Cryptococcus neoformans, a fungal pathogen of humans. We find that the RNase H depletion method is more efficient in depleting rRNA and more specific in recapitulating non-rRNA levels present in unenriched controls than the commonly-used Poly(A) isolation method. The RNase H depletion method is also more effective than the Ribo-Zero depletion method as measured by rRNA depletion efficiency and recapitulation of protein-coding RNA levels present in unenriched controls, while the Ribo-Zero depletion method more closely recapitulates annotated non-coding RNA (ncRNA) levels. Finally, we leverage these data to accurately map the C. neoformans mitochondrial rRNA genes, and also demonstrate that RNA-Seq data generated with the RNase H and Ribo-Zero depletion methods can be used to explore novel C. neoformans long non-coding RNA genes.RNA sequencing (RNA-Seq) experiments focused on gene expression involve removal of ribosomal RNA (rRNA) because it is the major RNA constituent of cells. This process, called RNA enrichment, is done primarily to reduce cost: without rRNA removal, deeper sequencing must be performed to compensate for the sequencing reads wasted on rRNA. The ideal RNA enrichment method removes all rRNA without affecting other RNA in the sample. We tested the performance of three RNA enrichment methods on RNA isolated from  RNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression in diverse organisms. Despite the rapid and continual decrease in sequencing costs, the expense of sequencing is often the limiting factor in designing RNA-Seq experiments. Due to this cost constraint, enrichment of the RNA classes of interest, hereafter referred to as “RNA enrichment,” is an important step in library preparation for most RNA-Seq experiments. Ribosomal RNA (rRNA) is the most abundant RNA, generally constituting more than 90% of the total RNA in a cell . They are also only applicable to eukaryotes, since mRNA in prokaryotes is generally not polyadenylated. Most rRNA removal methods involve hybridization of sequence-specific probes to rRNA. These probes target the rRNA for depletion. In the Ribo-Zero depletion method, the probes are synthesized with a molecular tag, which is used to bind the probe-rRNA complex to beads, allowing the complexed rRNA to be removed from solution (There are many different methods for RNA enrichment and many products available based on these different methods. When selecting an RNA enrichment method and product there are two key considerations: (1) the fraction of rRNA removed and (2) the side effects on other RNA in the sample. RNA enrichment methods either specifically target the RNA of interest, most commonly mRNA, for isolation or specifically target rRNA for removal (The Poly(A) isolation and DSN methods are attractive because they are broadly applicable without any organism-specific adaptation: the Poly(A) method works in all eukaryotes and the DSN method should work in any organism. However, the rRNA removal methods (Ribo-Zero and RNase H) are more targeted and are therefore expected to have fewer side-effects on biologically important RNA molecules, such as protein-coding RNA and ncRNA. The downside inherent in the targeted nature of the rRNA depletion methods is that the sequence-specific probes must be designed for the organism under experimentation or a close relative for maximal efficacy. Because rRNA is the most highly conserved sequence across the tree of life   isolation. The Ribo-Zero depletion  isolation methods in removing rRNA. Additionally, we report that the RNase H depletion method is highly specific. It more closely reflects protein-coding RNA levels present in unenriched controls than the other two methods, and reflects annotated ncRNA levels present in unenriched controls nearly as well as the Ribo-Zero depletion method. Because the RNase H and Ribo-Zero depletion methods both retain ncRNA, we leveraged these unique datasets to demonstrate the feasibility of identifying novel C. neoformans var. grubii H99 (MATα) wild-type strain was used for all experiments. This strain was maintained on yeast extract-peptone-dextrose (YPD) medium .The 9 cells from each sample were pelleted, resuspended in fresh YPD medium, and incubated at 30°C for 90 min with 150 rpm shaking. Cells were then pelleted, flash frozen on dry ice, and lyophilized for ∼18 h. Total RNA was isolated using the Qiagen RNeasy Plant Mini Kit ; on-column DNase digestion was performed to ensure elimination of contaminating genomic DNA. Total RNA quantity and quality were assessed using the Agilent 2100 Bioanalyzer. Purified total RNA was subsequently stored at −80°C. Aliquots from each total RNA sample were treated with one of three different RNA enrichment methods: the RNase H method for selective depletion of rRNA (® Poly(A) mRNA Magnetic Isolation Module (NEB #E7490) . RNA-Seq libraries were prepared from these enriched samples and from unenriched control samples  using the NEBNext® Ultra™ II Directional RNA Library Prep with Sample Purification Beads (NEB #E7765) and NEBNext® Multiplex Oligos for Illumina®  (NEB #E7600) . Libraries were pooled and sequenced by the Duke Sequencing and Genomic Technologies Shared Resource on an Illumina NextSeq 500 using the High-Output Kit to produce 75-basepair single-end reads.Three biological replicate samples  were used for all analyses. Samples were prepared by growing H99 to mid-logarithmic growth phase in three separate flasks of liquid YPD medium, with 150 rpm shaking. Approximately 1 × 10It should be noted that while all work was done with the same three total RNA samples, the enrichment, library preparation, and sequencing were done in two batches, approximately one year apart. In between, total RNA samples were stored at −80°C. Ribo-Zero-treated, Poly(A)-treated, and Unenriched RNA control libraries were prepared and sequenced in the first batch. RNase H-treated, replicate Poly(A)-treated, and replicate Unenriched RNA control libraries were prepared and sequenced in the second batch. All Unenriched RNA samples were compared and shown to be highly correlated , demonst® Ultra II Directional RNA Library Prep Kit for Illumina .The RNase H depletion method (C. neoformans var. grubii (accession GCA_000149245.3). The genome sequence and annotation were downloaded from release 39 of the Ensembl Fungi database .Coverage depth was plotted for all reads mapped to the mitochondrial chromosome for data generated from the first batch of Unenriched libraries. Visual inspection of these plots clearly indicated two regions with coverage depth several orders of magnitude higher than the rest of the chromosome. These regions do not overlap with any annotated feature in the mitochondrial chromosome. We determined the boundaries of these regions, extracted the sequences of the putative rRNA genes, and confirmed by BLASTn  and the newly annotated 15S and 21S rRNA mitochondrial genes. In order to guide degradation of all rRNA by RNase H, the DNA oligos must be complementary to the rRNA and completely tile the rRNA. For simplicity and cost minimization, the design goal for rRNA targeting oligos was for them to be 50 nucleotides in length with no gaps between adjacent oligos. For genes with lengths that were not multiples of 50 nucleotides, single nucleotide gaps were introduced between oligos to allow for end-to-end coverage. Two, 55 nucleotide oligos were used to tile CNAG_10503, which is 111 basepairs long. Oligos were validated by mapping them to the H99 genome and confirming that they tiled as expected and mapped to the antisense strand. This validation process identified several partial duplications of the mitochondrial rRNA, putative nuclear mitochondrial DNA (numts)  was less than $1000. This provided over 21 ml of pooled oligos (179 oligos at 120 µl per oligo), enough for over 33,000 reactions. Therefore, while the upfront cost of oligos is substantial, the per reaction cost is about $0.03.® Multiplex Oligos for Illumina®  . Reads were then mapped to the genome and read counts were generated using STAR (version 2.5.4b) . While we expect there to be some lane effects, we expect these to be less than fluidically independent lanes. Because of this, and for simplicity, reads were combined across all four lanes for analysis of depletion efficiency and specificity.Basic assessments of sequence data quality were performed using FastQC  and MultTo calculate the percentage of rRNA reads per library, Rsamtools (version 2.2.2) was used to extract reads from the STAR generated BAM files and determine the number of reads mapped to the rRNA genes. We did not use rRNA counts generated by STAR because STAR excludes multimapping reads from per gene read counts. As discussed above, several rRNA genes are partially duplicated elsewhere in the genome. Because STAR excludes multimapping reads from gene counts, it undercounts reads mapping to the rRNA genes that are partially duplicated. We confirmed the source of reads that mapped to rRNA duplicated regions by evaluating context: the count level of these reads corresponded to the level of expression of the rRNA genes from which the duplications seem to have arisen and not the level of expression of the genes  that seem to be the “acceptor sites” of these duplications. The percentage of total reads that mapped to rRNA genes was then calculated.Per gene read counts were generated by STAR as described above. Read counts for each library were combined across all four lanes using DESeq2::collapseReplicates, each library’s counts were normalized by its size factor, then an average count per gene was calculated for each enrichment method across all replicates. The mean normalized count of the Unenriched replicate libraries was considered the reference count for each gene. Specificity of each enrichment method was determined by calculating the Pearson correlation of the mean normalized count for each enrichment method with that of the Unenriched libraries. Variation among the Unenriched libraries was quantified by cross-correlation: Pearson correlation was calculated for each Unenriched replicate library with the normalized mean of the other five Unenriched replicate libraries. Scatterplots were generated to visualize the correlation of replicate enriched libraries with the Unenriched libraries. While calculation of mean normalized counts used all replicates for each method, scatterplots are only shown for one technical replicate of each RNA sample for each enrichment method . In addition to analyses across all genes, calculation of Pearson correlation and generation of scatterplots was repeated for subsets of genes, as annotated for “gene_biotype”: protein-coding genes, ncRNA, and tRNA, according to each gene’s annotation. rRNA genes and CNAG_13073 were excluded from all correlation analyses and scatterplots.To determine specifically which genes are “lost” by the Poly(A) isolation method, we identified genes with counts at least eight-fold lower in the Poly(A)-treated libraries than in the Unenriched libraries, after first excluding genes with very low expression in the Unenriched libraries . These thresholds were chosen to identify obvious outliers in the Poly(A)-treated libraries and were confirmed by visual inspection of the identified genes . We selected these thresholds to be more conservative than thresholds commonly used to identify genes with biologically relevant differences in expression . To explore whether deadenylation explains the failure of the Poly(A) isolation method to capture a subset of these protein-coding transcripts, we visualized read depth across each of the corresponding genes and compared these distributions to read depth across a random set of control genes . The control genes were selected from a subset that had total read counts in the same range as the “lost” protein-coding genes.C. neoformans data. Details of the forked repository are provided below.To demonstrate the feasibility of identifying novel lncRNA from RNA-Seq datasets generated using rRNA removal methods, we applied LncPipe (C. neoformans specific model for CPAT (one component of LncPipe), and generating a Bash script which itself runs LncPipe. LncPipe itself was run in Singularity with the bioinformatist/lncpipe Docker image built by the LncPipe developers (https://hub.docker.com/layers/bioinformatist/lncpipe/latest/images/sha256-9d97261556d0a3b243d4aa3eccf4d65e458037e31d9abb959f84b6fe54bb99a2?context=explore). Within LncPipe, STAR was used for mapping reads and the final step, LncPipeReporter, was not run.We developed an Rmarkdown document to perform all necessary pre-processing for running LncPipe  isolation methods, because, of the three RNA enrichment methods assessed here, they are the two that are still available for use. To provide some context to our RNase H depletion method results, we also include analyses on the Ribo-Zero depletion method, which was frequently used in fungal RNA-Seq experiments before its discontinuation.As an initial assessment, we evaluated the efficiency with which each enrichment method removed rRNA. To do so, we quantified the percentage of total reads that mapped to rRNA genes for each method and compared these percentages to those of Unenriched RNA control libraries generated by sequencing identical RNA samples without any enrichment. As expected, the vast majority (∼90–92%) of reads in the Unenriched RNA control libraries map to rRNA genes . Both thR = 0.983–0.997), demonstrating reproducibility between samples and batches  display a much better correlation with the Unenriched RNA control libraries than the Poly(A)-treated libraries (R = 0.793–0.820) for all reads mapping to non-rRNA genes (R = 0.932–0.954) display a much better correlation with the Unenriched libraries than the Poly(A)-treated libraries, but a slightly weaker correlation than the RNase H-treated libraries for reads mapping to all non-rRNA genes (We compared the abilities of the RNase H depletion method and the Poly(A) isolation method to retain all non-rRNA following rRNA depletion. To do so, we calculated the correlation coefficient of normalized reads mapped to all non-rRNA genes in RNase H-treated andPoly(A)-treated libraries with normalized reads mapped to all non-rRNA genes from the Unenriched RNA control libraries. We found that the RNase H-treated libraries (R = 0.986–0.998), demonstrating reproducibility between samples and batches  display a much better correlation with the Unenriched RNA control libraries than the Poly(A)-treated libraries (R = 0.810–0.838) for all reads mapping to protein-coding genes (R = 0.935–0.962) display a better correlation with the Unenriched libraries than the Poly(A)-treated libraries, but a slightly weaker correlation than the RNase H-treated libraries for reads mapping to protein-coding genes (We compared the ability of the RNase H depletion method and the Poly(A) isolation method to retain protein-coding RNA following rRNA depletion. To do so, we calculated the correlation coefficient of normalized reads mapped to protein-coding genes in RNase H-treated and Poly(A)-treated libraries with normalized reads mapped to protein-coding genes from the Unenriched RNA control libraries. We found that the RNase H-treated libraries (R = 0.835–0.990), demonstrating reproducibility between samples and batches  display a much better correlation with the Unenriched RNA control libraries than the Poly(A)-treated libraries (R = 0.139–0.149) for all reads mapping to annotated ncRNA genes (We compared the ability of the RNase H depletion method and the Poly(A) isolation method to retain annotated ncRNA following rRNA depletion. We calculated the correlation coefficient of normalized reads mapped to annotated ncRNA genes in RNase H-treated and Poly(A)-treated libraries with normalized reads mapped to annotated ncRNA genes from the Unenriched RNA control libraries. As expected, we found that the RNase H-treated libraries (R = 0.897–0.967) display a slightly better correlation with the Unenriched libraries than the RNase H-treated libraries for reads mapping to annotated ncRNA genes . There is no clear explanation for the poor performance of the RNase H depletion method with CNAG_12993.As a better assessment of the ability of the RNase H depletion method to retain annotated ncRNA, we compared it to the Ribo-Zero depletion method. Ribo-Zero-treated libraries .C. neoformans. We applied this modified LncPipe pipeline to identify predicted lncRNA within our RNase H depletion, Ribo-Zero depletion, and Unenriched RNA datasets. Our lncRNA discovery analysis identified 11 predicted lncRNA within the C. neoformans transcriptome  isolation method  selection method, are very efficient in removing fungal rRNA. Surprisingly, the Ribo-Zero depletion method showed poor efficiency in C. neoformans, despite previous work showing efficient removal of various bacterial rRNA  isolation method and somewhat more specific than the Ribo-Zero depletion method, in that it more closely reflects protein-coding RNA levels observed in the Unenriched controls.We evaluated the specificity of each RNA enrichment method in retaining annotated ncRNA. The Poly(A) isolation method is unable to retain ncRNA; this is as expected since it depends on 3′ polyadenylation, which is absent from ncRNA. The RNase H depletion and the Ribo-Zero depletion methods both perform well in retaining annotated ncRNA, with the Ribo-Zero depletion method being slightly more specific than the RNase H depletion method, in that it more closely reflects annotated ncRNA levels observed in the Unenriched controls.Cryptococcus, lack polyadenylation  isolation method may overlook important biological phenomena, including the dynamics of mRNA with rapid turnover.To determine a possible mechanism impacting specificity of the Poly(A) isolation method, we identified the genes that are most underrepresented in the Poly(A)-treated libraries compared to the Unenriched libraries . A total of 41 genes were identified as substantially underrepresented in the Poly(A)-treated libraries; as expected, 24 of these genes are non-coding genes (Supplementary Table S1). In analyzing the remaining 17 protein-coding genes that are underrepresented in the Poly(A)-treated libraries, we observed a pattern. The vast majority of these genes (12 of 17) are located on the mitochondrial chromosome (Supplementary Table S1). This observation is supported by previous work that has demonstrated that mitochondrial transcripts in fungi, including nylation . In seeknylation . Becausenylation . With thS. cerevisiae, Neurospora crassa, and Aspergillus flavus), in which lncRNA and natural antisense transcripts have been implicated in stress responses and development .Interest in ncRNA has recently expanded in the fungal genetics field. The majority of work has focused on ncRNA in model systems  isolation method is effective in many contexts. It does efficiently reduce rRNA reads , but it fails to capture biologically relevant RNA species that are not adenylated, including ncRNA, mRNA expressed in the mitochondria, and deadenylated mRNA. The RNase H depletion and the Ribo-Zero depletion methods both display strengths and weaknesses. The RNase H depletion method performs better in efficiency of rRNA reduction and specificity for protein-coding transcripts, while the Ribo-Zero depletion method performs moderately better in specificity for annotated ncRNA. While this work was being conducted, the Ribo-Zero product line was discontinued. It has since been replaced with the Illumina Ribo-Zero Plus rRNA Depletion Kit, which only targets human, mouse, rat, and bacterial rRNA. As a result, we conclude that the RNase H method may be the best option for RNA-Seq analysis of C. neoformans, as well as many other non-model organisms. While the RNase H depletion method has a substantial upfront cost to purchase DNA oligonucleotides (approximately $1000), we estimate that for this method our total cost per sample was less than $6.50 .In conclusion, based on the data presented here, we consider the RNase H depletion method to be the most generally effective in preparation of https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160397). The custom programs developed for processing and analyzing the RNA-Seq data are available in a GitHub repository (https://github.com/granek/rna_enrichment) and the version of the LncPipe pipeline that we modified to run on the H99 genome is available in a GitHub repository (https://github.com/granek/LncPipe) that was forked from the original. For purposes of reproducibility, all analyses were run within Singularity containers (v 3.5.2). All lncRNA discovery was performed using the bioinformatist/lncpipe Docker image (run within Singularity) provided by the LncPipe developers (https://hub.docker.com/layers/bioinformatist/lncpipe/latest/images/sha256-9d97261556d0a3b243d4aa3eccf4d65e458037e31d9abb959f84b6fe54bb99a2?context=explore). All other analyses were performed using a Singularity image which we built and is publicly available (library://granek/published/rna_enrichment). These resources include all programs, support files, and instructions for automatically replicating all analyses presented here using the data available from GEO.The RNA-Seq data analyzed in this publication have been deposited in NCBI’s Gene Expression Omnibus (GEO) (https://doi.org/10.25387/g3.15428664. Figure S1 contains a depth of coverage plot of the mitochondrial rRNA genes. Figures S2, S3, and S4 display scatterplot visualizations of rRNA depletion specificity summarized in All supplementary information is deposited in figshare:


GPEdit is a user-friendly and comprehensive database that provides an opportunity for a better understanding of the genetic impact and the effects on drug response of RNA editing in cancers.Altered A-to-I RNA editing has been widely observed in many human cancers and some editing sites are associated with drug sensitivity, implicating its therapeutic potential. Increasing evidence has demonstrated that a quantitative trait loci mapping approach is effective to understanding the genetic basis of RNA editing. We systematically performed RNA editing quantitative trait loci (edQTL) analysis in 33 human cancer types for >10 000 cancer samples and identified 320 029 edQTLs. We also identified 1688 ed-QTLs associated with patient overall survival and 4672 ed-QTLs associated with GWAS risk loci. Furthermore, we demonstrated the associations between RNA editing and >1000 anti-cancer drug response with ∼3.5 million significant associations. We developed GPEdit ( RNA editing is a unique type of post-transcriptional modification that alters specific nucleotide sequences originated from one organism's genome. The major form of RNA editing in metazoans is Adenosine to Inosine (A-to-I), a process that is catalyzed by the adenosine deaminase acting on RNA (ADAR) enzymes ,2. RNA ecis-regulatory mechanism of RNA editing patterns has been examined in animal models or normal human tissues. Genetic variants such as Single Nucleotide Polymorphism (SNP) that potentially affect RNA secondary structure are partially accounted for by the altered editing frequency. In human cancers, we have shown that RNA editing events are closely linked to clinical information such as patient overall survival (Previous works demonstrated that a quantitative trait loci (QTL) mapping approach is effective to understand the genetic basis of multiple molecular features in human cancers, e.g. gene expression, methylation, and alternative splicing . Severalsurvival . More imsurvival . In othesurvival .https://hanlab.uth.edu/GPEdit/).The significant impact of genetic variants on different noncoding RNAs ,17 and phttps://gdc.cancer.gov)  Figure . As desc) Figure ,22, autoWe have previously developed a computational pipeline to characterize A-to-I RNA editing profiles in 17 TCGA cancer types following a RADAR reference panel  of ∼1.4 P-value. For edQTLs identified in each cancer type, false discover rate (FDR) was controlled as <0.01.To emphasize the cis-regulatory role of detected edQTLs, only SNPs within 200 kb of editing sites were included in our analysis . To testEdQTLs were further examined against patients' overall survival time. For each edQTL, tumor samples were classified into three groups by their genotypes and log-rank tests were performed on them to examine the significance of the overall survival time difference among groups. Within each cancer type, an edQTL with FDR <0.1 was defined as survival associated edQTL. Kaplan-Meier (KM) curves were used to visualize the differences between groups.http://www.ebi.ac.uk/gwas/, access on September 2020) (https://data.broadinstitute.org/mpg/snpsnap) (R2 over 0.5. EdQTLs overlapped with GWAS tag SNPs were defined as GWAS associated edQTLs.Genome-wide association studies (GWASs) have been contributing to understanding relations between genetic risk loci and complex diseases . Here, wer 2020) . GWAS lisnpsnap) . The Eurhttps://osf.io/yatu3/)   and expa/yatu3/) ,21. For Rs = 0.73, P = 1.19 × 10–6), which is consistent with our previous pan-cancer QTL studies .GPEdit data portal was constructed based on Django framework with Bootstrap as front-end web interface and SQLite as a back-end database tool Figure  33). Va. Va33). There are four function modules available Figure  in GPEdiWithin each query function module, queries be made using a set of user-defined criteria such as SNP id (e.g. rs11024008), a specific genomic location (e.g. chr1:150000000-160000000), or the gene symbol  adjacent to RNA editing sites Figure . For exaP < 0.0001), and the risk table is listed below the KM plot.For other modules, making a query is similar to the ‘edQTL’ module, while the query options could be different. For example, the ‘GWAS-edQTL’ module has an additional ‘Linkage disequilibrium’ cutoff choosing option. The query returns are also different among modules Figure . Some moAdditionally, all the query return tables are searchable and can be downloaded in ‘Microsoft Excel’ compatible format. All the visualization plots can be saved in ‘png’ format as well as in ‘pdf’ format. A detailed tutorial of GPEdit can be found on the ‘Document’ page.We systematically investigated the genetic and pharmacogenomic basis of A-to-I RNA editing events in 33 human cancers. We constructed a user-friendly database, GPEdit, for users to query, browse and download edQTLs. Huge amounts of vector diagrams of edQTL box plots and KM plots are provided. GPEdit could serve as an important resource for human cancer genetics and provide opportunities to bridge the knowledge gap from variants in sequence to RNA editing. In addition, GPEdit provides the associations between RNA editing and >1000 anti-cancer drugs thus contributing to understanding the functional effects of RNA editing on drug response. Cancer Genomics is an explosively growing field in recent years , with thhttps://hanlab.uth.edu/GPEdit). Analytic codes for RNA editing calling and edQTL detection are available at GitHub site (https://github.com/hr1912/GPEdit).GPEdit is a data resource portal that is freely available at (


We inserted six CPVT-causing mutations via clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 into unc-68 and csq-1, the Caenorhabditis elegans homologs of RyR and CASQ, respectively. We characterized those mutations via video-microscopy, electrophysiology, and calcium imaging in our previously established optogenetic arrhythmia model. In this study, we additionally enabled high(er) throughput recordings of intact animals by combining optogenetic stimulation with a microfluidic chip system. Whereas only minor/no pump deficiency of the pharynx was observed at baseline, three mutations of UNC-68  reduced the ability of the organ to follow 4 Hz optogenetic stimulation. One mutation (Q4623R) was accompanied by a strong reduction of maximal pump rate. In addition, S2378L and Q4623R evoked an altered calcium handling during optogenetic stimulation. The 1,4-benzothiazepine S107, which is suggested to stabilize RyR2 channels by enhancing the binding of calstabin2, reversed the reduction of pumping ability in a mutation-specific fashion. However, this depends on the presence of FKB-2, a C. elegans calstabin2 homolog, indicating the involvement of calstabin2 in the disease-causing mechanisms of the respective mutations. In conclusion, we showed for three CPVT-like mutations in C. elegans RyR a reduced pumping ability upon light stimulation, i.e., an arrhythmia-like phenotype, that can be reversed in two cases by the benzothiazepine S107 and that depends on stabilization via FKB-2. The genetically amenable nematode in combination with optogenetics and high(er) throughput recordings is a promising straightforward system for the investigation of RyR mutations and the selection of mutation-specific drugs.Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited disturbance of the heart rhythm (arrhythmia) that is induced by stress or that occurs during exercise. Most mutations that have been linked to CPVT are found in two genes, i.e., ryanodine receptor 2 (RyR2) and calsequestrin 2 (CASQ2), two proteins fundamentally involved in the regulation of intracellular Ca In vertebrates, three RyR isoforms are present, whereas Caenorhabditis elegans has a single RyR gene encoded by the unc-68 locus , vulval muscles, and in the isthmus and terminal bulb regions of the pharynx  is a condition of abnormal heart rhythm (arrhythmia), induced by stress or physical activity. If untreated, CPVT is highly lethal. It often remains unnoticed due to normal baseline electrocardiograms and structurally normal hearts. The exact prevalence of CPVT is not known but is estimated to be around 1:10,000 . The meamyocytes . The car68 locus . Therefo68 locus . The exp68 locus . Two EF-etal RyR . Null muild type . This suild type . Calsequ pharynx . A csq-1eostasis . Polymer elegans . While j elegans , there astulated .2+ leakage from the SR, which leads to cytosolic Ca2+ overload, generating delayed afterdepolarizations (DADs), triggered activity, and ventricular arrhythmias, in particular, under adrenergic conditions  CPVT mutations and polymorphisms are known . These mnditions . There anditions , 2004. Rnditions . In addinditions . Those aium leak . Howeverium leak .via clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 into the C. elegans csq-1 and unc-68 loci. Using the C. elegans pharynx, a rhythmically active muscular pump acting as the feeding organ of the nematode, enabled us to study their effects on contraction and intracellular Ca2+ concentration in an entire organ of a living animal. Obvious similarities exist between the heart and the pharynx, i.e., both are tubes that pump materials along their lumina, both possess gap junctions to synchronize their contractions that can continue without neuronal input and the fact that the pharynx expresses orthologs of most of the proteins involved in human cardiac muscle physiology. These similarities suggest that the pharynx and the myocardium may represent convergent evolution between two muscular pumps faced with similar biological roles  and activates muscle contraction. EGL-19 slowly inactivates, and EXP-2, a voltage-gated K+-channel, causes membrane repolarization and action potential termination , a well-known activator of RyR, increased the spontaneous pump rate in EPG recordings of wild-type animals, while no effect of caffeine was observed in two different mutants lacking UNC-68  led to severe effects on optogenetically 4 Hz paced pumping in our pharynx model and was reversed by an expression of wild type UNC-68 in the deletion background  that enables higher throughput than conventional electrophysiological approaches. This method allows a precise light stimulation during recordings while registering pharyngeal potentials, similar to an electrocardiogram. Several mutations of UNC-68 led to a reduced ability to follow light stimulation. This effect was reversed in a mutation-specific way by the 1,4-benzothiazepine S107, which is suggested to stabilize RyR channels by enhancing the binding of calstabin , extracellular measurements of electrical events in the pharynx. The excitation phase consists of two positive peaks, the first one smaller than the second, that correlate with muscle contraction. The relaxation phase consists of two negative peaks, the second one smaller than the first, that correlate with muscle relaxation. A plateau phase is between the two phases . In addialstabin . In addimulation .C. elegans, we were also interested in the mode of interaction between CSQ-1 and UNC-68. This is especially because mutations of triadin are as well known to cause CPVT , ZX1423:zxEx795 , ZX1662:zxIs20 , GE24:pha-1(e2123) III, and RB2222:fkb-2(ok3007) I. Strains generated by SunyBiotech, China (SunyBiotech nomenclature in brackets): ZX2655:csq-1(zx7[K214N]) [PHX2058:csq-1(syb2058)], ZX2745:unc-68 [PHX2420:unc-68(syb2420)], ZX2747:unc-68(zx10[I5231M]) [PHX2421:unc-68(syb2421)], and ZX2749:unc-68(zx11[S2378L]) [PHX2494:unc-68(syb2494)].Worms were cultivated on nematode growth medium (NGM) plates , seeded with an ZX2255:fkb-2(ok3007) I; zxIs20 , ZX2256:pha-1(zx2); unc-68(zx4[Q4623R]), ZX2257:pha-1(zx2); unc-68(zx4[Q4623R]); zxIs20 , ZX2258:pha-1(zx2), ZX2259:pha-1(zx2); zxIs20 , ZX2260:pha-1(zx2); zxIs20 ; zxIs124, ZX2261:zxEx795 ; zxIs20 , ZX2264:zxIs124, ZX2266:pha-1(zx2); unc-68(zx5[R414Q]), ZX2267:pha-1(zx2); unc-68(zx5[R414Q]); zxIs20 , ZX2269:pha-1(zx2); unc-68(zx4[Q4623R]); zxIs20 ; zxIs20 ; zxIs124, ZX2270:fkb-2(ok3007) I; unc-68(zx4[Q4623R]); zxIs20 , ZX2567:csq-1(ok2672); zxIs20; zxIs124, ZX2568:unc-68(r1162)V; zxIs20; zxIs124, ZX2707:csq-1(zx7[K214N]); zxIs20 , ZX2708:unc-68(zx8[P2460S]), ZX2718:unc-68(zx8[P2460S]); zxIs20 , ZX2743:fkb-2(ok3007) I; unc-68(zx8[P2460S]); zxIs20 , ZX2744:unc-68(zx8[P2460S]); zxIs20 ; zxIs124, ZX2746:unc-68; zxIs20 , ZX2748:unc-68(zx10[I5231M]); zxIs20 , ZX2750:unc-68(zx11[S2378L]); zxIs20 , ZX2775:unc-68; zxIs20 ; zxIs124, ZX2777:unc-68(zx11[S2378L]); zxIs20 ; zxIs124, ZX2778:fkb-2(ok3007) I; unc-68(zx11[S2378L]); zxIs20 .We generated the following strains: pha-1(e2123) co-conversion approach, ZX2256, ZX2258, and ZX2266 were generated (pha-1(e2123) repair template RT01 (50 ng/μl), fusion PCR derived sgRNA template targeting the desired locus (25 ng/μl), and a linear oligo as a repair template carrying the desired mutation (50 ng/μl). Animals that were not embryonic lethal at 25°C were PCR genotyped for successful CRISPR events.Using a temperature-sensitive embryonic lethal mutation enerated . Severalpmyo-3::CFP (50 ng/μl) as an injection marker, KCl (300 mM), and HEPES (20 mM). Fluorescent progeny was picked and PCR genotyped for successful CRISPR events. For both methods, sgRNA target sites were predicted with the online tool CRISPOR  and subsequent backcrossing (4x) with N2 strain, ZX2261 was obtained.By integrating the extrachromosomal array of ZX1423 One day before experiments, L4 larvae were picked on NGM dishes seeded with OP50-1 culture. Spontaneous pumping of animals on food was video recorded . The NGM dish was manually directed to allow a continuous video recording of a freely moving animal. The grinder movements (pumps) were visually counted for 20 s, to gain a representative number of pumps per animal (about 80–100 pumps for wild type). Loss of focal plane, e.g., due to animals crossing borders of the bacterial lawn, and concomitant impossibility to count pumping, led to exclusion of single animals from the analysis. For the recording of swimming cycles, a 96-well plate was filled with 100 μl NGM and 200 μl M9 buffer. Up to ten young adult animals were picked into one well. Swimming cycles were video recorded  for 60 s and visually counted.InVivo Biosystems (formerly Nemametrix), United States) according to the manufacturer’s manual. Worms were sucked into ScreenChips™ 30 or 40, with respect to their size. Pharyngeal pumping was induced by applying 1.5 mW/mm2 of 470 nm light in 50 ms pulses through an inverse microscope (Leica DM IL LED) equipped with a 470 nm LED , 10x objective (Leica HI Plan I 10x/0.22 ∞/- Ph1), as well as a GFP filter set . Worms in the reservoir of the ScreenChip™ were protected from blue light with red foil . Stimulation was performed with 4 Hz pulses for 60 s. Recordings were performed with the software NemAcquire and analyzed with NemAnalysis (both open source from InVivo Biosystems). Further analysis was performed in OriginPro .One day before experiments, L4 larvae were transferred onto NGM plates seeded with OP50-1 from bacterial suspensions supplemented with ATR  with a final concentration of 0.3 mM. Prior to recording, young adult animals were collected in M9 buffer and washed three times. S107  was added in a final concentration of 50 μM (0.1% DMSO) and incubated for 30 min. EPGs were recorded with the ScreenChip™ System (2) in a stress test. The stimulation frequency was increasing stepwise: 1–7 Hz; pulse duration: 35 ms, over a period of 5 s each. The initial stimulation frequency of 1 Hz was held for 15 s to allow a stable rhythm without additional spontaneous pumps. The Review software  was used to translate PatchMaster files. The pump rate was analyzed by AutoEPG  directly posterior to the terminal bulb. Electrophysiology was performed as described in the study of  AutoEPG   to specifically target PMCs. Additionally, the nematodes cuticle would prevent uptake of bath-applied dyes. RCaMP1h is a red-shifted GECI that allows a parallel blue-light stimulation of ChR2 .C. elegans pharynx, young adult animals were placed onto 10% agarose pads (M9 buffer) mounted on microscope slides and were immobilized with polystyrene beads . Imaging was performed on an inverted microscope (Zeiss Axio Observer Z1), equipped with 40x oil immersion objective , a 470 nm LED for ChR2 excitation , and a 590 nm LED for RCaMP1h excitation (∼0.3 mW/mm2) , a 80R/20T beam splitter , and an EMCCD Camera , as well as the following filters: GFP/mCherry (479/585 nm) Dualband ET Filter was combined with a 647/57 nm emission filter and a 605 nm beamsplitter . The terminal bulb of one pharynx was focused. Movies were acquired at 2 ms exposure (ca. 156 fps) and a binning of 4 × 4. The blue-light stimulation started 3 s after the onset of the video-recording and lasted for 10 s to enable the achievement of a plateau phase. Raw image sequences were analyzed via ROI selection of signal  and background, a multi-measure function in ImageJ . Changes in fluorescence were calculated as ΔF/F0. The amplitude of ΔF/F0 was analyzed during the plateau phase (10–13 s). Importantly, only worms that were able to follow light stimulation have been included for analysis. Experiments were performed in the dark and illumination protocols were applied as short as possible to avoid degradation of photosensitive proteins or substances.A day before experiments, L4 larvae were transferred onto NGM plates seeded with OP50-1 from bacterial suspensions supplemented with ATR with a final concentration of 0.3 mM. For imaging of RCaMP1h in t-test or 1-way ANOVA with the Bonferroni post-hoc test) were calculated with OriginPro . Based on experimental settings, every animal was tested only once.Mean values, SEM, and further statistics  that enables higher throughput EPG recordings of intact nematodes in combination with optogenetic stimulation. We observed a significantly reduced ability to follow 4 Hz pacing over a period of 60 s for three of the CPVT alleles unc-68(S2378L), (P2460S), and (Q4623R) (unc-68(Q4623R), similar as already shown for unc-68(R4743C)  . This wa(Q4623R) . The fre(Q4623R) . Wild-ty(R4743C) , followsThe electrophysiological findings of ScreenChip™ and stress test recordings manifest a “worm arrhythmia” phenotype of several CPVT-related mutations in our optogenetic arrhythmia model. For all alleles showing reduced ability to follow the optogenetic stimulation, a decreased binding of calstabin2 was detected by another group . NeverthC. elegans, we decided to use a 1,4-benzothiazepine known to enhance calstabin binding and to reduce leakiness of RyR. The 1,4-benzothiazepine derivative JTV519 (a.k.a. K201) is a non-specific blocker of sodium, potassium, and calcium channels (unc-68 (RyR) specific effect of S107, that was not elicited via other, unknown channels  was present   . Since Ul EGL-19 , reversee 20 μM, . We did e 20 μM, , while te 20 μM, . Thus, tR allele .0 in comparison to wild type was observed for the csq-1 deletion, while reduced spontaneous pumping on food and reduced ability to follow optogenetic pacing have previously been shown for this allele. Nevertheless, one has to take into account that the effect of csq-1(ok2672) on pumping ability during light stimulation is about 3-fold smaller in comparison to unc-68(r1162) (No significant difference of the amplitude of ΔF/F8(r1162) .0 , with the neutral residue alanine (YVQAGAAGAAPARKC) to prevent an electrostatic interaction with the positively charged residues of CSQ-1.We were also interested in the interaction of UNC-68 and CSQ-1 in  triadin  and junc triadin . Whereas triadin . For the triadin . Based otructure , there s2+ imaging of the UNC-68 neutral luminal loop mutant reveals, as for the csq-1 deletion (2+ concentration (p = 0.06) based on RCaMP1h fluorescence (ΔF/F0) upon light stimulation  enables pacing up to 6 Hz for periods of > 1 min. In the present study, we established the insertion of CPVT mutations via CRISPR-Cas9 genome-editing into the optogenetic arrhythmia model. In contrast to extrachromosomal expression, genome-editing allows a stable expression level and a protein distribution that reflects the wild-type condition. In addition, there is no requirement of a manual preselection of transgenic animals, which facilitates higher throughput experiments. For the first time, we implemented here higher throughput electrophysiological recordings of C. elegans with optogenetic stimulation by combining a microfluidic ScreenChip™ system (InVivo Biosystems) with an Arduino controlled high power LED. This allows precise light stimulation in combination with EPG recordings, succeeded by an automated analysis of the recordings. In sum, it is an excellent option for patient-specific drug testing.We have previously established the  channel . An optounc-68(S2378L), (P2460S), and (Q4623R) show an arrhythmic phenotype characterized by a significantly reduced ability to follow 4 Hz pacing in EPG recordings  exhibit higher amplitudes and longer durations of spontaneous local Ca2+ release events already at a basal state adrenalin as well as the respective receptors (tyra-3), a G-protein-coupled catecholamine receptor for the invertebrate noradrenaline-like neurotransmitters tyramine and octopamine, acts in sensory neurons but is not expressed in PMCs , (P2460S), and (Q4623R)  had a significantly reduced ability to follow 4 Hz pacing over a period of 60 s, indicated by either following about every second stimulus with a pump or a complete stop of pumping ) after S107 treatment. This is consistent with results obtained in a murine knock-in model of RyR2-S2246L, where treatment with JTV519 (a.k.a. K201), a more unspecific 1,4-benzothiazepine derivative, was without effect on the inducible VT, presumably owing to the fact that the interaction between the 2246 domain (2234–2750 region) and the K201 binding domain (2114–2149 region) was so tight that the drug-binding domain was inaccessible , strong effects on swimming  for the homologous human mutations. Nevertheless, a combination of the calstabin mechanism with one of the other discussed CPVT mechanisms cannot be excluded. Furthermore, the selective effect of S107 on the different CPVT-mutations emphasizes the importance of the development of patient-specific drugs that could be tested in the C. elegans optogenetic arrhythmia model with high(er) throughput.In conclusion, we were able to shed some light on the mechanism of those CPVT mutations showing effects in The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.CS designed and directed the project. ME, YW, HK, and CS performed the experiments and analyzed the data. ME and CS wrote the article. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


RNA-seq analysis of 12-day- and 20-day-old Arabidopsis seedlings revealed candidates of U-to-C RNA editing events. Sanger sequencing of both DNA and cDNA for all candidate nucleotide conversions confirmed the seven U-to-C RNA editing sites. This work clearly demonstrated presence of U-to-C RNA editing for nuclear genes in Arabidopsis, which provides the basis to study the mechanism as well as the functions of the unique post-transcriptional modification.Cytosine-to-Uridine (C-to-U) RNA editing involves the deamination phenomenon, which is observed in animal nucleus and plant organelles; however, it has been considered the U-to-C is confined to the organelles of limited non-angiosperm plant species. Although previous RNA-seq-based analysis implied U-to-C RNA editing events in plant nuclear genes, it has not been broadly accepted due to inadequate confirmatory analyses. Here we examined the U-to-C RNA editing in Arabidopsis tissues at different developmental stages of growth. In this study, the high-throughput RNA sequencing (RNA-seq) of 12-day-old and 20-day-old Arabidopsis seedlings was performed, which enabled transcriptome-wide identification of RNA editing sites to analyze differentially expressed genes (DEGs) and nucleotide base conversions. The results showed that DEGs were expressed to higher levels in 12-day-old seedlings than in 20-day-old seedlings. Additionally, pentatricopeptide repeat (PPR) genes were also expressed at higher levels, as indicated by the log Physcomitrella patens [RNA editing, one of the most promising means of post-transcriptional gene regulation, has been widely investigated in various protozoa , mammalia patens . In flowPPR proteins constitute a large family of proteins, with more than 400 members  and are morf1 loss-of-function mutant, a single amino acid substitution in the conserved MORF domain abrogates the interaction of MORF1 with many PLS-class PPR proteins, implying that direct interaction with PPR proteins is required for the RNA editing function of MORF1 [P. patens, the upstream PPR stretch for RNA recognition linked in cis to the downstream E1, E2, and DYW domains is evident in all editing factors. Because of the simplicity of this model system, all organelle editing sites in the moss have been assigned to their corresponding DYW-type editing factors [E. coli cell as well as in vitro with a single DYW-type RNA editing factor from Physcomitrella patens suggests the DYW domain catalyzes the cytidine deamination.In addition to PPR proteins, MORF/RIP, ORRM, and OZ proteins are also required for successful RNA editing and play an important role in the editosome . In the of MORF1 . In P. p factors . ReconstWhile C-to-U RNA editing occurs in chloroplasts and mitochondria of the majority of terrestrial plants, U-to-C RNA editing is rare in land plants, except in hornworts, lycophytes, and ferns, and is, therefore, referred to as an occasional phenomenon . BecauseIn contrast to organellar RNA editing, RNA editing in nuclear genes of plants has not been widely accepted, though it has been suggested by few analyses based on the RNA-seq data. Recently, we also reported U-to-C and A-to-guanosine or inosine (G or I) nucleotide conversions in 12-d-old whole seedlings and leaves of 21-d-old seedlings, respectively ,24. HoweArabidopsis thaliana, which showed distinct RNA editing status at least at a single site in previous analysis. RNA-seq data can be used for sequence differences relative to the reference genome to identify both genomic SNPs and RNA editing events. The major challenge in identifying U-to-C RNA editing sites using RNA-seq data is the discrimination of RNA editing sites from genome-encoded, single-nucleotide polymorphisms and technical artifacts caused by sequencing or read-mapping errors. We comprehensively analyzed all candidates for U-to-C RNA editing by Sanger sequencing and confirmed the presence of genuine U-to-C RNA editing events in Arabidopsis nuclear genes.Here, we examined the U-to-C RNA editing in 12-day- and 20-day-old seedlings of Seeds of Arabidopsis thaliana col-0 were soaked in water and incubated in the dark at 4 °C for 2–3 days. Seeds were sown in paper pots containing a 1:2:1 mixture of horticultural perlite, peat moss, and vermiculite, and covered with a plastic wrap to maintain the moisture content. The pots were placed in a U-ING Green Farm hydroponic grow box  in a growth room at 22 °C temperature, 45% relative humidity, and a 16-h light/8-h dark cycle. After germination, water and fertilizers were supplied twice a week. Seedlings were harvested at different days and intervals.Total RNA was extracted from seedlings using the Qiagen Plant Mini Kit , according to the manufacturer’s instructions, and treated with DNase  to remove traces of contaminating genomic DNA. After DNase treatment, the samples were purified by phenol-chloroform extraction, followed by ethanol precipitation. The purified RNA was quantified using a NanoDrop Spectrophotometer . Subsequently, cDNA was synthesized using reverse transcriptase  and a random hexamer (oligo dT) primer. The sequences of forward and reverse primers are given in ® Ultra™ RNA Library Prep Kit for Illumina®  following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer 5X. First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H –). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase. After adenylation of 3′ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure was ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 150~200 bp in length, the library fragments were purified with AMPure XP system . Then 3 µL USER Enzyme  was used with size-selected, adaptor-ligated cDNA at 37 °C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The workflow for library preparation and transcriptome sequencing is shown in The mRNA from 12-d- and 20-day-old samples were enriched using oligo (dT) beads. A total amount of 3 µg RNA per sample was used as input material for the RNA sample preparations. Then, total RNA was extracted and was sent to the company, Novogene Co., Ltd., for Next Generation Sequencing analysis. Sequencing libraries were generated using NEBNextThe clustering of the index-coded samples was performed on a cBot Cluster Generation System using HiSeq PE Cluster Kit cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125-bp/150-bp paired-end reads were generated.Quality control. Raw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing poly- N, and low-quality reads from raw data. At the same time, Q20, Q30, and GC content from the clean data were calculated, as given in Reads mapping to the reference genome. Reference genome (TAIR 10) and gene model annotation files were downloaded from genome website directly. Index of the reference genome was built using Bowtie v2.2.3 and paired-end clean reads were aligned to the reference genome using TopHat v2.0.12. We selected TopHat as the mapping tool, as it can generate a database of splice junctions based on the gene model annotation file and, thus, a better mapping result than other non-splice mapping tools.Quantification of gene expression level. High-throughput sequencing  was used to count the reads’ numbers mapped to each gene. Then the FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of fragments per kilobase of transcript sequence per millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads’ count at the same time and is currently the most commonly used method for estimating gene expression levels . HTSeq sp-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted p-value < 0.05 found by DESeq were assigned as differentially expressed.  Prior to differential gene expression analysis, for each sequenced library, the read counts were adjusted by edgeR program package through one scaling normalized factor. Differential expression analysis of two conditions was performed using the DEGSeq R package (1.20.0). The p values were adjusted using the Benjamini and Hochberg method. Corrected p-value of 0.005 and log2(Fold change) of 1 were set as the threshold for significantly differential expression.Differential expression analysis . Differential expression analysis of two conditions/groups  was performed using the DESeq R package (1.18.0). DESeq provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting SNP analysis. Picard-tools v1.96 and samtools v0.1.18 were used to sort, mark duplicated reads, and reorder the bam alignment results of each sample. Genome Analysis Toolkit, GATK2 (v3.2) software was used to perform SNP calling. The mapping status of reads was provided in BAM files, which were visualized using the Integrative Genomics Viewer (IGV) software.After doing PCR with equal amounts of cDNA (100 ng) in each reaction of 20 μL volume, the PCR products were purified by 1% agarose gels and the bands were cut out and frozen. DNA was purified using the QIAquick Gel Extraction kit, and concentration was measured by Nano-Drop. Sequencing of the purified DNA was performed using the Big Dye Terminator v3.1 Cycle Sequencing Kit  using the forward and reverse primers . The raw2(Fold Change)| > 1 and q-value < 0.005. By contrast, in experiments with biological replicates, DESeq eliminates biological variation; therefore, we set our threshold as adjusted p-value (padj) < 0.05.  data of Arabidopsis for expressed PPR genes using the Bioinformatics & Evolutionary Genomics website. The expressed PPR genes are listed in descending order of expression in We selected the genes of both samples that had a minimum number of reads to be able to infer an editing event. This minimum number should be reasonably high to minimize the impact of sequencing artifacts. For example, the T-to-C change at position 14,198,871 in AT3G41768 was supported by 647(29%) and 240 (19%) in 12-d-old and 20-d-old seedlings, respectively. In addition, there were some variants that were supported by 100% of the reads in both samples (12- and 20-d-old). Therefore, these are several editing events that seem to be polymorphisms. For the same gene, we found many reads in the same U-to-C conversions. Genes with higher read coverage were further examined for the confirmation of U-to-C RNA editing sites. Genes, such as AT2G16586, AT5G02670, AT5G42320, AT4G16380, and AT5G08740, showed 249, 13, 55, 268, and 146 reads at the converted sites, respectively. Genes showing extremely low reads  were also analyzed by RT-PCR. However, very few sites were confirmed as editing events. Because many reads mapped to each U-to-C conversion site, we considered that these nucleotide conversions were caused by RNA editing . The floIn our knowledge, this is the first report of U-to-C RNA editing for nuclear genes confirmed by both RNA-seq and Sanger sequencing in flowering plants. In this study, total RNA extracted from 12-d- and 20-d-old seedlings was examined by high-throughputing RNA-seq.2FC values. These data suggest that DEGs are more likely to be expressed in young Arabidopsis seedlings than in older seedlings. Therefore, more mutations could occur at this stage of development because RNA editing events are more frequent in seedlings than in any other plant tissue.Total RNA isolated from 12-d-old seedlings was examined by NGS, and DEGs were identified based on FPKM values and read counts. The results showed that DEGs were expressed to higher levels in 12-d-old seedlings than in 20-d-old seedlings. This was confirmed by higher FPKM values and read counts and more upregulated genes in 12-d-old seedlings than in 20-d-old seedlings. The ANOVA test was performed for comparing the gene expression levels. The summary for regression analysis of differentially expressed genes among the replicates of 12-d- and 20-d-old seedlings is given in While investigating for RNA editing events to create a global map of high-quality candidates, an appropriate balance between sensitivity (identifying a highly inclusive set of possible edits) and specificity  is required. We considered it better to have a fewer number of candidate RNA editing events that are more likely to be true than to have a larger number with an increased percentage of false positives. We undoubtedly did not score a substantial number of true, low-level, U-to-C RNA editing events in the process. Up to 90% of nucleotide variants that are not SNPs (either in dbSNP or private genomic SNVs) are U-to-C calls; this suggests they are likely to be U-to-C editing candidates. Furthermore, more than 85% of these candidates are located in UTRs. Our candidate U-to-C RNA editing sites had a different variant frequency from known SNPs. They tended to cluster predominantly in the untranslated regions.We investigated single-nucleotide base changes and the percentage of read coverage was calculated . We predWhile RNA editing in introns or UTR regions can affect mRNA stability, translation, or splicing activity because of the modification of its secondary structure, those in coding region can also affect the translated polypeptide sequence ,29,30. IIn Arabidopsis, C-to-U and U-to-C RNA editing have been reported at the translation borders of nuclear transcripts, AT1G29930.1 and AT1G52400.1 . These dArabidopsis RNA-seq data generated by public laboratories, using online software http://signal.salk.edu/atg1001/3.0/gebrowser.php, accessed on 5 January 2021. All seven identified U-to-C RNA editing sites AT2G16586, AT5G42320, AT5G02670, AT3G41768, AT4G32430, AT3G47965, and AT5G52530 were aligned against the publicly available RNA-seq databases (Arabidopsis RNA-seq is shown in cis or trans regulatory elements, isolation of editing enzymes, and validation of editing sites.We further compared the gene expression levels for seven identified U-to-C RNA editing target genes among different tissues . The greatabases  and confArabidopsis thaliana. A comprehensive analysis of RNA-seq data to detect nucleotide base conversions was performed. In this study, we examined U-to-C RNA editing in Arabidopsis seedlings at different developmental stages. Sanger sequencing identified the sites and efficiency of seven U-to-C editing events. Most U-to-C RNA editing here identified occurred in the UTR of mature mRNAs. Thus, we confirmed the presence of U-to-C RNA editing in nuclear genes of plants. We provided the experimental basis to explore the mechanism involved in the amination of U-to-C editing and functions and effects of U-to C RNA editing on mRNA stability, other RNA modifications, and translation.Our findings confirm the uridine-to-cytidine RNA editing sites in some nuclear genes in


Coronavirinae family. Like other coronaviruses, SARS-CoV-2 is enveloped and possesses a positive-sense, single-stranded RNA genome of ~30 kb. Genomic RNA is used as the template for replication and transcription. During these processes, positive-sense genomic RNA (gRNA) and subgenomic RNAs (sgRNAs) are created. Several studies presented the importance of the genomic RNA secondary structure in SARS-CoV-2 replication. However, the structure of sgRNAs has remained largely unsolved so far. In this study, we probed the sgRNA M model of SARS-CoV-2 in vitro. The presented model molecule includes 5′UTR and a coding sequence of gene M. This is the first experimentally informed secondary structure model of sgRNA M, which presents features likely to be important in sgRNA M function. The knowledge of sgRNA M structure provides insights to better understand virus biology and could be used for designing new therapeutics.SARS-CoV-2 belongs to the  Coronaviridae family, which also includes alpha-, gamma- and deltacoronaviruses [Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 19 (COVID-19) and is responsible for widespread infection/death and concomitant disruptions to health services, travel, trade, education and has a negative impact on people’s physical and mental health [aviruses . SARS-Coaviruses . SARS-Coaviruses . The genaviruses . Genomicaviruses ,7,8,9.RNA-dependent RNA polymerase (RdRP) pauses when it crosses a body TRS and switches the template to the leader TRS. As a result, sgRNA possess a leader sequence derived from the 5′ untranslated region of the genome and a TRS 5′ of the open reading frame [In coronaviruses, each sgRNA possesses a short 5′-terminal leader sequence derived from the 5′ end of the genome. Transcription regulatory sequences (TRS) are necessary to add the leader sequence to sgRNAs . TRSs arng frame ,12.RNA secondary structure in untranslated and coding regions play a key role in the viral replication cycle . The secIn this study, we performed probing of the sgRNA M sequence of SARS-CoV-2 in vitro. We used selective 2’-hydroxyl acylation analyzed by a primer extension (SHAPE) method and chemical mapping with dimethyl sulfate (DMS) and 1-cyclohexyl-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate (CMCT) to obtain the secondary structure of sgRNA M. This is the first experimentally informed secondary structure model of sgRNA M, which presents features likely to be important in sgRNA M function. Several chemical reagents combined with a bioinformatics program allowed for the prediction of a high confidence structure. The comprehensive characterization of sgRNA M structure will provide important insights to better understand virus biology and could be used for designing new therapeutics.https://www.european-virus-archive.com, accessed on: 9 July 2020). Next, three PCR reactions using cDNA as template and specific primers . The DNA template was cloned into pUC19 and sequenced using the M13F and M13R primers for confirmation of proper sequence  with Random Primers Mix  on RNA of SARS-CoV-2 from strain Slovakia/SK-BMC5/2020 (received from sequence .Primers for reverse transcription were synthesized by the phosphoramidite approach on a MerMade synthesizer. Primers for reverse transcription were synthesized with fluorophores: 6-carboxyfluorescein (6-FAM) and 5-carboxy-4’,5’-dichloro-2’,7’-dimethoxyfluorescein (5-JOE) on the 5′-end . PrimersTM PCR Micro Kit . The in vitro transcription reaction was performed using a MEGAscript™ T7 Transcription Kit (Thermo Fisher Scientific) according to the manufacturer’s protocol. RNA product was purified using RNeasy MiniElute Cleanup Kit . The integrity and purity of samples were checked on an agarose gel.The DNA template for in vitro transcriptions of sgRNA M was obtained by PCR from a modified puC19 plasmid using primers FM and RM . DNA was2, 50 mM HEPES, pH 7.5. RNA integrity and homogeneity after folding were analyzed by native gel electrophoresis using 0.8% agarose gel running at 4  °C with low voltage. Under these conditions, one band was observed  [The sequence of the in vitro probed sgRNA M underwent covariation analysis via the cm-builder pipeline, and details of this process are available . Brieflyry 2021) ,38.https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 10 December 2021) database and yielded 453 homologous sequences. These sequences were subsequently MAFFT (Multiple Alignment using Fast Fourier Transform) [Additionally, the sgRNA M sequence was queried against the nucleotide BLAST   aligned 2, 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), pH 7.5) to obtain a single RNA conformation, as assessed by non-denaturing agarose gel . A leader sequence is characteristic for the sgRNA of the Coranaviridae family, and “leader to body fusion” takes place during discontinuous transcription [rose gel . Chemicarose gel .DMS strongly modified 123 nucleotides (nt), meaning that 32.6% of all adenosines and cytidines in sgRNA M were structurally accessible. CMCT modified 106 nts, which represents 40.9% of all uridines. The SHAPE reagent NMIA strongly modified 119 nts, and 56 nts had moderate modifications signals, representing 22.2% of all sgRNA M nucleotides see . ChemicaTo assess prediction quality and identify well-defined structural regions, we calculated the secondary structure partition function using RNAstructure 6.2 and, from this, determined the base pair probabilities for model pairs . For theTo predict the secondary structure of sgRNA M based on the experimental probing data, the results of chemical mapping were used to constrain predictions in the RNAstructure 6.2 program. SHAPE data were loaded as pseudoenergy constraints  and DMS and CMCT modifications were included as chemical mapping constraints (highly reactive nts are forbidden to be in Watson–Crick base pairs flanked by Watson–Crick base pairs). The default values for slope and intercept in the RNAstructure 6.2 program were used. The default values of these parameters were determined by optimizing the accurate modelling of the SHAPE data set with sequences of known structures .37 of the entire folded secondary structure is −416 kcal/mol. We showed that most of the inaccessible regions defined by chemical mapping correspond to areas containing base pairs. The 5′end of the sgRNA M model was folded into three hairpins: SL1, SL2 and SL3. These three hairpins also occur in the 5′UTR of SARS-CoV-2 in its 5′ 300 nt fragment [Coronaviridae family [Our model of sgRNA M is highly structured with plenty of accessible bulges and loops . RNA motfragment ,41,42 anfragment ,22,23,43fragment  and in sfragment . Moreovefragment . This infragment . SL1 is e family . SL2 is e family . SL3 is e family ,44.Recently, a prediction of interaction between the SARS-CoV-2 genome and the human proteome indicated that a highly structured region at the 5′ end had a large number of interactions with proteins such as (1) ATP-dependent RNA helicase—DDX1, which was previously reported to be essential for Avian infectious bronchitis coronavirus replication ,47, (2) RNA structure probing coupled with nanopore direct-RNA sequencing were used to map sgRNAs with NAI in living cells, but the structure of sgRNA M was not proposed . NMIA reWe also compared long-range RNA–RNA interactions within the secondary structure of sgRNA M mapped in vitro  with theWe additionally compared our sgRNA M model  with a pin vitro model of the whole SARS-CoV-2 genome [We compared our sgRNA M model with the corresponding region of an 2 genome  to check2 genome . However2 genome .Coronaviridae sequences, yielded no base pairs with statistically significant covariation. Covariation  is often used to support the potential for an RNA motif to be functional as the structure is being preserved even when the primary sequence is not. Importantly though, a lack of covariation does not indicate a lack of potential functionality.The covariation analysis of the sgRNA M model presented here, utilizing the cm-builder pipeline with an alignment against 25,571 An analysis of the conservation of the sgRNA M model base pairs against an alignment of 453 homologous sequences showed an average base pair conservation of 97.57%, with stem SL1  having aThe high degree of conservation of most base pairs in the model may be partially responsible for the lack of detectable covariation. This lack of significant covariation is in line with previous studies ,19,56. DThe SARS-CoV-2 genome constantly evolves, new mutations appear and virus variants are monitored. Changing of RNA sequence must occur in a frame to not be lethal for the virus and to preserve the function of proteins and also RNA structure. Emerging mutations should retain base pairs in RNA structure motifs that are important in the viral cycle. Therefore, we analyzed nucleotide mutations in the sequence of the M gene of SARS-CoV-2 variants. For the first time, the secondary structure of sgRNA M was determined based on the experimental data from several chemical mapping methods and bioinformatic analyses. The secondary structure model contains unique features likely to be important for sgRNA M functions. The structure also includes several of the same motifs as the genomic M fragment in the SARS-CoV-2 genome . Previou


CoCoNet that is based on a combination of a Coevolutionary model and a shallow Convolutional Neural Network. Despite its simplicity and the small number of trained parameters, the method boosts the positive predictive value (PPV) of predicted contacts by about 70% with respect to DCA as tested by cross-validation of about eighty RNA structures. However, the direct inclusion of the CoCoNet contacts in 3D modeling tools does not result in a proportional increase of the 3D RNA structure prediction accuracy. Therefore, we suggest that the field develops, in addition to contact PPV, metrics which estimate the expected impact for 3D structure modeling tools better. CoCoNet is freely available and can be found at https://github.com/KIT-MBS/coconet.Co-evolutionary models such as direct coupling analysis (DCA) in combination with machine learning (ML) techniques based on deep neural networks are able to predict accurate protein contact or distance maps. Such information can be used as constraints in structure prediction and massively increase prediction accuracy. Unfortunately, the same ML methods cannot readily be applied to RNA as they rely on large structural datasets only available for proteins. Here, we demonstrate how the available smaller data for RNA can be used to improve prediction of RNA contact maps. We introduce an algorithm called  Ribonucleic acid (RNA) is one of biomolecular key players in cells and plays significant roles in many biological activities such as the coding, regulation and expressions of genes. For example, non-coding RNA is involved in genetic regulation acting on transcriptional and translational machinery ,2 thus ein-silico tools they can be used alone or in combination with experimental and statistical methods. When direct structure determination is not feasible, indirect measurement might still be possible. To improve the interpretation of such indirect experimental data, this data can be integrated in computational modeling tools. For instance, small angle X-ray scattering (SAXS), and single molecule Förster Resonance Energy Transfer (FRET) data have been fruitfully used in combination with molecular dynamics simulations of proteins  of RNA families can be utilized as spatial constrains in molecular modeling tools ,20–22. STo evaluate the performance of DCA-based methods on RNA contact prediction, we compared different methods on a well curated dataset of RNA structures . In the These approaches, however, are characterized by a huge number of free parameters and require big datasets of 3D structures for their training and thus cannot be easily extended to RNA structure prediction due to the limited number of available experimentally resolved structures. Here, we thus focus not on deep but on shallow Neural Networks. In particular, we construct our approach CoCoNet as combination of the mean-field DCA approach with a shallow Convolutional Neural Network. We will demonstrate the approach’s ability to improve RNA contact prediction, while keeping the number of free parameters to train the network limited to assure the generalization of its performance.Mutations play an essential role in shaping the evolution of all biomolecules. Their large majority have a neutral or, more often, deleterious effect on biomolecular fitness. Only few mutations lead to new functions. Evolutionary pressure acts on the biomolecules to counteract deleterious effects and restore their functional states favoring secondary compensatory mutations. The interactions between these mutations can be traced in the biomolecule’s evolution and be observed in multiple sequence alignments (MSAs) of homologous proteins or RNA. A series of co-evolutionary methods have been developed to capture the sequence variability in MSAs such as the Direct coupling analysis (DCA) . DCA is a1a2a3...aL of length L containing residues or a gap at sites 1, 2, 3, ..., L. The probability P of observing this sequence in a MSA is given by the following expression:Jij are the couplings and hi(ai) are local fields. Finding a solution for equation Let consider a sequence of nucleotide bases σ =  (mfDCA) , which s (mfDCA) . It is a (mfDCA) do not le (mfDCA) ,20,21 wefi(ai) be single-site frequency counts of the MSA for column i when occupied by a nucleotide/gap ai, and fij be the pair-site frequency counts for columns i and j when occupied by nucleotide/gap ai and aj, respectively. These quantities are computed from the MSA asm is weight of sequence m which is defined as the reciprocal of the number of similar sequences for a particular sequence similarity threshold; and Meff is the effective number of sequences which is the sum of sequence weights. The correlation matrix C has elements Cij = fij − fi(ai)fj(aj). The couplings of the model are obtained fromi and j. The nucleic acid pairs are scored using the direct-information that is given byIn mfDCA, the couplings are computed from the inverse of the empirical correlation matrix obtained from the MSA. Let rmations  or compermations .Convolutional neural networks (CNNs) have been extensively used in the last decades in a wide range of applications that range from accurate learning of patterns in images to speech recognition ,37,41. TThe tremendous effort devoted to the improvements of CNN architectures aims to make CNN scalable to larger and increasingly complex systems. Indeed from the simple LeNet architecture introduced in  consistiThe increased level of complexity of these networks is reflected in the number of free parameters to train that range from 60k for LeNet to about 1380k for VGGNet. However, despite the accurate performances of these networks, this huge number of parameters makes the training slow and limits generalization .When the training dataset is very small as for a RNA structure dataset, the deeIn order to improve contact prediction accuracy from RNA multiple sequence alignments, we here design a method called CoCoNet that is based on a combination of DCA with convolutional neural network approaches. CoCoNet is motivated by the simple observation that contact maps of RNA are not random but instead show ordered patterns of contacts. It’s very likely that nucleotide pairs close to other pairs that are in physical contact are also true contacts themselves. CNNs are a systematic method to identify patterns from DCA contact map prediction and filter out noisy and unwanted artifacts. The architecture of our CoCoNet method is schematically depicted in Figure L with its homologous. Note that MSA are trimmed by selecting only positions corresponding to the specific RNA target sequence.The input layer is simply given by the MSA of the target RNA sequence of length L × L is then constructed from these DCA scores assigning to each  pair of the target sequence the corresponding DCA score.The first layer is the coevolutionary layer. In this layer the DCA scores of nucleotide-nucleotide pairs are computed using a mean-field DCA approach. This step is performed using the mean-field algorithm implementation in pydca . A 2D map = (d − 1)/2. Then a d × d filter matrix  is used to perform convolution across the 2D DCA contact map obtained from the previous padded layer. This results in a new 2D contact map of size L in which each entry corresponds to a sort of re-weighted DCA scores.The second layer is the convolutional layer. As a first step we perform a padding operation of size n pairs from the previous layer map having the n highest re-weighted DCA scores and consider them as true contacts while giving a vanishing score for all the others. The choice of the number of contacts n that leads to reliable predictions is discussed in the results section. It depends on the system size as well as on the contact definition.The output layer consist in selecting the Meff greater than, and less than or equal to 70.0. Indeed nucleotide contact prediction performance depend on Meff  that are less than 10 Å apart. For nucleotide pairs fulfilling this condition they are assigned a value of one in the real contact map, zero otherwise.R belonging to the training dataset, we can define a functiond × d i, j), respectively. The delta function i and j are in physical contact in the PDB structure and zero otherwise.Given a target RNA sequence trix eq.  centeredi, j entries corresponding to  and  contacts are considered while the remaining entries of The convolution operation can in principle be done using several filter matrices. To limit the number of free parameters, CoCoNet is designed to use a maximum of two filter matrices. Their total number range from 9, for a single 3 × 3 filter matrix up to 98 for two 7 × 7 filter matrices. When two filter matrices are used, one of them performs convolution with Watson–Crick nucleotide pairs and the other on non-Watson–Crick pairs. More in detail, the modified convolution occurs between the WC-filter and the R represents the summation over all the entries in the training dataset and that of i and j over all nucleotide pairs that are separated at least four nucleobases in the sequence of R. The cost function is minimized using Limited-memory BFGS algorithm using a standard implementation in Python’s Scipy library . Four of them belong to the For the modeling of the 3D structure, we use the tool SimRNA  that is From all generated RNA conformations, we selected the 1% with lowest energy and clustered them. We then selected twenty clusters and computed their averaged RMSD value with respect to the experimental structure.Here, we analyze the structural patterns observed in the coevolutional layer of our network since their understanding provides insight on how CoCoNet is able to identify them and enhance nucleotide–nucleotide contact prediction. In particular, we study these structural features by investigating the average DCA scores in a 7 × 7 window around nucleotide pairs following a similar approach to the one employed in  for protr between the closest heavy atoms (i.e. non-hydrogen) of a nucleotide pairs. At short distance  that is the widely used metric for contact prediction. In addition, we also have evaluated CoCoNet's performance using the Mathews correlation coefficient (MCC)  such that |i − j| > 4  between DCA-based method and CoCoNet, the predictive capacity of CoCoNet is superior to mfDCA if more lower ranks are taking into account. Among the different filter sizes, the 3 × 3 filter matrix performs slightly better than other filter matrices up to ranks of about hundred and slightly worse beyond that limit.Meff > 70 the average PPVs are significantly better than those of families that have lower effective number of sequences (Meff ≤ 70). This trend is consistent for both classes of contacts, i.e., all and tertiary contacts as we can see in Figures The performance of our method depend, as expected, on the effective number of homologous RNA sequences in the corresponding RFAM family of the target RNA. For families with L contacts are displayed for different network characteristics. When all contacts are considered, the performances of mean-field DCA that shows an average PPV of 45% are drastically increased to 74.5% and 77% for single and double filter versions of CoCoNet, respectively. No filter-size dependence is observed here but a slight improvement occurs by using double filter convolution with respect to the single filter ones.We also report the CoCoNet numerical results in Table Tertiary contact prediction capability is also significantly improved by our method  weight each predicted contact in its assumed benefit for 3D modeling ideally in (B) the context of the other predicted contacts. (A) could be realized, for example, by weighting contacts with measures such as contact order from the protein folding field . (B), hoWe can therefore explore multiple directions to further improve our method to better understand the structural properties of RNA molecules. First of all, when more 3D RNA structures will be experimentally available we could exploit more complex neural networks architecture to improve the accuracy of our method. In addition, although CoCoNet is able to enhance RNA tertiary contact prediction, their prediction accuracy remains limited and thus needs to be further improved. This is a challenging issue since as we have seen in previous sections the co-evolutionary signals are dominated by the secondary structures. Also, we believe that with a dedicated analysis, we could find an alternative way to integrate CoCoNet constraints in molecular modeling tools and improve and boost the structural RNA models’ accuracy. Additionally, as discussed in the last paragraph, we would suggest discussion in the community to identify a robust quantitative measure of contact map prediction quality beyond PPV, which also takes into account the information content improvement for 3d structural modeling of RNA.gkab1144_Supplemental_FileClick here for additional data file.


A change from RNA- to DNA-based genetic systems is hypothesized as a major transition in the evolution of early life forms. One of the possible requirements for this transition is a change in the substrate specificity of the replication enzyme. It is largely unknown how such changes would have occurred during early evolutionary history. In this study, we present evidence that an RNA replication enzyme that has evolved in the absence of deoxyribonucleotide triphosphates (dNTPs) relaxes its substrate specificity and incorporates labeled dNTPs. This result implies that ancient replication enzymes, which probably evolved in the absence of dNTPs, could have incorporated dNTPs to synthesize DNA soon after dNTPs became available. The transition from RNA to DNA, therefore, might have been easier than previously thought. One of the major evolutionary events in primordial life would have been the change in the genetic material from RNA to DNA. It is hypothesized that RNA played the role of the genetic molecule in the ancient RNA and RNA–protein worlds, and that it was replaced by DNA in early evolution . To dateOne of the possible prerequisites for this transition is the change in the substrate specificity of an RNAP from ribonucleotide triphosphate (NTP) to deoxyribonucleotide triphosphate (dNTP). Standard RNAPs have strict substrate specificity; RNAPs selectively use NTPs even in the presence of a high concentration of dNTPs in the cell  because Escherichia coli [Evolutionary experiments with replicating molecules have been conducted for decades. The first example is the evolution of the Qβ RNA by Spiegelman’s group, in which the genomic RNA of bacteriophage Qβ was continuously replicated by the RNA-dependent RNA polymerase protein (Qβ replicase) of the bacteriophage in a test tube and through a serial dilution cycle . Mutatiohia coli ,15, by whia coli . In prevIn our previous evolutionary experiments, we first performed 96 rounds of serial dilution cycles while repressing the amplification of parasitic RNAs (parasite-absent condition), i.e., small RNAs that lost the replicase gene and thus replicated relying on the replicase translated from other RNAs . Next, wE. coli at 37 °C for 2.5 h. In this reaction, the RNAPs were translated from the RNA and they in turn replicated the RNA  was incubated in the reconstituted translation system of  the RNA b. RNA co the RNA c. The coWe next established a sensitive detection method for dNTP incorporation as dNTP incorporation by RNAP was expected to be very low. The procedure is illustrated schematically in To understand whether the above-mentioned trend is specific to labeled deoxyribonucleotides or is also observed for labeled ribonucleotides, we next measured the incorporation of 5-ethynyl-2′-uridine 5′-triphosphate (EUTP) into RNA. We used the cell-free translation reaction solution containing 1.1 mM EUTP and 0.11 mM UTP and performed the same experimental procedure. The pattern of the RNA concentrations before the recovery step was similar to the results obtained with EdUTP, but the absolute values were much lower d, probabThere were 13, 18, and 17 nonsynonymous mutations fixed in the Qβ replicases encoded by RNA1, 2, and 3, respectively . These mWe demonstrated that RNAPs that experienced evolution in the absence of dNTPs incorporated noncanonical substrates, namely, ethynyl-labeled dUTP and UTP. One possible explanation for this enhanced incorporation is as follows: in the evolutionary experiment without dNTPs, the RNAPs did not have to distinguish between canonical nucleotides, NTPs, and non-canonical nucleotides, dNTPs. Therefore, mutations that impaired the ability to discriminate between NTPs and dNTPs could have accumulated. Such mutations might distort the structure or electric distribution around the nucleotide binding pocket, one of the selection mechanisms for NTPs . In addiDuring the early stages of life before the advent of DNA, RNAP might have evolved in the absence of dNTPs and thus might not have had strict substrate specificity. Such loose substrate specificity might have been a common feature of ancient enzymes . If the The transition from RNA replication to DNA replication also requires changes in template specificity; RNAP must have used DNA instead of RNA as a template. Thus far, we have not obtained any results showing that mutant RNAPs can use DNA as a template. Understanding changes in template specificity is an important challenge, and we need further evolutionary experiments to elucidate this matter.Plasmids encoding the RNAs used in this study were constructed as described previously ,18. DNA E. coli [Each RNA (10 nM) was incubated at 37 °C for 2.5 h in the reconstituted translation system of E. coli  preparedE. coli , except The mixture (18 µL) after the replication reaction was purified using the RNeasy Mini Kit (QIAGEN) and eluted with 21 µL water. The ethynyl group on the RNA was conjugated with biotin by click reaction using a Click-it Nascent RNA Capture Kit (Invitrogen). The mixture was rotated gently around 23 °C for 30 min and then purified in the same manner as described above. The elution was carried out with 27 µL water. The purified RNA was mixed with 10 µL of streptavidin-coated magnetic beads (Dynabeads MyOne Streptavidin T1) and rotated gently at room temperature for 30 min. The beads were then washed 10 times using a magnet on ice. The beads were suspended in 10 µL of phosphate-buffered saline containing 0.1% sodium dodecyl sulfate solution and heated at 95 °C for 5 min to separate the RNA from the beads. After recovery, the RNA solution was diluted 100-fold with 1 mM EDTA aqueous solution and analyzed by RT-qPCR as described above.