Rockefeller University). The mouse RBM47 expression vector (pCMV RBM47) was obtained by inserting a PCR fragment containing the coding sequence of mouse RBM47 (transcript variant 4, mRNA sequence ID: GenBank: NM_001291226.1) into the mCherry-Apob-EGFP cut with NheI-BsrGI. The amplification was done using oligos #3–4 on RNA of jejunal epithelial cells from the small intestines of C57BL/6 mice25Rosenberg B.R. Hamilton C.E. Mwangi M.M. Dewell S. Papavasiliou F.N. Transcriptome-wide sequencing reveals numerous APOBEC1 mRNA-editing targets in transcript 3′ UTRs.Nat. Struct. Mol. Biol. 2011; 18: 230-236Crossref PubMed Scopus (159) Google Scholar and the cloning with NEBuilder HiFi DNA assembly master mix (NEB). LentiCRISPRv2 was a gift from Dr. Feng Zhang (Addgene, plasmid #52961; http://addgene.org/52961; RRID:Addgene_52961).26Sanjana N.E. Shalem O. Zhang F. Improved vectors and genome-wide libraries for CRISPR screening.Nat. Methods. 2014; 11: 783-784Crossref PubMed Scopus (2334) Google Scholar DNA oligos #5–6 were cloned into this plasmid following the “lentiCRISPRv2 and lentiGuide oligo cloning protocol” (Addgene plasmid #52961) to generate lenti-CRISPR-ADAR1 exon 4 (from Pestal et al.27Pestal K. Funk C.C. Snyder J.M. Price N.D. Treuting P.M. Stetson D.B. Isoforms of RNA-editing enzyme ADAR1 independently control nucleic acid sensor MDA5-driven autoimmunity and multi-organ development.Immunity. 2015; 43: 933-944Abstract Full Text Full Text PDF PubMed Scopus (234) Google Scholar). As a non-editing transduction control, lenti-CRISPR-NT (Lenti-NT) was cloned accordingly using oligos #7–8. pCMV-DR8.91 (coding for HIV gag-pol) and pMD2.G (encoding the VSV-G glycoprotein) were kind gifts from Prof. Didier Trono (Lausanne, Switzerland). pSpCas9(BB)-2A-GFP (PX458) was a gift from Feng Zhang (Addgene plasmid #48138; http://addgene.org/48138; RRID:Addgene_48138).28Ran F.A. Hsu P.D. Wright J. Agarwala V. Scott D.A. Zhang F. Genome engineering using the CRISPR-Cas9 system.Nat. Protoc. 2013; 8: 2281-2308Crossref PubMed Scopus (5735) Google Scholar The plasmid was digested with BsbI (NEB) and dephosphorylated with a RAPID DNA Dephos and Ligation kit (Roche). Oligos #9–12 are all 5′ phosphorylated. The oligo pairs #9–10 and #11–12 containing complementary sequences were annealed to each other and then ligated to the dephosphorylated PX458 to generate plasmids PX458-iv-single guide RNA (sgRNA)-A1_11 (cutting in exon 4) and PX458-iv-sgRNA-A1_39 (cutting in exon 5), respectively. The mCherry-APOB-EGFP W58X plasmid (CAGX) was obtained by site-directed mutagenesis using oligos #13–14 and QuikChange Lightning site-directed mutagenesis kit (Agilent, #210518) on the original plasmid mCherry-APOB-EGFP.24Severi F. Conticello S.G. Flow-cytometric visualization of C>U mRNA editing reveals the dynamics of the process in live cells.RNA Biol. 2015; 12: 389-397Crossref PubMed Scopus (10) Google Scholar 4λN-DD E488Q ADAR2 (4λN) and U6 pENTR gRNA vectors were a kind gift of Dr. Joshua Rosenthal (University of Chicago).8Montiel-González M.F. Vallecillo-Viejo I.C. Rosenthal J.J.C. An efficient system for selectively altering genetic information within mRNAs.Nucleic Acids Res. 2016; 44: e157PubMed Google Scholar The NLS version of 4λN plasmid (4λN-NLS) was created by adding the c-myc NLS to the C-terminus of 4λN. The U6 pENTR gRNA vector was linearized by PCR using oligos #15–16 and Q5 high-fidelity DNA polymerase (NEB). The sequence containing the gRNA to induce specific A-to-G editing on the W58X of CAGX (Rosenthal fashion8Montiel-González M.F. Vallecillo-Viejo I.C. Rosenthal J.J.C. An efficient system for selectively altering genetic information within mRNAs.Nucleic Acids Res. 2016; 44: e157PubMed Google Scholar) was inserted into the linearized pENTR using oligo #17 and NEBuilder HiFi DNA assembly master mix (NEB). A549 cells (A-549, RRID:CVCL_0023, DKFZ Germany) were cultured at 37°C, 5% CO2 in high-glucose DMEM (Sigma) supplemented with 10% fetal bovine serum (FBS, PAN Biotech) and penicillin/streptomycin (Sigma). HEK293T cells (ATCC-CRL-3216) were cultured at 37°C, 5% CO2 in high-glucose DMEM (Sigma) supplemented with 5% FBS (PAN Biotech) and penicillin/streptomycin (Sigma). RAW 264.7 cells (ATCC TIB-71) were cultured at 37°C, 5% CO2 in high-glucose DMEM (Sigma-Aldrich) supplemented with 5% endotoxin low FBS (Sera Pro FBS, PAN Biotech), 1% glutamine, and 1% penicillin/streptomycin (Sigma). The cell lines were regularly tested for mycoplasma contamination in our facility Multiplexion (F020, DKFZ) (https://www.multiplexion.de) Lenti-CRISPR-ADAR1 exon 4 or NT in combination with pCMV-DR8.91 and pMD2.G were calcium phosphate transfected in HEK293T cells for lentiviral particle production (ratio 3:1:3). 48–72 h after transfection, cell-free supernatant was harvested and used for transduction of A549 cells. The transduced cells were selected with puromycin (1 μg/mL). Immediately after the selection control (non-transduced A549) died, limiting dilution in 96-well plates was performed for ADAR1 KOs (0.5 cell/well) and clonality was validated by visual inspection with a microscope; the Lenti-NT control was kept polyclonal. KO of ADAR1 was validated by western blot (anti-human ADAR1 [D7E2M] rabbit monoclonal antibody [mAb], Cell Signaling Technology, cat. #14175). Two clones, numbers 5 and 7, resulted in a completely abolished ADAR1 (p110 and p150) expression (Figure S3A). For further experiments we used only clone #5. PX458-iv-sgRNA-A1_11 and PX458-iv-sgRNA-A1_39 plasmids were co-transfected using the Amaxa cell line Nucleofector kit V (Lonza) into RAW 264.7 cells following the manufacturer’s protocol for RAW 264.7 cells and a Nucleofector 2b device (Lonza). 48 h post-transfection GFP-positive cells were single cell sorted into 96-well plates and clonality was validated by visual inspection with a microscope. Clones were screened by amplifying targeted regions from genomic DNA (produced by a High Pure PCR template preparation kit [Roche]) using oligos #20–21 and #22–23 and then Sanger sequencing. This was followed by additional cloning of amplified regions using a CloneJET PCR cloning kit according to the manufacturer’s instructions and transforming DH5α bacteria with ligated product. Ten resultant bacteria colonies were sent for sequencing to determine genetic changes to the targeted region. One clone that was subsequently used contained in the region targeted by PX458-iv-sgRNA-A1_39 either a 1-bp deletion or a 2-bp deletion. KO was further confirmed by RT-PCR (using a One-step RT-PCR kit [QIAGEN]) amplification of B2m the 3′ UTR region from extracted RNA defined by oligos #24–25 known to be edited and determining absence of editing compared to the amplified region from the parental cells (Figure S3A). RNA was extracted using an RNeasy mini kit (QIAGEN) and treated with DNase (Turbo DNA-free kit, Invitrogen). All of the PCRs on RNA were performed with gene-specific primers (Table S3) and a One-step RT-PCR kit (QIAGEN). Primers were designed using Primer-BLAST29Ye J. Coulouris G. Zaretskaya I. Cutcutache I. Rozen S. Madden T.L. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction.BMC Bioinformatics. 2012; 13: 134Crossref PubMed Scopus (3052) Google Scholar or AmplifX 2.0.7 (https://inp.univ-amu.fr/en/amplifx-manage-test-and-design-your-primers-for-pcr) to obtain 350- to 700-bp PCR amplicons. At this stage, PCR clean up usually is sufficient, however, gel extraction is required when amplification results in multiple bands (Macherey-Nagel NucleoSpin gel and a PCR clean-up kit was used). The fragments were then subjected to Sanger sequencing (Eurofins Genomics, GATC services, Germany, or Microsynth, Switzerland) and the resulting .ab1 files were analyzed by MultiEditR. For the C-to-U editing HEK293T cells were transfected with CmAG (50 ng), APOBEC1 (200 ng), and RBM47 (200 ng) expression vectors or CmAG (50 ng) alone. For transfection we used a mix of plasmid DNA and polyethylenimine (PEI) in an approximately 1:4 ratio (450 ng of DNA/2 μg of PEI). 72 h after transfection RNA was extracted and cDNA was amplified from Apob (using oligos #26 and #2). This allowed us to obtain Apob fragments heavily edited or not edited, respectively. These two fragments were cloned into a CloneJET PCR cloning kit (Thermo Scientific), and several colonies were screened by sequencing. From this screening we obtained two pJET vectors containing Apob with no editing (pJET-CmAG-WT) and six edited sites (pJET-CmAG-6x). These two vectors were then mixed together in titrated amounts from 0% to 100% and subjected to capillary Sanger sequencing with universal primers pJET1.2 forward and reverse. RNA-seq libraries were prepared in duplicate from A549 WT and ADAR1 KO clone 5 (Figure S3) and in triplicates from RAW 264.7 WT and RAW 264.7 APOBEC1 KO. Total RNA was extracted from 10,000,000 cells in duplicate (A549 WT and ADAR1 KO) or triplic" @default.
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• W3186299673 title "MultiEditR: The first tool for the detection and quantification of RNA editing from Sanger sequencing demonstrates comparable fidelity to RNA-seq" @default.
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