Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the COVID-19 pandemic. 2′-O-RNA methyltransferase (MTase) is one of the enzymes of this virus that is a potential target for antiviral therapy as it is crucial for RNA cap formation; an essential process for viral RNA stability. This MTase function is associated with the nsp16 protein, which requires a cofactor, nsp10, for its proper activity. Here we show the crystal structure of the nsp10-nsp16 complex bound to the pan-MTase inhibitor sinefungin in the active site. Our structural comparisons reveal low conservation of the MTase catalytic site between Zika and SARS-CoV-2 viruses, but high conservation of the MTase active site between SARS-CoV-2 and SARS-CoV viruses; these data suggest that the preparation of MTase inhibitors targeting several coronaviruses - but not flaviviruses - should be feasible. Together, our data add to important information for structure-based drug discovery. SARS-CoV-2 expresses a 2′-O RNA methyltransferase (MTase) that is involved in the viral RNA cap formation and therefore a target for antiviral therapy. Here the authors provide the structure of nsp10-nsp16 with the panMTase inhibitor sinefungin and report that the development of MTase inhibitor therapies that target multiple coronoaviruses is feasible. Coronaviruses have long been a threat, but recent developments show that they should be classified as extremely dangerous pathogens and that we must develop effective means to suppress and treat the diseases caused by these viruses2. Currently, the arsenal of approved treatments for diseases caused by coronaviruses is rather limited and therefore there is a pressing need for the discovery and development of therapeutic agents for treatment of COVID-19 and other coronavirus infections3. Directly acting antiviral agents provide a backbone for treatment of numerous viral disease such as hepatitis B and C and AIDS5 and such a compound, remdesivir, was also very recently FDA approved for emergency treatment of COVID-19 patients. These therapeutics directly aim at a certain viral protein and, therefore, a deeper understanding of the function of individual viral proteins is needed to derive future therapies of COVID-19 and other possible coronavirus infections.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the coronavirus disease (COVID-19) global pandemic7. SARS-CoV-2 exploits the cell environment to its full advantage for its use and replication8. Importantly, viral RNA must be protected from the cellular innate immunity. One of the most important elements that ensures the integrity of viral RNA is the cap, a specific arrangement at the 5 ‘end of the RNA molecule that consists of a N-methylated guanosine triphosphate and C2′-O-methyl-ribosyladenine . First, the γ-phosphate from a 5′-triphosphate end of the nascent RNA is removed by 5′-RNA triphosphatase. Second, a guanosine monophosphate (GMP) is attached to the formed 5′-diphosphate end of RNA by a guanylyltransferase using GTP as the source of GMP. Finally, the methylation steps take place. In this case, two separate enzymatic steps are required: one for N-7 methylation of the GTP nucleobase (N-7 methyltransferase) and the other for C2′-O methylation of the following nucleotide.Coronaviruses have the largest genomes of all RNA viruses. In particular, the genome of SARS-CoV-2 has ~29 800 bases, which encodes 4 structural and 16 nonstructural proteins (nsp1–nsp16) that are essential for the lifecycle of this virus12. The primary function of nsp13 is the unwinding of the viral RNA during replication. Therefore, it is considered to be essentially the helicase. However, it is also a protein with 5′-RNA triphosphatase activity responsible for cleaving monophosphate at the 5′-end of the nascent RNA to provide a diphosphate13. There is still no clear evidence that any of the coronavirus proteins possess the guanylyltransferase functionality associated with the cap creation12. Nsp14 and nsp16 are responsible for the methylation of the cap on the guanine of the GTP and the C2′ hydroxyl group of the following nucleotide, respectively. Both nsp14 and nsp16 are S-adenosylmethionine (SAM)-dependent methyltransferases (MTases) and seem to be essential for the viral lifecycle7. In particular, nsp16 appears to be a very promising molecular target from the perspectives of medicinal chemistry and drug design. It has been shown that this 2′-O methyltransferase (MTase) is indispensable for replication of coronaviruses in cell cultures15. Enzymatic activities of both these MTases (nsp14 and nsp16) are significantly enhanced by nsp10, which is a necessary cofactor for their proper function19.Coronaviruses use sequence installation of the cap that is performed by several nonstructural proteins (nsp) encoded by their genome. For coronaviruses, nsp10, 13, 14, and 16 appear to be involved in this processStreptomyces griseoleus20. The structure reveals an overall fold similar to SARS-CoV nsp10-nsp16, and, importantly, reveals atomic details in how sinefungin inhibits the nsp16 MTase. This provides the starting point for specific inhibitor design.Here we report on the crystal structure of SARS-CoV-2 nsp10-nsp16 in complex with sinefungin, a pan- MTase inhibitor originally isolated from E. coli. The complex was stable during protein purification suggesting suitability for structural analysis , a short helix α5 and several coiled-coil regions. A key feature of the nsp10 fold are two zinc binding sites. One is formed by three cysteine residues  and a histidine residue (His83) and is located between the helices α2 and α3 and appears to stabilize them in the observed conformation. The other zinc binding site is formed by four cysteine residues  and stabilizes the very C-terminus of the nsp10 protein.2 large and it is formed by the nsp10 helices α2, α3, α4 and a coiled-coil region connecting helix α1 and the sheet β1 (residues Asn40 to Thr49) and the inner side of the nsp16 J-motif including sheets β4 and helices α3, α4, and α10 stability of the RNA, fidelity of the RdRps, and its ability to correct excessive mutations and by the limited space for a nucleic acid within the capsid. Therefore, every viral enzyme is a potential drug target because RNA viruses do not have the luxury to encode nonessential accessory proteins. In this study, we have structurally analyzed SARS-CoV-2 2′-O-ribose methyltransferase, an essential enzyme involved in RNA cap formation which ensures stability of the viral RNA because non-methylated RNA located in cytoplasm is prone to degradation and cannot be efficiently translated. Our analysis revealed overall fold similarity between SARS and SARS-CoV-2 nsp10-nsp16 complexes (RMSD = 0.747) and also a high conservation of the SAM binding site among coronaviruses, in fact, all the residues that are involved in ligand-hydrogen bonding are absolutely conserved between SARS, SARS-CoV-2, and MERS. The cocrystal structure we obtained is, however, not with the natural methyl donor, SAM, but with the pan-MTase inhibitor sinefungin.23. Therefore, it is expected that nsp10 induces a conformational change in the nsp16 MTase that switches nsp16 in a productive enzyme, although the direct evidence is missing so far. In principle, the nsp10-nsp16 interface is a drug target. However, given the large area of this interface and complex network of hydrogen bonds and hydrophobic interactions it is unlikely that it could be targeted by a small drug-like molecule.Interestingly, the nps16 MTase is not active without the accessory protein nsp10. The mechanism of nsp16 activation is elusive because we do not have a structure of any unliganded coronaviral nsp16 protein. Structural analysis of the SARS-CoV nsp10 protein in an unliganded form and in complex with nsp16 reveals no significant conformational change of the nsp10 upon nsp16 bindingC6′ of sinefungin has to be directed toward the RNA cap binding site. Therefore, modifications on this part of the molecule, based on a rational structure design, has led to the preparation of highly active inhibitors of various MTases as shown previously on various sinefungin-related derivatives24.The structure of the nsp10-nsp16 complex reveals several important factors that can be exploited to target the installation of a viral cap on a nascent viral RNA molecule in therapeutic design. The nsp10-nsp16 complex must be able to bind the previously introduced methylated GTP, and recognize at least the first nucleotide of the RNA strand, as well as the substrate for the methylation reaction, SAM. Based on the position of sinefungin in our structure, it is apparent where both of these two reaction partners bind to nsp16. These two binding sites form well-defined canyons in the structure of nsp16 as seen in Fig. Also detailed knowledge of the amino acid moiety on the sinefungin binding site is extremely important for design of potential SARS-CoV-2 therapeutics, since the possible design of specific bioisosteres of the amino acid scaffold may play a vital role in the development of cell permeable compounds as potential inhibitors of this essential MTase.25 and with the Zika virus (ZIKV) MTase  the Zika Asp114 and CoV-2 Asp131 that make a hydrogen bond with the adenine base which is essential for its recognition and (ii) Zika Asp130 and CoV-2 Asp146 that are close to the methylation reaction center and important for catalysis. Also, this shows that there is a significant lipophilic cavity in close proximity to the adenine nucleobase of sinefungin in both SARS-CoV-2 and ZIKV MTase. In the case of flavivirus MTases, it has been shown, that this part of the enzyme can be effectively targeted by MTase inhibitors without affecting human proteins. Therefore, we believe that this part of the nsp16 protein may play a very important role in future design of COVID-19 therapeutics. Although there is still a long way to go, through preclinical and clinical testing of inhibitors before they can be introduced into clinical practice, we believe this research provides a solid foundation.We next sought to determine whether an inhibitor of a SERS-CoV-2 MTase could be potentially active against other viruses. We performed a structural alignment of the SARS-CoV-2 MTase with SARS-CoV MTase Fig. 25 and wiase Fig. 26. The sIn conclusion, we have acquired a crystal structure of SARS-CoV-2 nsp10-nsp16 complex, the activated 2′-O-methyltransferse, which is essential for RNA capping during the viral cycle. Since this process is essential for viral survival and replication in cells, it can be targeted by chemical compounds based on this structural information.8×-SUMO tag. The proteins were expressed and purified using our standard protocols28. Briefly, E. coli Bl21 (DE3) cells were transformed with the expression vector and grown at 37 °C in LB medium supplemented with 25 µM ZnSO4. After OD600 nm reached 0.5, the protein expression was induced by addition of IPTG to final concentration 300 µM and the protein was expressed overnight at 18 °C. Bacterial cells were harvested by centrifugation, resuspended in lysis buffer  and lysed by sonication. The lysate was cleared by centrifugation. Next, the supernatant was incubated with NiNTA agarose (Machery-Nagel), extensively washed with the lysis buffer and the protein was eluted with lysis buffer supplemented with 300 mM imidazole. The proteins were dialyzed against lysis buffer and digested with Ulp1 protease at 4 °C overnight. The SUMO tag was removed by a second incubation with the NiNTA agarose. Finally, the proteins were loaded on HiLoad 16/600 Superdex 200 gel filtration column  in SEC buffer . Purified proteins were concentrated to 7 mg/ml and stored at −80 °C until needed.Artificial codon optimized genes encoding SARS-CoV-2 nsp10 and nsp16 proteins were commercially synthesized (Invitrogen) and cloned in a pSUMO vector that encodes an N-terminal His121 spacegroup. Data were integrated and scaled using XDS29. The structure was solved by molecular replacement (nps10-nsp16 complex PDB ID 6W4H as the search model) and further refined in Phenix30 and Coot31 to good R-factors (Rwork = 18.05% and Rfree = 20.86%) and good geometry as summarized in Table Crystals grew in sitting drops consisting of 300 nl of the protein and 150 nl of the well solution  in five days. Upon harvest the crystals were cryo-protected in well solution supplemented with 20% glycerol and frozen in liquid nitrogen. Diffraction data were collected at the home source. The crystals diffracted to 2.4 Å and belonged to the trigonal P3Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


The internet is a well-known source of information that patients use to better inform their opinions and to guide their conversations with physicians during clinic visits. The novelty of the recent COVID-19 outbreak has led patients to turn more frequently to the internet to gather more information and to alleviate their concerns about the virus.The aims of the study were to (1) determine the most commonly searched phrases related to COVID-19 in the United States and (2) identify the sources of information for these web searches.Search terms related to COVID-19 were entered into Google. Questions and websites from Google web search were extracted to a database using customized software. Each question was categorized into one of 6 topics: clinical signs and symptoms, treatment, transmission, cleaning methods, activity modification, and policy. Additionally, the websites were categorized according to source: World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), non-CDC government, academic, news, and other media.In total, 200 questions and websites were extracted. The most common question topic was transmission , followed by clinical signs and symptoms  and activity modification . Notably, the clinical signs and symptoms category captured questions about myths associated with the disease, such as whether consuming alcohol stops the coronavirus. The most common websites provided were maintained by the CDC, the WHO, and academic medical organizations. Collectively, these three sources accounted for 84.0% (n=168) of the websites in our sample.In the United States, the most commonly searched topics related to COVID-19 were transmission, clinical signs and symptoms, and activity modification. Reassuringly, a sizable majority of internet sources provided were from major health organizations or from academic medical institutions. Since its emergence in late 2019 in Wuhan, China, COVID-19, the disease caused by the novel coronavirus SARS-CoV-2 has drastically impacted daily life around the world . Nearly Previous studies have shown that patients frequently use the internet to research their conditions and inform their discussions in clinic ,6. As phSearch terms related to COVID-19 were entered into Google web search using a clean-installed Google Chrome browser on May 30, 2020, in New York, NY. Google web search is by far the most widely used internet search engine in the United States . Due to The questions were first categorized according to Rothwell’s classification system by a single trained reviewer ,12. ThisActivity modification consisted of questions regarding the effectiveness of various activities or lifestyle changes in preventing COVID-19. Policy included questions detailing local or national policy changes enacted in response to COVID-19, including questions about economic support. A full listing of the criteria for each topic category is listed in In line with previous studies, the websites were categorized according to source: World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), non-CDC government, academic, news, and other media ,17 Tabl. SpecifiIn total, 200 questions and their corresponding source of information were extracted; the top 25 questions are listed in Most questions regarding the transmissibility of the coronavirus asked about specific modes of transmission such as spread through food, feces, air conditioning units, delivery packages, and mosquitoes. Interestingly, the clinical signs and symptoms category captured questions about myths associated with the disease, such as whether consuming alcohol stops the coronavirus. In the activity modifications category, there were many questions about staying at home, wearing masks, and managing pre-existing travel plans. The most commonly asked question—“Can antibiotics treat the coronavirus disease?”—was classified as treatment, which, in total, comprised 9% (n=18) of the searched questions.Can antibiotics treat the coronavirus disease?Is headache a symptom of the coronavirus disease?Is there a vaccine for coronavirus?Are masks effective against the coronavirus disease?Can children pass on the coronavirus disease?Can coronavirus spread through mosquito bite?Can I stay at home if I have the coronavirus disease?Can the coronavirus disease spread faster in an air-conditioned house?Can the coronavirus disease spread through delivered packages?Can the coronavirus disease spread through feces?Can the coronavirus disease spread through food?Can you get coronavirus from talking to someone from a distance?Can you have the coronavirus disease without a fever?Does drinking alcohol kill the coronavirus?How long does the coronavirus stay on clothing?Is bleach an effective cleaning agent for the coronavirus disease?Is the coronavirus disease more severe than the flu?Should I accept packages from China?Should I cancel my trip due to coronavirus?Should I wear a face mask out in public?What antiviral drugs are available to treat the coronavirus disease?What is a pandemic?What is the recovery time for the coronavirus disease?Who gets a stimulus check?Are rashes a symptom of the coronavirus disease?Can hand sanitizer explode in a hot car?With respect to sources of information, the most common websites provided were maintained by the CDC , academic medical organizations , and the WHO  . With anIn the midst of a highly unprecedented pandemic with significant economic and public health implications, the internet is a crucial source of information for the general public in order to guide their everyday life. As information is changing rapidly and is compounded by fallacies originating from places of authority, we believe that the pandemic highlights the role of physicians in providing patients the most reliable information based on the highest quality of evidence. Thus, the present study effectively characterized the intellectual curiosity of millions of Americans into 6 easily categorizable groups and demonstrated the origin of the general public’s sources of information.Previous studies have examined search and Twitter trends related to the COVID-19 pandemic from regions around the world, including the United States, China, Italy, and Spain -15,18-20Thus, the present study revealed that the most commonly searched criteria about COVID-19 included information about transmission, clinical signs and symptoms, and activity modification. Understanding what matters to our patients should compel us to be well informed on these topics. We believe that as physicians, we should collectively take responsibility to provide reliable information based on the best available evidence . Even ifFurther, the present study revealed that the sizable majority of internet sources provided were from major health organizations or from academic medical institutions. While we find this to be encouraging, we must remember that our patients also consume information from multiple other sources. The social media “echo chamber” phenomenon is an active area of study for sociologists and computer scientists and has been shown to rapidly propagate rumors or misinformation on a mass scale -25. AgaiThere are several limitations to the present study. First, the COVID-19 pandemic is rapidly changing, and the results of the present study captured web searches as of May 2020. Due to the changes in pandemic characteristics, such as the emergence of new hotspots, it is entirely plausible that the focus of web searches has changed as well. In addition, the present study only captures the web searches of users in New York during May 2020, as the Google COVID-19 database generates search results based on the date of search and user location. Thus, we are unable to analyze trends in either. However, other published studies have examined Google searches in other regions of the world, including China, Taiwan, and Italy -15,19. TPeople use Google Web Search to identify sources of information about COVID-19. In the United States, the most commonly searched topics related to COVID-19 were transmission, clinical signs and symptoms, and activity modification. Reassuringly, the majority of information in the present study came from highly reputable sources, including the CDC, academic websites, and the WHO.


Herbaspirillum seropedicae is a diazotrophic bacterium from the β-proteobacteria class that colonizes endophytically important gramineous species, promotes their growth through phytohormone-dependent stimulation and can express nif genes and fix nitrogen inside plant tissues. Due to these properties this bacterium has great potential as a commercial inoculant for agriculture. The H. seropedicae SmR1 genome is completely sequenced and annotated but despite the availability of diverse structural and functional analysis of this genome, studies involving small non-coding RNAs (sRNAs) has not yet been done. We have conducted computational prediction and RNA-seq analysis to select and confirm the expression of sRNA genes in the H. seropedicae SmR1 genome, in the presence of two nitrogen independent sources and in presence of naringenin, a flavonoid secreted by some plants.cis-encoded and trans-encoded categories and among them 20 have Rfam homologs. The housekeeping sRNAs tmRNA, ssrS and 4.5S were found and we observed that a large number of sRNAs are more expressed in the nitrate condition rather than the control condition and in the presence of naringenin. Some sRNAs expression were confirmed in vitro and this work contributes to better understand the post transcriptional regulation in this bacterium.This approach resulted in a set of 117 sRNAs distributed in riboswitch, H. seropedicae SmR1 express sRNAs in the presence of two nitrogen sources and/or in the presence of naringenin. The functions of most of these sRNAs remains unknown but their existence in this bacterium confirms the evidence that sRNAs are involved in many different cellular activities to adapt to nutritional and environmental changes. Herbaspirillum seropedicae SmR1 is a diazotrophic and endophytic bacterium that belongs to the β-proteobacteria. This microorganism fixes nitrogen under microaerobic conditions inside the plant tissues of the economically important cereal crops wheat, rice, maize and sorghum [H. seropedicae strains also appears associated with sugar cane and forage grasses [Herbaspirillum-plant interaction through the increase of the biomass of the inoculated plant [H. seropedicae SmR1 fixes nitrogen to ammonia in a reaction catalysed by the enzyme nitrogenase [Arabidopsis thaliana by H. seropedicae Z67 [H. seropedicae SmR1, naringenin regulates the expression of several genes, positively or negatively [ grasses , 3, frui grasses  and comm grasses . Severaled plant –10. Nitrrogenase , 12. Therogenase . Plants rogenase , 15. It icae Z67 . In H. sgatively . This migatively .H. seropedicae SmR1 strain was sequenced and 4804 open reading frames were annotated [H. seropedicae SmR1 [trans-encoded RNAs (traRNA) and cis-encoded RNAs (caRNA) [trans-encoded sRNAs that are partially complementary to their target [H. seropedicae SmR1 contains a conserved Hfq protein with a classic hexameric ring shape, observed in all available Hfq structures, with sRNA and mRNA contact surfaces [H. seropedicae SmR1 genome and RNA-seq analysis to confirm their expression in bacteria grown in the presence of two nitrogen sources (ammonia or nitrate) and in the presence of naringenin. A set of 117 sRNAs transcripts were confirmed and some of them showed sequence identity with well-characterized sRNAs in other bacteria. Some sRNAs were experimentally detected confirming their existence.The single circular chromosome of the nnotated . Since tcae SmR1 , 20–22, cae SmR1 –25. Theycae SmR1 , 27. Thecae SmR1 . sRNAs c (caRNA) . The caR (caRNA) –33. The r target  and the r target , 35. Ther target . The parr target , 38. H. surfaces . The presurfaces –42. The surfaces . In thisH. seropedicae SmR1 we used the nocoRNAc software [H. seropedicae SmR1 using Cufflinks [4Cl as nitrogen source, (ii) presence of naringenin (NAR) - bacteria cultured in NFbHPN medium containing NH4Cl and the flavonoid naringenin, and (iii) nitrate (NIT) - bacteria grown in NFbHP medium containing KNO3 as nitrogen source. Using the coverage criterion which established a minimum coverage ≥5 as a confidence level to select sRNAs in at least one of the three culture conditions we were able to verify the expression of 117 sRNAs transcripts in H. seropedicae SmR1 which have been termed Hsnc001 to Hsnc117  and trans-encoded (81) categories  returned only 20 sequences with hits and information about putative function  and sucA RNA motif were found essentially in β-proteobacteria [H. seropedicae SmR1 were Hsnc59, Hsnc63, Hsnc86 and Hsnc107  encoding the phosphomethylpyrimidine synthase and is known to bind directly to thiamine pyrophosphate (TPP) turning off TPP biosynthesis [ribE) encoding the 6,7-dimethyl-8-ribitylllumazine synthase which catalyses one of last steps in the biosynthesis of riboflavin. FMN binds to the FMN aptamer and regulates the ribE expression [H. seropedicae SmR1 still presents the SAH riboswitch (Hsnc113), Cobalamin riboswitch (Hsnc115), ZMP / ZTP riboswitch (Hsnc029), Yybp-ykoY (Hsnc112) and Fluoride riboswitch (Hsnc002). The SAH riboswitch is upstream HSERO_RS21435, encoding S-adenosyl- (L) -homocysteine (SAH), and is involved in S-adenosyl- (L) -methionine (SAM) regeneration cycle [H. seropedicae SmR1 showed this riboswitch (Hsnc029) upstream to the HSERO_RS06140 (glyA) encoding serine hydroxymethyltransferase, a pyridoxal phosphate-dependent enzyme that plays an important role in the cellular pathways of a carbon [We also found the TPP (Hsnc109) and FMN (Hsnc035) riboswitches. The TPP riboswitch is immediately upstream of HSERO_RS02120 , 4.5S RNA (Hsnc001) and tmRNA (Hsnc083). These sRNAs associate with proteins and are highly expressed in the cell. The 6S interacts with the primary form of holoenzyme of RNA polymerase, negatively regulates transcription and is involved in modulating stress and optimizing survival during nutrient limitation [Some well-preserved housekeeping sRNAs were also found in mitation . The 4.5mitation . In mostmitation .H. seropedicae SmR1 . A heat map that includes the 117 sRNA reveals different expression profiles according to the growth condition of mR1 Fig. . We obseWe also used the coverage criterion of RNA-seq reads greater than or equal to five, in at least one of the three culture conditions  and stationary phase (after 10 h of culture). H. seropedicae SmR1 was cultured under the CRT, NAR and NIT conditions and the total RNA was extracted and hybridized with specific radiolabelled probes. We were able to confirm that the five selected sRNAs are expressed in H. seropedicae SmR1  than the length initially predicted by bioinformatics (182 nt and 157 nt respectively) Fig . We suggleotides . The HsnH. seropedicae SmR1, a highly versatile diazotrophic bacterium capable of metabolizing a wide range of carbon and nitrogen sources. Initially, we showed that 2164 sRNAs were predicted by bioinformatics tools Cufflinks and nocoRNAc that uses a specific prediction method for prokaryotic sRNA. To confirm which of these sRNAs are expressed in H. seropedicae SmR1, we analysed RNA-seq data from three culture conditions  and verified the expression of 117 sRNAs, each expressed in at least one of the three conditions analysed. The number of sRNAs predicted by bioinformatics tools varies among species as already described for Streptomyces coelicolor (843 sRNAs) [Burkholderia pseudomallei (1306 sRNAs) [B. cenocepacia J2315 (213 sRNAs) [H. seropedicae SMR1, we should also consider the stringent criterion adopted as a confidence level for sRNA expression (minimum coverage ≥5) to avoid data noise and false-positives from the biocomputational prediction. Besides that, considering the size of the H. seropedicae SmR1 genome and the number of ORFs, we can expect a larger number of different sRNAs being expressed in other culture metabolic conditions. When we submitted the expressed sequences of the H. seropedicae SmR1 sRNAs  , Burkhol6 sRNAs)  and B. c3 sRNAs) . We can s aureus , and othcterized , 77, 78.H. seropedicae SmR1 sRNAs had a mean GC content (54.97% GC) below the mean of the genome content (63.4% GC). However, there are some sRNAs with slightly higher GC content such as riboswitches (61.81% GC) and known housekeeping sRNAs . RNAs that exhibit high GC content generally exhibit a more rigid and conserved structure wich is probably necessary for the regulation of specific target molecules, such as maintenance genes or molecules that hardly evolve. In contrast, sRNAs that have a more flexible structure with a low GC content are probably involved in regulating the expression of several different genes or molecules that frequently evolve [trans-encoded and [Many processes could be controlled by bacterial sRNAs induced by specific metabolic or environmental signals  or exprey evolve , 80, 81.oded and  and possH. seropedicae SmR1 can assimilate nitrate [H. seropedicae SmR1, more than 37% of the genes were differentially expressed in the nitrate condition and the carbon consumption was increased [H. seropedicae SmR1 was cultivated in the presence of nitrate Bonato and collaborators [sucA and sucB (7.3 and 7.7 respectively). In our work we observed the sucA RNA motif (Hsnc006) is highly expressed in the presence of nitrate (10 mmol/L) when compared to the control (20 mml/L NH4Cl)  as well as for sm84 (30 nt of difference) and sm270 (10 nt of difference) [H. seropedicae SmR1 presents RNase E (Hsero_RS09410) an endoribonuclease which preferentially cleaves AU-rich regions [We were able to validate by Northern blot the expression of the Hsnc050 (ssrS RNA), Hsnc028, Hsnc042, Hsnc073 and Hsnc082 sRNAs. According to the Northern blot Hsnc073 and Hsnc082 sRNAs had smaller lengths than those predicted by bioinformatics. As we said earlier this difference could be due to sRNA processing or even imprecision in the prediction. Klein et al. (2202) also observed a length difference of sRNAs predicted for ted size . Sinorhiference) . The sRNference) . H. sero regions  and affe regions . PNPase  regions . In factH. seropedicae SmR1 genome in the presence of two nitrogen sources and/or in the presence of naringenin. The functions of the novel sRNAs remain unknown but their existence in this bacterium confirms the evidence that sRNAs are involved in many different cellular activities to adapt bacterium to nutritional and environmental changes. Some of them may participate in the regulation of nitrogen metabolism or in the bacterial-plant interaction. The discovery and knowledge of these sRNA molecules in this nitrogen fixation bacterium is very important due to its biotechnological significance.We reported the expression of several sRNA in H. seropedicae SmR1 (NCBI sequence: NC_014323.1) was grown at 30 °C and with agitation of 120 rpm in NFbHPN medium containing 80 μg/mL streptomycin [4Cl as nitrogen source; (ii) Naringenin condition (NAR), bacteria grown in NFbHPN medium in the presence of flavonoid naringenin (100 μM); (iii) Nitrate condition (NIT), bacteria grown in NFbHP medium, using malate as carbon source and 10 mmol/L KNO3 as nitrogen source [ptomycin . Three gn source .H. seropedicae SmR1 genome was screened with the computational tool nocoRNAc [Cufflinks [The nocoRNAc  to searcufflinks  to verifufflinks . The cooH. seropedicae SmR1 genome with the tRNA, mRNA, rRNA and their 50 flaking nucleotides masked. We used the default parameters on the Cufflinks program to localize transcribed/expressed regions on the genome likely to encode sRNAs.We assessed RNA-seq data to uncover sRNAs based only on read alignments. The reads from RNA-seq were mapped to H. seropedicae SmR1 reference genome, the rRNA sequences were masked using the cross-match program. Recursive trimming of the reads at 5′ and 3′ to 35 nucleotides were performed using a Perl script and the Mate-Paired reads were aligned to the reference genome using the alignment tool Short Read Mapping Program SHRiMP [Before mapping the short reads to the m SHRiMP . The prom SHRiMP  was usedm SHRiMP . A sequewww.ebi.ac.uk/arrayexpress) under accession number E-MTAB-3435 e E-MTAB-3646. Small non-coding RNA expression profiles was obtained with Artemis [The RNA-seq data sets used in this work are available in the ArrayExpress database  and stationary phase (10 h of growth). Culture samples were withdrawn and mixed with an equal volume of RNA stop buffer . The total RNA extraction followed the protocol of cell lysis and phenol:chloroform extraction (adapted from [H. seropedicae SmR1 and the primers listed in Table 32P (T7 probes) or ɣ-32P ATP (primer probes) were purified on G25 Microspin columns . Hybridizations were carried out overnight at 42 °C or 68 °C with the PerfectHyb Plus Hybridization Buffer (Sigma). RNA Decade marker (Ambion) was used when detecting non-coding RNAs up to 150 nt; for longer transcripts, the 100–1000 bp Ladder (Biotools) was used. All radiochemicals were purchased from Perkin-Elmer.Overnight cultures grown in the CRT, NAR and NIT conditions were diluted in fresh medium to an initial ODted from . After aAdditional file 1. Herbaspirillum seropedicae SmR1 predicted sRNAs. Expression of the 117 sRNAs of H. seropedicae SmR1 are shown in the table by Coverage and RPKM using RNA-seq data under CRT, NAR and NIT conditions. Defined identity sRNAs have the Rfam code listed. All sRNAs have features as the tools by which the sRNA was predicted (nocoRNAc and / or Cufflinks), category , percentage of G + C in the sRNA sequence, initial and final position of the sRNA found in the genome of H. seropedicae SmR1 as well as the sense of the DNA strand (sense or antisense) and the size of the sRNA.


Quantification of plasma microRNAs (miRNAs) as non-invasive disease biomarkers is subject to multiple technical variabilities. This study aimed to develop an optimized protocol for miRNA quantification from rodent plasma. We hypothesized that a fixed small RNA concentration input for reverse transcription (RT) reaction will provide better miRNA quantification than a fixed RNA volume input. For this, tail-vein plasma was collected from 30 naïve, adult male Sprague-Dawley rats. Plasma hemolysis was measured with NanoDrop-1000 and Denovix DS-11 spectrophotometers. Plasma was then pooled, and RNA was extracted from 50-μl, 100-μl or 200-μl pool aliquots. Small RNA concentration was measured with Qubit miRNA assay. A fixed RNA volume  or a fixed small RNA concentration was used for RT . The method was setup with miR-23a-3p and validated with miR-103a-3p and miR-451a. Hemolysis measurements from Denovix and NanoDrop strongly correlated. Qubit revealed increased small RNA concentrations with increasing starting plasma volumes. With concentration-normalization, miRNA levels from 100-µl and 200-µl plasma volume groups mostly normalized to the level of the 50-µl in ddPCR. Our results indicate that miRNA quantification with ddPCR should be performed with small RNA concentration-normalization to minimize variations in eluted RNA concentrations occuring during RNA extraction. Dysregulation of miRNA levels is observed in many disease conditions, both in the affected tissue and the circulation5. Circulating serum/plasma miRNAs are extremely stable under appropriate sampling and storage conditions10, and are a potential source of non-invasive diagnostic, prognostic, and predictive biomarkers of disease states12.MicroRNAs (miRNAs) are small non-coding RNAs (~22 nucleotides long) that regulate the expression of protein-coding genes through translational repression13. Second, the total amount of small RNA in the plasma is usually low, making it challenging to obtain accurate concentration measurements using common methods like the NanoDrop or Agilent Bioanalyzer14. Third, there are no endogenous normalizer miRNAs that can be used as an internal standard15. Some attempts have been made to overcome these challenges. For example, multiple methods are proposed for detecting plasma hemolysis18. The need to carefully report the protocols used for blood collection, centrifugation, storage, and handling of plasma has been emphasized, because these factors profoundly affect circulating miRNA levels, and the harmonization of these techniques is important for standardizing plasma biomarker research across laboratories22. The addition of carrier RNAs and synthetic spike-ins during RNA extraction and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is also useful24. Further, fluorometric assays such as Qubit have been developed to quantify small RNA concentrations from low-abundance samples with higher specificity and sensitivity than the NanoDrop or Bioanalyzer14. To circumvent the problem of normalization, absolute quantification methods such as droplet digital PCR (ddPCR) have been developed25.Despite the stability of circulating plasma miRNAs, there are several challenges associated with their reliable and reproducible quantification as biomarkers. First, hemolysis in plasma impairs the detection of circulating cell-free miRNAs27. Eluted RNA concentrations from the same starting plasma volumes might differ due to sample-to-sample variations in RNA concentrations that occur during extraction. Thus, with fixed plasma/serum volumes, normalization of ddPCR data remains a requirement.One factor that remains to be addressed, however, is the use of a fixed RNA sample volume for cDNA synthesis due to the poor estimation of the eluted RNA concentration28, we focused on rodent plasma samples as a pre-clinical scenario. We selected miR-23a-3p to setup a method for a-priori small RNA concentration normalization for cDNA synthesis, instead of using fixed RNA sample volumes. We selected miR-23a-3p because of its relatively stable expression abundance in the plasma of healthy humans16 and naïve rats19. To validate the method, we analysed plasma miR-103a-3p and miR-451a levels, since these miRNAs also have an abundant expression in human and rat plasma29. Adding to our previous knowledge19, we first optimized our hemolysis measurement protocol to select the most suitable rat tail-vein plasma aliquots for miRNA biomarker analysis. Next, we measured the eluted small RNA concentrations using the Qubit miRNA assay and evaluated the assay performance. In addition to assessing the fixed RNA sample volume input, we also assessed a fixed small RNA concentration input for cDNA synthesis, based on the Qubit measurements. Finally, we performed absolute quantification of miR-23a-3p, miR-103a-3p and miR-451a from both the volume-normalized and small RNA concentration-normalized samples using ddPCR. Based on the data from setup and validation phases, we propose that plasma miRNA biomarker analysis can be performed from a fixed volume of plasma, followed by small RNA concentration-normalization at the reverse transcription (RT) step and absolute quantification of the target miRNA copy number with ddPCR.In this work, we examined the sample volume normalization issue. As plasma is preferred over serum for miRNA biomarker analysisThe animal numbers and study design are summarized in Fig. www.nc3rs.org.uk/rat-tail-vein-non-surgical) and following our previously optimized protocol19. Briefly, 1 ml of whole blood was drawn from the lateral tail vein under isoflurane anesthesia  into two Microtainer K2 EDTA-tubes , using a 25 G butterfly needle . The blood samples were thoroughly mixed with EDTA and immediately placed on ice. For each animal, the two K2 EDTA-tubes were labelled as EDTA-A and EDTA-B. Tube A was always filled first followed by tube B. Whole blood samples were centrifuged at 1300 g  for 10 min (+4 °C) to obtain plasma. The hemolysis coefficient of the plasma was measured at 414 nm (UV-Vis module), using a NanoDrop-1000 spectrophotometer. For this measurement, 1.5 μl of plasma was pipetted approximately from the middle of the plasma layer in the EDTA tube.Tail-vein plasma was sampled from the rats (n = 30) according to the 3Rs . Plasma from the EDTA-A tube was aliquoted in five 50-μl aliquots (labelled A1-A5), whereas the plasma in the EDTA-B tube was aliquoted into two 80-μl aliquots (labelled B1-B2) and two 50-μl aliquots (labelled B3-B4). Manual notes were prepared by an experienced researcher pipetting the aliquots, regarding (a) observable red blood cell contamination, (b) if the pipetted volume in the Eppendorf tube was less than intended, and (c) if the last aliquoted tubes were not complete due to a smaller plasma volume obtained than anticipated. The aliquots were then frozen in dry ice and stored at −70 °C until processed.For method setup, a plasma pool was prepared by combining plasma from all available A4, A5 and B4 aliquots , and 20 A3 aliquots from the first 20 rats of the cohort . For the validation phase, a second plasma pool was prepared using all available B3 aliquots (30 B3 tubes), the remaining 10 A3 aliquots and 20 A2 aliquots from the first 20 rats of the cohort . The remaining A1-A2 and B1-B2 aliquot tubes were preserved for future studies. For creating the plasma pools, frozen aliquots were thawed on ice and the hemolysis of each individual aliquot was measured with the NanoDrop-1000 and Denovix DS-11 spectrophotometers. Thawed plasma from all tubes were pooled and hemolysis in the total plasma pools were again measured with the NanoDrop-1000 and Denovix DS-11. Finally, various volumes of aliquots were taken from the plasma pools for RNA extraction.https://www.qiagen.com/fi/resources/resourcedetail?id=1076a54d-7967-4bc0-a34e-e3f574641d92&lang=en). The protocol was slightly modified: the addition of the spike-in control was omitted, and final elution of RNA was performed in 30 μl nuclease-free water, instead of 14 μl. The same RNA isolation protocol was also applied to a 50-μl water sample as a no-template control. RNA elutions were used for miR-23a-3p quantification with the TaqMan and miRCURY chemistries.Total RNA was extracted from five 50-μl aliquots, five 100-μl aliquots, and five 200-μl aliquots of the first plasma pool using the miRNeasy serum/plasma protocol . After pipetting 250 μl Qiazol lysis reagent into each plasma sample, we added 1 μg of MS2 bacteriophage carrier RNA  into each tube. We performed an additional wash step with RPE buffer (provided in the miRNeasy serum/plasma kit)30. For this, measurements were performed (a) immediately after RNA extraction (m1) and 1 day later (m2) after a single freeze-thaw cycle of the RNA. Further, each of these measurements were performed using (a) either a 10-μl pipette  or (b) a 5-μl pipette  to pipette the RNA eluate to the Qubit assay. Because the RNA elution volume was limited to 30 μl, we used the traditional forward pipetting method instead of the reverse pipetting method. The RNA volume added to the Qubit reaction was limited to 1 μl to maintain the required amount of eluted RNA for downstream RT-qPCR and ddPCR applications.Measurement of small RNA concentration with the Qubit microRNA assay kit. For the 15 plasma RNA samples extracted without carrier, small RNA concentrations in the RNA eluates were measured with the Qubit miRNA assay kit . We investigated if one freeze-thaw cycle of the eluted RNA affected the small RNA concentration measurements in Qubit. We also investigated if a difference in the choice of pipettes used to add the RNA eluate to the Qubit assay affected the assay variability, as noted in an application note from Thermofisher Scientific for Qubit assaysFor the plasma RNA samples with the carrier RNA, Qubit measurements were obtained as for the samples without the carrier, but only the 10-μl pipette was used as our data indicated no difference in performance between the two pipettes (see Results).Reverse transcription from samples with un-normalized small RNA concentration. A fixed volume of RNA eluate from the different starting plasma volume groups  was used as input for the RT reaction.https://assets.thermofisher.com/TFS-Assets/LSG/manuals/4364031_TaqSmallRNA_UG.pdf) according to the manufacturer’s instructions. Samples were stored at −20 °C until further processed.TaqMan chemistry. Total RNA (with and without carrier RNA) was transcribed to complimentary DNA (cDNA) with the TaqMan miRNA Reverse Transcriptase Kit  according to the manufacturer’s instructions. Samples were stored at −20 °C until further processed.miRCURY chemistry. Total RNA (with and without carrier RNA) was transcribed to cDNA with the miRCURY LNA RT Kit . For this, small RNA concentrations of the 15 plasma samples isolated without carrier RNA were adjusted to the concentration in the sample with the lowest concentration of small RNAs measured with Qubit. Thus, the RNA sample with the lowest concentration was used undiluted in the cDNA reaction. The other samples were then diluted with nuclease-free water to adjust their concentration of small RNA applied to the cDNA reaction to be comparable to that of the undiluted sample. cDNA synthesis was performed using the TaqMan and miRCURY chemistries as described above. Small RNA concentration normalization was not performed for the carrier added samples, since Qubit did not accurately estimate the concentrations in the presence of carrier (see Results).The PCR reaction setups and thermal cycling conditions used for plasma miR-23a-3p quantification are summarized in Supplementary Table https://assets.thermofisher.com/TFS-Assets/LSG/manuals/4364031_TaqSmallRNA_UG.pdf). For samples with carrier RNA, the analysis was performed only from un-normalized cDNA.The plasma miR-23a-3p level was determined from both un-normalized and normalized cDNA samples (without carrier RNA) using the TaqMan Small RNA Assays protocol (https://www.qiagen.com/fi/resources/resourcedetail?id=7ab5f614-f5d6-4bdc-b22b-246ec3601588&lang=en). cDNA samples were diluted 30-fold with nuclease-free water for the RT-qPCR reaction.Plasma miR-23a-3p levels from the un-normalized and normalized cDNA samples (without carrier) were also analyzed with miRCURY chemistry. For samples with carrier RNA, the analysis was performed only from un-normalized cDNA. RT-qPCR reactions were performed according to the protocol  for the synthetic spike-in UniSp6 added during the miRCURY RT reaction. RT-qPCR was performed in the same way as described above for miR-23a-3p, except that the UniSp6 primer assay  was used.31. For each sample, the reaction was performed in duplicate, and the mean of miR-23a-3p copies/20-μl PCR reaction from the two replicates was calculated and used for representation in the figures (with automatic amplitude threshold). No ddPCR was performed for the samples containing carrier RNA.For ddPCR, 1.33 μl of un-normalized and normalized cDNA templates from the TaqMan RT reaction  were added to a 20-μl reaction mixture containing 10 μl Bio-Rad 2x ddPCR Supermix for probes , 1 μl 20x miR-23a-3p PCR primer, and 7.67 μl nuclease-free water. DdPCR reaction was performed as described previously32. No ddPCR was performed for the samples containing carrier RNA.For miRCURY ddPCR, we tested the un-normalized cDNA under several conditions , since the setup phase indicated no difference in measured small RNA concentrations over different days or with using different pipettes (see Results). Small RNA concentration normalization was performed as described above. Plasma levels of miR-103a-3p  and miR-451a  were analyzed from the un-normalized and small RNA concentration-normalized samples using miRCURY RT-qPCR and ddPCR. For this, RT reaction products were diluted 30-fold for miR-103a-3p, and 60-fold for miR-451a with nuclease-free water. The miRCURY chemistry was chosen since (i) it requires smaller RNA volume for PCR analysis due to the universal RT reaction in comparison to the miRNA-specific RT reaction in TaqMan, (ii) the setup phase indicated comparable performance of the two chemistries (see Results). Further, since miRCURY ddPCR setup 6 performed most optimally in the setup phase (see Results), we chose only that setup for miR-103a-3p and miR-451a analysis. In the validation phase, miR-103a-3p, miR-451a levels and miR-23a-3p were also analyzed with miRCURY ddPCR from the un-normalized samples with carrier RNA.U test was applied for post hoc analysis33. For three or more related samples, the Friedman test was used. The Wilcoxon signed rank test was used for pairwise comparisons. Correlations were analyzed based on Spearman’s rho (ρ). A P-value less than 0.05 was considered statistically significant. All the statistical tests performed in this study are summarized in Supplementary Table Statistical analyses were performed using IBM SPSS Statistics 25.0 . Graphs were prepared with GraphPad Prism . The non-parametric Kruskal-Wallis test was used for comparison of three or more independent groups, and the Mann-Whitney 18. NanoDrop-1000 analysis of the EDTA-A and EDTA-B tubes immediately after centrifugation indicated that 1 of the 30 EDTA-A tubes and none of the 30 EDTA-B tubes contained a hemolysed sample  was calculated using the following formula:value(A) = 0.25, Denovix(A) = absorbance value of each plasma aliquot measured with Denovix DS-11, NanoDrop(A) = absorbance value of each plasma aliquot measured with NanoDrop-1000, n(samples) = total number of plasma aliquots measured. With this equation, the cut-off threshold for detecting hemolysis in the plasma samples with the Denovix DS-11 was defined as >2.5.We then compared the NanoDrop-1000 and Denovix DS-11 instruments for measuring hemolysis in the individual A2, A3, A4, A5, B3 and B4 plasma aliquots that were pipetted from the centrifuged EDTA tubes, with later aliquots being closer to the erythrocyte layer than earlier aliquots. Based on a cut-off absorbance value of >0.25 in the NanoDrop assay, the cut-off threshold for the Denovix  of the tubes. When the aliquots were thawed and a second hemolysis measurement was performed using NanoDrop and Denovix, hemolysis was detected in 29% (28/96) of all samples by at least one method (A414nm > 0.25 or >2.5 respectively) , 23% (6/26), 39% (9/23) and 44% (12/27) of the tubes respectively, with both instruments  detected by either instrument. With both instruments, however, the hemolysis coefficients were lower for the A4 tubes than for the A5 (p < 0.01) or B4 (p < 0.05) tubes. The hemolysis coefficients for the A3 tubes were also lower than those for the A5 (p < 0.001) or B4 tubes (p < 0.001). With the NanoDrop, the hemolysis coefficients did not differ between the A3 and A4 tubes (p > 0.05). The Denovix, however, revealed less hemolysis in the A3 tube than in the A4 tube  of the tubes. When the aliquots were thawed and hemolysis was again measured with NanoDrop and Denovix, NanoDrop detected hemolysis in 8.3% (5/60) of the aliquots (A414nm > 0.25) and Denovix detected hemolysis in 3.3% (2/60) of the aliquots (A414nm > 2.5)  were also compared with that of the individual aliquot tubes (after melting). Since hemolysis was measured before freezing with NanoDrop-1000, comparisons were performed only based on NanoDrop-1000 measurements. There was correlation between the EDTA-B tube and its respective aliquots (B3 and B4) but not for the EDTA-A tube and its aliquots  and the second measurement , the amount of small RNA eluted from the 100-μl plasma aliquot was greater than that obtained from the 50-μl aliquot . Similarly, the amount of small RNA eluted from 200-μl aliquots was greater than that from the 50-μl (p < 0.01 in both m1 and m2) and 100-μl (p < 0.05 in both m1 and m2) aliquots. For each starting plasma volume, the amount of small RNA measured did not differ between m1 and m2 Fig. . Very siThe percentage of coefficient of variation (CV%) between the two measurements from each pipette was calculated for every plasma volume group . Similarly, the amount of small RNA eluted from 200-μl aliquots was greater than that from the 50-μl and 100-μl aliquots , Qubit measurement was performed only with the 10-μl pipette due to the comparable performance of the two pipettes. For these samples, Qubit detected a very high concentration. When the concentrations were compared with corresponding elutions from 50-μl plasma without carrier, a 13-fold increase in detection was obtained in the setup phase (mean concentration: 3340.8 ng/ml vs. 262.6 ng/ml). Surprisingly, for the control sample where RNA extraction was performed from 50-μl of nuclease-free water with MS2 carrier RNA added during the extraction process, Qubit also detected a small RNA concentration of 3154.4 ng/ml. This indicated that for carrier-added samples, Qubit detects the carrier RNA, and the real eluted small RNA concentration is masked. Similar results were obtained in the validation phase. A 33-fold increase in small RNA concentration was measured with Qubit from the carrier added samples, in comparison to the corresponding elutions from 50-μl plasma without carrier (mean concentration: 4968.7 ng/ml vs. 150.5 ng/ml). Thus, small RNA concentration normalization was not performed for these samples in any of the two phases.In the setup phase, only miR-23a-3p was analysed. In the validation phase, miR-103-3p and miR-451a levels were analysed.As a first step, RT-qPCR was performed from the un-normalized RNA. As expected, mean miR-23a-3p Cq decreased with a corresponding increase in the starting plasma volumes. Cq values were lower for the 100-μl plasma volume group than the 50-μl group (p < 0.01). Cq values were also lower for the 200-μl plasma volume group than the 50-μl or 100-μl group (p < 0.01 for both) Fig. . The meaNext, we performed miR-23a-3p RT-qPCR from the small RNA concentration-normalized samples. We expected that with this normalization, all samples would exhibit similar Cq values for miR-23a-3p irrespective of the starting plasma volumes, because the small RNA input for the cDNA reactions was the same for all samples. The normalized 100-μl plasma volume group had a similar Cq as the normalized 50-μl group (p > 0.05). The normalized 200-μl group still had a lower Cq than either the 50-μl or 100-μl group (p < 0.01 for both). As a result of the small RNA concentration-normalization, standard deviation (SD) and CV% within each group reduced for the 50-µl and 100-μl groups, but not for the 200-μl . With this chemistry, however, the unnormalized 200-μl group had a high within-group SD, because of which the mean Cq in this group was not different from that in the 100-μl (p > 0.05) Fig. . Unlike As with TaqMan RT-qPCR from small RNA concentration-normalized samples, the 50-μl and 100-μl plasma volume groups had similar Cq values (p > 0.05). The 200-μl group still had lower Cq in comparison with the other groups (p < 0.01 for both). SD and CV% within each group were reduced for all the three plasma volume groups after normalization . In contrast to TaqMan RT-qPCR where the mean miR-23a-3p Cq correlated with the Qubit concentration only in the 50-μl plasma volume group, the mean miR-23a-3p copy number with TaqMan ddPCR correlated with Qubit concentrations only in the 100-μl group . Similarly, the 200-μl group also had higher mean miR-23a-3p copy number than the 100-μl group (p < 0.01) Fig. . In contSimilar to TaqMan RT-qPCR, the mean miR-23a-3p copy number in the normalized 100-μl plasma volume group was not different from that in the normalized 50-μl group (p > 0.05), but the normalized 200-μl group still had a higher copy number than either of the other plasma volume groups (p < 0.01 for both). SD within each group reduced for all the three plasma volumes whereas CV% reduced only for the 50-µl and 100-μl , but not with setup 6 (p > 0.05)  miRNA ratio or visual inspection 18. In that study, we concluded that similar to human plasma22, absorbance > 0.20–0.25 at 414 nm is also indicative of hemolysis in rodents. In the present study, we investigated the pattern of rat tail-vein plasma hemolysis in further detail. Although NanoDrop measurements revealed that plasma from the individual EDTA-A or the EDTA-B tubes were not hemolysed, aliquoting revealed that the later aliquots were more hemolysed than the first ones. This was because the hemolysis values of the individual aliquots were affected by their vicinity to the erythrocyte layer. As a result, hemolysis values measured from the EDTA tubes and the individual plasma aliquots mostly did not correlate. This indicated that manual pipetting of plasma increases the risk of contamination, which is why automated pipettes are currently gaining popularity in clinical settings35. When comparing hemolysis values of individual aliquot tubes measured NanoDrop-1000 and Denovix DS-11 spectrophotometer, we identified comparable performance of the machines.We identified that plasma aliquots pipetted from the vicinity of the erythrocyte layer had a higher proportion of hemolysis. Several methods for detecting hemolysis in human serum/plasma samples have been published36, as well as circulating DNA37 and total RNA39. Superior performance has also been reported for the Qubit miRNA assay in quantifying circulating miRNA concentration in human plasma samples, compared with spectrophotometric methods14. However, the previous study also highlighted that the Qubit miRNA assay detects all forms of small RNAs and are not specific to miRNAs. In the present study, we evaluated the Qubit miRNA assay for our rat plasma samples. We performed the measurements with two different pipettes and on two separate days, involving one freeze-thaw cycle of the RNA. With both pipettes and measurements, an increase in the small RNA concentration was observed with an increase in starting plasma volume. Importantly, there was no difference between the two measurements on successive days, indicating that the small RNA concentrations were unaffected by one freeze-thaw cycle. Qubit revealed a mean CV of 11% when the two pipettes and measurements were compared, which is consistent with the observations in the human plasma study14. The 5-μl pipette did not reduce the CV% in comparison with the 10-μl pipette. However, the 200-µl plasma volume group showed lower CV% compared to the 50-µl, with significant correlations when the different measurements and pipettes were compared. This indicates that the Qubit measurements were more consistent for RNA extracted from a larger plasma volume, given it is within the limit of saturation. Good performance of Qubit from 200-µl plasma has also been observed in another recent study40.For our rat tail-vein plasma samples, the Qubit miRNA assay detected an increase in small RNA concentrations with an increase in the starting plasma volumes. The Qubit fluorometer has already been identified as a reliable platform for quantification of RNA from tissue41, but reports have been contradictory regarding the performances of MS2, yeast RNA, or glycogen as carriers43. Further, these studies have been performed in human plasma samples, and Qubit was not used to measure eluted RNA concentrations. With our rodent plasma samples, Qubit detected a very high small RNA concentration when extractions were performed in presence of the MS2 carrier. Ramon-Nunez et al. had also observed highest total RNA concentration with addition of MS2 carrier, but based on electropherogram analysis, they concluded yeast RNA to be a better carrier for small RNA and miRNA extraction than MS224. Importantly, we also observed a very high small RNA concentration with Qubit even from the no-template control where RNA extraction was performed from a water sample with MS2 carrier addition. This finding revealed that the carrier RNA gets eluted when the miRNeasy columns are used. Further, even though its size is larger than that of the small RNAs, the Qubit miRNA assay detects it. Thus, isolated RNA concentrations get masked with the use of MS2 carrier24.Addition of a carrier RNA during RNA extraction from plasma increases miRNA recovery24. In the setup phase MS2 carrier-added samples showed higher mean miR-23a-3p Cq levels than the samples without carrier, whereas in the validation phase, no difference in miR-23a-3p copy numbers was observed with ddPCR. This indicates a possible inhibition of RT-qPCR due to the co-extracted carrier RNA which is resolved with the more tolerant ddPCR method45. However, ddPCR revealed that the levels for miR-103a-3p and miR-451a doubled with carrier addition, whereas there was no improvement in miR-23a-3p yield, indicating that the addition of MS2 carrier RNA impacts the extraction of miRNAs differently.Further, while most studies emphasize the fact that carrier RNA addition improves miRNA recovery from serum/plasma samples, it is also highlighted that the recovery depends on the relative abundance of the miRNA in the sample (SRA), ΔG and GC content47. According to Van Vliet et al18., a 50–200 μl plasma volume is suitable for miRNA analysis from rodents. In our study, we observed an increase in the concentrations of the analysed miRNAs with increase in starting plasma volumes. Thus, our findings confirm that 50–200 μl of starting plasma volume is suitable for rodents, with no significant PCR inhibition.A limited number of analyses of miRNAs from rat plasma have been performed. Two of these studies used 200 μl plasma for RNA extraction48. In comparison with RT-qPCR, most studies have also demonstrated similar or improved sensitivity, precision, and reproducibility of ddPCR49, with very good correlation between the two techniques51. Studies comparing the TaqMan and miRCURY chemistries have revealed similar performance of both chemistries for abundantly expressed miRNAs53. For low-abundance miRNAs, however, miRCURY is preferred over TaqMan53, although some variability in the measurements has been observed52. With ddPCR, performance of both chemistries is comparable32. In our study, performance of the TaqMan and miRCURY chemistries were comparable for the un-normalized and the concentration-normalized samples with RT-qPCR and ddPCR.The major advantage of ddPCR over RT-qPCR is its reduced sensitivity to PCR inhibitors and its ability to perform absolute quantification, thereby eliminating the need for endogenous references or standard calibrator curves54. For circulating miRNA analysis, a fixed RNA sample volume is typically used for cDNA synthesis due to poor estimation of the eluted RNA concentrations27. In this study, we attempted to normalize the plasma miR-23a-3p, miR-103a-3p and miR-451a levels of the individual samples to the total small RNA content based on Qubit assay detection. With this normalization, similar miRNA concentration could be achieved from different starting plasma volumes. The 100-μl plasma volume group had similar miRNA concentration as the 50-μl for all the three tested miRNAs . The 200-μl group also had similar miR-451a concentration as in 50-µl and 100-µl, but for miR-23a-3p and miR-103a-3p it showed a trend towards normalization. Since these variations were observed with both PCR chemistries, it indicated that normalization performed based on the total small RNA concentration measurement from Qubit does not perfectly normalize miRNA concentrations when 200 μl plasma volume is used as a starting material.Total RNA content as a normalizer for miRNA RT-qPCR data was previously studied in tissue samples, but did not perform wellWe also compared our a-priori normalization method with a posteriori normalization, wherein miRNA levels from the un-normalized samples were normalized to the Qubit concentration. Overall, the a-priori and posterior normalizations resulted in similar miRNA expression patterns. However, since ddPCR based on a-priori normalization yields absolute miRNA copy numbers, this method may be preferred over the posteriori to generate cut-off thresholds for circulating miRNA biomarker analysis.Within each starting plasma volume group, the normalized samples mostly had a lower CV% for miR-23a-3p in comparison to the un-normalized condition. However, reduction in CV% as a result of normalization was not that prominent for miR-103a-3p or miR-451a, both with a-priori and posteriori methods. This plausibly indicates that the total small RNA concentration-based normalization affects individual miRNA concentrations differently. Nevertheless, performing the small RNA concentration based normalization and comparing it to the un-normalized condition is crucial for individual miRNA candidate, particularly in settings where RNA isolation has to be performed from different starting plasma volumes , and where variations in RNA extraction efficiencies are observed.Hemolysis measurements from the plasma samples were performed only with the NanoDrop-1000 and Denovix DS-11, as these were the only two spectrophotometers available to us. Thus, performance of the Denovix DS-11 in comparison with other spectrophotometers, e.g., the NanoDrop-2000, was not evaluated. For TaqMan, the standard chemistry with miRNA specific cDNA synthesis was only analysed. The recently introduced TaqMan advanced miRNA assays that implement the universal cDNA synthesis similar to miRCURY chemistry was not evaluated. Further, the TaqMan and miRCURY primer assays are designed for RT-qPCR systems, making it essential to evaluate their performances in ddPCR for each miRNA assay. We recommend testing and optimizing the chemistries and the normalization strategies described here for every target miRNA.Our data suggest that spectrophotometry-based detection of hemolysis in rodent plasma samples should be performed to select good quality plasma for miRNA biomarker analysis. The Qubit performs well in measuring small RNA concentrations from low abundance samples like the plasma. Further, starting from a fixed volume of non-hemolysed rodent plasma, we can apply the small RNA concentration-normalization approach with absolute quantification in ddPCR for plasma miRNA biomarker analysis.Supplementary information.


Pyrococcus furiosus. The large subunit (LSU) rRNA is produced as a circularly permuted form through circularization followed by excision of Helix 98. As a consequence, the terminal domain VII that comprise the binding site for the signal recognition particle is appended to the 5´ end of the LSU rRNA that instead terminates in Domain VI carrying the Sarcin-Ricin Loop, the primary interaction site with the translational GTPases. To our knowledge, this is the first example of a true post-transcriptional circular permutation of a main functional molecule and the first example of rRNA fragmentation in archaea.Synthesis and assembly of ribosomal components are fundamental cellular processes and generally well-conserved within the main groups of organisms. Yet, provocative variations to the general schemes exist. We have discovered an unusual processing pathway of pre-rRNA in extreme thermophilic archaea exemplified by  Synthesis and processing of rRNA is fundamental to all living organisms. A highly conserved feature at the transcriptional level is that the two main species, SSU and LSU rRNAs, are co-transcribed with the consequence that they are expressed directly into 1:1 stoichiometry. 5S rRNA on the other hand can be part of the same transcriptional unit or transcribed elsewhere in the genome. Another common principle applies to early pre-rRNA processing, namely that the 5´ and 3´ ends of each of SSU and LSU rRNA comes together as a prerequisite for their release from the ribosomal precursor. As a consequence, only fully transcribed precursors give rise to mature rRNA. In bacteria, the nucleotides corresponding to the mature rRNA ends as well as immediate flanking regions base pair to form stem structures that are recognized by RNase III, and the mature ends are subsequently formed by exonucleolytic trimming. In archaea, Bulge-Helix-Bulge (BHB) motifs are formed, cleaved by tRNA splicing endonuclease, and ligated by tRNA ligase to form circular intermediates. This is followed by further endonucleolytic cleavages and exonucleolytic trimming to form the mature rRNA ends. In the most studied models of eukarya (yeast and human), the primary transcript is not looped through base pairing and the initial cleavages are not coordinated in a single cleavage activity. Instead, multiple endonucleolytic cleavages and exonucleolytic trimming reactions result in the release of the mature rRNA species. The details of pre-rRNA processing and variations on the general schemes have been the subject of numerous reviews in bacteria , archaeaThermoproteus, the SRP RNA gene is genomically rearranged, such that the transcription is initiated from a position that is normally found within the gene , the mature LSU RNA appears as a linear RNA species that has helices 99–101 appended to the 5´ end and terminates in helices H94–97 comprising the Sarcin-Ricin Loop (SRL).Here, we demonstrate that the LSU rRNA of the extreme thermophile Pyrococcus furiosus cells were grown in 1/2 SME medium (pH 6.5) supplemented with 0.1% yeast extract, 0.1% peptone, and 0.1% starch. The gas phase was N2/CO2 (80:20) and the incubation temperature was 95°C. The culture was grown for 30 h to a final cell density of 3 × 107 cells/ml. RNA was extracted by resuspending 0.5 g of pelleted cells in 2.5 ml TMN buffer . Cells were run twice through a French press, followed by addition of 4 ml TRIzol (Life Technologies). After 5 min incubation at room temperature, 0.8 ml chloroform was added. The samples were centrifuged at 12,000 × g for 5 min and the aqueous phase transferred to a new tube for extraction with phenol/chloroform. Total RNA was then precipitated by ethanol precipitation.Arabidopsis tRNA ligase. Finally, the library was sequenced on the Ion Proton sequencing platform. The reads were mapped to non-coding RNAs annotated in the P. furiosus genome (GenBank: CP003685.1) and scored for read-end counts. Analyses of transcriptomics data were based on datasets deposited at the European Nucleotide Archive: SRX501747 (Thermococcus kodakarensis), SRX2118858 (Pyrobaculum aerophilum), SRX3467357 , and SRX5547671 (Pyrococcus furiosus).The RiboMeth-seq analysis was performed in duplicates according to previously described protocols . Briefly6 primers, followed by standard PCR.Northern Blot analysis was performed according to standard protocols as described in http://rna.tbi.univie.ac.at/) and supported by covariance analysis of sequences from species of Thermococcales. Structure figures were prepared using PyMol and UCSF-Chimera.RNA base pairing schemes were assisted by RNAfold from the ViennaRNA Web Services  that would exist as a separate fragment after H98 excision in the absence of ligation of the 5´ and 3´ end of LSU rRNA. We detected the free 129 nt fragment at a level of <5% compared to 5S rRNA based on the hybridization analysis as well as a gel stain , but absent in the two representatives of Nanoarchaeota, e.g., Nanoarchaeum equitans. In Euryarchaeota, it is found in, e.g., Methanococcales  and Thermococcales, but appears absent in, e.g., Methanomicrobiales, Halobacteriales, and Archaeoglobales  and proposed to circularize many different cellular RNAs -H99 region (data not shown). In addition to the circularly permuted forms in P. furiosus and T. kodakarensis  is involved in docking of the signal recognition particle (SRP) in bacteria and chloroplasts . Second,P. furiosus. This organism is widely studied and the organization of its rRNA was not anticipated in previous studies. However, re-analysis of existing data, e.g. from , 4V9F (H. marismortui 50S subunit), 4ADX, (M. thermautotrophicus 50S subunit), 4V6 (P. furiosus 70S ribosome), and 4V6U (T. kodakarensis 70S ribosome).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at UB and HN planned the RiboMeth-seq and biochemical experiments that were conducted by UB. DW supervised the cryo-EM analysis. DW and BB prepared the structure Figures. HN wrote the first manuscript draft. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Non-coding regulatory RNAs are generated as a core output of the eukaryotic genomes, being essential players in cell biology. At the organism level, they are key functional actors in those tissues and organs with limited proliferation capabilities such as the heart. The role of regulatory networks mediated by non-coding RNAs in the pathophysiology of cardiovascular conditions is starting to be unveiled. However, a deeper knowledge of the functional interactions among the diverse non-coding RNA families and their phenotypic consequences is required. This review presents the current knowledge about the functional crosstalk between circRNAs and other biomolecules in the framework of the cardiovascular diseases. In eukaryotic cells, circular RNAs (circRNAs) are covalently closed RNA molecules generated by non-canonical back-splicing of coding and non-coding transcriptional units ,2. Besidtrans-acting factors that regulate the formation of several circRNAs [CircRNAs biogenesis proceeds following a unique mechanism among non-coding RNAs (ncRNAs) since they are generated as by-products during the post-transcriptional processing of other RNA species (coding or non-coding). Jeck and coworkers proposed the term back-splicing to describe the pre-mRNA process of circularization  and covacircRNAs ,13. RegacircRNAs . Once thcircRNAs ,16, whiccircRNAs .CircRNAs can be classified in three major groups: exonic circRNA (EciRNA or ecircRNA) generated by the circularization of one or more exons , introniCircRNAs, together with other ncRNA species such as miRNAs and lncRNAs, are pivotal players in the regulation of cardiovascular development and pathophysiology ,24,25. CRegulatory crosstalk between non-coding RNA species was exemplified by the characterization of competing endogenous RNA (ceRNA) networks where an anchor RNA molecule was able to capture miRNAs by sequence complementarity, removing them from the medium and decreasing their regulatory effects over target mRNA transcripts. This phenomenon was firstly described in the differentiation of muscle, where the MD1 lncRNA acts as a molecular sponge of miR-133, activating muscle-specific gene expression , and alsSeveral bioinformatics tools, such as CircInteractome, CircNet, Circ2Traits, and StarBase v2.0 have been of great benefit to predict miRNA binding sites in circRNAs ,32,33,34Cdr1as (ciRS-7) is a very well-known circRNA that acts as a miRNA sponge in numerous and different cell types such as cardiomyocytes, neural cells, and hepatic cells ,29,44. CSmyd4) gene [The mitochondrial fission and apoptosis-related circRNA (MFACR) is generated from the exon 5 of the SET and MYND domain-containing 4 (d4) gene . This cid4) gene ,48. Simid4) gene . In cultd4) gene .ncx1) gene and also mediates cardiomyocyte apoptotic events. Higher levels of reactive oxygen species (ROS) in the myocardium are related to increased levels of circNCX1 and may intensify the effects of I/R damage. In fact, circNCX1 can act as an miRNA sponge, harboring 8 binding sites for miR-133a-3p. This miRNA was previously described as cardioprotective in hypertrophic cardiomyopathy and heart failure by targeting the cell death-inducing p53-target protein 1 (CDIP1), an apoptotic transducer. Thus, circNCX1 sponge activity may suppress miR-133a-3p, leading to an increased number of apoptotic cardiomyocytes that intensify MI effects such as myocardial damage [CircNCX1 is generated from the second exon of the sodium/calcium exchanger 1 , generated from the murine  failure . HRCR ac failure .Another circRNA that plays a major role in cardiac hypertrophy is circSlc8a1. This circRNA was previously shown to be abundant in the human heart . Lately,Myo9a) gene. It contains the exons 7 to 15 and respective flanking sequences of the introns 6 and 15. Despite its large size, this circRNA was shown to only harbor two MREs for miR-26b-5p, a suppressor of Col1a2 and CTGF expression [CircRNA_000203 is a nine exon circRNA transcribed from the Myosin 9a  and circZNF609. MICRA is an 874 nt long circRNA originated from exon 1 of the iomarker . Interesiomarker . Adding iomarker , it is piomarker . Moreovene locus . In thisne locus . On the ne locus . In factne locus ,60.CircRNAs have been implicated in hypoxia-induced angiogenic events by sponging miRNAs. In an early study, hsa_circ_000595 RNA was associated with the increase of apoptosis in human aortic smooth muscle cells. Data showed that hsa_circ_000595 could target miR-19a and overexpression of the circRNA resulted in miR-19a down-regulation .More recently, the circRNA hsa_circ_0010729 was described as a vascular cell apoptosis suppressor by inhibiting the activity of miR-186 . In humaIschemic stroke events and other cerebral ischemic diseases are responsible for significant and serious impairment in the brain, with increased morbidity and mortality. Rising research on circRNAs has shown that these molecules are enriched in multiple organs and are highly expressed in the brain, suggesting that circRNAs may play an important role in regulating neurophysiological and pathophysiological processes .DLGAP4 gene, may be implicated in the physiology of ischemic stroke [Recent research provides evidence that the circular RNA circDLGAP4, originated from the back-splicing of exons 8 to 10 of the c stroke . CircDLGc stroke .HECTD1 locus is related to ischemic stroke development and regulation through the formation of the circular RNA circHECTD1 that contains exons 23 and 24 of the HECTD1 gene [The TD1 gene . CircHECTD1 gene .Recent studies started to show that circRNAs may also interact and modulate the cellular roles of many RNA-binding proteins (RBPs). These interactions are very complex, as different RBP/circRNA interactions could result in diverse regulatory events. However, two main circRNA/RBP interactions can be distinguished: RBP sponging and RBP scaffolding ,41,66.Drosophila melanogaster muscleblind protein (MBL) was the first RBP found to strongly regulate circRNA synthesis from the Mbl locus and was also the first protein noticed to undergo RBP/circRNA sponging. Cellular MBL protein abundance is responsible for its own pre-mRNA back-splicing and circularization. This process prevents the formation of a linear transcript and consequent MBL protein formation and accumulation, in a negative feedback mechanism. More interesting is the fact that mature circMbl molecules contain many MBL binding sites with the ability to intake these proteins and balance their cellular amount [The RBP sponging function is very similar to the previously described miRNA sponging function. Essentially, some circRNAs contain RNA-protein binding sites that present high-affinity to certain RBPs that prevent their availability and consequent roles in the cell. CircRNA/protein interactions started to be described in 2014. r amount .Foxo3) gene, acts as a sponge for certain transcription factors and antisenescence proteins but also functions as a scaffold for p21 protein. circFoxo3 contains p21 binding sites and the interaction they establish facilitates the communication between p21 and Cdk2 protein, triggering the chain of events that prevent cell cycle progression [Though many circRNAs act as RBP sponges, many other circRNAs own the ability to modulate protein function by acting as scaffolds that stabilize and enable protein–protein interactions. For instance, it was previously reported that circFoxo3, generated from the forkhead box-O 3  and E2F1 (E2F transcription factor 1) and the antisenescence proteins HIF1α  and FAK . The interaction between these molecules results in the retention of these proteins in the cytoplasm. The decrease of ID1, E2F1, and HIF1α in the nucleus, and FAK in the mitochondria, is responsible for cellular stress and posterior senescence  noncoding RNA in the INK4 locus, abbreviated as ANRIL. The same locus may produce a circular RNA form, circANRIL, which is associated with atherosclerotic vascular disease (ASVD). It was shown in HEK-293 cells that this circRNA interacts with the pescadillo homolog (PES1) protein. PES1 is a component of the PeBow complex and is responsible for the maturation and formation of the 60S ribosome subunit, being crucial in cell cycle progression. In atherosclerotic plaques, PES1 sponging by circANRIL has consequences in cell proliferation suppression and a consequent increase of cell apoptosis in proliferating cells. Thus, circANRIL is responsible for atheroprotection [The otection ,68.CircAmotl1 was shown to suppress apoptosis events in a doxorubicin-induced cardiomyopathy mouse model. In the cytoplasm of cardiomyocytes, circAmotl1 acts as a scaffold facilitating the interaction between the serine/threonine kinase AKT and the 3-phosphoinositide-dependent protein kinase 1 (PDK1). PDK1 phosphorylates AKT trigger the translocation of phosphorylated AKT (pAKT) into the nucleus. In the nucleus, pAKT participates in the regulation of cell survival processes. An experiment using antisense oligonucleotides for the AKT and PDK1 binding sites showed that the amount of dephosphorylated AKT in the cytoplasm was increased and therefore it could not be translocated into the nucleus. Furthermore, the use of ribonuclease A was enough to suppress the interaction between AKT and PDK1. These experiments suggested that circAmotl1 is a crucial “platform” for AKT activation and translocation into the nucleus and protects the heart from cardiomyopathy . InteresTTN) gene is the largest coding gene found in mammals. The RNA that results from its transcription may contain up to 363 exons [TTN isoforms are crucial in muscle contraction, regulation of calcium content in the muscle fiber, and cell cycle upkeep [TTN gene may also produce circular RNA forms [The titin  patients. Two recent reports emphasized the possibility of using MICRA from blood as a biomarker to infer on left ventricular (LV) dysfunction and remodeling four months after AMI. MICRA’s blood levels were shown to be low in a cohort of MI patients when compared to healthy volunteers. Salgado-Somoza et al. also reported that lower levels of MICRA in the blood of patients at reperfusion time will present a reduced heart ejection fraction and that higher levels of blood MICRA are indicative of a mid-range or preserved ejection fraction [MICRA, previously mentioned in the fraction ,78.Another circRNA that may be used to diagnose AMI is circRNA_081881. This molecule is down-regulated in the plasma of AMI patients alongside the peroxisome proliferator-activated receptor gamma (PPARγ) transcription factor. Deng et al.  performeThe dynamic output of the human genome in the form of RNA transcripts contains a wide range of molecular species with different functions. The non-coding transcriptome constitutes by far the more diverse part of the genomic output, harboring regulatory ncRNAs that can control the information flow from the coding genome. The intricated functional relationships established between the different species belonging to the non-coding transcriptome are starting to be characterized in cell biology, but also in human disease. Among the ncRNAs, lncRNAs and circRNAs are very diversified in their functions due to their abilities to interact with different biomolecules including DNA, RNA, and proteins. In cardiovascular diseases, circRNAs have been characterized as important players in the control of the cellular levels of miRNAs and RNA-binding proteins, with different functional and pathological consequences ranging to direct involvement in pathology to protection from a cardiovascular condition.Despite of the importance of circRNAs and the established regulatory networks, the availability of data about their roles in cardiovascular diseases is still limited due to the lack of knowledge about the rules governing their biogenesis from splicing events, the limitations of laboratory models, and also the lack of consensus within the scientific community in circRNA annotation and databases. A stronger effort is required to circumvent these limitations, since the central position of circRNAs in the regulatory events leading to some cardiovascular conditions may lead to potential new therapeutic and diagnostic strategies for better healthcare.