N6-Methyladenosine (m6A), the most common form of mRNA modification, is dynamically regulated by the m6A RNA methylation regulators, which play an important role in regulating the gene expression and phenotype in both health and disease. However, the role of m6A in papillary renal cell carcinoma (pRCC) is unknown. The purpose of this work is to investigate the prognostic value of m6A RNA methylation regulators in pRCC; thus, we can build a risk score model based on m6A RNA methylation regulators as a risk signature for predicting the prognosis of pRCC. Here, we investigated the expression and corresponding clinical data by bioinformatic analysis based on 289 pRCC tissues and 32 normal kidney tissues obtained from TCGA database. As a result, we identified the landscape of m6A RNA methylation regulators in pRCC. We grouped all pRCC patients into two clusters by consensus clustering to m6A RNA methylation regulators, but we found that the clusters were not correlated to the prognosis and clinicopathological features of pRCC. Therefore, we additionally built a two-m6A RNA methylation regulator risk score model as a risk signature by the univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. The risk signature was constructed as follows: 0.031HNRNPC + 0.199KIAA1429. It revealed that the risk score was associated with the clinicopathological features such as pT status and pN status of pRCC. More importantly, the risk score was an independent prognostic marker for pRCC patients. Thus, m6A RNA methylation regulators contributed to the malignant progression of pRCC influencing its prognosis. The papillary renal cell carcinoma (pRCC) subtype accounts for approximately 10-20% of RCC which is a kind of lethal cancers in the genitourinary system . CurrentRNA methylation modification accounts for more than 60% of the 171 known RNA posttranscriptional modifications which play a fundamental role in many biological and disease processes . N6-MethGrowing evidence has proved that m6A modification plays a critical role in gene expression regulation, as well as the carcinogenesis, progression, and prognosis of various human malignancies . Howeverhttps://cancergenome.nih.gov/) [The RNA-seq transcriptome and corresponding clinical datasets of the pRCC cohort were downloaded from TCGA Research Network (ih.gov/) . The verp < 0.05 was considered statistically significant. The heatmap and vioplot were performed, respectively, by the “pheatmap” package (http://cran.r-project.org/web/packages/pheatmap/index.html) and the “vioplot” package (https://cran.r-project.org/web/packages/vioplot/index.html) visualizing the expression levels and differences of the 13 m6A RNA methylation regulators in pRCC. Furthermore, to identify the potential correlation of the 13 m6A RNA methylation regulators, Spearman correlation analysis was performed by the “corrplot” package (https://cran.r-project.org/web/packages/corrplot/index.html).The expression matrix of the 13 m6A RNA methylation regulators was extracted from all mRNASeq gene expression matrix data. To investigate the function of m6A RNA methylation regulators in pRCC, the “limma”  package http://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.html). Moreover, to verify the results of the grouping, PCA was performed using the “ggplot2” package (http://cran.r-project.org/web/packages/ggplot2/index.html).To identify whether the cancer tissues can be grouped into different clusters based on the expression similarity of m6A RNA methylation regulators, we grouped 289 cancer tissues using the “ConsensusClusterPlus” package . Besides, we also investigated the expression of the 13 m6A RNA methylation regulators and the clinicopathological features of pRCC in clusters using the “pheatmap” package.To identify the difference in the OS rate in the clusters, the K-M survival analysis of the clusters was performed using the “survival” package (https://cran.r-project.org/web/packages/glmnet/index.html) and the “survival” package. Based on the minimum criteria and the coefficients obtained from the LASSO algorithm, it would select appropriate m6A RNA methylation regulators to build the risk signature to calculate the risk score for pRCC patients [p < 0.05 was considered to indicate a statistically significant difference.To better understand the prognostic value of m6A RNA methylation regulators in pRCC, firstly, a univariate Cox regression analysis was performed based on the expression levels of m6A RNA methylation regulators and corresponding clinical survival data using the “survival” package. Then, the statistically significant m6A RNA methylation regulators were used for the least absolute shrinkage and selection operator (LASSO) Cox regression , 24. Thepatients . Based ohttp://cran.r-project.org/web/packages/timeROC/index.html). Besides, to determine whether the risk score was an independent prognostic indicator for pRCC patients, we performed univariate and multivariate Cox regression analyses using the “survival” package and “forestplot” package (https://cran.r-project.org/web/packages/forestplot/index.html). The confounding factors included age, gender, stage status, pT status, pM status, and pN status. A p < 0.05 was statistically significant.To better understand the role of the risk score in the clinical outcomes of pRCC, we systematically investigated the relationship between the risk score and the clinicopathological characteristics by the “pheatmap” package, including age, gender, stage status, pT status, pM status, and pN status. We also investigated the relationship between the risk score and the expression levels of the selected risk signatures using the “pheatmap” package. Furthermore, to verify the predictive accuracy of the risk score, a receiver operating characteristic (ROC) curve of 5-year survival rates for pRCC patients was performed and the area under the curve (AUC) was calculated using the “survival” package and “timeROC” package , KIAA1429 , and RBM15  had a worse OS rate in patients with pRCC . The LASInvestigating the relationship between the risk score and the clinicopathological characteristics, it was revealed that the risk score was closely correlated to pT status and pN status of pRCC patients, and the high risk was generally along with the higher pT status and pN status . MoreoveThe m6A RNA methylation is dynamically regulated by 13 widely reported m6A RNA methylation regulators, which are widely distributed in various types of RNA, such as mRNA, transport RNA (tRNA), and ribosomal RNA (rRNA), and play an important role in regulating splicing, stability, localization, transportation, and translation of target mRNAs in the posttranscriptional level , 27. As This study attempted to investigate the role of the 13 widely reported m6A RNA methylation regulators in the prognosis of pRCC. Firstly, we described the expression landscape of the 13 m6A RNA methylation regulators in 289 pRCC tissues compared with 32 normal kidney tissues. There were 2 highly expressed regulators (HNRNPC and YTHDF1) and 5 lowly expressed genes  in pRCC tissues. The correlation between the 13 m6A RNA methylation regulators was positively correlated, and the correlation was weak to moderate, and METTL14 and ZC3H13 were the most relevant. More importantly, we constructed a risk signature (risk score = 0.031HNRNPC + 0.199KIAA1429) by using 2 m6A RNA methylation regulators, including HNRNPC and KIAA1429. Based on the median cutoff point (7.772) of the risk score, we divided all patients into the high-risk group and low-risk group. The result of the univariate Cox regression analysis indicated that the risk score, age, stage status, pM status, and pN status were closely correlated to the prognostics of pRCC. And the result of the multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for pRCC patients .Up to now, the role and specific mechanism of m6A RNA methylation regulators are not fully understood and lack specificity. Though we have demonstrated the prognostic values of m6A RNA methylation regulators in pRCC patients, their accurate mechanism in the progression and prognosis of pRCC remains unclear. Recently, using TCGA database, several similar studies provided us with clues to understand the role and mechanism of the m6A RNA methylation regulators. In gastric cancer, a risk signature was constructed using 3 m6A RNA methylation regulators , which not only was an independent prognostic marker but could also predict the clinicopathological features of gastric cancer . In bladIn summary, our results systematically demonstrated the expression and prognostic value of m6A RNA methylation regulators in pRCC. Using HNRNPC and KIAA1429, we constructed a two-m6A methylation regulator risk score model as a risk signature, which could serve as an independent prognostic biomarker to predict the prognosis of pRCC patients.


RNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process.https://github.com/BioinfoUNIBA/REDItools2.In this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. Availability: HPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores. Advances in next generation sequencing (NGS) technologies have led to the production of an unprecedented amount of omic data  changing science and medicine in ways never seen before and entering the “big data” era. The scale and efficiency of NGS poses the relevant challenges of sharing, archiving, integrating and analyzing these vast collections of omic data. Although tools and algorithms to handle and analyse large NGS datasets are now appearing, widespread software for common NGS analyses are yet multi-threaded or serial and not ready for the “big data” era revolution. As a consequence, the investigation of specific and important biological phenomena in large NGS datasets from international projects - such as The Cancer Genome Atlas (TCGA) 1 or The RNA editing is a widespread post-transcriptional mechanism that alters primary RNA sequences through specific base modifications and increases the transcriptome complexity of eukaryotic organisms. In humans, RNA editing affects nuclear and cytoplasmic transcripts mainly by the deamination of adenosine (A) to inosine (I) through the ADAR family of enzymes  that actRNA editing has relevant and serious biological and physiological implications. Indeed, its deregulation has been linked to several nervous and neurodegenerative diseases such as epilepsy, schizophrenia, major depression, Alzheimer and amyotrophic lateral sclerosis . In addiDespite its importance in modulating gene expression and maintaining a correct cellular homeostasis, the A-to-I landscape in human is still incomplete and main biological roles are yet elusive. Indeed, the de novo detection of RNA editing in humans has been performed in a limited number of samples, tissues and experimental conditions.Thanks to international consortia, thousands of transcriptome experiments (RNA-seq) have been performed and publically released through specialised web archives such as dbGAP (the database of Genotypes and Phenotypes ) or SRA https://github.com/BioinfoUNIBA/REDItools2.In order to detect RNA editing sites in RNA-seq data, we developed the REDItools package , 14, a bRegion) and genomic coordinate (column Position). In addition, each editing position includes additional attributes, such as the reference nucleotide (column Reference), the list of observed substitutions (column AllSubs), the RNA editing frequency (column Frequency) (representing the editing level per site), the number and base count of reads supporting the position , the strand  and the mean quality of supporting nucleotides (column MeanQ).RNA-editing events detected by REDItools are represented in a machine-readable tabular format as reported, for example, in Fig. The identification of RNA editing events in a classical RNA-seq experiment (including at least 50-60 million reads) through the first implementation of REDItools  can takeData loading optimization: the new code has been fully rewritten to optimise data loading and improve computational speed when launched in serial mode;Dynamic Interval Analysis: we have implemented a novel algorithm, called Dynamic Interval Analysis (DIA), to solve the problem of high-density genomic regions that improves the software in terms of workload balance;Parallelization: the current code is HPC-aware supporting the parallel analysis on thousands HPC nodes.Our novel software, called HPC-REDItools, introduce at least three big novelties over the previous implementation:Below we describe in detail algorithmic novelties illustrating speed improvements over the original REDItools implementation.pysam module, a wrapper of the widespread SAMtools package for reading and manipulating large raw BAM files [M splits the input whole genome into a set of genomic intervals D and, then, dispatches each interval to n free parallel slave processes by sending a COMPUTE message and until all processes are kept busy. Each slave process S . When all intervals have been analysed, the master process M sends a FINISH message to each slave process S notifying the end of the computation so that they can gracefully exit. A final procedure  and for its biological purpose (RNA editing detection) employs the AM files  containiAM files    and when treated equally result in an unbalanced workload across processes with sub-optimal performances . Such strategy aims at finding an optimal interval division, which is able to guarantee a balanced processing time across processes while maintaining the number of intervals comparable to that of processes. Formally, the goal is to find a set D of n intervals (n fixed) such that the processing time T(I) of each interval I is approximately the same, that is:HPC-REDItools introduce a novel strategy called T(Ii)≈cn, meaning that the time to process a given interval is constant /n, where T(G) is the total time required to analyse the whole genome G in a serial fashion). Since the function T is not known, it is necessary to find the best estimate T which is able to predict the execution time over an interval. First, we define the time needed to analyse an interval I as the sum of the single contributions over each position i in the interval, thus reducing the problem of estimating the processing time over single positions:t(i) is a function that estimates the processing time of the i-th position. For simplicity, we assume that the processing time to compute a certain position depends only on the number of reads supporting such position . This is equivalent to say that D of dynamic intervals and then calculate the processing-time estimate GC for the whole genome (lines 3-6). We then calculate AC as the average coverage time of the ideal interval (line 7); this is the ideal constant processing time for an interval. Since intervals cannot overlap and are sets of contiguous positions, the algorithm starts at the first position of the genome and then proceeds by expanding the partial interval until a stop condition is reached. There are three possible stop conditions:max-weight: the weight of the interval under analysis exceeds the constant AC ;max-width: the width of the interval exceeds the desired threshold max (to encourage process recycling);single-chromosome-span: the end of a chromosome has been reached .As shown in Algorithm 1, we first initialise the set Finding time estimate. To find the best estimate for t, we selected a random sample of 1,000 equal-size intervals uniformly distributed across all human chromosomes, in order to take into account also short sequences . We analysed each interval with HPC-REDItools in serial mode  and calculated its average coverage. Figure x,y) point represents an interval with average coverage ex which has taken ey seconds to be completed. As shown, the plot reveals two main clouds, the first representing intervals whose log average coverage is between 4 and 12 and the second which includes higher-coverage intervals. This plot is meaningful in the sense that it gives a pragmatic indication of the correlation between an interval coverage and the processing time it requires to analyse it. Since the analytical continuous function t is not known a priori, we tested several t, including constant, linear and polynomial functions. However, the plot suggests that the average time to elaborate a given interval correlates in a cubic manner with its mean coverage and the light-blue line in the figure represents the function which best fits the given discrete point distribution.To experimentally test the speed improvement between HPC-REDItools and REDItools, we created a dataset consisting of 10 RNA-seq samples randomly selected from the GTEx project. Both programs were launched on the same computer machine in order to predict RNA editing events occurring in the chromosome 21. In Fig. averaged-sized samples from the GTEx repository and then we performed 7 experiments involving the analysis of 1, 2, 10, 20, 50, 100 and 200 samples, respectively. For each experiment we ran HPC-REDItools with a number of nodes equal to the number of selected samples . All computations were executed on the Knight-Landing (KNL) partition of Marconi, a Tier-0 cluster available at CINECA, the Italian biggest non-profit organization offering support to national and international research projects as well as one the most powerful calculus infrastructures in EuropeW using 1 core over the time required to perform the same amount of work using n cores; for example, s=1, corresponding to the implementation that achieves the maximum parallelism possible. As shown in the figure, in which blue line corresponds to HPC-REDItools timings and red line to the optimal speed-up, HPC-REDItools achieves a very good scaling, demonstrating the ability to optimally exploit the computational power offered by a HPC infrastructure.To formally assess the scalability of HPC-REDItools on a High Performance Computing architecture, we generated a dataset comprising 200 x=8,17,34,68) on a single KNL node  high parallelism to employ the computational power available at High Performance Computing infrastructures; ii) optimised data loading that dramatically reduces computing time per genomic interval; iii) Dynamic Interval Analysis approach to improve workload balance across parallel processes. Our results indicate that HPC-REDItools are ready to analyse RNA editing in a variety of samples. Indeed, we plan to apply our software to explore the RNA editing landscape in large NGS datasets, thus providing a more reliable overview of the role of RNA editing in eukaryotic organisms.Project name: HPC-REDItoolshttps://github.com/BioinfoUNIBA/REDItools2Project home page: Operating system(s): Platform independentProgramming language: Python ≥2.7Other requirements: NoneLicense: GPL-3.0


Exosomes exist in almost all body fluid and contain diverse biological contents which may be reflective of disease state. Circular RNAs (circRNAs) are stable in structure and have a long half-life in exosomes without degradation, thus making them reliable biomarkers. However, the potential of exosomal circRNAs as biomarkers of coronary artery disease (CAD) remains to be established. Here, we aimed to investigate the expression levels and the potential use of exosomal circRNAs as diagnostic biomarkers for CAD.  CircRNA expression levels in exosomes obtained from three plasma samples of CAD patients and three paired controls were analyzed using RNA sequencing. Exosomal circRNAs obtained in the profiling phase were then verified in two-center validation cohorts. Finally, the ability of exosomal circRNAs, adjusting for Framingham Heart Study (FHS) risk factors, was determined to discriminate between CAD patients and non-CAD controls. P < 0.05) as candidate biomarkers for further validation. Our data showed that only hsa_circ_0005540 was significantly associated with CAD (P < 0.0001). After adjustment for risk factors, hsa_circ_0005540 showed a high discriminatory power for CAD in ROC analyses  = 0.799 − 0.906, P < 0.001).  355 circRNAs were differentially expressed between these two groups: 164 were upregulated, and 191 were downregulated. Here, we selected the potential circRNAs (fold change > 4,  Our results suggest that plasma exosomal hsa_circ_0005540 can be used as a promising diagnostic biomarker of CAD. CAD is one of the most common forms of cardiovascular diseases and remains a leading cause of mortality worldwide. It poses a high economic burden to the national healthcare system mainly due to its late diagnosis and severe complications , 2. AmonExosomes are a class of nanoscale (30-150 nm) extracellular vesicles that can be generated and released by most cell types . They arCircRNAs are a large novel type of endogenous transcripts and represent a recent research hotspot in the field of RNA. They form covalently closed loop structures with neither 5′-3′ polarities nor a polyadenylated tail, which makes them much more stable than linear RNAs –20. ReceRecent studies confirmed that circRNAs are enriched and stable in exosomes and can continually play their roles after exosomes are secreted from the parent cells into the bodily fluid , 29. TheSixty-one CAD patients and thirty-eight non-CAD controls enrolled from center 1  between March 2018 and August 2018 formed the profiling and internal validation phases. Forty-seven CAD patients and 51 non-CAD controls recruited at center 2  between January 2019 and May 2019 constituted the external validation phase. The diagnosis of CAD was based on the percentage narrowing of each coronary artery segment, with 50% stenosis of at least one of the major coronaries . Patients with impaired ejection fraction, heart failure, or acute myocardial injury were excluded. Age-, sex-, and ethnicity-matched subjects were used as non-CAD controls. All the controls have normal electrocardiogram (ECG) records and no evidence of CAD. General exclusion criteria were a known history of leukopenia, thrombocytopenia, or severe hepatic or renal dysfunction, as well as evidence for inflammatory or malignant disease. Written informed consent was obtained from each of the participants. This study was approved by the Ethics Committee of the First People's Hospital of Foshan and the Affiliated Hospital of Guangdong Medical University under the guidance of the Helsinki Declaration. The characteristics of the study subjects are shown in For exosomal RNA extraction, an exoRNeasy Serum/Plasma Midi kit  was used to isolate total exosome RNA from 1 ml prefiltered plasma for validation cohorts according to the manufacturer's protocol. Briefly, prefiltered plasma was mixed 1 : 1 with 2x binding buffer (XBP) and added to the exoEasy membrane affinity column to bind the exosomes to the membrane. After centrifugation, the flow-through was discarded and the wash buffer (XWP) was added to the column to wash off nonspecifically retained material. After another centrifugation and discarding of the flow-through, the plasma exosomes were lysed by adding QIAzol to the spin column, and the lysate was collected by centrifugation. Following the addition of chloroform, thorough mixing and centrifugation to separate organic and aqueous phases, the aqueous phase was recovered and mixed with ethanol. The sample-ethanol mixture was added to the RNeasy MinElute spin column and centrifuged. The column was washed once with buffer RWT, and then twice with buffer RPE followed by elution of RNA in water. Collected RNA was then used in the subsequent experiments.Total RNA for RNA-seq analysis was extracted using an exoRNeasy Serum/Plasma Maxi kit  from 4 ml prefiltered plasma according to the manufacturer's protocol. The purity and concentration of RNA were determined from the OD260/280 readings using a spectrophotometer (NanoDrop ND-1000). The RNA integrity was assessed using Agilent Bioanalyzer 2100 (Agilent Technologies). Total RNA from each sample was subjected to the RiboMinus Eukaryote Kit  to remove ribosomal RNA before the construction of RNA-seq libraries. Strand-specific RNA-seq libraries were prepared using NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB) following the manufacturer's instructions. Briefly, approximately 50 ng of ribosome-depleted RNA samples were fragmented and then used for first- and second-strand cDNA syntheses with random hexamer primers. For second-strand cDNA synthesis, dUTP mix (without dTTP) was used which allows the removal of the second strand. An End-It DNA End Repair Kit was used to repair the ends of the double-stranded cDNA fragments, which were then modified by the Klenow fragment so that an A was added to the 3′ end of the DNA fragments; the fragments were finally ligated to adapters. The ligated cDNA products were purified and treated with uracil DNA glycosylase to remove the second-strand cDNA. Purified first-strand cDNA was subjected to 14 cycles of PCR amplification, followed by library analysis with a Bioanalyser 2100 . The strand-specific RNA-seq libraries were sequenced using Illumina HiSeq™ 2500 platform.ACTIN was used to normalize the RNA preparation. The relative fold-change was calculated using the 2ΔΔCT- method. Primer sequences are listed in TABLE Total RNAs were converted to cDNA using PrimeScript™ RT Reagent Kit . The differentially expressed exosomal circRNAs selected from the profiling data were measured by quantitative real-time RT-PCR using the LightCycler® 96 detection system at 95°C for 3 min and amplified by 40 cycles of denaturing at 95°C for 10 s and 60°C for 30 s. The CT value was the fractional cycle number at which the fluorescence exceeded the given threshold. t-test and χ2 test for continuous variables and categorical variables, respectively. Data were expressed as percentages for the qualitative variables and mean ± standard deviation (SD) for the quantitative variables. In the scatter plot depicting the circRNA expression, the horizontal lines represent the medians. The area under the receiver operator characteristic (ROC) curve (AUC) was used to evaluate the diagnostic value of exosomal circRNAs for CAD. The corresponding sensitivity and specificity were identified through the ROC curve analysis. Multivariate logistic regression models were constructed with stepwise addition of FHS risk factors  for each circRNA. All statistical analyses were performed using SPSS software. P < 0.05 was considered statistically significant.The differences of the demographic, clinical pathological characteristics and circRNA expression levels between the CAD patients and non-CAD controls were estimated using Student's n = 3) and non-CAD controls (n = 3) were assessed using an Illumina HiSeq™ 2500. As illustrated in the hierarchical clustering analysis (P < 0.05).To identify alterations in circRNA expression in circulating exosomes between CAD patients and non-CAD controls, we first performed circRNA screening. Specifically, the circRNA profiles in isolated circulating exosomes from CAD patients , whereas there was no significant difference for hsa_circ_0000676 or hsa_circ_0007385 between the two groups (Figures To further explore the applicability of these exosomal circRNAs as potential diagnostic biomarkers for CAD, we tested these circRNAs in an external validation cohort consisting of 47 CAD patients and 51 non-CAD controls. As shown in  Figures .P < 0.0001, r = 0.212, P < 0.01, Supplementary Figure P < 0.0001, As the above results were obtained in two different cohorts, we further assessed hsa_circ_0005540 as a biomarker for CAD in all individuals, including 105 CAD patients and 86 non-CAD controls. Levels of hsa_circ_0005540 were significantly increased in CAD patients as compared with the non-CAD controls (Currently, CAD can be diagnosed by either noninvasive or invasive methods, but each has its limitations. Therefore, a highly sensitive and specific biomarker is urgently needed to facilitate the diagnosis of CAD. In this study, we first explored the differentially expressed exosomal circRNAs in CAD patients and non-CAD controls, and potential circRNAs were selected for verification. Our results from clinical samples demonstrated that the levels of exosomal hsa_circ_0005540 in plasma from patients with CAD were significantly elevated compared with those non-CAD controls. The present work has led us to conclude that exosomal circRNAs can be clinically practicable biomarkers for CAD diagnosis.To date, clinical studies of circRNAs expression in CAD are limited and have produced varying results. Several cross-sectional studies have reported differences in some circRNAs among patients with CAD compared to controls –34. A stMCTP1, which encodes multiple C2 domain transmembrane protein 1. There is currently no definitive evidence demonstrating the biological function of hsa_circ_0005540. CircRNAs can regulate gene expression by acting as miRNA sponges. There are some binding sites for miR-221 and miR-145 in hsa_circ_0005540, and these miRNAs were associated with CAD [hsa_circ_0005540 is located at chr5:94204037-94248681, and the name of its source gene is with CAD –38. Morewith CAD . miR-145with CAD , 38. TheIn addition to CAG, many methods are currently used to diagnose CAD, including routine ECG, Holter monitoring, TET, and CTA. The sensitivities of these methods have been shown to be 0.29, 0.65, 0.79, and 0.92, respectively –42. The It should be pointed out that there were some limitations in our study. Although 2 independent cohorts for validation had been performed in our study, a larger clinical population is warranted to further validate the performance of the association of exosomal hsa_circ_0005540 as a potential blood-based signature in CAD. Furthermore, since the levels of exosomal circRNAs may be affected by multiple parameters such as the change in expression in the tissue and the release of exosomes by the cells into the circulation, further studies are necessary to explore the mechanisms underlying the dysregulation and the putative impact of the changes of exosomal hsa_circ_0005540 level in the pathophysiology.In conclusion, our study reported a novel signature of exosomal hsa_circ_0005540, allowing to distinguish patients with CAD from non-CAD controls, and provided first insights into the levels of exosomal circRNAs in patients with CAD that could be translated into noninvasive blood-based biomarker.


An acute respiratory disease, caused by a novel coronavirus , the coronavirus disease 2019 (COVID-19) has spread throughout China and received worldwide attention. On 30 January 2020, World Health Organization (WHO) officially declared the COVID-19 epidemic as a public health emergency of international concern. The emergence of SARS-CoV-2, since the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002 and Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012, marked the third introduction of a highly pathogenic and large-scale epidemic coronavirus into the human population in the twenty-first century. As of 1 March 2020, a total of 87,137 confirmed cases globally, 79,968 confirmed in China and 7169 outside of China, with 2977 deaths (3.4%) had been reported by WHO. Meanwhile, several independent research groups have identified that SARS-CoV-2 belongs to β-coronavirus, with highly identical genome to bat coronavirus, pointing to bat as the natural host. The novel coronavirus uses the same receptor, angiotensin-converting enzyme 2 (ACE2) as that for SARS-CoV, and mainly spreads through the respiratory tract. Importantly, increasingly evidence showed sustained human-to-human transmission, along with many exported cases across the globe. The clinical symptoms of COVID-19 patients include fever, cough, fatigue and a small population of patients appeared gastrointestinal infection symptoms. The elderly and people with underlying diseases are susceptible to infection and prone to serious outcomes, which may be associated with acute respiratory distress syndrome (ARDS) and cytokine storm. Currently, there are few specific antiviral strategies, but several potent candidates of antivirals and repurposed drugs are under urgent investigation. In this review, we summarized the latest research progress of the epidemiology, pathogenesis, and clinical characteristics of COVID-19, and discussed the current treatment and scientific advancements to combat the epidemic novel coronavirus. R0) of SARS-CoV-2 to be around 2.2 [In December 2019, a cluster of pneumonia cases, caused by a newly identified β-coronavirus, occurred in Wuhan, China. This coronavirus, was initially named as the 2019-novel coronavirus (2019-nCoV) on 12 January 2020 by World Health Organization (WHO). WHO officially named the disease as coronavirus disease 2019 (COVID-19) and Coronavirus Study Group (CSG) of the International Committee proposed to name the new coronavirus as SARS-CoV-2, both issued on 11 February 2020. The Chinese scientists rapidly isolated a SARS-CoV-2 from a patient within a short time on 7 January 2020 and came out to genome sequencing of the SARS-CoV-2 [ound 2.2 , or evenound 2.2 , and famound 2.2  outbreaksarbecovirus, Orthocoronavirinae subfamily) [The SARS-CoV-2 is a β-coronavirus, which is enveloped non-segmented positive-sense RNA virus (subgenus bfamily) . Coronavbfamily) . It was bfamily) , to infeThe epidemic of unknown acute respiratory tract infection broke out first in Wuhan, China, since 12 December 2019, possibly related to a seafood market. Several studies suggested that bat may be the potential reservoir of SARS-CoV-2 . HoweverHuman-to-human transmission of SARS-CoV-2 occurs mainly between family members, including relatives and friends who intimately contacted with patients or incubation carriers. It is reported  that 31.Isolated from a COVID-19 pneumonia patient, a worker in the Wuhan seafood market, the complete genome of Wuhan-Hu-1 coronavirus (WHCV), one strain of SARS-CoV-2, is 29.9 kb . While SACE2, found in the lower respiratory tract of humans, is known as cell receptor for SARS-CoV  and reguBecause the binding of SARS-CoV-2 Spike (S) glycoprotein and ACE2 receptor is a critical step for virus entry, virus-receptor binding affinity is under intensive study through different approaches. Systematic detection of β-CoV receptors showed that human cells expressing ACE2, but not human Dipeptidyl peptidase-4 (DPP4) or APN (Aminopeptidase N), were enhanced entry of SARS-CoV-2 . While, As an emerging acute respiratory infectious disease, COVID-19 primarily spreads through the respiratory tract, by droplets, respiratory secretions, and direct contact  for a loThe viral research institution in China has conducted preliminary identification of the SARS-CoV-2 through the classical Koch’s postulates and observing its morphology through electron microscopy . So far,A recent study led by Prof. Nan-Shan Zhong’s team, by sampling 1099 laboratory-confirmed cases, found that the common clinical manifestations included fever (88.7%), cough (67.8%), fatigue (38.1%), sputum production (33.4%), shortness of breath (18.6%), sore throat (13.9%), and headache (13.6%) . In addiIn laboratory examination results, most patients had normal or decreased white blood cell counts, and lymphocytopenia , 54. ButMoreover, the CT imaging showed that computed tomography on the chest was ground-glass opacity (56.4%) and bilateral patchy shadowing (51.8%) , sometimBased on the current information, most patients had a good prognosis, while a few patients were in critical condition, especially the elderly and those with chronic underlying diseases. As of 1 March 2020, a total of 79,968 confirmed cases, including 14,475 (18.1%) with severe illness, and 2873 deaths (3.5%) in mainland China had been reported by WHO , acute kThe immune response is vital for the control and resolution of CoV infections, while it can also lead to immunopathogenesis, associated with the immune response out of control. The S proteins of Coronavirus binds to the host cells by ACE2, fusing to the membrane and release the viral RNA. The viral RNAs, as pathogen-associated molecular patterns (PAMPs), are detected by the pattern recognition receptors (PRRs). Usually, Toll-like receptor (TLR) 3, TLR7, TLR8, and TLR9 sense viral RNA and DNA in the endosome , 61. TheGiven the lack of effective antiviral therapy against COVID-19, current treatments mainly focused on symptomatic and respiratory support according to the Diagnosis and Treatment of Pneumonia Caused by COVID-19 (updated to version 6) issued by National Health Commission of the People’s Republic of China . Nearly Based on the experience of fighting the epidemic SARS-CoV and MERS-CoV previously, we may learn some lessons for some treatment strategies against coronavirus . AntivirScientists previously confirmed that the protease inhibitors lopinavir and ritonavir, used to treat infection with human immunodeficiency virus (HIV) , could iThe outbreak of COVID-19 swept across China rapidly and has spread to 85 countries/territories/areas outside of China as of 5 March 2020 [Given more than 80% of patients are confirmed in Hubei province, the hospitals and medical workers in Hubei are facing and bearing enormous pressure and severe challenge, including a high risk of infection and inadequate protection, as well as overwork, frustration and exhaustion . Chinese


Only 1.5% of the human genome encodes proteins, while large part of the remaining encodes noncoding RNAs (ncRNA). Many ncRNAs form structures and perform many important functions. Accurately identifying structured ncRNAs in the human genome and discovering their biological functions remain a major challenge.Here, we have established a pipeline (CM-line) with the following features for analyzing the large genomes of humans and other animals. First, we selected species with larger genetic distances to facilitate the discovery of covariations and compatible mutations. Second, we used CMfinder, which can generate useful alignments even with low sequence conservation. Third, we removed repetitive sequences and known structured ncRNAs to reduce the workload of CMfinder. Fourth, we used Infernal to find more representatives and refine the structure. We reported 11 classes of structured ncRNA candidates with significant covariations in humans. Functional analysis showed that these ncRNAs may have variable functions. Some may regulate circadian clock genes through poly (A) signals (PAS); some may regulate the elongation factor (EEF1A) and the T-cell receptor signaling pathway by cooperating with RNA binding proteins.By searching for important features of RNA structure from large genomes, the CM-line has revealed the existence of a variety of novel structured ncRNAs. Functional analysis suggests that some newly discovered ncRNA motifs may have biological functions. The pipeline we have established for the discovery of structured ncRNAs and the identification of their functions can also be applied to analyze other large genomes.The online version contains supplementary material available at 10.1186/s12864-021-07474-9. The Human Genome Project shows that only 1.5% of the human genome encodes proteins. The subsequent ENCODE project showed that approximately 75% of the human genome can be transcribed into RNAs, of which 74% are non-protein-encoded RNAs (ncRNAs) .Some of these ncRNAs form complex structures that perform important biological functions. tRNA  forms a Different methods and algorithms have been developed to search for structured ncRNAs. One approach is to look at large gaps between protein-coding regions and highly conserved sequences among closely related bacterial species . AnotherFugu and zebrafish [Recently, several algorithms including QRNA , RNAz 1, CMfindeebrafish . In 2006ebrafish . In 2008ebrafish . In 2010ebrafish . In 2013ebrafish . In 2017ebrafish .SDC RNA motif could reduce gene expression. In addition, similar pipelines were applied to discover three novel classes of self-cleaving ribozymes [As early as 2004, the group of Breaker  began usibozymes . These eIn the current study, we have modified the old pipeline which was driven by CMfinder and had been used for fungal ncRNA discovery  to find yjdF riboswitch [The functional analyses include the assay of the genomic location of the motif and its associated upstream and downstream genes, the RegRNA assay , the RBPboswitch , and thrboswitch , 44, 45 Through our pipeline, many structured ncRNA candidates in humans were discovered, and 11 of the selected candidates are reported here. These ncRNAs have many different functions. Some may regulate circadian clock genes by cooperating with poly (A) signals (PAS), some may regulate elongation factors by cooperating with RNA binding proteins, some may regulate T-cell receptor signaling pathways. Importantly, our pipeline can also be applied to analyze other large genomes to discover structured ncRNAs and the identification of their functions.Promising ncRNA candidates were identified by applying our computational pipeline to compare the human genomes with selected 23 metazoan and three protist genomes , 15,151 candidates were discovered . Then, R-scape  which usEEF1A2–70,236.Below are described the most promising 11 candidate motifs that likely serve as functional ncRNAs. The alignments including sequences and species for each sequence for these motifs in Stockholm format are presented in the Additional files EEF1A2–70,236 motif . The moEEF1A2 . The human example of this motif is located in the 2th intron of the gene EEF1A2 on the chromosome 20 (NCBI BLAST (version 2.2.25) [This motif in many other species appears to be in the intron of the gene  2.2.25) ), which  2.2.25) .In order to analyze RNA regulatory elements that are embedded in the RNA motif, we used a web-based RegRNA  system tHere, we used RBPmap  to show ZNF516–12356 motif consensus model is based on 120 examples from 102 species. Note that some of the examples have the same sequences although they are from different species. The secondary structure includes two hairpins with the support of seven covariations, three of which are significant covariations detected by R-scape [The  R-scape , indicatZNF516 on the chromosome 18, encoding a zinc finger protein, which is a transcriptional regulator that binds to the gene promotor to control brown adipose tissue (BAT) differentiation, thereby regulating thermogenesis [ZNF516 gene is highly expressed in certain cancer types and is a potential suppressor of EGFR, which is related to breast carcinogenesis [The human example of this motif is located in the 3rd intron of the gene ogenesis . The canogenesis , 38 analogenesis . RegRNA2ogenesis  showed togenesis  predicteogenesis . One hypMotif-3778 motif have been identified from 34 species. The consensus sequence and secondary structure model based on these sequences reveal that this motif may form a long hairpin structure with several bulges  on the chromosome 14. According to UCSC browser, this intron is alternatively spliced. It is complementary to part of the NONCODE  RNA tranRBPmap  predicteACVR2B-21,548 motif includes 33 examples from 18 species. The predicted secondary structure includes three hairpins with a three-way junction, which is supported by a few covariations and some compatible mutations , which encodes a transmembrane serine/threonine kinase activin type-2 receptor, transducing the activin signal from the cell surface to the cytoplasm to regulate many biological processes [Two human examples of this motif are both located in the 5th intron region of the rocesses . AccordiBoth RegRNA and RBPmap predicted that some of the sequences of the motif might have transcriptional regulatory  motifs aANTXR2–33250, represented by 75 sequences from 53 species (humans and other metazoans). The consensus sequence and secondary structure model based on these sequences reveal that this motif may form a structure with a three-stem junction, supported by several covariations and compatible mutations . If the predicted PAS is true and it is used by the RNA motif, it is likely that RNA spliced isoforms use this PAS as polyadenylation processing signals and therefore the RNA motif may interact with this PAS to regulate gene expression.Represented by 55 distinct examples from 43 species, the motif typically forms a long hairpin structure with one internal loop  and RNA binding protein binding sites. However, since these regions are not highly conserved, we did not further investigate these elements.DPH1–85951 motif is represented by 19 examples from 15 species, typically forming a simple hairpin with two bulges  for RNA binding protein SRSF1. Since SRSF1 is an arginine/serine-rich splicing factor and is a powerful splicing enhancer , the funCAPN6–33096 motif is based on 79 examples from 60 species. Its secondary structure includes two stems and one big internal loop, of which the formation of P1 and P2 stems is supported by seven covariations and five compatible mutations. One of the covariations is significant  or long mRNA (full length).RegRNA analysis predicted that most of the examples of the To investigate whether these RNA motifs are expressed in the cells, whether these motifs affect the splicing patterns of their pre-mRNAs and whether their gene expression levels are as high as those of house-keeping genes, we use Reverse transcriptase (RT) PCR to detect the gene expression.ZNF516–12356 motif and the exons adjacent to the motif could produce the expected PCR products, indicating that the cell can produce this motif and the exon RNAs  in the case of the ARNTL-1638 motif  was detected) due to unknown reasons.In addition, real-time PCR showed that the expression of the RNA motif was much lower than the house-keeping gene  produced an alternative splicing product [Regarding the effect of RNA motifs on pre-mRNA splicing patterns, we did not observe alternative splicing products during the RT-PCR for the motif  product .In order to better understand the mechanism of RNA function, further experiments such as Northern blotting, RNA structural analysis , aAbout 20 years ago, the first metabolite sensing structured RNA which was coined as riboswitch was discovered by Breaker’s lab . Later sOver the past few years, other groups also have applied CMfinder  and otheTo identify the function of an RNA motif, one of the important steps is to identify the RNA regulatory elements that are embedded inside or near the RNA motif. These regulatory elements in eukaryotes include RNA processing sites, poly (A) signals (PAS), splicing sites, binding sites for RBPs, intron or exon enhancers or repressors, miRNA target sites, etc. These elements could be utilized by RNA motifs to fulfill their functions, therefore identification of these elements will help infer the functions of RNA motifs.glmS ribozyme [Another important step is to find genes that are associated with these RNA motifs. Riboswitches are often involved in genes involved in metabolite pathways directly related to their cognate ligand. The ribosomal protein-associated RNA motifs found in fungi are usually located in noncoding regions of genes encoding ribosomal proteins . Ribozymribozyme , which iThrough the study of the functions of riboswitches, we speculate that many of the motifs we identified in humans may bind to proteins to regulate gene expression in a manner similar to riboswitch regulation. However, in these cases, one RBP could bind to the RNA motif, and the binding could alter the conformation of the expression platform, such as an alternative splicing site or a poly (A) signal sequence to regulate the gene expression.The covariations reported in many earlier studies , 31, 75 However, other subtle mutations can also be used to indicate the formation of RNA structures, especially for those closely related species. Covariations in the structured RNAs are produced during evolution . They caHence, we used both R2R and R-scape to report covariations because we believe each has its advantage especially when we compared these two methods to predict covariations for purine and TPP riboswitches. However, eleven ncRNA candidates reported here all contain at least one significant covariations.ARNTL-1638, ZNF516–12356, ICE2–92051 and CAPN6–33096 motifs contain a poly (A) signal (PAS) embedded in their structure. These motifs might regulate the availability of the PAS to regulate the stability of mRNAs. The CD247–2015 motif may regulate the formation of T-cell receptor-CD3 complex to recognize intracellular signals. However, at this stage, more bioinformatics data and the use of more extensive biological experimental procedures are still needed to further understand their functions.We found that the Many ncRNAs have been identified in the three domains of life. Many ncRNAs form structures and perform a diversity of important biological functions. However, identifying structured ncRNAs in the human genome and discovering their biological functions are still challenging.In this work, we have established the CM-line with a CMfinder algorithm, making it suitable for analyzing large genomes of humans and other animals. Through CM-line, we have discovered many structured ncRNA candidates in humans, and report 11 of the selected candidates here. These ncRNAs have various functions. Some may regulate circadian clock genes by cooperating with poly (A) signals (PAS), some may regulate elongation factors by cooperating with RNA binding proteins, some may regulate T-cell receptor signaling pathways. The pipeline we have established for the discovery of structured ncRNAs and identification of their functions can also be applied to analyze other large genomes.P-value cutoff of 0.001, indicating that they are unlikely to encode proteins. The remaining clusters were fed into CMfinder [The human genome, 23 metazoan and three protist genomes  and comCMfinder  was usedCMfinder  to get rSeveral algorithms have been developed to assess the covariations in sequence alignments. However, currently available algorithms cannot consistently handle problems such as incorrect alignments which could predict invalid covariation . We haveWhen using both R-scape  and R2R To determine whether the predicted structured RNA candidates were reported previously, we used Infernal to compare them with known RNA motifs in the Rfam database . Novel Rhuman embryonic kidneys)  were passaged twice before collecting cells for RNA extraction. HEK293 were cultured in DMEM medium and supplemented with 10% fetal bovine serum, 100 mg/mL penicillin, and 100 units/mL streptomycin . Cells were maintained in a humidified 37 °C incubator with 5% CO2.Cell culture follows standard protocol . BrieflyRNA extraction was performed by using the TIANGEN RNAprep Kit  and following the manufacturer’s protocol.RT-PCR was performed as previously described . For motReal-time PCR was performed on the Agilent Mx3000P real-time PCR system using the TransStart® Top Green qPCR SuperMix kit according to the manufacturer’s instructions .Additional file 1 Table S1. Species, their classification and links to genome download sites (from NCBI). Table S2. Other related databases and their download sites. Table S3. Motif basic information. Table S4. Primers and product sizes.Additional file 2 Fig. S1. Comparison of covariations reported by R2R and R-scape. Fig. S2. Analysis of structured ncRNA candidates.Additional file 3. Motif ACVR2B_21,548 Stockholm file.Additional file 4. Motif ARNTL-1638 Stockholm file.Additional file 5. Motif CAPN6_33,096 Stockholm file.Additional file 6. Motif CD247_2015 Stockholm file.Additional file 7. Motif DPH1_85,951 Stockholm file.Additional file 8. Motif EEF1A2_70,236 Stockholm file.Additional file 9. Motif ICE2_92,051 Stockholm file.Additional file 10. Motif_33,250 Stockholm file.Additional file 11. Motif_59,232 Stockholm file.Additional file 12. Motif MYH7_3778 Stockholm file.Additional file 13. Motif ZNF516–12356 Stockholm file.Additional file 14 Fig. S3. Genomic locus diagram.Additional file 15 Fig. S4. The full-length original agarose gels for Fig. Additional file 16 Fig. S5. Real-time PCR for the NPTN-6924 motif.Additional file 17. Pictures of sequence alignments for 11 RNA motifs.Additional file 18:. txt file for 11 motifs.Additional file 19:. txt file for all 11,763 motifs part1.Additional file 20:. txt file for all 11,763 motifs part2.Additional file 21:. txt file for all 11,763 motifs part3.Additional file 22:. txt file for all 11,763 motifs part4.Additional file 23:. txt file for all 11,763 motifs part5.Additional file 24. Bioinformatics steps and configurations.Additional file 25. Fig. S6. The full-length original agarose gel for Fig. S5b.


Remdesivir (RDV) is a direct-acting antiviral agent that is used to treat patients with severe coronavirus disease 2019 (COVID-19). RDV targets the viral RNA-dependent RNA polymerase (RdRp) of severe acute respiratory syndrome coronavirus 2 (SARS–CoV-2). We have previously shown that incorporation of the active triphosphate form of RDV (RDV-TP) at position i causes delayed chain termination at position i + 3. Here we demonstrate that the S861G mutation in RdRp eliminates chain termination, which confirms the existence of a steric clash between Ser-861 and the incorporated RDV-TP. With WT RdRp, increasing concentrations of NTP pools cause a gradual decrease in termination and the resulting read-through increases full-length product formation. Hence, RDV residues could be embedded in copies of the first RNA strand that is later used as a template. We show that the efficiency of incorporation of the complementary UTP opposite template RDV is compromised, providing a second opportunity to inhibit replication. A structural model suggests that RDV, when serving as the template for the incoming UTP, is not properly positioned because of a significant clash with Ala-558. The adjacent Val-557 is in direct contact with the template base, and the V557L mutation is implicated in low-level resistance to RDV. We further show that the V557L mutation in RdRp lowers the nucleotide concentration required to bypass this template-dependent inhibition. The collective data provide strong evidence to show that template-dependent inhibition of SARS–CoV-2 RdRp by RDV is biologically relevant. Filoviridae (e.g. Ebola virus) and Paramyxoviridae (e.g. Nipah virus and measles virus) as well as in vitro activity against viruses in the Pneumoviridae  family (–The U.S. Food and Drug Administration has recently issued an emergency use authorization (EUA) for the investigational drug remdesivir (RDV) to treat infection with severe acute respiratory syndrome coronavirus 2 SARS–CoV-2) . The EUA) family –7. AntivCoV-2 . T) family –12. Seve family –, 12.50) and a high barrier to the development of resistance were observed in cell cultures  revealed two mutations in the RdRp enzyme that confer low-level resistance to RDV . Using the same construct design, we generated complexes with mutations at position Ser-861 in nsp12 that are expected to affect efficiency of delayed chain termination with RDV-TP. The steric clash hypothesis predicts that smaller side chains will reduce delayed chain termination and larger side chains will increase inhibition, provided that substitutions do not impede regular NTP incorporations.Several recent structural and biochemical studies have demonstrated that the active SARS–CoV-2 RdRp complex is composed of the three nonstructural proteins nsp7, nsp8, and nsp12 –24. Reco–A). The WT enzyme shows delayed chain termination at position 12 (i + 3) , which is illustrated by more than 95% reduction in the full template length-to–(i + 3) product ratio in the presence of RDV-TP as compared with the full template length-to–p12 product ratio in the presence of ATP . Termination efficiency is gradually reduced by increasing the concentration of the next incoming nucleotide (UTP), as previously reported . Delayed chain termination is not observed with the S861G mutant. These findings are consistent with the removal of a steric clash when serine is replaced with glycine. Full-length product formation is already seen to near completion at UTP concentrations of ∼1 μm . The S861A mutation shows only subtle reductions in delayed chain termination (A and B), which is in agreement with a previous report by Wang and colleagues (A and C).We compared RNA synthesis of the WT SARS–CoV-2 RdRp complex with S861G, S861A, and S861P mutants . ReactioFig. 1reported . 50% realleagues . We furtTemplate A) or RDV (Template R) at the same strategic position 11 .The high efficiency of read-through with increasing NTP concentrations suggests that the newly synthesized copy of the RNA genome contains several RDV residues. This raises the question whether RDV may also inhibit RNA synthesis when present in the template . The cha32P]GTP (A). With Template A, a mixture of minimal concentrations of 0.1 μm CTP and UTP, and 1 μm ATP yields predominantly the full-length 20-mer RNA product (B). Increasing the concentration of UTP shows no significant increases in RNA synthesis. With Template R, we observe an intermediate product at position 10 at the same base concentrations of CTP, UTP, and ATP (B). These findings demonstrate that 0.1 μm UTP is not sufficient for incorporation opposite RDV, which is a marked difference to results obtained with Template A. Increasing concentrations of UTP can overcome the inhibitory effects, and we observe an accumulation of product at position 11, which points to a second site of inhibition immediately after the template-embedded RDV. A similar effect is seen with increasing the concentration of UTP and CTP that is required later at position 14. Increases only in CTP did not yield significant amounts of product at position 11, suggesting that incorporation of CTP opposite RDV is negligible. Higher concentrations of UTP and ATP or UTP, ATP, and CTP gradually reduced the inhibitory effects and increased the yield of full-length product. Overall inhibition is driven by the accumulation of products at positions 10 and 11. Together these findings show that a single RDV residue in the template inhibits efficiency of incorporation of the complementary UTP and the adjacent NTP (ATP in this particular sequence context). NTP concentrations required to overcome these obstacles are higher than observed with delayed chain termination (C).A 4-mer primer was used to initiate RNA synthesis with SARS–CoV-2 RdRp in the presence of [α-32P]GTP A. With T product B. Increa and ATP B. These mination C.m to 100 μm showed the following effects (A). The primary observation is that the UTP concentration required to overcome inhibition at position 10 and to generate the product at position 11 is lower with the Val-557 mutant  (B). The associated steady-state kinetic parameters show that UTP incorporation is ∼5-fold more efficient with the mutant enzyme (Table S1). The effect of V557L is specific to incorporation of UTP opposite RDV. Incorporation of the next nucleotide (ATP) is not significantly affected by the V557L mutant enzyme .We next asked whether such template-dependent inhibition provides a biologically relevant mechanism of action of RDV. To address this question we attempted to study possible neutralizing effects of known resistance conferring mutations. In nsp12, two amino acid substitutions have been associated with low-level resistance to RDV . The eff effects A. The prctively) B. The asA). These findings suggest that regular nucleotide incorporations might be compromised, which is not unusual for enzymes that contain resistance-conferring mutations. Steady-state kinetic parameters show that the efficiency of ATP incorporation is ∼3-fold reduced with the mutant enzyme (Table S2). However, it is important to note that the selectivity (Vmax/mK (ATP)/Vmax/mK (RDV-TP)) is almost identical for V557L and the WT enzyme. Thus, V557L does not show a resistance phenotype at the level of incorporation of RDV-TP. Moreover, V557L is also not significantly different from the WT in assays monitoring delayed chain termination with either single or multiple sites of incorporation for RDV-TP . The collective data provide evidence to show that the effect of V557L is specific to RDV embedded in the template, which in turn suggests that this type of inhibition is biologically relevant. Moreover, the S861G mutant specifically reduces delayed chain termination and does not affect template-dependent inhibition of RNA synthesis . However, this mutation is here newly described, and it remains to be seen whether the corresponding virus is viable and may affect drug susceptibility.However, the increase in efficiency of UTP usage with the V557L mutant does not translate in increases in the amount of the full-length product A. These Building on our previous models of the pre-incorporation state of the replicating SARS–CoV-2 nsp7/nsp8/nsp12 complex , we founi.e. three residues downstream of the site of incorporation. However, higher concentrations of NTP pools can reduce the efficiency of delayed chain termination, concomitantly enhancing read-through and full-length product formation  to i + 4 (post-translocation), which liberates the nucleotide binding site. For HIV type 1 (HIV-1) reverse transcriptase (RT), we developed site-specific footprinting techniques that demonstrated such nucleotide-dependent translocation . Seifertm are required to bypass this obstacle (stage 6). This concentration is significantly lowered with the V557L mutant, which provides a mechanism for drug resistance (The 3′–5′ proofreading ExoN activity of the replication complex plays another important role in the generation of RNA strands with embedded RDV residues. ExoN(−) MHV shows an ∼5-fold increase in RDV susceptibility; however, potent inhibition was still seen with an intact ExoN . This pasistance . Val-557m and rescue of RNA synthesis remains incomplete even at higher NTP concentrations (stage 8). Finally, we note that template-dependent inhibition mechanisms have also been demonstrated with other nucleotide analog inhibitors . This effect has been demonstrated with various RdRp complexes . RDV-TP was provided by Gilead Sciences . NTPs were purchased from GE Healthcare. [α-supporting information.SARS–CoV-2 RdRp WT and mutant  were expressed and purified as we reported previously , 14. Addm for WT, S861A, S861G, and S861P mutants, and 0.45 μm for the V557L mutant.RNA synthesis assays involving RDV-TP incorporation by SARS–CoV-2 RdRp complex, data acquisition and quantification were done as previously reported by us , 33, 34.m followed by a full template-length RNA synthesis (data not shown). The following DNA template  was used as the starting material: 3′-AAACAACAATAAAAAGCGCA-5′. Underlined portion indicates the region which is complementary to the 5′-monopyosphorylated RNA primer: 5′-pUUUGUUGUU. The template residue “T” indicates the site of the RDV-TP incorporation into the RNA primer. Therefore, the fully extended primer will contain an embedded remdesivir (R) and may serve as a 20-nt RNA template for the RNA synthesis by SARS–CoV-2 RdRp complex: 5′-pUUUGUUGUURUUUUUCGCGU-3′, where underlined portion is complementary to the RNA primer 5′-pACGC used in SARS–CoV-2 RNA synthesis reactions. Note that this RNA primer can only anneal to a fully synthesized 20-nt RNA template. The T7 RNA polymerase reaction mixtures contained 30 units of the polymerase, 200 μm RNA primer, 100 μm DNA template, 100 μm UTP/CTP/GTP mixture, and 100 μm ATP or RDV-TP in a 25 mm Tris-HCl buffer (pH 8). Reactions were started with 5 mm MgCl2, incubated at 37°C for 90 min, boiled for 10 min, and incubated with 2 units of Turbo DNase (Thermo Fisher Scientific) at 37°C for 30 min. The reaction mixtures were then extracted with phenol/chlorophorm  and buffer-exchanged consecutively three times using the size-exclusion chromatography Bio-Rad p6 spin columns (Bio-Rad Laboratories).Our initial tests revealed that T7 RNA polymerase (Thermo Fisher Scientific) readily accepted short RNA/DNA primer/templates for RNA synthesis and was capable of incorporating RDV-TP at high concentrations of 100 μRUUUUUCGCGU-3′, where underlined portion is complementary to the RNA primer 5′-pACGC), or with adenosine at the equivalent position (5′-pUUUGUUGUUAUUUUUCGCGU-3′), data acquisition and quantification were done as previously reported by us . The pre-incorporation state incorporated elements from the previous models into this new structure, with optimization achieved via minimization and conformational searches done with the Schrödinger suite of programs .Modeling was done following the general approaches outlined previously , 14. A mAll data are included within this article.


After the increasing number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections all over the world, researchers and clinicians are struggling to find a vaccine or innovative therapeutic strategies to treat this viral infection. The severe acute respiratory syndrome coronavirus infection that occurred in 2002, Middle East respiratory syndrome (MERS) and other more common infectious diseases such as hepatitis C virus, led to the discovery of many RNA-based drugs. Among them, siRNAs and antisense locked nucleic acids have been demonstrated to have effective antiviral effects both in animal models and humans. Owing to the high genomic homology of SARS-CoV-2 and severe acute respiratory syndrome coronavirus (80–82%) the use of these molecules could be employed successfully also to target this emerging coronavirus. Trying to translate this approach to treat COVID-19, we analyzed the common structural features of viral 5’UTR regions that can be targeted by noncoding RNAs and we also identified miRNAs binding sites suitable for designing RNA-based drugs to be employed successfully against SARS-CoV-2. Similarly to the severe acute respiratory syndrome coronavirus (SARS-CoV) outbreak in 2002, the scientific community is struggling to find novel therapies, as neither vaccines or specific treatments are currently available. Among the first therapeutic options that have been employed since the beginning we recall the antiviral therapies ORF1b , it is surely a valid strategy to reduce the viral load and the severity of the disease. In any case, the idea that RNAi can be used to mitigate the symptoms of new coronavirus infections and other emerging infectious diseases was a debated concept in the past , but it Supplementary data). Owing to their high similarity, the region that encompasses the TRS  is identical in SARS-CoV and SARS-CoV-2 (Supplementary data). More recently, a preliminary work of Taiaroa and his group reports the use of the leader sequence of SARS-CoV-2 for the identification of subgenomic mRNAs [To evaluate whether the concepts already found for 5’UTR of SARS-CoV can be transferred also to SARS-CoV-2, we calculated the similarity between the 5’UTR of the two coronaviruses. We found that the similarity of these two regions is 88.76% B and seqRS-CoV-2 B and conic mRNAs . This seIn 2006, to test the promoter activity of the 5′UTR of SARS-CoV in eukaryotic cells and identify the fundamental regions necessary for viral replication, Zhang and collaborators assessed the function of many 5’UTR clones lacking increasing portions of the SARS-CoV 5’UTR sequence . They foin vitro but outlined the importance of the secondary structure of the 5’UTR region in coronavirus RNA synthesis. The presence of these stem-loops is highly conserved in many coronaviruses derived from all three major CoV groups [The RNA synthesis processes of coronaviruses takes place in the cytoplasm and is regulated by proteins of the host cell. Among these proteins, the zinc-finger and RNA binding motif MADP1 was able to interact with the 5’UTR region of the SARS-CoV genome and this interaction was confirmed also in coronavirus infectious bronchitis virus . InteresV groups . MoreoveV groups ,36. By aV groups . HoweverTherefore, all of these data outline once more the importance of 5’UTR when devising therapeutic strategies for coronaviruses.RNAi is not the only strategy that can be adopted to target the 5’UTR of coronaviruses. A few years ago an innovative antiviral strategy for hepatitis C virus (HCV), another common widespread virus, based on a locked nucleic acid (LNA)–modified DNA phosphorothioate antisense oligonucleotide, named Miravirsen, has been proposed . This apSupplementary data). We would like to emphasize that this score is only an indicative in silico prediction score and does not necessarily reflects the real hybridization behavior in in vitro or in vivo studies. In any case, this score is a good starting point for further experimental considerations. Through this approach, up to 41 sites for miRNA binding were identified on the 5’UTR of SARS-CoV-2. Of note, several miRNAs such as miR-4507, miR-638, miR-3150b-3p and miR-602 can bind conserved regions of the 5’UTR of SARS-CoV-2. In HCV infection, miR-122 is highly expressed in the liver and this organ represents the primary target site of the virus. Similarly, we focused to find highly expressed miRNAs in the lungs that are the main target organs for SARS-CoV-2. Therefore, we extracted and analyzed the expression data contained in the human miRNA tissue atlas [To translate this approach into the identification of therapeutic molecules for the potential treatment of COVID-19 infections, we searched for potential miRNAs binding sites targeting the 5’UTR of SARS-CoV-2. Target recognition algorithms such as PITA  allowed ue atlas  to gain Supplementary data) where other cis-acting region are present, although the regulatory mechanisms underlying these interactions are still not completely understood [However, many other miRNAs resulted to bind coding regions and 3’UTR of the coronavirus  have been proposed as effective therapeutics against CoVs infection [Flaviviridae and Filoviridae families together with molecules such as BXC4430, favipiravir and 3-deazaneplanocin A. However, the efforts and the development stage of these approaches are different as recently reported in an editorial paper by Brinton and colleagues [For nfection  and furtlleagues .One of the main problems associated to the delivery of RNA-based drugs is the intracellular presence of RNAses that can rapidly degrade the delivered cargo thus reducing the efficacy of the treatment . TherefoAnother less common antiviral approach is the exploitation of a fundamental property of ssRNA viruses: the extensive cytoplasmic RNA replication that leads to an extreme transgene expression. It has been demonstrated that alphaviruses, flaviviruses, rhabdoviruses and measles viruses can be employed for immunization against several infectious agents, thus providing a strong immune response against challenges with lethal viral loads . In factClinicalTrials.gov Identifier: NCT02410733).Encapsulation of nucleoside-modified RNA molecules within lipid-nanoparticles is another relatively new approach already employed successfully in the immunization of mice and nonhuman primates against Zika virus  an approwww.modernatx.com/pipeline), whereas RNAi-loaded lipid nanoparticles targeting chronic hepatitis B virus are actually in Phase II .In viral infection, RNA-based drugs are in different clinical Phase trials . For exain vitro and in vivo appeared on the market. These oligonucleotides have particular properties imparted by the linking of the 2’-O and 4’-C atoms of the ribose ring that lead to a ‘locked’ conformation, which is ideal for Watson-Crick pairing. The antisense ‘locked’ (or ‘bridged’) nucleic acid  GapmeRs can pair more rapidly with a complementary nucleotide strand and the stability of the resulting duplex is increased compared with traditional oligonucleotides. GapmeRs are generally designed to have a DNA portion flanked by LNA  and we identified many regions of the 5’UTR where the putative GapmeRs could be designed  and effectively employed to inhibit the replication activity of SARS-CoV-2.We have summarized and discussed here the panorama of noncoding RNA molecules that can be employed to target the 5’UTR region of SARS-CoV-2 with the aim to find novel drugs or innovative therapeutic strategies .Recently, a couple of papers emphasized the directions of RNAi therapeutics and their possible use as a tool to control viral infections ,82. Morein vivo and to enter into cells without the need of delivery vehicles or transfecting agents . This is an advantageous property as generally RNA molecules are not very effective per se. In fact, they require a mean to reach the cells, such as in the case of the recent Phase I clinical trial on COVID-19 patients that employs mRNA-1273 delivered by lipid nanoparticles .The process to commercialize a novel drug is lengthy and costly owing to the regulatory processes related to preclinical and clinical studies. Despite this, we think that many of the molecules already studied and developed after the previous SARS-CoV infection in 2002 can be the starting point to develop effective molecules also for the recent SARS-CoV-2 infection. One of the interesting aspects of these molecules is the possibility to alter not only the functioning of the viral replication machinery but also to impair the secondary structure of the crucial 5’UTR genomic region. However, many aspects must be considered to optimize this approach: the amount of drugs in relation to the viral genome to target, the presence of mutated viruses  that may be implicated in viral escape  and the Independently by the use of siRNAs, miRNAs or GapmeRs, the arsenal that can be employed to fight SARS-CoV-2 is promising and potentially very powerful. In principle, some of these therapeutic options could alleviate COVID-19 symptoms or reduce viral replication. We strongly believe that none of these possibilities should be left unassessed, as the discovery of an effective vaccine could be a very long and uncertain path.One of the possible future directions in the optimization of RNA-based drug efficiency and in the development of novel therapeutic molecules could be focusing the research on the chemistry of these antisense molecules. In particular, designs employing chemistries other than the phosphorothioates  have shown promises in the improvement of affinity and stability of RNase H–oligonucleotide duplex . MoreoveThe 5’UTR region is crucial to design novel antiviral molecules.The 5’UTR region encodes for a ‘leader’ sequence important for viral replication.The 5’UTR of severe acute respiratory syndrome coronavirus (SARS-CoV) can be effectively targeted by RNA-based drugs .The 5’UTR and the leader sequence of severe acute respiratory syndrome coronavirus 2 are very similar to those of SARS-CoV.The secondary structure of SARS-CoV 5’UTR is very similar to that of severe acute respiratory syndrome coronavirus 2.Deletions or RNAi within the 5’UTR region can suppress SARS-CoV replication.Locked nucleic acids and GapmeRs are two innovative RNA-based molecules that showed promises as antiviral molecules.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Here, we show that primase can use metabolic cofactors as initiating substrates, instead of its canonical substrate ATP. DnaG primase of Escherichia coli initiates synthesis of RNA with NADH (the reduced form of nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide) in vitro. These cofactors consist of an ADP core covalently bound to extra moieties. The ADP component of these metabolites base-pairs with the DNA template and provides a 3′-OH group for RNA extension. The additional cofactors moieties apparently contact the ‘basic ridge’ domain of DnaG, but not the DNA template base at the –1 position. ppGpp, the starvation response regulator, strongly inhibits the initiation with cofactors, hypothetically due to competition for overlapping binding sites. Efficient RNA primer processing is a prerequisite for Okazaki fragments maturation, and we find that the efficiency of primer processing by DNA polymerase I in vitro is specifically affected by the cofactors on its 5′-end. Together these results indicate that utilization of cofactors as substrates by primase may influence regulation of replication initiation and Okazaki fragments processing.To initiate replication on a double-stranded DNA  Therefore, all organisms and some viruses employ a primase, a specialized RNA polymerase (RNAP) that synthesizes short RNAs which DNAPs can then extend , synthesizes DNA in 5′−3′ direction, only one strand (leading) can be synthesized continuously, whereas another strand (lagging) has to be synthesized initially in discrete 1–2 kb long Okazaki fragments. Therefore, while leading strand synthesis involves very few priming events, each Okazaki fragment must start with an RNA primer that is synthesized by the primase DnaG. To switch synthesis from RNA to DNA, primase is displaced and the primer is elongated by Pol III (Replicative DNA polymerases (DNAPs) are unable to initiate synthesis of DNA n extend . The Esc Pol III . Replica Pol III . To resc Pol III ,10. Over Pol III , tightly Pol III , regulat Pol III ,13,14. I Pol III .E. coli, DnaG primase recognises a consensus 3′-GTC-5′ motif in DNA and makes a 10–12 nucleotide long RNA primer, starting synthesis with ATP opposite the central thymine of this element . These molecules can replace ATP in initiation of RNA synthesis by base-pairing with the template DNA using their ADP part and providing an accessible 3′ hydroxyl group for RNA extension. Taking into account its strong preference for ATP as a first nucleotide, primase could potentially utilize them as initiating substrates. RNAPs of transcription  which generates a 5′ monophosphorylated RNA species that are quickly degraded in the cell  were red of RNA  during id of RNA ,21. As athe cell .E. coli DnaG primase is capable of initiating RNA synthesis using a number of ADP-containing cofactors in vitro, including NADH, FAD and DP-CoA, but not NAD+. This reaction requires amino acid residues of the DnaG ‘basic ridge’ region and is inhibited by the global starvation alarmone ppGpp. We also show that cofactors on the 5′-end of an RNA primer specifically and differentially affect its processing by DNA Pol I.In the present study we sought to address two questions: i) whether primase could use cofactors as the initiating nucleotide, and if the answer is yes, ii) what potential physiological consequences this non-canonical initiation can cause. We found that +, NADH and FAD, were from Sigma Aldrich, ppGpp from Tebu-Bio.ATP, GTP and UTP were from GE Healthcare; AMP, ADP, NADdnaG and dnaB genes were the corresponding pCA24N overexpression vectors from the ASKA collection. For subsequent expression and isolation dnaG and dnaB genes were transferred to plasmid pET28a. This pET28a-dnaG WT (wild type) plasmid was used to generate DnaG mutants with amino acid substitutions K229A, Y230A, K241A and D309A by QuickChange mutagenesis kit and protocol (Agilent).The original source of 6-tagged DnaG, DnaB and NudC were expressed in E. coli T7 express strain (New England Biolabs). Cells were grown to an OD600 = 0.5 at 37°C before induction with IPTG (1 mM), and the growth was continued at ambient temperature overnight. Cells were lysed by sonication in grinding buffer . Proteins were purified by affinity chromatography on Ni-NTA-sepharose  in 20 mM Tris–HCl pH 8.0, 600 mM NaCl, 5% glycerol buffer. Cell lysate clarified by centrifugation was loaded onto the column in the presence of 10 mM imidazol to reduce non-specific protein binding. The column was washed with buffer containing 25 mM imidazole, and bound proteins eluted with buffer containig 200 mM imidazole. Fractions containing target proteins were pooled and further purified by ion exchange chromatography on ResourseQ column . The pure protein fractions were pooled and dialysed against storage buffer .Plasmid –encoded wild type and mutant C-terminally HisRNAs were synthesized by DnaG primase (1 μM) on single stranded 5′- biotynilated DNA template with the sequence 5′-bio-CGGACACACACACACTGCGAAGC; or hairpin template for subsequent Pol I primer degradation experiments 5′- bio-TTTACGCTTCGTTGACACACACACTGCGCGTTTGGGAAAACTCTTTCCCAAAC. For the experiments with DnaG mutants and for Pol I degradation, the primer was synthesized at the presence of DnaB helicase (3 μM).32P]-GTP 5 Ci/mmol  in primase reaction buffer  at 30°C for 10 min, unless otherwise indicated in the Results section. The reactions were stopped by addition of formamide-containing loading buffer . Products were separated on denaturing polyacrylamide gels , revealed by PhosphorImaging , and analysed using ImageQuant software .Reactions contained 500 μM ATP or cofactors, 100 μM UTP and 10 μM /(Km + [S]) using non-linear regression in SigmaPlot software to produce KM values.For apparent ppGpp at final concentrations of 100 μM, 600 μM and 1 mM was added to the primase reaction prior the initiating substrates. Initiating substrates were used at 100, 600 and 1 mM concentrations, to generate relative rates of primer synthesis for plots on the Figure 32P]-GTP 5 Ci/mmol, 100 μM UTP, in primase reaction buffer  at 30°C for 10 min. The reaction was stopped by adding a final concentration of 1 M NaCl, and proteins were removed from reaction by binding to Ni-NTA agarose beads for 5 min at ambient temperature, and pelleting the beads by low speed centrifugation. The supernatant was transferred to a gel filtration column . DNA–RNA hybrid was immobilized on streptavidine beads via biotin on the DNA template, and non-bound material removed by washing with PolI buffer . Pol I  was added at 0.25 U/μl, alongside 10 μM dNTPs. Reactions were incubated at 37°C and stopped after 5, 10, 20, 30, 45, 60, 120 and 180 s with formamide-containing loading buffer as above. Products were separated on the denaturing 23% polyacrylamide gel, revealed by PhosphorImaging , and bands’ intensities quantified using ImageQuant software . The percentage of the initial RNA13 band, compared to the total radioactivity in the lane, was plotted against time to generate plots on Figure Primer was generated on the hairpin template by DnaG (1 μM) and DnaB (3 μM) premixed at ambient temperature with initiating substrate , 10 μM [αE. coli primase (PDB ID 1DDE) and Staphylococcus aureus primase in complex with ATP and ppGpp  were superimposed using the structure visualization and model building tool COOT  was used. Fitting was optimised to match the phosphate, sugar and base moieties in each ligand with those in the ATP bound form of S. aureus primase using angle editing tools, followed by geometry optimisation within COOT. The remaining regions of each ligand were modelled such as any clashes with surrounding residues were minimised. ppGpp was modelled into the E. coli primase structure based on the superposition of the protein models, followed by geometry optimisation in COOT to avoid clashes with surrounding residues.Structural models of ool COOT . The prode novo RNA synthesis on DNA. We wanted to test if primase can initiate synthesis using ADP- containing metabolic cofactors  and monophosphorylated RNA species. We found that after synthesis by DnaG, the 5′-NADH of the primers was susceptible to NudC activity , which is proven to incorporate non-canonical initiating substrates in vivo. Nonetheless, based on these calculations and observations it would be unreasonable to expect a large extent of non-canonical priming, and therefore a strong effect on replication in E. coli. Perhaps under specific conditions, such as at the chromosome replication origin, or during replication restart, the non-canonical initiation might become more significant. We can also envisage a higher impact of cofactor-dependent priming in eukaryotes where Okazaki fragments are roughly 10 times shorter due to more frequent initiating events, leading to a higher probability for primase to initiate with cofactors.Here we showed that DnaG primase of ectively ,28. Thes+/NADH and FAD very efficiently both in vitro and in vivo . We suggest that the competitive component of inhibition is more prominent for initiation with cofactors due to a more extensive binding site being shared by ppGpp and cofactors, and by ppGpp interference with basic ridge residues which coordinate non-canonical substrates according to our structural modelling. The non-competitive component of the inhibition might come from ppGpp interference with template binding, as suggested in .+ very inefficiently compared to NADH, in contrast to bacterial and mitochondrial transcriptases, which do not distinguish between the reduced and oxidised forms (This work highlights the differences between RNA polymerases (primase versus transcriptase) utilizing this non-canonical initiation mechanism. Notably, DnaG uses NADed forms ,21. Thised forms . This reE. coli systems (RNA pieces of Okazaki fragments are destined to be removed. Despite the transient nature of these RNAs, a balance between the kinetics of primer removal and extension influences the mean size and length distribution of Okazaki fragments, and ultimately replication kinetics . We show systems .Potential primer processing defects caused by cofactors might also lead to increased retention of the short oligoribonucleotides within genomic DNA. These pieces may function as a ribonucleotide imprint, when short RNA regions in DNA serve as epigenetic switches, such as the mating locus switch in yeast, as well as markers for repair machinery recognition to distinguish between old and new DNA strands .Here, we explored the ability of DnaG to incorporate NADH, FAD and DP-CoA cofactors, well established as substrates for RNAPs of transcription. Currently, more potential nucleotide analogues, including dinucleotide polyphosphates incorporated into a 5′-position of cellular RNAs are being discovered in both kingdoms . The rolgkaa447_Supplemental_FileClick here for additional data file.