Post-transcriptional modifications have been recently expanded with the addition of RNA editing, which is predominantly mediated by adenosine and cytidine deaminases acting on DNA and RNA. Here, we review the full spectrum of physiological processes in which these modifiers are implicated, among different organisms. Adenosine to inosine (A-to-I) editors, members of the ADAR and ADAT protein families are important regulators of alternative splicing and transcriptional control. On the other hand, cytidine to uridine (C-to-U) editors, members of the AID/APOBEC family, are heavily implicated in innate and adaptive immunity with important roles in antibody diversification and antiviral response. Physiologically, these enzymes are present in the nucleus and/or the cytoplasm, where they modify various RNA molecules, including miRNAs, tRNAs apart from mRNAs, whereas DNA editing is also possible by some of them. The expansion of next generation sequencing technologies provided a wealth of data regarding such modifications. RNA editing has been implicated in various disorders including cancer, and neurological diseases of the brain or the central nervous system. It is also related to cancer heterogeneity and the onset of carcinogenesis. Response to treatment can also be affected by the RNA editing status where drug efficacy is significantly compromised. Studying RNA editing events can pave the way to the identification of new disease biomarkers, and provide a more personalised therapy to various diseases. RNA modifications refer to alterations in the chemical structure of RNA molecules occurring after DNA transcription and synthesis by the RNA polymerase enzyme. They were first described in 1968 with the discovery of RNA methylation in Hela cells .A unique type of RNA modification in trypanosome mitochondrial mRNA was discovered ~ 30 years ago . The proSaccharomyces cerevisiae alanine tRNA . M. M41]. MIn humans, three ADAR enzymes are expressed Fig. b. ADAR1 RNA editing occurs primarily within noncoding regions, and only a small percentage takes place in coding regions resulting in amino acid change. In humans, A-to-I editing mostly occurs in introns and untranslated regions (UTRs) of protein coding genes . A-to-I Ala have been reported [MicroRNAs (miRNAs) are also processed by ADARs (ADAR1 and ADAR2) for A-to-I editing in order to control miRNA biogenesis , 84. In reported . Even threported .APOBEC1 has a preference for AU-rich sequences within mRNA 3′ UTRs . APOBEC3Classical molecular biology and sequencing techniques have contributed significantly in the discovery and early detection of RNA editing. The emergence NGS technologies in combination with accurate bioinformatic pipelines made possible the detection of thousands of new RNA editing sites , 93. Mathttps://github.com/zhqingit/giremi), RNAEditor (http://rnaeditor.uni-frankfurt.de/) and DeepRed (https://github.com/wenjiegroup/DeepRed) from RNA-seq data in the absence of relevant genomic data [High-throughput sequencing methods like RNA-seq have now become readily available in the scientific community, leading to a wealth of accumulating transcriptomic data of various tissues and cell populations . This exmic data , 80, 96.https://omictools.com/the-rna-editing-atlas-tool) is the first human inosinome atlas, comprising > 3.0 million A-to-I events identified in six tissues from three healthy individuals. Matched directional total-RNA-Seq and whole genome sequence datasets were generated and analysed within a dedicated computational framework, also capable of detecting hyper-edited reads. Inosinome profiles within the RNA Editing ATLAS are tissue specific and edited gene sets, consistently show enrichment of genes involved in neurological disorders and cancer. The RNA Editing ATLAS reports that overall, the frequency of editing varies, but is strongly correlated with the expression levels of ADAR [Furthermore, freely available databases are now available, where one can explore RNA editing collections, in humans and other model organisms. The RNA Editing ATLAS ( of ADAR .http://rnaedit.com/) presents a comprehensive collection of A-to-I editing sites in human, mouse, and fly transcripts [Moreover, the RADAR database  is another freely available database collecting > 4.5 millions of A-to-I editing events in 55 body sites of 150 healthy individuals from the GTEx project. In REDIportal, RNA editing sites can be searched by genomic region, gene name and other relevant features as the tissue of origin. Recently, REDIportal started collecting A-to-I events in non-human organisms [REDIportal  provides a comprehensive resource for the functional prediction of RNA editing in long noncoding RNAs (lncRNAs) in Homo sapiens, Macaca mulatta, Mus musculus and Drosophila melanogaster [Furthermore, LNCediting  implicated ADARs and A-to-I editing post-transcriptionally regulates circadian rhythm and sleep [Drosophila led to constitutive release of neurotransmitter in glutamatergic neurons promoting sleep [2+, whilst regulating Ca2+ influx; a prerequisite for normal neuronal function  and class switch recombination (CSR). AID plays an important role in antibody diversity through both SHM and CSR . Two modAPOBECs are heavily implicated in viral immune defense. APOBEC3G is the first family member that was identified as an antiviral factor and the most well studied , 122. InDifferent levels of antiretroviral activity among APOBECs have also been demonstrated against Simian immunodeficiency virus (SIV), adeno-associated virus type 2 (AAV-2), murine leukemia virus (MLV), equine infectious anemia virus (EIAV), and foamy viruses (FVs) , 125–128Apart from viral C-to-U DNA editing, APOBEC3-mediated C-to-U RNA editing is also observed in HIV genomic RNA . APOBECsADARs  target RNA viruses like measle virus, infuenza A virus, Rift Valley fever virus (RVFV), lymphocytic choriomeningitis virus (LCMV) and hepatitis C virus (HCV) by A-to-I hypermutation –137. HowAID/APOBEC proteins have the capacity to restrict LTR and non-LTR retrotransposons , 139. ReThe contribution of RNA editing in the brain and CNS is not always beneficial. Evidence have emerged implicating RNA editing in the pathogenesis of neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), Parkinson and Alzheimer diseases . In ALS,Cancer pathogenesis is primarily attributed to genetic mutations in proto-oncogenes and tumour suppressor genes, which transform a normal cell into a malignant cancer cell. The rapid progress of NGS technologies and the readily available transcriptomic data have revealed a significant contribution of RNA editing in the pathogenesis and progression of cancer Table  153, 15. TranscrAs mentioned above, editing sites fall within noncoding regions of the genome, and this is also true for cancer genomes. MiRNAs are also important editing targets in cancer . In glioEditing in the protein coding region is far less frequent, but it has major consequences in the regulation and function of the affected gene. Sixty recoding events have been identified in a large scale study that are associated with tumours . Other rCytidine deaminase mutations have also been observed in an array of human cancers . APOBEC kataegis has been associated with programmed cell-death receptor-1 (PD-1) overexpression, an immune checkpoint molecule, leading to immune tolerance and exhaustion [Another important role of RNA editing in cancer is its involvement in the ability of tumours to evade immune responses. APOBEC overexpression and haustion . Convershaustion .https://proteomics.cancer.gov/programs/cptac), Peng and colleagues present evidence that the A-to-I RNA editing events in cancer are manifested in protein diversity of cancer cells through changes in amino acid sequences. These intriguing observations suggest that RNA editing is a novel source of cancer protein heterogeneity.In cancers, RNA editing not only changes the sequence of RNAs and their expression but also contributes to proteomic diversity , 192. CoIncreasing evidence proclaims that the levels of RNA editing, along with the expression of adenosine and cytidine deaminases and specifically edited genes , could all be used as important prognostic biomarkers in the pathogenesis and progression of cancer , 171. DeOverall, the modulation and application of RNA editing is an area of great potential. Besides traditional deaminase inhibitors to control expression, molecular tools such as antisense oligonucleotides are potent and selective inhibitors of RNA editing on targeted RNAs . EngineeIn this review, we have discussed the diverse aspects of RNA editing, from its discovery to physiological function and involvement in human diseases. The road ahead still seems bright for RNA editing with new exciting findings from modern transcriptomics and new therapeutic developments in associated disorders.


Trypanosoma brucei. In this mSphere of Influence article, he reflects on how the paper “Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS)” by Queiroz et al.  influenced his research by providing a tool to capture RNA-protein complexes on a global scale using acid guanidinium thiocyanate-phenol-chloroform (AGPC), an old method hitherto applied for RNA, DNA, or protein purification.Calvin Tiengwe works on posttranscriptional gene regulation and iron homeostasis in the parasitic protozoan  Trypanosoma brucei. In this mSphere of Influence article, he reflects on how the paper “Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS)” by Queiroz et al.  influenced his research by providing a tool to capture RNA-protein complexes on a global scale using acid guanidinium thiocyanate-phenol-chloroform (AGPC), an old method hitherto applied for RNA, DNA, or protein purification.Calvin Tiengwe works on posttranscriptional gene regulation and iron homeostasis in the parasitic protozoan  It became apparent that current methods rely on derivatives of cross-linking and pulldown experiments where live cells are first irradiated with UV at a defined wavelength (UV cross-linking) and lysed, and then associated RNA-protein complexes are captured by either oligo(dT)-coupled beads or (co)immunoprecipitations (IP). These methodologies have been widely applied to a variety of organisms but sparingly so for Trypanosoma brucei, a relatively well-studied and well-established experimentally tractable system. After many unsuccessful attempts to replicate these methods in T. brucei, a colleague of mine  brought to my attention the article by Queiroz et al. published in Nature Biotechnology  allowed the authors to identify entirely novel RNA-binding proteins (RBPs) not captured by other methods. The new method is efficient, easily reproducible, and less expensive, and its broad application in any organism is particularly attractive for my research in that it could be adopted to identify novel posttranscriptional regulatory factors of iron deficiency in T. brucei. This is potentially interesting for trypanosomes because control of gene expression is posttranscriptional, relying almost exclusively on the temporal dynamics, activities, and functions of RBPs.Acid guanidinium thiocyanate-phenol-chloroform (AGPC) is a widely used reagent for isolation of RNA (or in some cases DNA and proteins) from cells. It relies on the ability of macromolecules  to partition between distinct liquid phases in a single-step reaction. Once added to cells, upon centrifugation, the mixture separates into a lower organic layer, an interphase, and an upper aqueous phase containing RNA. Despite its wide application as a routine laboratory technique for separating these macromolecules, the interface has always been excluded in RNA preparations. In this paper, Queiroz et al. reasoned that prior to lysis with AGPC, irradiating cells with UV at 254 nm will result in accumulation of RNA-protein complexes at the interface. Several rounds of AGPC separation significantly enrich exclusively for RNA-protein adducts that can be methanol precipitated and further applied to a variety of downstream applications such as next-generation sequencing or proteomics. In this work, a well-established classical method was thus repurposed and optimized for capturing and identifying the complete RNA-bound proteome of three human cell lines and the bacterium T. brucei, we had initially identified an RBP and hypothesized that it was a key posttranscriptional regulator of iron uptake genes. Functional characterization of this RBP showed that its role in iron regulation may be much more complex than we initially thought. Since our major goal is to identify regulatory RNAs or RBPs that respond to an iron stress signal, we had to go back to the drawing board to find an unbiased approach. OOPS is an important step forward in the way we think about studying RNA-protein interactions in T. brucei. Not only can we now capture the total RNA-interactome from this system , but overexpressing, knocking out, or silencing specific RNA-binding proteins could be potentially useful for dissecting subcomplexes within a specific pathway of interest. Using chemical agents to perturb the system, it is now possible to study in a dynamic fashion how regulatory RNAs, mRNAs, and their associated interactors respond to these agents or stimuli.As it is often the case when one first attempts to dissect complex processes in any organism, there are bound to be setbacks, especially when trying to develop new or reproduce existing methodologies. In the search for iron regulatory proteins in It is worth mentioning that since coming across this paper, alternative variants of this method have been published –5. It tu–


Circular RNAs (circRNAs) are recently discovered as a special novel type of endogenous noncoding RNAs (ncRNAs), which form a covalently closed continuous loop and are highly represented in the eukaryotic transcriptome. Recent research revealed that circRNAs can function as microRNA (miRNA) sponges, regulators of splicing and transcription, as well as interact with RNA-binding proteins (RBPs). In this review, not only the function and mechanism, but also the experimental methods of circRNA are summarized. The summary of the current state of circRNA will help us in the discovery of novel biomarkers, the therapeutic targets and their potential significance in diagnosis and treatment of diseases. CircRNAs might play important roles in cancers especially in hepatocellular carcinoma, gastric carcinoma and colorectal cancer as well as serving as diagnostic or predictive biomarkers of some diseases and providing new treatments of diseases. Unlike linear RNAs that are terminated with 5′ caps and 3′ tails, circRNAs form covalently closed loop structures with neither 5′-3′ polarities nor polyadenylated tails, which makes them much more stable than linear RNA and insusceptible to degradation by RNA exonuclease or RNase R The competitive endogenous RNAs (ceRNAs) can compete for miRNAs binding with their MREs Some research revealed that the knockdown of ciRNA derived from intron of ANKRD52 (Ci-ankrd52) leads to reduced expression of their parent genes by combinding RNA Pol II, and lines of evidence suggest one possible function for circRNAs as positive regulators of RNA Pol II transcription Multiple evidence demonstrated noncoding RNA controls gene expression both at the transcriptional and post-transcriptional level through physical interaction with RBPs or other noncoding RNAs As a member of ncRNAs, few circRNAs can be translated. Evidences are presented based on electron microscopy and electrophoretic behaviour that hepatitis delta virus (HDV) contains a single stranded circular RNA molecule The circRNAs chip  containing 5396 probes specific for human circular RNAs splicing sites can be used to investigate different expressions of circRNAs between tumor tissues and normal tissues. After hybridization and washing with samples, samples (tumor tissues and matched non-tumor tissues) were analyzed on the circRNAs chips. These chosen circRNAs are screened for further analysisAfter being extracted from each sample using a homogenizer and TRIzol regent, total RNA is digested with RNase R to remove linear RNA and enrich circRNA. Subsequently, the enriched circRNA is amplified and transcribed into fluorescent cRNA (complementary RNA) utilizing a random priming method. After hybridizing, hybridized arrays are supposed to be scanned. When comparing two groups of profile differences, the fold change (i.e. the ratio of the group averages) between the groups for each circRNA is computed cDNA is synthesized with the Reverse Transcription System using random primers. qRT-PCR analysis is carried out to detect circRNAs expressions FISH can be used to investigate the expression and intracellular location of circRNAs in tissue and cell lines Cell proliferation rates are detected with CCK-8 GO analysis (geneontology.org) can be used to construct meaningful annotation of gene products. GO contains three domains, including biological process (BP), cellular components (CC) and molecular function (MF) KEGG is utilized to harvest pathway clusters covering the knowledge of the molecular interaction and reaction networks in genes producing differentially expressed circRNA http://genome.ucsc.edu) and associated tools continue to grow, providing a comprehensive resource of genome assemblies and annotations to scientists and students worldwide Launched in 2001 to showcase the draft human genome assembly, the UCSC Genome Browser database (http://circnet.mbc.nctu.edu.tw/) provides the following resources: (i) novel circRNAs, (ii) integrated miRNA-target networks, (iii) expression profiles of circRNA isoforms, (iv) genomic annotations of circRNA isoforms (e.g. 282 948 exon positions), and (v) sequences of circRNA isoforms CircNet database (CIRI is able to unbiasedly and accurately detect circRNAs from transcriptome data by employing multiple filtration strategies. By applying CIRI to encode RNA-seq data, the prevalence of intronic/intergenic circRNAs as well as fragments specific to them in the human transcriptome can be identified and experimentally validated The circular RNA database circBank was officially launched on July 7th, 2018. A total of 140,790 human circRNA records are recorded in the circBank database, which also develops a dedicated ID number, based on the name of its' Host gene and the corresponding location.Growing evidence indicates that circRNA expression alterations have a broad impact in biological characteristics of HCC. CircRNAs act as oncogenes or tumor suppressors in HCC. Furthermore, circRNAs interfere with hepatitis virus infection Present study finds that different expressions of circRNAs and the corresponding miRNAs interact through circRNA binding sites to regulate the expression of target genes Bachmayr-Heyda A et al. were the first to report a global reduction of circular RNA abundance in CRC cell lines and tumor tissues compared to normal tissues, and they discovered a negative correlation of global circular RNA abundance and proliferation During the last decade, studies have convincingly documented that ncRNAs participate in regulating of cellular structure, function, and physiological development, and they may contribute to the pathogenesis and development of cancer. Among them, circRNAs are considered a new star in the field of ncRNAs research and extensively investigated. Considering the stability and cytoplasmic localization circRNAs, engineered circRNAs could be exploited for a range of molecular tools or therapies We also discuss the experimental methods of circRNAs for the further research. A better understanding of circRNAs in diseases may contribute to the development of novel detection methods, resulting more reliable diagnosis and treatment in cancer.


However, this method typically requires 300 μg of total RNA, which limits its application to patient tumors. In this study, we present a refined m6A MeRIP-seq protocol and analysis pipeline that can be applied to profile low-input RNA samples from patient tumors. We optimized the key parameters of m6A MeRIP-seq, including the starting amount of RNA, RNA fragmentation, antibody selection, MeRIP washing/elution conditions, methods for RNA library construction, and the bioinformatics analysis pipeline. With the optimized immunoprecipitation (IP) conditions and a postamplification rRNA depletion strategy, we were able to profile the m6A epitranscriptome using 500 ng of total RNA. We identified approximately 12,000 m6A peaks with a high signal-to-noise (S/N) ratio from 2 lung adenocarcinoma (ADC) patient tumors. Through integrative analysis of the transcriptome, m6A epitranscriptome, and proteome data in the same patient tumors, we identified dynamics at the m6A level that account for the discordance between mRNA and protein levels in these tumors. The refined m6A MeRIP-seq method is suitable for m6A epitranscriptome profiling in a limited amount of patient tumors, setting the ground for unraveling the dynamics of the m6A epitranscriptome and the underlying mechanisms in clinical settings.N6-Methyladenosine (m 6A) is one of the most abundant and conserved mRNA modifications. It has been reported to influence multiple steps of RNA life cycle and play an important role in the initiation and progression of human cancers. m6A RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq) is a powerful technique to map the m6A location transcriptome-wide. However, this method typically requires 300 μg of total RNA, which limits its application to patient tumors. In this study, we presented an optimized MeRIP-seq protocol that allows us to profile m6A epitranscriptiome using as low as 500 ng of total RNA. By applying our refined protocol to 2 lung cancer patient tumors and integrating with proteomic data, we identified dynamics at the m6A level that account for the discordance between mRNA and protein levels in these tumors.N6-Methyladenosine (m A secon Input]) . Next, w49 cells . With 1 100-fold . Consistnd of IP , althoug6A profiling, 2 analysis approaches have been applied for m6A peak calling. The first one employed and tuned the most popular peak calling software, MACS  > 1) and protein (Tumorlight/Stable Isotope Labeling by Amino acids in Cell culture [SILAC]heavy > 0.5) levels, and 18 of these 45 genes have discordant fold changes between the 2 tumors at RNA and protein levels (SLC2A1), while the mRNA levels were similar between the 2 tumors, the protein level was 1.9-fold higher in tumor1 compared with tumor2  in the A549 cell line. The knockdown efficiency was confirmed by both RT-qPCR and western blot  has been reported to be able to drive Epidermal Growth Factor Receptor (EGFR) protein translation ) > 1  > 1 , and thoS1 Fig(A) Different RNA samples were chemically fragmented for the specified time points, ethanol-precipitated, and separated on the High Sensitivity RNA ScreenTape. After 6 minutes, RNA fragments centered around approximately 200 nt.  Representative electropherogram of fragmented RNA using 5 μg total RNA from A549 cells after 6- and 7-minute incubation.(TIFF)Click here for additional data file.S2 Fig6A motifs detected from top 5,000 m6A summit centered around 200 nt regions. (D) The location and frequency of the top motif to the summit; ***p < 1 × 10−4. (E) The distribution characteristic of the peaks detected by MACS based on 2 published m6A datasets. (F) Motif discovered by MACS based on 2 published m6A datasets. Data related to this figure can be found in 6A, N6-methyladenosine; MeRIP-seq, m6A RNA immunoprecipitation followed by high-throughput sequencing.(A) Peaks detected by MACS and MeTPeak with and without the duplication reads. (B) Changes in peak number with increasing depth of uniquely mapped reads for IP/Input. The dashed line means the overlapped peaks called by both MeTpeak and MACS. (C) Consistent m(TIFF)Click here for additional data file.S3 Fig6A peaks from different libraries. Right: the motif called based on Millipore or Synaptic 32 μg unique peaks. (B) Analysis of the percentage of m6A peaks in each of the 5 nonoverlapping transcript segments. (C) Relative enrichment of m6A peaks around the TSS and stop codon region. (D) Sequence logo representing the deduced top motifs for 12 μg and 6 μg libraries. (E) Density curves of the motif flanking the peak summit.  Comparison between the Millipore 2 μg and the control dataset. Data related to this figure can be found in 6A RNA immunoprecipitation followed by high-throughput sequencing; TSS, transcription start site.(A) Comparison between the control dataset (300 μg) and the Millipore or Synaptic 32 μg library data. Left: Venn diagrams show the overlap between m(TIFF)Click here for additional data file.S4 FigSETD7/GAPDH with different starting amount of total RNA. (B) Correlation between 2 replicates for both Input (top) and IP (bottom) of the 0.5 μg MeRIP-seq data. (C) The S/N ratio of SETD7/GAPDH (top) and the SETD7 IP yield (percentage of the input) (bottom) using different amounts of Millipore antibody  with fixed amount of total RNA (2 μg). Data related to this figure can be found in 6A RNA immunoprecipitation followed by high-throughput sequencing; S/N, signal-to-noise.(A) The S/N ratio of (TIFF)Click here for additional data file.S5 FigE. coli K-12 total RNA before and after DNase treatment on the High Sensitivity RNA ScreenTape. (B) Representative electropherogram of E. coli K-12 total RNA before and after purification and DNase treatment.(A) The gel image separation profile of (TIFF)Click here for additional data file.S6 Fig6A peaks before normalization (bottom). (C) The correlation between IP fold change and Input fold change (top). MA plots for different m6A peaks after normalization were shown in the bottom. (D) Number of differential peaks distributed along the log-transformed fold change. Data related to this figure can be found in (A) Venn diagram of peaks detected in 2 ADC tumors (tumor1 and tumor2). (B) The correlation between IP fold change and Input fold change before the ERCC spike-in normalization (top). MA plots for different m(TIFF)Click here for additional data file.S7 FigSETD7/GAPDH. Bottom: SETD7 IP yield (percentage of the input). (B) MeRIP-seq library of different amplification cycles using SMARTer Stranded Total RNA-Seq Kit version 2  kit. One out of 50 μl of PCR product was used for gel electrophoresis by DNA tape station. The smear centered at 300 bp is the library DNA. IP, immunoprecipitation; MeRIP-seq, m6A RNA immunoprecipitation followed by high-throughput sequencing; S/N, signal-to-noise.(A) Comparison of low-salt, high-salt, and low/high salt combination washing conditions. Top: pulldown efficiency as measured by S/N ratio of (TIFF)Click here for additional data file.S1 Table6A RNA immunoprecipitation followed by high-throughput sequencing.MeRIP-seq, m(XLSX)Click here for additional data file.S2 Table6A RNA immunoprecipitation followed by high-throughput sequencing.MeRIP-seq, m(XLSX)Click here for additional data file.S3 Table6A, N6-Methyladenosine.lincRNA, long intergenic noncoding RNA; m(XLSX)Click here for additional data file.S4 Table6A, N6-Methyladenosine; MeRIP-seq, m6A RNA immunoprecipitation followed by high-throughput sequencing.m(XLSX)Click here for additional data file.S5 Table6A RNA immunoprecipitation followed by high-throughput sequencing.MeRIP-seq, m(XLSX)Click here for additional data file.S1 Text6A RNA immunoprecipitation followed by high-throughput sequencing.MeRIP-seq, m(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


We report cellular RNA editing by adenine base editors and new ABE variants with reduced RNA editing activity. Adenine base editors (ABEs) enable precise and efficient conversion of target A•T base pairs to G•C base pairs in genomic DNA with a minimum of by-products. While ABEs have been reported to exhibit minimal off-target DNA editing, off-target editing of cellular RNA by ABEs has not been examined in depth. Here, we demonstrate that a current ABE generates low but detectable levels of widespread adenosine-to-inosine editing in cellular RNAs. Using structure-guided principles to design mutations in both deaminase domains, we developed new ABE variants that retain their ability to edit DNA efficiently but show greatly reduced RNA editing activity, as well as lower off-target DNA editing activity and reduced indel by-product formation, in three mammalian cell lines. By decoupling DNA and RNA editing activities, these ABE variants increase the precision of adenine base editing by minimizing both RNA and DNA off-target editing activity. Base editors enable the precise installation of targeted point mutations in genomic DNA without creating double-stranded DNA breaks (Off-target base editing can arise from guide RNA–dependent or guide RNA–independent editing events (Escherichia coli TadA (tRNA-specific adenosine deaminase) monomer that plays a structural role during base editing, a laboratory-evolved E. coli TadA monomer (TadA*) that catalyzes deoxyadenosine deamination, and a Cas9(D10A) nickase (E. coli TadA natively acts as a homodimer to deaminate an adenosine located in a transfer RNA (tRNA) anticodon loop . In this study, we measured, with high sensitivity, A-to-I editing that can be attributed to overexpression of ABEmax, the most efficient ABE variant reported to date (CTNNB1), calnexin (IP90), and ribosomal L1 domain containing 1 (RSL1D1). We chose CTNNB1 and IP90 as two examples of abundant mRNAs in HEK293T cells, and we studied RSL1D1 because it contains a region highly homologous to the 20-nt region of E. coli tRNAArg2 that is the native substrate of TadA  generation from polyadenylated cellular mRNA, we performed high-throughput sequencing (HTS) on 220- to 250-nt (nucleotide) regions of three mRNA amplicons: beta catenin-1  nor ABEmax edit RNA  or DNA , with onubstrate . Togetheubstrate .LDLR)–targeting sgRNA. Targeting the base editors to an expressed gene mimics their typical use , ABEmax(TadA* E59A), and ABEmax. We transfected cells with plasmids expressing the base editor and a low-density lipoprotein receptor  resulted in 53,917 A-to-I edits, similar to the 53,334 A-to-I edits detected in the Cas9 nickase–only control. ABEmax(TadA E59A) resulted in 12,142 more A-to-I edits than the Cas9 nickase–only control, 19% fewer additional A-to-I edits than the 14,959 mediated by ABEmax . The aveE. coli TadA homodimer bound to RNA, we used the crystal structure of Staphylococcus aureus TadA, which has high sequence homology to E. coli TadA  could further improve the DNA versus RNA editing specificity of ABEmax(TadA E59A). Argdenosine . We replr Asn108  or Val10108  mutants and measured the resulting on-target DNA A•T-to-G•C base editing at the seven genomic loci tested . We used3T cells  before t47 in ABEmax(TadA E59A) with Gln, Met, Phe, or Trp maintained relatively high DNA base editing efficiency, particularly at sites where the target A is at protospacer position 5 [counting the protospacer-adjacent motif (PAM) as positions 21 to 23]. Average editing efficiencies were reduced from a mean of 47 ± 3.9% for ABEmax(TadA E59A) to a range of 31 to 41% for the four TadA* Arg47 variants. Among the four Arg47 mutants tested, ABEmax and ABEmax, the most efficient variants for DNA base editing, showed little or no reduction in RNA editing activity compared to ABEmax(TadA E59A)  and ABEmax, resulted in up to a twofold reduction in the number of edited adenosines in the interrogated transcripts  . The twonscripts . Unfortunscripts . These d108 in ABEmax(TadA E59A) generally preserved DNA base editing at sites in which the target A was at protospacer position 5 but greatly reduced DNA editing at other target sites. The most active Asn108 mutant, ABEmax, mediated an average of 25 ± 0.2% on-target DNA editing (108 mutants assayed (108 in ABEmax(TadA E59A) to Phe, Trp, Gln, or Met greatly reduced RNA editing compared to ABEmax in the three transcripts sequenced at depth to levels statistically indistinguishable from background RNA editing observed in the Cas9(D10A)-only controls  and is also essential for RNA editing. The ABEmax or ABEmax variants may be useful when the target A is at protospacer position 5 and minimizing RNA editing is critical.Mutation of TadA* Asn editing , a 1.9-f assayed . Mutatio106 in ABEmax(TadA E59A) resulted in variants that exhibited much lower RNA editing while maintaining DNA editing levels similar to those of ABEmax(TadA E59A) and ABEmax. All four Val106 mutants mediated effective DNA base editing across the seven genomic loci tested; the most efficient DNA base editor among these mutants was ABEmax, hereafter referred to as ABEmaxAW, which yielded an average of 36 ± 1.4% A•T-to-G•C DNA editing [compared to 41 ± 5.4% for ABEmax(TadA E59A) and 47 ± 3.9% for ABEmax]. ABEmaxAW exhibited both the highest level of DNA base editing and the lowest level of RNA off-target editing among the Val106 mutants tested , ABEmaxAW, and ABEmax in HeLa cells (fig. S2) and ABEmax and ABEmaxAW in U2OS and K562 cells (fig. S3). DNA base editing efficiencies among unsorted HeLa and U2OS cells were uniformly lower than in HEK293T cells (fig. S2A), possibly due to poorer transfection or nucleofection efficiencies . Unexpectedly, average RNA editing was reduced compared to the 48-hour treatment; ABEmax yielded an average frequency of 0.29 ± 0.063% A-to-I editing across the 182 adenosines sequenced (compared with 0.49 ± 0.13% at 48 hours). The average frequency of A-to-I mutation with ABEmaxAW after 5 days  remained close to the background frequency associated with Cas9(D10A) nickase alone of 0.051 ± 0.010% . We speculate that the steady loss (or silencing) of transfected plasmids expressing base editors, coupled with the constant degradation and replenishment of the transcriptome, may result in lower RNA editing rates at longer time points.These TadA* mutations might further weaken the ability of ABEmax variants to bind off-target DNA sequences that are already more weakly bound by Cas9. To test this possibility, we measured the levels of off-target DNA editing by ABEmax and a subset of the ABEmax variants described above. We used HTS to assess the frequencies of off-target A·T-to-G·C base editing and indel formation at 12 known off-target sites associated with HEK site 2, HEK site 3, and HEK site 4 (figs. S5 to S7) . The reason for this reduced indel frequency is unclear, but we hypothesize that indel formation may be dependent on the structure or activity of the wild-type TadA monomer. Consistent with this hypothesis, ABEmax(TadA E59A) also shows reduced average indel formation (1.1 ± 0.24%), and ABEmax(TadA* E59A), which cannot perform DNA base editing, induces indels at an elevated frequency of 4.3 ± 0.45% (fig. S1).To further illuminate the impact of V106W in TadA* on the DNA and RNA editing activities of ABE, we generated and tested two additional ABEmax mutants: ABEmax (ABEmaxEW) and ABEmax (ABEmaxQW) (fig. S8). ABEmaxEW displayed slightly higher DNA on-target editing frequencies than ABEmaxAW (fig. S8A) but also greater indel (fig. S8B) and RNA editing frequencies , confirming that mutation of both the wild-type and the evolved TadA monomers is required for the most effective reduction in RNA editing and indel frequencies. ABEmaxQW performed as well as or slightly better than ABEmaxAW at on-target DNA base editing (fig. S8A) and displayed similarly low levels of off-target RNA editing . Consistent with our observations that the wild-type TadA monomer plays a role in indel formation (fig. S1), both ABEmaxEW and ABEmaxQW displayed substantially higher indel frequencies than ABEmaxAW (fig. S8B). These comparisons together indicate that both inactivation of the wild-type TadA and mutation of the evolved monomer with V106W are required to minimize off-target RNA editing, and ABEmaxQW may display higher on-target base editing efficiency at some sites than ABEmaxAW but without the consistently lower indel frequencies of ABEmaxAW.Last, we performed RNA-seq to identify transcriptome-wide A-to-I editing frequencies associated with ABEmaxAW. We confirmed robust on-target DNA editing activity in the RNA-seq samples treated with ABEmax, ABEmax(E59A), and ABEmaxAW by observing substantial U-to-C mutation in the LDLR mRNA, which resulted from base editing the corresponding genomic DNA site directed by the LDLR-targeting sgRNA . While tTo determine the potential biological significance of the A-to-I edits observed with ABEmax and ABEmaxAW, we used the Ensembl Variant Effect Predictor to determine where the edits were located within mRNA transcripts in our transcriptome-wide sequencing data . The RNAIn this study, we identified widespread, low-level cellular RNA editing from ABEs that was greatly reduced, without substantially sacrificing on-target DNA editing, by introducing the E59A or E59Q mutation into TadA and the V106W mutation in TadA*. In addition to decoupling DNA and RNA editing activities, the ABEmaxAW variant substantially reduced off-target DNA editing activity and the formation of indel by-products. Although we note that even ABEmax-mediated RNA editing is both low level  and transient given the short half-life of most cellular RNAs (E. coli (Thermo Fisher Scientific) cells were used for plasmid construction.All mammalian cell expression plasmids were constructed by USER cloning from gBlock gene fragments (Integrated DNA Technologies), as previously described ] and HeLa cells  were cultured in Dulbecco’s modified Eagle’s medium plus GlutaMax (Thermo Fisher Scientific) supplemented with 10% (v/v) fetal bovine serum (FBS). K562 cells  were cultured in RPMI 1640 medium plus GlutaMax (Thermo Fisher Scientific) supplemented with 10% (v/v) FBS. U2OS cells  were cultured in MyCoy’s 5A medium plus GlutaMax (Thermo Fisher Scientific) supplemented with 10% (v/v) FBS.All cells were cultured and maintained at 37°C with 5% COg for 5 min at 4°C) and purified using ZymoPURE II Plasmid Midiprep Kits (Zymo Research), according to the manufacturer’s instructions with the inclusion of the optional step of passing the plasmid across the EndoZero Spin Column (Zymo Research). Plasmid yield was quantified using a NanoDrop and by electrophoresis on a 1% agarose tris/borate/EDTA gel supplemented with ethidium bromide.To obtain endotoxin-free plasmids for transfection, 45 ml of Mach1 cells (Thermo Fisher Scientific) expressing freshly transformed plasmid was pelleted by centrifugation (6000d-lysine coated plates (Corning) 18 to 20 hours before lipofection. Lipofection was performed at a cell density of 65%. Unless otherwise stated, cells were transfected with 462 ng of nuclease or base editor expression plasmid DNA, 138 ng of sgRNA expression plasmid DNA, and 100 ng of TadA dimer expression plasmid if this was included for “in trans” analysis of RNA editing. Lipofectamine 2000  was used per well. Cells were harvested 48 hours or 5 days, as indicated, after transfection.HEK293T cells were seeded on 48-well poly-HeLa cells were seeded in 250 μl of medium on 48-well collagen-coated plates (Corning) at a density of 70,000 to 80,000 cells/ml 20 to 24 hours before lipofection so cells were approximately 85% confluent at the time of transfection. A total of 200 ng of plasmid was used per well, consisting of a mixture of 154 ng of base editor or Cas9 nickase plasmid and 46 ng of sgRNA expression vector plasmid. 1 μl of HeLaFect (OZ Biosciences) was used per well according to the manufacturer’s instructions. DNA extraction was performed exactly as described above for HEK293T cells.Medium was removed, and cells were washed once with 1× Dulbecco’s phosphate-buffered saline (Thermo Fisher Scientific). Genomic DNA extraction was performed by addition of 100 μl freshly prepared lysis buffer  directly into the 48-well culture well. The extraction solution was incubated at 37°C for 60 min and then 80°C for 20 min.5 cells per sample (program FF-120), according to the manufacturer’s protocol. U2OS cells were nucleofected using the SE Cell Line 4D-Nucleofector X Kit (Lonza) with 3 × 105 to 4 × 105 cells per sample (program DN-100), according to the manufacturer’s protocol. RNA and DNA were isolated 48 hours after nucleofection. U2OS cells were trypsinized and resuspended in phosphate-buffered saline (PBS), and K562 cells were directly resuspended in PBS before being spun down by centrifugation (800g for 2 min) to isolate cell pellets. Cell pellets were resuspended in PBS (20 μl), and 3 μl was placed in 50 μl of DNA lysis buffer , which was incubated on a heat block at 37°C for 60 min and then 80°C for 20 min. The remaining 17 μl of cells suspended in PBS was pelleted again by centrifugation (800g for 2 min), and RNA extraction was begun on these pellets with the addition of RLT Plus Lysis Buffer (QIAGEN) to the cell pellet. RNA isolation proceeded with the RNeasy Plus Mini Kit (QIAGEN), as described below.We combined 560 ng of Cas9(D10A) or base editor expression plasmid with 240 ng of sgRNA-expression plasmid in a volume that did not exceed 1.5 μl. Detailed plasmid maps for plasmids ABEmax and ABEmaxAW are in fig. S10. This combined plasmid mixture was nucleofected in a final volume of 20 μl per sample in a 16-well Nucleocuvette strip (Lonza). K562 cells were nucleofected using the SF Cell Line 4D-Nucleofector X Kit (Lonza) with 5 × 10Cells were transfected with the indicated construct, and unless otherwise stated, an sgRNA for the LDLR target site. In the case of HEK293T cells, at the same time, as genomic DNA was harvested from one set of wells that had been transfected with editor in combination with LDLR sgRNA, a second set of wells that had undergone identical treatment were lysed for RNA harvest. RNA isolation was performed with the RNeasy Plus Mini Kit (QIAGEN) according to the manufacturer’s instructions. In short, RNA isolation began with removal of the culture medium and washing of the cells with 1× DBPS (Thermo Fisher Scientific). We added 350 µl of RLT Plus Buffer (QIAGEN) to each well; cells were homogenized by pipetting and transferred into a DNA eliminator column, and the subsequent binding and washing steps for RNA isolation using the RNeasy columns were performed as recommended by the manufacturer. Upon elution of RNA from the RNeasy column with 45 μl of RNase (ribonuclease) free water (QIAGEN), 2 μl of RNaseOUT inhibitor (Thermo Fisher Scientific) was added to prevent RNA degradation, and RNA was stored at −80°C.cDNA generation was performed with SuperScript IV (Thermo Fisher Scientific) according to the manufacturer’s instructions. A poly-T primer was used to selectively amplify mRNAs in the cDNA synthesis step. The optional step of RNase degradation before amplification of cDNAs was included to improve the efficiency of PCR. We note that this step was particularly important for RSL1D1 PCR.A two-step PCR protocol was performed as previously reported  was used for all experiments to avoid overamplification of the cDNA. RSL1D1 required more PCR cycles (34) than IP90 and CTNNB1 (32 each) using the program: 98°C for 1 min and 30 s, then cycles of , followed by a final extension of 2 min at 72°C. No reverse transcriptase controls and no input controls were also processed by qPCR and carried forward onto the MiSeq for each experiment. In no instances did either control exceed 2.5% of the number of aligned reads for the particular experiment when compared to the corresponding RNA samples.For assessing the number of adenosines within an amplicon that showed greater than 0.1% editing, the percentage of G for each adenosine position was measured and counted in Microsoft Excel using the formula = COUNTIF, where C85:HS85 represents the range of cells containing the frequency of bases called as a guanosine when the interrogated nucleoside is an adeosnine .Batch analysis with CRISPResso2 .Prism (GraphPad) was used to generate dot plots and bar plots of these data. For instances in the text where means have been calculated across multiple genomic or transcriptomic loci, the SDs reported represent the SD of the mean for all biological replicates.25 Dynabeads (Thermo Fisher Scientific) to enrich for polyadenylated transcripts. Stranded RNA-seq libraries were generated from these samples using the PrepX mRNA 48 Kit (Takara Bio) on the Apollo 324, followed by barcoding and amplification (12 cycles). Following PCR and bead cleanup with AMPure XP beads (Beckman Coulter), libraries were visualized on a 2200 TapeStation (Agilent Technologies) and quantified using the Library Quantification Kit (KAPA Biosystems) for multiplexing. Libraries were sequenced on a NextSeq high-throughput flow cell (Illumina) as 150-bp paired-end reads.Total RNA was applied to Oligo-dThttps://ccb.jhu.edu/software/hisat2/index.shtml. The resulting sam files were sorted and indexed using the SAMtools software package. Sorted bam alignments from three biological replicates were combined using SAMtools to increase coverage and provide high-quality variant calls. Combined bams were randomly down-sampled to 120 million aligned reads for each condition using a random number generator. The SEM was found by repeated random down-sampling  and measuring the spread in the variant calling results, which arise from different random sampling events.Analysis of the transcriptome-wide editing RNA-seq data was performed as follows. Before the analyses described below, Fastq files were generated using bcl2fastq2 and then trimmed using Trimmomatic version 0.32 to remove adaptor sequences, unpaired sequences, and low-quality bases. We created sam alignments using HISAT2 to align paired reads from each of three biological replicates to the hg38 human reference genome . Precomputed HISAT2 indexes were obtained from https://github.com/ekg/freebayes), an inherently probabilistic measure that accounts for error. The resulting VCF files were filtered with vcftools to retain only A-to-G variants, common variants, and variant calls with a call quality greater than or equal to 20, thus removing sites with less than a 0.99 probability of corresponding to a position where a real A-to-I edit has occurred. Thus, the variant calling performed here considers read depth at a specific adenosine, number of edited reads at that position, mapping quality, and base call quality, and, using all of these indicators, returns the probability that there is bona fide RNA editing at that given adenosine.Variant calling was performed using the freebayes software package version 1.2.0 (https://sift.bii.a-star.edu.sg/) was used to predict the outcome of nonsynonymous mutations on protein function. High- and low-confidence calls were made using standard SIFT parameters.The Variant Effect Predictor (Ensembl) was used to determine the location within a transcript of each A-to-I edit found in the sample treated with ABEmax, Cas9(D10A), or ABEmaxAW and whether the mutation was synonymous or nonsynonymous. The category “downstream gene variant” includes mutations found within a region 5-kb downstream of the start of a gene, and the category “upstream gene variant” includes mutations found in the region 5-kb upstream of a protein-coding region. “Intergenic regions” includes A-to-I mutations occurring in noncoding regions more than 5 kb away from the beginning or end of a coding region. SIFT (https://github.com/tflati/reditools2.0). We removed all nucleotides except adenosines from our analysis and then removed all adenosines with a read coverage of less than 20 to avoid errors due to low sampling. Next, we calculated the number of adenosines converted to an inosine in each sample and divided this by the total number of adenosines in our dataset after filtering to obtain a percentage of adenosines edited to inosine in the transcriptome. The calculation of SEM was performed as described in the variant calling section.To calculate the average frequency of A-to-I RNA editing among adenosines sequenced in transcriptome-wide sequencing analysis, we used REDItools to quantify the percentage of A-to-I editing in each sample (The transcriptome-wide RNA-seq data were demultiplexed and aligned as described above. Bins of 1 million nt were created along the human genome using bedtools makewindows. The high-confidence A-to-I edits were counted per bin using bedtools coverage. Last, the data were plotted in R using plot_ly and IdeoViz to show single-nucleotide polymorphism density per bin.


S. cerevisiae, enabling many studies identifying host factors and viral interactions essential for these processes. To better define and understand the conserved, core pathways of bromovirus RNA replication, we tested the ability of CCMV to similarly support spherule formation and RNA replication in yeast. Paralleling BMV, we found that CCMV RNA replication protein 1a was the only viral factor necessary to induce spherule membrane rearrangements and to recruit the viral 2a polymerase (2apol) to the endoplasmic reticulum. CCMV 1a and 2apol also replicated CCMV and BMV genomic RNA2, demonstrating core functionality of CCMV 1a and 2apol in yeast. However, while BMV and CCMV 1a/2apol strongly replicate each others’ genomic RNA3 in plants, neither supported detectable CCMV RNA3 replication in yeast. Moreover, in contrast to plant cells, in yeast CCMV 1a/2apol supported only limited replication of BMV RNA3 (<5% of that by BMV 1a/2apol). In keeping with this, we found that in yeast CCMV 1a was significantly impaired in recruiting BMV or CCMV RNA3 to the replication complex. Overall, we show that many 1a and 2apol functions essential for replication complex assembly, and their ability to be reconstituted in yeast, are conserved between BMV and CCMV. However, restrictions of CCMV RNA replication in yeast reveal previously unknown 1a-linked, RNA-selective host contributions to the essential early process of recruiting viral RNA templates to the replication complex.Positive-strand RNA viruses generally assemble RNA replication complexes on rearranged host membranes. Alphaviruses, other members of the alpha-like virus superfamily, and many other positive-strand RNA viruses invaginate host membrane into vesicular RNA replication compartments, known as spherules, whose interior is connected to the cytoplasm. Brome mosaic virus (BMV) and its close relative, cowpea chlorotic mottle virus (CCMV), form spherules along the endoplasmic reticulum. BMV spherule formation and RNA replication can be fully reconstituted in  Positive-strand RNA viruses are the largest genetic class of viruses and include many important human pathogens such as the Zika, Chikungunya, MERS, SARS, and Dengue viruses. There is a significant need for the development of therapeutic treatments for these and other viruses. Broad spectrum antivirals that target highly conserved viral functions are particularly sought after. For positive-strand RNA viruses, such a universal feature and potential antiviral target is the assembly of cytoplasmic membrane-associated RNA replication complexes , 2. The Bromoviridae family of viruses and has been previously studied as a model for varied replication steps within the alphavirus-like superfamily [Vigna unguiculata) and other dicotyledonous plants and efficiently carries out RNA replication in protoplasts from a wider range of plants, including some monocotyledons like barley (Hordeum vulgare) [Cowpea chlorotic mottle virus (CCMV) is a member of the erfamily , 7. CCMVvulgare) , 9. CCMVvulgare) .CCMV infection of plants induces dilation of and vesicular invaginations along the endoplasmic reticulum (ER) membrane that remain connected to the cytoplasm –13. Simipol and CCMV 1a + 2apol support replication and subgenomic mRNA transcription of either BMV or CCMV RNA3 to similar levels, although heterologous 1a/2apol pairings do not support full RNA replication [BMV systemically infects a range of plants and BMV RNA replication is supported in protoplasts from many plants including cowpea and barley. In such plant cells, BMV 1a + 2alication . Many stlication –22.S. cerevisiae [pol proteins from non-replication competent mRNAs directs positive- and negative-strand RNA synthesis from replication competent viral RNA templates provided in trans from plasmids. In plant and yeast cells, the BMV 1a protein is necessary and sufficient among viral proteins to induce spherule membrane rearrangements [pol to the replication complex [BMV RNA replication and encapsidation can be fully reconstituted in the model yeast revisiae –25. Plasngements , 26. Eacngements , 28, memngements , 30 1a pngements . BMV 1a  complex .S. cerevisiae has a rapid cell cycle, a small and extremely well annotated genome, and many advanced tools available for genomic manipulation. Many host pathways required for BMV RNA replication have been revealed by classical yeast genetic studies as well as systematic, high throughput study of over 5,000 individual yeast gene using deletion and conditional allele expression libraries [pol proteins, membrane association of 1a, 2apol, and viral RNA, 1a-induced membrane rearrangements, negative-strand RNA synthesis, positive-strand RNA synthesis, and subgenomic RNA synthesis. Yeast also support the selective encapsidation of BMV RNAs [The ability to carry out RNA replication in yeast has greatly facilitated many studies of BMV and other positive-strand RNA viruses , 32. S. ibraries , 34. ManBMV RNAs .pol and selected genomic RNAs, and investigated their ability to support RNA replication in yeast. We show that many BMV 1a protein functions required for RNA replication complex assembly, including perinuclear localization, membrane association and rearrangements, self-interaction, and recruitment of 2a polymerase, are conserved in the related CCMV 1a in yeast. CCMV 1a and 2apol also support replication of CCMV RNA2, BMV RNA2, and BMV RNA3, although to levels lower than BMV 1a and 2apol. No replication of CCMV RNA3 was detected in yeast despite the ability of both viruses to replicate CCMV RNA3 in plants. These data indicate different template-dependent host requirements for the replication of bromovirus RNAs. In the absence of BMV or CCMV 2apol, CCMV 1a induced lower levels of BMV RNA3 accumulation and recruited less BMV RNA3 to a membrane-associated state than BMV 1a, indicating 1a-dependent host contributions to RNA recruitment. Further investigation of these differences will be a valuable tool to dissect essential processes such as recruiting viral RNA templates to the replication compartment.To take advantage of the benefits of the BMV / yeast system and the power of viral hybrid studies, we have now generated yeast plasmids expressing CCMV 1a, 2apol proteins to direct RNA replication in yeast, we generated plasmids expressing CCMV 1a and 2apol mRNAs as well as a full-length CCMV RNA3 replication template , we used N-terminal GFP fusions. As in a previous report [pol showed function near that of wildtype BMV 2apol in replicating BMV RNA3   was found to allow better visualization of membrane rearrangements , 46. TheUsing HPF to examine yeast cells expressing BMV or CCMV 1a, we again observed typical spherular vesicles in the lumen of the perinuclear ER membrane . No membS. cerevisiae. Here we examined RNA replication and replication complex assembly of another bromovirus, CCMV, in yeast to better define and understand conserved features of bromovirus replication, and to develop further tools to study these processes. Our results show that CCMV RNA replication protein 1a parallels BMV 1a in inducing spherule membrane rearrangements along the perinuclear ER , and components of coat protein complex II (COPII), as well as an acyl-coA binding protein involved in lipid synthesis –60. Addipol to ER membranes by interaction of 1a’s NTPase/helicase-like domain with an N-terminal 2apol region preceding the polymerase domain [pol from a diffuse cytoplasmic distribution to the ER membrane  results in a frameshift and subsequent stop codon twenty amino acids into the open reading frame. GFP from GFP-BMV2apol (pB2YT5-G2 [pol (pCC2GCL-G). Sec63-GFP was expressed from pJK59 [pol were expressed from pB1YT3H[+ derivative of pB2YT5). Corresponding CCMV 1a and 2apol plasmids pCC1GCH and pCC2GCU were generated as described above. A plasmid expressing a frame-shifted CCMV RNA2 template (pCow2C) was created by replacing BMV RNA2 in pB2NR3-M1 with CCMV RNA2 sequence from pCC2TP2. Nucleotides 221–231 were deleted, removing the ClaI site, resulting in a frame-shift and introducing a stop codon at that position (amino acid 38).eviously , 62. BMV pB3MS82 , 36, 41. pB3MS82  to generom pJK59 . In expeom pJK59 ), BMV 1aCell fractionation was done as described previously . Briefly32P-labelled RNA probes and the radioactivity was measured using storage phosphor screens imaged on a Typhoon scanner . Viral RNA isolated from yeast was detected using strand-specific probes complementary to BMV RNA3/4 nt 1250–1755 [2, 1mM EGTA, 2 mM EDTA, 15 mM, protease inhibitor cocktail (Sigma)). The resulting lysate was mixed with an equal volume of SDS lysis buffer , boiled for 10 minutes, centrifuged at 20,000xg for 3 minutes, and the supernatant was recovered for analysis by SDS/PAGE and western blotting. A polyclonal rabbit antibody raised against an N-terminal fragment of BMV 1a [Total yeast RNA was harvested from whole cells or fractionated lysate using hot phenol . RNA ele250–1755 , CCMV RNf BMV 1a  was usedBands were quantified as indicated in each figure, all quantified samples within each blot were compared in a pairwise fashion using a two-tailed Welch's t-test.pol constructs and the Sec63-GFP ER marker [Yeast were prepared for confocal microscopy as previously described . Goat-α-R marker . ConfocaR marker . ColocalYeast were prepared for electron microscopy by traditional chemical fixation using glutaraldehyde and paraformaldehyde followed by osmium staining as described previously . High pr


Many neurodegenerative diseases are characterized by accumulation of proteins such as tau, a microtubule stabilization protein. Toxic tau forms tangles and affects neuronal synapse function, an early step of neurodegeneration. To focus on synapse function, we highlighted Caprin-1 protein. Through gene ontology, Caprin-1 is related to RNA granule proteins, important for transport and local translation in dendrites. Caprin-1 is of interest for neurodegeneration because it is a memory related protein, transports mRNA in RNA granules, and knock out mice demonstrate memory deficits. As we age, the performance of local translation in the dendrites is compromised and leads to synapse dysfunction. We hypothesize Caprin-1 binds to Tau and becomes disrupted. This leads to the dissolution of RNA granules, inhibition of mRNA transport in dendrites, suppression of translation, and failure of synapse. Early western blot data showed reduced Caprin-1 in PS19 Tau+, supporting our model that Caprin-1 is disrupted in disease models. Through immunohistochemistry, we investigated the localization of Caprin-1 in the mouse hippocampus. We observed Caprin-1 localization to dendrites of CA1 neurons in the hippocampus. Furthermore, Caprin-1 exhibited colocalization with Rps6, an RNA granule marker. This suggests Caprin-1 associates with RNA granules in mouse hippocampus. Finally, we investigated the localization of Caprin-1 in human iPSC-derived neurons. Similar to the mouse hippocampus, we observed localization of Caprin-1 to dendrites of human neurons. In future directions, we will examine whether pathogenic tau alters the association of Caprin-1 with RNA granules and the mechanisms by which pathogenic tau negatively effects synapse function.


O-ribose methylation and pseudouridylation of ribosomal RNAs (rRNAs), respectively. Emerging evidence has demonstrated that snoRNAs are involved in various physiological and pathological cellular processes. Mutations and aberrant expression of snoRNAs have been reported in cell transformation, tumorigenesis, and metastasis, indicating that snoRNAs may serve as biomarkers and/or therapeutic targets of cancer. Hence, further study of the functions and underlying mechanism of snoRNAs is valuable. In this review, we summarize the biogenesis and functions of snoRNAs, as well as the association of snoRNAs in different types of cancers and their potential roles in cancer diagnosis and therapy.Small nucleolar RNAs (SnoRNAs) are a class of non-coding RNAs divided into two classes: C/D box snoRNAs and H/ACA box snoRNAs. The canonical function of C/D box and H/ACA box snoRNAs are 2'- O-ribose methylation, and H/ACA box snoRNAs direct the pseudouridylation of NTs  are an extensively studied non-coding RNAs that primarily accumulate in the nucleoli and consist of 60–300 nucleotides (NTs). SnoRNAs are mostly responsible for the posttranscriptional modification and maturation of ribosomal RNAs (rRNAs), small nuclear RNAs (snRNAs), and other cellular RNAs. SnoRNAs are divided into two classes: C/D box snoRNAs and H/ACA box snoRNAs. C/D box snoRNAs guide−2′-n of NTs –4.The box C/D family of snoRNAs is characterized by a kink-turn (k-turn) (stem–bulge–stem) structure and contains two conserved sequence elements: box C (RUGAUGA) and box D (CUGA) located at the 5′ and 3′ ends of the RNA molecule, respectively  1). The. The1). H/ACA box snoRNAs are composed of conserved box H and box ACA motifs . Box H iIn vertebrates, most snoRNAs are encoded in the introns of protein-coding or non-coding genes, except for a small number of snoRNAs transcribed autonomously by RNA polymerase II . The bioO-methylation or pseudouridylation residues located within conserved and functional regions of rRNAs have been found. A sequence upstream of box D and/or box D' recognizes target RNAs and results in the 2′-O-methylation of the fifth NT by the methylase fibrillarin  –30. SNORin vivo  by its target mRNA. Four snoRNAs encoded in Rpl13a, snoRNAs U32A, U33, U34A, and U35A, regulated systemic glucose metabolism  encoded in the ribosomal protein L13a (Rpl13a) locus were increased significantly under oxidative stress induced by palmitate and hydrogen peroxide treatment. Knockdown of these three snoRNAs simultaneously increased resistance against palmitate in vivo . Furtherin vivo . Brandisfficking . Other rtabolism , 43.SnoRNAs are widely involved in many physiological and pathological processes; indeed, emerging evidence suggests that snoRNAs have tumor-suppressive or oncogenic functions in various cancer types . SnoRNAsin vitro and in vivo -based genome-wide association studies (GWAS), a low-frequency duplication at 15q13.3 containing SNORA18L5 was strongly correlated to risk of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Further studies showed that SNORA18L5 was overexpressed in HCC tissues compared with adjacent normal tissues and played an oncogenic role in HCC . Overexpvia inducing PHLPP expression –AKT and Wnt/β-catenin pathways discussed here. The PI3K–AKT pathway is a highly conserved cell signaling system in most multicellular organisms, which is critical to diverse cellular processes such as stemness, cell proliferation, differentiation, and cell death . The rolpression . In the pression .Arabidopsis, and fission yeast  and found independent classes of small RNAs originating from noncoding RNAs . Since ton yeast . In receon yeast . SNORD28on yeast . At the on yeast . miR-768on yeast .via base-pairing recognition (Homo sapiens) and bearing modification of 2′-O-methylation at the 3′ terminus (in vitro and in vivo by upregulating the expression of TRAIL (PIWI-interacting RNAs are identified as inhibitor of transposable elements (TEs) in the germline and play an important role in regulating target RNA to silence its expression ognition . In additerminus . Microarterminus . Pi-sno-of TRAIL . Uzunovaof TRAIL .O-methylation are essential for the AML1–ETO–induced self-renewal of leukemia cells in vitro and in vivo  has been demonstrated. Moreover, Valleron et al. found that the overexpression of snoRNA HBI-239 and HBI-239-processed miRNA predicted good prognosis in angio-immunoblastic T-cell lymphoma (AITL) and PTCL not otherwise specified (PTCL-NOS) . BerquetSince snoRNAs participate in tumorigenesis, tumor aggressiveness, and staging, they are ideal candidates for cancer therapy. The regulation of snoRNA expression may contribute to the goal of cancer treatment. For example, antisense oligonucleotide (ASO)-mediated downregulation of SNORA23 expression reduced tumor growth, dissemination, and liver metastasis in pancreatic ductal adenocarcinoma . Our stuAlthough the landscape of snoRNA function in its entirety remains still unclear, previous studies have confirmed the critical roles of snoRNAs in rRNA processing, gene transcription, RNA splicing, and other cellular processes. As sequencing and microarray analysis technologies evolve, more and more tumor-related snoRNAs are being discovered. Recent studies have demonstrated that aberrant expression and mutations in specific snoRNAs were associated with tumorigenesis and metastasis. The tissue- and cancer-specific expression of snoRNAs can be used as reliable prognostic markers for cancer diagnosis and novel therapeutic targets. However, the field is still in its infancy, and our understanding of the role of snoRNAs in tumors remains incomplete. Currently, the effect of snoRNA on cellular signaling pathways is also poorly understood. Functional screening of RNA interference libraries and/or clustered regularly interspaced short palindromic repeats (CRISPR)-based libraries is necessary to explore snoRNA functions in signaling pathways. SnoRNAs do not always act directly; indeed, most of their actions are indirect binding to other molecules. The identification of more molecules that interact with snoRNAs using RNA precipitation combined with high-throughput mass spectrometry and RNA immunoprecipitation (RIP) technologies is promising. Moreover, animal models are important for understanding the function of non-coding RNA in tumorigenesis and development. However, animal models of snoRNAs in cancer remain insufficient to date. Furthermore, emerging evidence suggests that some non-coding RNAs contain translated open reading frames that are able to encode proteins. This hints at whether snoRNAs also have the ability to encode proteins and provides a whole new direction for investigating snoRNA function. A better grasp of the role of snoRNAs in tumors will help us to understand tumors more comprehensively and may offer not only novel diagnostic biomarkers but also effective therapeutic targets in the near future.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


There is an error in the Funding statement. The correct name for the Funder is “Shanghai Municipal Commission of Health and Family Planning.”Additionally, in the published article, there were errors in affiliations 1 and 4. Instead of “Department of Clinical Laboratory, Pudong Hospital Affiliated to Fudan University, Shanghai, China,” it should be “Department of Clinical Laboratory, Shanghai Pudong Hospital, Fudan University, Shanghai, China,” and in affiliation 4, instead of “Department of Clinical Laboratory, The Fourth People's Hospital of Shanghai, Shanghai, China,” it should be “Department of Clinical Laboratory, Shanghai Fourth People's Hospital affiliated to Tongji University School of Medicine, Shanghai, China.”The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


Marnaviridae, and expand our view of the diversity of this important group. Overall, our data and analyses provide a foundation for interpreting marine RNA virus diversity and evolution.Very little is known about aquatic RNA virus populations and genome evolution. This is the first study that analyzes marine environmental RNA viral assemblages in an evolutionary and broad geographical context. This study contributes the largest marine RNA virus metagenomic data set to date, substantially increasing the sequencing space for RNA viruses and also providing a baseline for comparisons of marine RNA virus diversity. The new viruses discovered in this study are representative of the most abundant family of marine RNA viruses, the  Picornavirales, are widespread and abundant in the ocean. Gene surveys suggest that there are spatial and temporal patterns in the composition of RNA virus assemblages, but data on their diversity and genetic variability in different oceanographic settings are limited. Here, we show that specific RNA virus genomes have widespread geographic distributions and that the dominant genotypes are under purifying selection. Genomes from three previously unknown picorna-like viruses  assembled from a coastal site in British Columbia, Canada, as well as marine RNA viruses JP-A, JP-B, and Heterosigma akashiwo RNA virus exhibited different biogeographical patterns. Thus, biotic factors such as host specificity and viral life cycle, and not just abiotic processes such as dispersal, affect marine RNA virus distribution. Sequence differences relative to reference genomes imply that virus quasispecies are under purifying selection, with synonymous single-nucleotide variations dominating in genomes from geographically distinct regions resulting in conservation of amino acid sequences. Conversely, sequences from coastal South Africa that mapped to marine RNA virus JP-A exhibited more nonsynonymous mutations, probably representing amino acid changes that accumulated over a longer separation. This biogeographical analysis of marine RNA viruses demonstrates that purifying selection is occurring across oceanographic provinces. These data add to the spectrum of known marine RNA virus genomes, show the importance of dispersal and purifying selection for these viruses, and indicate that closely related RNA viruses are pathogens of eukaryotic microbes across oceans.RNA viruses, particularly genetically diverse members of the IMPORTANCE Very little is known about aquatic RNA virus populations and genome evolution. This is the first study that analyzes marine environmental RNA viral assemblages in an evolutionary and broad geographical context. This study contributes the largest marine RNA virus metagenomic data set to date, substantially increasing the sequencing space for RNA viruses and also providing a baseline for comparisons of marine RNA virus diversity. The new viruses discovered in this study are representative of the most abundant family of marine RNA viruses, the Marnaviridae, and expand our view of the diversity of this important group. Overall, our data and analyses provide a foundation for interpreting marine RNA virus diversity and evolution. Viruses shape the ecology and evolution of marine microbial communities. They are the most abundant biological entities in the ocean and are estimated to kill about 20% of its living biomass each day, thus affecting food web dynamics and biogeochemical cycling , 2. MostHeterosigma akashiwo  genes revealed that most sequences fell into a distinct phylogenetic cluster, which are likely viruses primarily infecting protists  (Chaetoceros tenuissimus RNA virus 01 (CtenRNAV01) (3 versus 104) and a longer infection cycle (2 days versus <24 h), so it should have a lower distribution potential.Not only are the abundance and diversity of viruses in the oceans high, but there are patterns in their distribution. Although identical or nearly identical viral genotypes have been documented in geographically and environmentally separate locations , 11–13, 11–(RsRNAV)  and ChaenRNAV01)  have difHeterocapsa circularisquama RNA virus (HcRNAV) and Heterosigma akashiwo RNA virus (HaRNAV) infect widely distributed taxa, although the viruses are also strain specific  is tightly coupled with the virus hosts. Host traits, such as abundance, distribution, size, and physiological state, affect the biogeography of their viruses . Strain specific , 19–21, cific 19– and the cific 19– imply th−4 substitutions per nucleotide per replication cycle  changes than synonymous changes (dS), indicating elevated purifying (or negative) selection pressure compared to that of viruses transmitted by other routes, probably due to the required interactions of virus capsids with insect cellular receptors  of viruses with ancient evolutionary origins and phylogenetic primacy among modern RNA viruses  were assembled from metagenomic data collected at Jericho Pier in April 2014 (JP14) . The virThe geographical distributions of the six viruses were explored using 17 RNA virus metagenomic data sets (15 generated in the present study and 2 from previous studies). Library size potentially affects the detection of rarer viral genomes, but there was no trend between the number of reads recruited to genomes and library size see  to the o10.1128/mSphereDirect.00157-19.3FIG S1FIG S1, EPS file, 0.10 MB.Comparison of metagenomic library size and number of reads that mapped to the six genomes. Each library/location is represented by a different color, with the size of the circle denoting the mean of how many times reads were represented more than once/how often reads mapped to more than one genome on average. Download Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the To assess the distribution of close relatives of the six marine RNA viruses, reads were recruited to their genomes from 17 marine and 7 fresh and reclaimed water RNA virus metagenomic data sets collected from widespread locations  and S3. 10.1128/mSphereDirect.00157-19.4FIG S2−10. The position of the colored bars on the y axis indicates the percentage amino acid sequence identity of the read to the virus, while the width of the bar covers the region of the genome where the similarity was observed. The bars are displayed at 0.5 transparency to make overlap more evident. Each genome map for each virus is superimposed on the plot with domains with designations from left to right as follows: helicase, protease, RNA-dependent RNA polymerase, VP2, VP4, VP3, and VP1. Download FIG S2, EPS file, 0.1 MB.Prevalence of reads with amino acid sequence identities greater than 85% mapped to the six marine RNA viruses. Fragments were recruited from marine RNA virus metagenomic data sets generated during this study  (A) and from publicly available metagenomics data sets from Palmer Station (Antarctica) and Kaneohe Bay (Hawaii) (B). Metagenomic reads were recruited against each of the six virus genomes using tBLASTx with an E value of 10Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphereDirect.00157-19.5FIG S3Heterosigma akashiwo RNA virus (HaRNAV). The superimposed domains are from left to right: helicase, protease, RNA-dependent RNA polymerase, VP2, VP4, VP3, and VP1. Download FIG S3, EPS file, 0.9 MB.Reads mapping to six marine RNA viruses represented by metagenome. Reads from Antarctica (A), the Arctic (B), Bering Sea (C), Cape Point (D), Chile (E), Queen Charlotte Strait (F), Peru (G), Pendrell Sound (H), Nunavut 3 (I), Nunavut 2 (J), Laguna Madre (K), Kenton-on-Sea (L), Kaneohe Bay (M), Johnstone Strait (N), Jericho Pier 14 , and Jericho Pier 13 (Q) mapped to marine RNA viruses BC-1, BC-2, and BC-3, JP-A, JP-B, and Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Nonetheless, deduced amino acid sequence with high levels of identity to the six viruses were detected at numerous locations ; Fig. S2Many reads in the JP14 sample mapped with high identity to BC-1, -2, and -3; however, there was wide variation in identity levels across the genomes ; Fig. S2Across all locations, a large proportion of the reads showed low levels of amino acid sequence identity to the six viruses, ranging from about 20% to 80% across both structural and nonstructural domain regions, although many reads mapped to RdRp domains. BC-1 and HaRNAV were exceptions, and fewer environmental reads mapped to their genomes overall see . GeneralMany reads from the assembled metagenomic data obtained from the five lakes and two reclaimed water sites recruited to the six marine RNA virus genomes, some with identities as high as 90%, although most were between 30 and 60% . As in tdN/dS of <1.0 for all data below the 75th percentile indicates that purifying selection was acting on these sequences. No difference was observed among the calculated means for the six domains , but based on the medians, all groups did not have the same distributions of dN/dS ratios . Multiple pairwise comparisons showed that the VP1 domain had a statistically significant larger median dN/dS ratio distribution than the RdRp and VP2 domains   . Severalctively) .10.1128/mSphereDirect.00157-19.6FIG S4dN/dS ratio based on the calculated conserved amino acid sequences of the six marine RNA virus genomes. Domains are, from left to right, helicase, protease, RNA-dependent RNA polymerase, VP2, VP4, VP3, and VP1. Download FIG S4, EPS file, 0.06 MB.The number of amino acid groups per Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P = 0.05343; Kruskal-Wallis rank sum, P = 0.09858). Calculated dN/dS ratios were very low apart from outliers for the VP1 domain of virus BC-2 in Queen Charlotte Strait and the helicase domain of JP-A in Johnstone Strait and Kenton-on-Sea.Selection analyses of the metagenomic data that mapped to the six genomes reflected purifying selection acting on these viruses . There wSingle-nucleotide-variant (SNV) analyses  indicate10.1128/mSphereDirect.00157-19.7FIG S5Heterosigma akashiwo RNA virus (HaRNAV) is shown. Triangles (▲) indicate synonymous mutation variants, and circles (●) indicate nonsynonymous variants. The locations/samples where they were detected are illustrated in different colors. The SNV analysis was conducted using the quality-based variant detection tool of CLC Genomics Workbench. Download FIG S5, EPS file, 0.1 MB.Environmental quasispecies of marine RNA viruses. The distribution of single-nucleotide variants (SNVs) along the helicase (A) and VP4 (B) domains of marine RNA viruses BC-1, BC-2, and BC-3, JP-A, JP-B, and Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Single-nucleotide variants that were more frequent than 99% were abundant and detected at multiple locations . These aUnlike the RdRp domain, fewer high-frequency SNVs were associated with other domains, especially for the helicase, VP4, and VP1. SNVs that were concentrated in the same region of the VP3 domain of HaRNAV were identified in data from Bering Sea (3 SNVs), Jericho Pier 13 (2 SNVs), Johnstone Strait (6 SNVs), Laguna Madre (2 SNVs), Nunavut 2/3 , Pendrell Sound (2 SNVs), Peru (3 SNVs), Queen Charlotte Strait (14 SNVs), and South Chile (4 SNVs).Of the three new genomes, BC-1 was the only one in which high-frequency SNVs were identified; SNVs in the VP2 domain were detected from Nunavut 3 (11 SNVs). SNVs in the VP2 domain of HaRNAV were also detected at Nunavut 3 (4 SNVs), Bering Sea (9 SNVs), and Pendrell Sound (4 SNVs). Detection of SNVs in multiple domains of a single virus at specific locations supports the existence of biogeographical variations for the virus, with established genome variants existing in these locations.Metagenomic analysis uncovered sequences for three previously unknown marine picorna-like RNA viruses . The distributions of these and three related viruses  , 44 withBacillariophyta), which are abundant, genetically diverse, and globally distributed. In contrast, the host of HaRNAV is Heterosigma akashiwo, a member of the Raphidophyta, a family comprised of relatively few and less diverse protists  were assembled from the Jericho Pier 14 sample , adding protists ; HaRNAV 10.1128/mSphereDirect.00157-19.8FIG S6-joining trees for the trimmed helicase , RdRp , VP2 , VP4 , VP3 , and VP1  domains. The percentage of trees in which the associated taxa clustered together is shown next to the branches . Darker shading indicates more closely related sequences. This indicates that the six viruses are all distantly related, with BC-2 and -3 comparatively more similar to one another and HaRNAV the most divergent of the six . Trees were obtained by applying neighbor-joining and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model. The percentages of trees in which the associated taxa clustered together are shown next to the branches . Download FIG S7, EPS file, 0.3 MB.Pairwise amino acid sequence similarity and neighbor-joining trees of the conserved domains of the six analyzed marine RNA viruses. The respective amino acid sequences were aligned for each domain and trimmed at the edges so that the same regions of the domains were compared for all six viruses. The percent identities and Jukes-Cantor distances are displayed, along with neighbor-joining trees, for the trimmed helicase , RdRp , VP2 , VP4 , VP3 , and VP1  domains of marine RNA viruses BC-1, BC-2, and BC-3, JP-A, JP-B, and Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Sequence divergence is different among the viral sequence domains. In particular, the protease is most divergent, whereas VP1 is the most divergent among the conserved structural proteins . This is congruent with read recruitment , in whicAlthough all metagenomic data sets were interrogated for complete genomes, the three new genomes originated from a single sample, Jericho Pier 14. This may be because Jericho Pier 14 was the newest sample and the RNA genomes may have been less degraded than in the older samples , which w10.1128/mSphereDirect.00157-19.1TABLE S1Table S1, DOCX file, 0.02 MB.Environmental descriptions of stations from which viral metagenomes were generated. Download Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the The six viruses were selected for this study because they were all discovered in waters adjacent to Vancouver. Despite the large number of reads from Jericho Pier 14 that recruited to the three new genomes , few reads recruited to these genomes from a sample (Jericho Pier 13) collected a year earlier from the same location . In contH. akashiwo) that is sporadic in occurrence  emptying into the marine water body from which the BC viruses were assembled suggests that these viruses are not of freshwater origin, as very few, if any, reads could be recruited (unpublished data).The freshwater metagenomes  provide dN) to synonymous changes (dS) of nucleotides in a genome. Our data imply that purifying selection acts on both structural and nonstructural domains of the virus genomes, with overall dN/dS values of <1 . The nucleotide sequence alignments were constructed based on the corresponding amino acid sequence alignments, with the edges trimmed to remove regions not represented in all viruses. Download FIG S8, EPS file, 0.5 MB.Nucleotide alignments of the RdRp, helicase, VP2, VP-4, VP-3, and VP-1 domains of marine RNA viruses BC-1, BC-2, and BC-3, JP-A, JP-B, and Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphereDirect.00157-19.2TABLE S2Table S2, DOCX file, 0.01 MB.Stochastic dominance observed in the domain sample pairs, analyzed using the Conover-Iman test. Download Copyright © 2019 Vlok et al.2019Vlok et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the The existence of quasispecies was validated for the six viruses by examining single-nucleotide variants (SNVs) in the sequence data from different locations. Not surprisingly, because they were assembled from the Jericho Pier 14 samples, BC-1, -2, and -3 had the greatest genome coverage, yet BC-3 recruited about four times more data than BC-1 and -2 but fewer SNVs for the RdRp, helicase, VP2, and VP3 domains A.If a population of viruses contains an abundance of the fittest genotype surrounded by rarer quasispecies variants, most genotypes should resemble the original virus that replicated. This suggests that BC-3 stemmed from a lytic event that occurred shortly before sample collection, resulting in higher abundance and lower diversity. Similarly, an analysis of quasispecies in an Antarctic lake revealed that an abundant RNA virus genotype, APLV1, had few SNVs, with most occurring in <10% of the sequences, despite the virus being ecologically successful .The patterns in the SNVs in the environmental data that mapped to JP-A and HaRNAV indicated that quasispecies-like populations occurred at locations beyond the original site of discovery . For exadN/dS ratio previously discussed, suggest that VP1 domains experience different degrees of evolutionary pressure in comparison to the other capsid proteins, perhaps due to the role of VP1 in host recognition.For the BC-3 virus, only the VP1 domain had enough variation to suggest a possible quasispecies . This doGiven the coevolutionary arms race between viruses and their hosts, and the competitive advantage of existing as quasispecies, some low-frequency variants should be selected for and become established as the fittest genotype. This would result in turnovers in the dominant viral genotypes, as frequently occurs in viruses of multicellular organisms , 65, 66.Most SNVs in the HaRNAV and JP-B genomes were synonymous, indicating pressure to conserve amino acid sequences. In contrast, the SNVs in the RdRp of JP-A were more equally distributed between synonymous and nonsynonymous. The JP-A RdRp sequences originated from samples collected at Kenton-on-Sea, on the southeast coast of South Africa, which is very distant from the coast of southwestern Canada, where the JP-A genome originated. The large proportion of nonsynonymous substitutions suggests that the Kenton-on-Sea variant is evolutionarily relatively distant from the JP-A genome, emphasizing that marine picorna-like viruses are diverse and widespread in the world’s oceans.Assembly of marine RNA virus metagenomic data from a single metagenomic sample in British Columbia revealed three previously unknown viruses, BC-1, -2, and -3. Recruitment of sequences from 17 metagenomic data sets to these and other previously characterized marine RNA virus genomes uncovered biogeographic patterns and evidence for quasispecies and purifying selection in marine RNA viruses across distant locations.The occurrence of genetically related viruses differed across oceans and hemispheres, implying that biotic factors such as virus life cycles and host traits, rather than solely abiotic factors, affected their distribution. In addition, differences in community composition across locations and years attest to the dynamic nature of marine RNA virus assemblages and are consistent with the idea that these dynamics probably result from episodic infections .dN/dS ratio than the other domains, the highest distance scores in pairwise similarity analyses, and a unique “cloud” of high-amino-acid-identity sequences. These observations are congruent with the conjecture of VP1 being important in host receptor binding ; one composite of 10 samples from the Canadian Arctic (Arc); two from the Nunavut coastal ocean (Nun2 and Nun3) representing the northern subpolar regions; one in the temperate/subpolar Bering Sea (BerS); one from Laguna Madre, representing the subtropical Gulf of Mexico (LagM); one from tropical coastal Peru (Peru); one from temperate southern Chile (SChil); and two from the coastal warm Indian Ocean Aghulas (Kenton-on-Sea [KoS]) and cold Southern Atlantic Benguela (Cape Town [CapeP]) currents of South Africa . The samSamples were collected using either Niskin bottles mounted on a CTD rosette (∼150 to 200 liters) or by bucket from the surface  (∼2 to 20 liters). Particulate matter was removed by pressure filtering (<17 kPa) the water through glass fiber filters  and polyvinylidene difluoride filters , after which viruses were concentrated using either ultrafiltration  through g for 5 h at 8°C), and each pellet was resuspended overnight at 4°C in 400 µl of supernatant liquid. Nucleic acids were extracted using the PureLink viral RNA/DNA mini kit (Thermo Fisher Scientific), and the DNA was removed using TURBO DNase (Thermo Fisher Scientific). Five replicates of cDNA were synthesized, with various concentrations of template RNA, by sequence-independent single-primer amplification (SISPA) using both random-hexamer  and nonamer  primers (Viruses 70 ml) were further concentrated by ultracentrifugation  per the manufacturer’s protocol, with the addition of 5% (vol/vol) dimethyl sulfoxide (DMSO) and various concentrations of MgCl2. All replicates were pooled, cleaned using the Genomic DNA Clean and Concentrator kit (Zymo), and sheared to approximately 300 bp using an S220 focused-ultrasonicator (Covaris).Amplification of cDNA was carried out in four replicates using primers AD89 (5′-CCTGAATTCGGATCCTCC-3′) and SMA-P2 (5′-GACATGTATCCGGATGT-3′) , 70 and Size selection (>200 bp) was done using Agencourt AMPure XP beads (Beckman Coulter), and libraries were prepared using NEXTFLEX-96 DNA barcodes (Bioo Scientific) and the NxSeq DNA sample prep kit (Lucigen) per the manufacturer’s protocol. Pooled libraries were sequenced on an Illumina HiSeq 2000 (2 × 100 bp) at the Biodiversity Research Centre, University of British Columbia, Vancouver, Canada.de novo assembly algorithm in the CLC Genomics Workbench v7.5 (CLCBio). The contigs and unassembled reads for each metagenome were combined and reassembled using the same parameters.Primer sequences were removed and quality trimmed (PHRED score of 30) using Trimmomatic . Paired Open reading frames (ORFs) were identified using the CLC Genomics Workbench v7.5. ORFs smaller than 350 bp were not included in further analyses. The ORFs were compared to the Conserved Domain Database (CDD)  October Decembe. Contig NP944776, BAE47143, YP001429583, AGZ83339, AFM44930, AHA44480, YP009047193, BAG30951, BAE79742, YP002647032, YP009230124, YP009230118, YP009230120, YP001429581, YP009111336, AYD68773, AYD68775, AYD68777, and BAK40203.RdRp alignments were generated using MUSCLE v3.8.425 with the default parameters  and thenMG584187, MG584188, and MG584189) and JP-A (NC_009757), JP-B (NC_009758), and HaRNAV (NC_005281) using tBLASTx . Conflicts were resolved based on the highest bit score and lowest E value. Translated reads were mapped onto the genomes based on percent amino acid identities and visualized with a 0.5 alpha setting to visualize overlapping reads using R 3.2.2  analysis was conducted using the quality-based variant detection tool in CLC Genomics Workbench v7.5 (minimum read count of two).MG584187, MG584188, and MG584189. Sequencing data were deposited in the BioSample database under the following numbers: SAMN08391118 to SAMN08391132.The newly assembled viral genomes are available from GenBank under the following accession numbers: