A-to-I editing, as a post-transcriptional modification process mediated by ADAR, plays a crucial role in many biological processes in metazoans. However, how and to what extent A-to-I editing diversifies and shapes population diversity at the RNA level are largely unknown. Here, we used 462 mRNA-sequencing samples from five populations of the Geuvadis Project and identified 16,518 A-to-I editing sites, with false detection rate of 1.03%. These sites form the landscape of the RNA editome of the human genome. By exploring RNA editing within and between populations, we revealed the geographic restriction of rare editing sites and population-specific patterns of edQTL editing sites. Moreover, we showed that RNA editing can be used to characterize the subtle but substantial diversity between different populations, especially those from different continents. Taken together, our results demonstrated that the nature and structure of populations at the RNA level are illustrated well by RNA editing, which provides insights into the process of how A-to-I editing shapes population diversity at the transcriptomic level. Our work will facilitate the understanding of the landscape of the RNA editome at the population scale and will be helpful for interpreting differences in the distribution and prevalence of disease among individuals and across populations. As a post-transcriptional modification mediated by adenosine deaminases acting on RNA (ADARs)2, A-to-I editing plays an important role in biological processes by affecting targets such as neuronal receptors3, ion transporters4, and immune response receptors5. A-to-I editing can contribute to transcriptomic and phenotypic diversity by recoding proteins6, affecting alternative splicing7, modifying microRNAs8, and altering microRNA target sites9.A-to-I editing, which is the most common type of RNA editing in metazoansDrosophila13, mice14 and primates16. Genetic cis-regulation is important in the comparison of editing levels between genetically diverse outbred mice14, different Drosophila species12, and Drosophila from a common environment13. Recently, researchers identified dozens of differentially edited sites between flies from two opposing slops and showed the relative importance of cis-regulation and environmental regulation in determining these differences11. The complex cis- and trans-regulation of RNA editing has been observed by exploring the A-to-I editing profiles of 8,551 human samples from the Genotype-Tissue Expression (GTEx) project15. Although cis variation in RNA editing has a pronounced impact on RNA secondary structure, it is still largely unknown if there is a difference in the cis- or trans-sequence of A-to-I editing at the human population level.The cis-regulation of A-to-I editing has been intensively investigated in 17 is characterizing the geographic and functional spectrum of human genetic variation to improve the understanding of genetic contributions to disease. By sequencing 2,504 samples from 26 populations, the consortium recently presented an integrated map of structural variants (SVs). This SV catalogue greatly facilitates studies of population genetics, structural variant demography, functional impacts and disease associations at the DNA level. In contrast to the extensive research on genetic variation in human genomes, how and to what extent A-to-I editing diversifies and shapes population diversity at the RNA level are not fully characterized. In the Geuvadis (Genetic European Variation in Disease) project18, proposed by Lappalainen in 2013, mRNA sequencing was performed on 465 lymphoblastoid cell line (LCL) samples from the following populations of the 1000 Genomes Project: CEPH (CEU), Finns (FIN), British (GBR), Toscani (TSI) and Yoruba (YRI). The data from the Geuvadis project allowed us to investigate intra- and inter-population diversity at the transcriptomic level and to explore the landscape of the A-to-I RNA editome at the population scale.The 1000 Genomes ProjectFST) among five populations. We observed higher diversity of RNA editing between population pairs from different continents relative to those from the same continent, suggesting geographic differentiation of RNA editing. In addition, we analysed the structural motifs of cis- and trans-RNA editing QTLs (edQTLs) in each population separately and discovered the population-specific patterns of these sequence features. Altogether, our results suggested that RNA editing could be used to characterize the nature and structure of populations at the RNA level, which provides insights into how A-to-I editing shapes population diversity at the transcriptomic level. Our study will facilitate the understanding of the RNA editome landscape at the population scale and will be helpful for interpreting differences in the distribution and prevalence of disease among individuals and across populations.In this study, we identified 16,518 A-to-I editing sites with false detection rate of 1.17% from 462 mRNA-sequencing samples of the Geuvadis project and explored transcriptomic variation among multiple human populations. We compared the probability of RNA editing being shared by two individuals within a population with the probability of sharing by two random individuals from 462 samples. Then, we examined the sharing of editing sites occurring in less than nine individuals across all samples. Our results showed that the sharing of rare RNA editing sites was non-random, suggesting that rare editing sites are likely geographically restricted. Next, we identified 253 highly differentiated editing sites between populations and compared the RNA editing level and the fixation index  samples of the Geuvadis projectRI Table . These f1, we restricted our analysis to A-to-I RNA editing. To identify A-to-I RNA editing candidates among the 462 human genomes from the five populations, we used a pipeline similar to that described in a previous study19. To ensure the accuracy of identification, we only retained 16,518 A-to-I editing sites annotated in DARNED and RADAR in the subsequent analyses. Compared with the percentage of non-canonical mismatches identified by our pipeline, the percentage of A-to-I mismatches ranged from 84.86% to 95.25%, with an average of 89.81%  and found that the majority of editing sites were located in 3′ UTRs (47.68%), followed by intronic (26.26%), non-coding RNA  and intergenic regions (4.61%)  Fig. . Among t1%) Fig. . Coding p-value < 2.2 × 10−16), FIN  and TSI . Together, these data provide a broad representation of the A-to-I RNA editome of human genomes.For each population, we identified 10,469, 11,590, 12,395, 10,931 and 11,469 A-to-I editing sites in CEU, FIN, GBR, TSI, and YRI, respectively. We use the number of RNA editing sites per million uniquely mapped reads to avoid the effect of different numbers of mapped reads between individuals. The number of A-to-I editing sites was similar in each population but largely varied among individuals, ranging from 62 per million uniquely mapped reads in one CEU individual to 179 per million uniquely mapped reads in one GBR individual Figs  and S3. p-value < 2.2 × 10−16) and rare RNA editing sites tend to cluster within private populations .The reference set of the A-to-I RNA editome provides a prime resource for systematically analysing the patterns in which A-to-I RNA editing sites are shared or monopolized among individuals and populations. To this end, we first investigated the distribution of RNA editing across multiple populations by examining the frequency distribution of RNA editing sites present across all 462 individuals. With an increasing frequency of RNA editing, the proportion of RNA editing shared within a population decreased, and the proportion of RNA editing shared across all populations increased Fig. . Among tp-value = 0.91). In contrast, the probability of rare editing sites being shared within a population is significantly higher than the probability of rare editing sites being shared by two random individuals across all individual pairs. This result suggested that the sharing of rare editing sites was non-random.To investigate the pattern of shared RNA editing within populations, we compared the probability of RNA editing sites being shared by two individuals within a population with the probability of RNA editing sites being shared by two random individuals selected from all 462 samples Fig. . The prop-value = 5.06 × 10−2). Next, we examined the distribution of rare RNA editing sites sharing between individuals. We found that GBR exhibited the greatest number of rare editing sites shared within a population, at 1064, followed by YRI, FIN, TSI and CEU. The number of editing sites shared by individuals within a population ranged from 509 to 1,064, whereas the number of rare editing sites shared by individuals from different populations ranged from 81 to 277. Within each population, more than half of the rare RNA editing sites were characterized by population-specific sharing. The sharing of rare editing within population is significantly higher than that sharing between populations . These results suggested that the rarely shared editing sites were much more likely to be shared between individuals from the same population. Together, our results revealed that rare editing sites were likely to be geographically restricted.Furthermore, we explored the relationship of the RNA editing sharing between the five populations and the frequency of RNA editing among 462 individuals. We found that population-specific RNA editing (RNA editing private to a population) decreased, while population-shared RNA editing increased as the frequency of RNA editing increased Fig. . The samp-value = 7.51 × 10−3). The number of highly differentiated RNA editing sites in population pairs from different continents was significantly higher than that for population pairs from the same continent . We chose 0.15, 0.2 and 0.25 as the relative differences in frequency and obtained consistent results  in RNA editing in one population, but rare  in another population; this scenario could be observed between any two populations Table . This relts Fig. . Our resp-value < 2.2 × 10−16) between the CEU population and FIN population to 0.983  between the GBR population and TSI population. However, the correlation coefficient of editing levels between populations from Europe and YRI was slightly lower than that between population pairs from Europe . FST between population pairs from Europe ranged from 1.81 × 10−3, between the FIN and TSI populations, to 4.38 × 10−3, between the CEU and GBR populations. However, FST between the YRI population and the various European populations ranged from 5.92 × 10−3, between the FIN and YRI populations, to 7.82 × 10−3, between the CEU and YSI populations. These results suggested that editing sites could be used to characterize the extent of diversity between populations from different continents well.Finally, we performed a pairwise ons Fig. . Typical25 separately for each population . Limiting the analysis to SNPs within 200 kb upstream and downstream of the RNA editing sites, we identified 85, 90, 128, 128 and 140 cis-edQTLs at a significance threshold of 1e-8 in CEU, FIN, GBR, TSI, and YRI, respectively  associated with changes in editing levels. In this analysis, we employed only 445 human lymphoblastoid cell lines for which both genomic and transcriptomic data were available. To address the question of whether there is any difference in edQTLs between populations, we ran MatrixEQTLely Fig. . With a ely Fig. . To veriely Fig. . Howeverely Fig. .Figure 4Next, we examined the location of edQTL editing sites in gene regions. The location of edQTL editing sites was similar to that of ordinary A-to-I editing sites. The majority of edQTL editing sites were located in 3′ UTRs, followed by intronic, non-coding RNA and intergenic regions Fig. . In addi26 was used to scan motifs in the sequences 250 bp upstream and downstream of edQTL editing sites. For background control, we used the sequences of the 250 bp flanking regions of 16,518 A-to-I editing sites to exclude the structural features of ordinary A-to-I editing sites . In total, we identified 69 motifs at cis-edQTL editing sites (cis-motif) and 229 motifs at trans-edQTL editing sites (trans-motif)  Fig. . Among tif) Fig. .Figure 527 and DREME28 . The sequences of the 250 bp flanking regions of all 16,518 A-to-I editing sites were used to generate a second-order Markov model as a background control. We identified 28 novel cis-motifs and 51 novel trans-motifs with a very stringent cut-off (P < 1e-8). We annotated the most likely transcription factor for each motif by comparing it with public transcription factor database TRANSFAC29 using TOMTOM30  analysis in human populations. They discovered that SNPs associated with the variation in RNA editing function more closely to their respective editing sites and that some of these SNPs are linked to genome-wide association study (GWAS) signals of complex traits or diseases among genetically distinct individuals32. Their work demonstrated the important functional impact of RNA editing on biology and disease in human cells. The population-specific pattern of edQTL editing sites could aid in interpreting the distribution of disease among populations. These results suggest that genetic mutation or RBPs other than ADARs affect the differences in RNA editing between populations, but how and to what extent this regulation modulates RNA editing have yet to be explored.Finally, we explored the association between genetic SNPs and transcriptomic A-to-I editing sites in human populations via cis- and trans-edQTLs. We observed that YRI presented more cis- and trans-edQTL editing sites than the other populations, especially for population-specific edQTL editing sites. Furthermore, the structural motifs of edQTL editing sites were enriched in a population-specific manner, suggesting the existence of different regulatory patterns between populations. Two other recent studies also performed RNA editing analysis on the same RNA-seq data from these human populations that we used. Xiao’s group discovered differences in RNA editing prevalence within populations and low-level differences in the shared editing sites of the populations17 identified millions of genetic variants in humans and interpreted their functional effects to understand the genetic basis of variation in human traits. In addition, the Consortium showed that the majority of rare variants was shared by individuals from the same population and that rare variants had arisen more recently than distinct populations. These results demonstrated that rare (<0.5%) variants are highly informative regarding population structure and recent demography, suggesting that these variations can reflect substantial local differentiation, in line with population history. Genetic variability is considered key to evolution. Strikingly, our analysis demonstrated that rare RNA editing sites can illustrate the properties of populations well, consistent with rare genetic variants33. The only difference is that rare genetic variants characterize the nature of a population at the DNA level, whereas rare transcriptomic RNA editing characterizes the nature of a population at the RNA level. Consistent results were obtained regardless of the application of relatively loose filters, requiring RNA editing sites with coverage of at least five reads and at least two edited reads or the selection of the strict GIREMI34 method to identify RNA editing sites, which demonstrated that our results were reliable and were not affected by the adopted filtering steps or identification methods  RNA-seq samples and included 462 individuals with available mRNA after the application of quality control measures. We obtained mRNA-seq data from the 462 Geuvadis project samples, which came from the following populations of the 1000 Genomes Project17: CEU, FIN, GBR, TSI and YRI 38 to align RNA-seq reads to the hg19 human reference with default parameters. Then, we only considered uniquely mapped reads with tag ‘NH:i:1’ and used Picard (https://broadinstitute.github.io/picard/) to remove duplicated reads that mapped to the same location. Reads with a mapping quality <20 were removed by SAMtools (Version: 1.3.1) with the parameter ‘−q 20’. Next, we called variants using the GATK (version 3.5.0)39 HaplotypeCaller tool with the options stand_call_conf of 20 and stand_emit_conf of 0. Then, we removed all variants present in dbSNP  and the 1000 Genomes Project database. We discarded variants located within the first 6 bp from either end of a sequence read. In non-Alu regions, we removed sites within simple repeats according to RepeatMasker annotation, discarded intronic candidates if they were located within 4 bp of all known splicing junctions according to Ensembl gene annotations, and removed sites in homopolymer runs of ≥5 bp. We also excluded variant sites in regions showing high similarity to other parts of the genome using the BLAST-like alignment tool (BLAT)40. Finally, we required each RNA editing site to exhibit coverage of at least ten reads and at least three edited reads and to be present in more than three individuals of a population. We inferred the editing type of each site based on the strand of overlapping annotated genes and retained only A-to-I editing sites annotated in the DARNED and RADAR databases for subsequent analysis.The Geuvadis projectRI Table . These f41 and RADAR42, which include 333,214 and 259,5361 events, respectively. In total, the two databases compile 2,598,505 editing sites. Among our identified A-to-I editing sites, 11,511 (68.46%) and 16,499 (98.12%) were found in the DARNED and RADAR databases, respectively. Collectively, 16,518 (98.23%) of our identified editing sites were present in these two databases.To validate the identified A-to-I editing events, we compared our editing sites with the RNA editing databases DARNEDTo assess the error rate arising from our identification strategy, we calculated the false detection rate of this methodology. Assuming that all non-A-to-I mismatches were false and that the error rate for all 12 mismatch types was equal, we use this formula to estimate our false detection rate:This formula denotes the ratio of the number of any other one type of non-A-to-G mismatches to that number of A-to-G mismatches. Where AG% is the ratio of A-to-G editing sites in all identified RNA editing sites, nonAG% is the ratio of any other one type of non-A-to-G editing sites in all identified RNA editing sites (referred to as nonAG%), All is the number of identified RNA editing sites, AG is the number of A-to-G editing sites, nonAG is the number of any other one type of non-A-to-G editing sites.23, with the gene definitions of GENCODE24 (V24) . The A-to-I editing sites within exonic regions were further defined as “synonymous” or “non-synonymous” based on whether they change amino acids in the protein products.For each RNA editing site, functional annotation was performed using AnnovarWe defined the frequency of an RNA editing site by calculating how many times that RNA editing site occurred across all 462 samples. In terms of the frequency of RNA editing, we classified RNA editing sites into three categories: rare editing sites , low-frequency editing sites  and common editing sites . In terms of the sharing number of population, RNA editing sites can be classified into population-specific, share-in-two-populations, share-in-three-populations, share-in-four-populations and share-in-all-populations.To further verify whether A-to-I editing sites could reflect a population’s characteristics, we measured the ratio of the probability of editing sites being shared by two individuals within a population compared with the probability of being shared by two random individuals selected from all 462 samples.We employed RNA editing sites shared by all populations to analyse the divergence of RNA editing levels between populations. For each RNA editing site, we chose the average editing level of individuals in that population as the editing level of the population. We defined the distance of editing levels as 1-Spearman’s rho between the editing levels of two populations. Then, we performed hierarchical clustering analysis of editing levels for all population pairs using the function hclust in R.FST), a measure of population differentiation due to genetic structure. We used VCFtools43 to estimate FST for each pairwise population comparison and chose Weir and Cockerham’s weighted method as our estimator.To compare the level of differentiation between populations, we calculated the fixation index  For RNA editing sites in each population, we removed sites with low variance (coefficient of variance <0.8). (2) In each population, only variants that were heterogeneous in at least one sample in the population were used to map edQTLs. Variants located within a 400 kb window centred at the editing site were used to map cis-edQTLs. (3) MatrixEQTL was employed to map cis- and trans-edQTLs. The results were filtered separately with significance thresholds of 1e-8 and 1e-10. FDRs were calculated with MatrixEQTL. (4) For each RNA editing site, the SNP with smallest p value located at the shortest distance was chosen as the edQTL SNP. For trans-edQTLs with the same smallest p-value, the edQTL SNP was chosen randomly among these edQTLs. The associations between edQTL editing sites and SNPs were drawn with CircosFor each edQTL editing site, the sequence of the 500 bp window centred on the editing site was used to scan potential transcript factor binding sites. FIMO was employed to scan motifs from TRANSFAC in the window against three different backgrounds (the backgrounds provided by FIMO and TRANSFAC and the background generated from the window sequence). The p-values for each motif occurrence are converted to q-values following the method of Benjamini and Hochberg. Binding sites that passed the q-value < 1e-6 were counted as potential TF binding sites. The density of a transcription factor was defined as theTo illustrate the relative enrichment of TF binding sites in the five population, we used the 500 bp window sequences centred on all 16,518 RNA editing sites as a control. The enrichment of TFs in each population was defined asThe populations were clustered using Spearman correlation of TF enrichment as a distance metric. For the Alu region background control, we randomly selected the 500 bp window sequence centred on Alu regions.For each edQTL editing site, the 500 bp window centred on the editing site was used to identify the sequence motif with MEME and DREME. For the background control, we employed two second-order Markov models generated from the 500 bp window sequences centred on the 16,518 RNA editing sites and Alu regions separately. The width of the motifs identified by MEME was limited to 10 bp. All motifs identified with an E-value < 1e-8 were compared with the motifs in TRANSFAC using TOMTOM.Supplementary informationSupplementary tables


Neisseria gonorrhoeae is an urgent public-health threat, with continued worldwide incidents of infection and rising resistance to antimicrobials. Traditional culture-based methods for antibiotic susceptibility testing are unacceptably slow (1–2 days), resulting in the use of broad-spectrum antibiotics and the further development and spread of resistance. Critically needed is a rapid antibiotic susceptibility test (AST) that can guide treatment at the point-of-care. Rapid phenotypic approaches using quantification of DNA have been demonstrated for fast-growing organisms (e.g. E. coli) but are challenging for slower-growing pathogens such as N. gonorrhoeae. Here, we investigate the potential of RNA signatures to provide phenotypic responses to antibiotics in N. gonorrhoeae that are faster and greater in magnitude compared with DNA. Using RNA sequencing, we identified antibiotic-responsive transcripts. Significant shifts (>4-fold change) in transcript levels occurred within 5 min of antibiotic exposure. We designed assays for responsive transcripts with the highest abundances and fold changes, and validated gene expression using digital PCR. Using the top two markers (porB and rpmB) we correctly determined the antibiotic susceptibility and resistance of 49 clinical isolates after 10 min exposure to ciprofloxacin. RNA signatures are therefore promising as an approach on which to build rapid AST devices for N. gonorrhoeae at the point-of-care, which is critical for disease management, surveillance, and antibiotic stewardship efforts.Antimicrobial-resistant  Neisseria gonorrhoeae is the second most common sexually transmitted bacterial infection in the United States, with about 460,000 cases reported in 2016, an 18.5% rise since 20151. Worldwide, it is estimated that about 78 million new N. gonorrhoeae infections occur annually2. N. gonorrhoeae infections can lead to heart and nervous system infections, infertility, ectopic pregnancies, newborn blindness, and increased risk for other sexually transmitted infections, including HIV3. The CDC has identified N. gonorrhoeae as one of the three most urgent drug-resistant bacterial threats3. N. gonorrhoeae has developed resistance to all of the most commonly used antibiotics  leaving only one last effective class of antibiotics, cephalosporins. However, there have even been worldwide reported cases of decreased susceptibility to the cephalosporin ceftriaxone8, and therefore an imminent threat of widespread untreatable N. gonorrhoeae. An important factor leading to the widespread development of antibiotic resistance is the liberal use and misuse of antibiotics. Critically needed is a rapid antibiotic susceptibility test (AST) that can guide treatment at the point-of-care—both to provide correct treatment and to facilitate antibiotic stewardship.N. gonorrhoeae susceptibility to antibiotics is the culture-based agar dilution test, which is unacceptably slow (1–2 days). More rapid genotypic approaches, involving detection of gene mutations, are available for a subset of antibiotics in N. gonorrhoeae10, but such approaches are inherently limiting, as they require knowledge of the mechanisms of resistance. Moreover, N. gonorrhoeae is naturally competent for transformation, and can take up gonococcal DNA from the environment and recombine it with its own genome, resulting in frequent gene mutations12. Given the high rate at which new resistance emerges, relying solely on genotypic methods is not an acceptable long-term solution. Phenotypic methods involving growth measurements have enabled faster ASTs that are independent of resistance mechanisms16. However, such growth-based methods are challenging for N. gonorrhoeae, which is slow-growing and fastidious17. Another phenotypic approach for antibiotic susceptibility testing is quantification of nucleic acids19. We have previously demonstrated a rapid (30 min) phenotypic AST using quantification of DNA replication by digital PCR (dPCR) to assess the antibiotic susceptibility of Escherichia coli in clinical urine samples20. However, AST methods that quantify changes in DNA replication require a longer antibiotic-exposure step for slow-growing pathogens such as N. gonorrhoeae, which has a doubling time of about 60 min21, compared with the 20 min doubling time of E. coli22.The gold standard for determining 23, far before phenotypic changes in growth can be observed. Quantifying changes in RNA signatures is therefore a particularly appealing approach for slow-growing organisms. RNA has previously been used to differentiate antibiotic susceptibility and resistance in organisms where the transcriptional response is well characterized25. More recently, RNA sequencing (RNA-seq) has been used to measure the transcriptome response of Klebsiella pneumoniae and Acinetobacter baumanii to antibiotic exposure25. Although the N. gonorrhoeae transcriptome has been previously sequenced27, to our knowledge, no one has characterized the transcriptome response of N. gonorrhoeae to antibiotic exposure. Unlike most bacteria, N. gonorrhoeae lacks the classic transcriptional SOS response to DNA damage whereby DNA repair is induced and the cell cycle is arrested29. The SOS response promotes survival to certain antibiotic classes, such as the fluoroquinolones, which act by directly inhibiting DNA synthesis30. The recA or recA-like proteins are essential for the induction of the SOS response28. However, neither recA transcripts nor recA protein levels increase in N. gonorrhoeae upon exposure to DNA damaging agents32.A complementary approach to DNA quantification is measuring the pathogen’s RNA response to antibiotic exposure. Transcriptional responses are among the earliest cellular changes upon exposure to antibioticsN. gonorrhoeae upon exposure to ciprofloxacin. Ciprofloxacin is a fluoroquinolone and functions by inhibiting the enzymes topoisomerase II (DNA gyrase) and topoisomerase IV, thereby inhibiting cell division33. Ciprofloxacin was chosen in this study to gain insight into transcriptional changes that occur upon DNA damage in an organism lacking the classic SOS response. Here, we address the following questions: (1) How does the transcriptome of N. gonorrhoeae respond to ciprofloxacin exposure? (2) What is the shortest antibiotic exposure time at which we can still observe significant changes (>4-fold) in RNA expression? (3) Which transcripts provide the largest and most abundant fold-changes per cell, which is an important consideration for clinical samples that have low numbers of pathogens? (4) Will candidate markers respond consistently across a large pool of isolates with wide genetic variability?In this work, we explore the transcriptome response of N. gonorrhoeae after 5, 10, and 15 min of ciprofloxacin exposure . The magnitude of fold change at which most genes were distributed was approximately 2-fold. The tail of the distribution illustrates that a few genes responded to antibiotic exposure with changes as large as 6-fold within 5 min. Increasing the antibiotic exposure time further shifted the distribution to larger negative log2 fold-change values. The transcriptional response in resistant isolates was tightly distributed around a fold-change value of 1 at every time point, indicating that the transcriptome of the resistant isolates did not respond significantly to antibiotic exposure  against the −log2(expression) for all genes and fit a negative exponential curve to the outer edge of each plot. We then averaged the curves from all six datasets and added a 90% confidence interval to the average curve by assuming a Gaussian fit for the error distribution, which we define as our threshold of significance. Genes with a −log2(C:T ratio) value above or below the upper and lower thresholds were identified as differentially expressed. Downregulated genes (fold changes below the significance threshold) appeared as early as 5 min after antibiotic exposure  <=0.015 mg/mL) and resistant isolates  to ciprofloxacin for 15 min and extracted RNA for sequencing , rpmB , tig (molecular chaperone), yebC , pilB (pilus assembly ATPase), and cysK (cysteine synthase). The candidate marker with the highest abundance and largest fold change upon antibiotic exposure was porB, which is a membrane channel forming protein and the site of antibiotic influx into the cell35.RNA expression in response to antibiotics can be heterogeneous among different isolates of the same species36. By linear regression we captured the relationship between ERCC copies added to the samples and ERCC quantified by sequencing. Using the linear regression, we converted gene expression values from RNA sequencing (in TPM) to approximate copy numbers per cell (see Methods). The transcript copies per cell estimated for the candidate markers using the sequencing approach were within the same order of magnitude as the absolute copies per cell measured by dPCR . We designed primers for the candidate markers . We found that the C:T ratios measured by the dPCR assay agreed with the C:T ratios obtained through sequencing  in response to ciprofloxacin compared with all other genes in ing Fig. , confirmporB and rpmB) to determine the susceptibility of 49 clinical isolates, with a wide range of MIC values (Supplementary Table N. gonorrhoeae panel of the Centers for Disease Control (CDC) Antimicrobial Resistance Isolate Bank. The MIC values were representative of the population-wide distribution values reported by the European Committee on Antimicrobial Susceptibility Testing37. We exposed each clinical isolate to ciprofloxacin for 10 min and measured the fold change in expression of the two candidate markers between the control and treated sample using dPCR , which play a prominent role in assembly and function of the ribosomes and are essential for cell growth. Mutations in ribosomal proteins have been reported to confer resistance to different classes of antibiotics38. Another top marker identified in this study was porB, which is a membrane channel forming protein (porin) responsible for uptake of small nutrients and the site of antibiotic influx into the cell. The expression of porins is highly regulated in response to environmental stimuli39. Reducing permeability to decrease intracellular antibiotic concentration is a known mechanism for bacteria to confer antibiotic resistance38. The downregulation of porB observed in this study can be attributed to a halt in growth processes caused by ciprofloxacin damage and possibly an attempt to reduce influx of antibiotic.When characterizing the global transcriptional response of N. gonorrhoeae to ciprofloxacin, but this was not the focus of our study. Furthermore, the short exposure times potentially introduce a bias in selection of transcripts present at low abundances. For transcripts present at high abundance to display the same fold change as low abundance transcripts, a substantially higher number of mRNA molecules must be transcribed, which would require longer timescales. As an example, a 4-fold change from 1 to 4 transcripts requires 3 additional mRNA to be produced, whereas a 4-fold change from 20 to 80 requires 60 mRNA to be transcribed. This bias also holds true in downregulation, where mRNA continues to be transcribed in the control samples, whereas transcript levels drop in treated samples due to degradation of RNA, and/or a reduction in the rate of transcription.A key aim of this study was to identify RNA markers that would yield a measurable response after only a short antibiotic exposure (<15 min) to ensure this approach can fit within the required timescale for a rapid AST. It is possible that longer exposure times could provide additional insight into the biological response of 20. To measure the abundance of the candidate markers, we used both dPCR measurements and ERCC spike-ins for RNA sequencing to obtain approximate RNA copies/cell. Both methods yielded results within the same order of magnitude. To our knowledge this is the first quantitative measurement of RNA abundance per cell in N. gonorrhoeae.We identified candidate markers with consistent differential expression across three sets of susceptible and resistant pairs. Among the candidate markers, one of our criteria for selection was transcript abundance, which is of particular importance in clinical samples with low cell numbers. Furthermore, marker abundance affects measurement sensitivity and limits of detection, as has been previously demonstrated in AST methods based on quantification of DNA replicationporB and rpmB, with 49 clinical isolates. Both markers were consistent in their ability to correctly determine susceptibility or resistance of all 49 clinical isolates. porB demonstrated C:T ratios between 2.5 to 7 and rpmB demonstrated C:T ratios between 2 and 6 after 10 min of antibiotic exposure in the nine susceptible clinical isolates. The large fold changes highlight the significance of using RNA response as an AST marker compared with quantification of DNA replication. Our previous work using dPCR quantification of DNA replication demonstrated C:T ratios between 1.2 and 2.4 for 15 min of antibiotic exposure in E. coli20, which has a doubling time approximately 3 times shorter than N. gonorrhoeae.We separately validated the performance of the two most abundant candidate markers, porB against other prokaryotes and found it to be specific to the Neisseria genus. AST markers should be specific to the pathogen of interest because additional bacterial species are likely to be present in clinical samples. Additional experiments with mixtures of bacteria would be required to further confirm the specificity of the markers identified in this study. We additionally measured the 16S rRNA to normalize C:T ratios, which inherently enables pathogen identification as well. A combination of identification and susceptibility testing in a single integrated platform is important for correct and rapid diagnosis.We performed an alignment search of N. gonorrhoeae after short antibiotic exposure times, a requirement for a rapid phenotypic AST. N. gonorrhoeae is a fastidious slow-growing organism, presenting challenges to growth-based AST methods. Additional work will be needed to yield a clinic-ready, rapid RNA-based AST for N. gonorrhoeae. Additional background matrices of clinical samples, both urine and swab samples, that could possibly affect speed and sensitivity of an AST, must be further evaluated. Digital isothermal chemistries, such as digital loop-mediated isothermal amplification (dLAMP) should be considered to speed up quantification times relevant to point-of-care settings20. Follow-up studies should also examine the transcriptional response of N. gonorrhoeae to other classes of antibiotics and identify responsive RNA markers for class-specific antibiotics. Overall, as a first step, the work described here demonstrates the potential of a phenotypic RNA-based approach for a rapid AST in N. gonorrhoeae at the point-of-care, which is critically needed for disease management, surveillance, and antibiotic stewardship.This paper demonstrates that RNA markers can be used to determine antibiotic susceptibility of 2). Cells were re-suspended in Hardy Fastidious Broth (HFB) and incubated for 45 min  with shaking (800 rpm) to an OD600 between 1 and 5. Cultures were diluted (5X) into HFB. Each isolate culture was split into “treated” and “control” tubes. Ciprofloxacin was added to the “treated” tubes  and water was added to the “control” tubes; cultures were incubated  for 15 min. During incubation, samples were collected for RNA sequencing at 5, 10, and 15 min  for immediate RNA stabilization). In addition, a sample was collected for RNA sequencing immediately before ciprofloxacin was added. To quantify CFU, the sample at t = 15 min was serially diluted (10X), plated on a Chocolate Agar plate, and incubated overnight .Antibiotic susceptible and resistant clinical isolates were obtained from the University of Washington Neisseria Reference Laboratory and the University of California, Los Angeles, Clinical Microbiology Laboratory. Isolates were plated from glycerol stocks onto Chocolate Agar plates and grown in static incubation overnight . Cells were re-suspended in pre-warmed HFB + 5 mM sodium bicarbonate and incubated for 30 min  with shaking (800 rpm) to an OD600 between 1 and 5. Cultures were diluted (100X) into HFB + 5 mM sodium bicarbonate. Each isolate culture was split into treated  and control (water instead of antibiotic) samples. Samples were incubated at 37 °C for 10 min on a static hot plate. A 90 µL aliquot of each sample was placed into 180 µL of Qiagen RNA Protect Reagent for immediate RNA stabilization. A 5 µL aliquot of each sample was plated onto a Chocolate Agar plate and incubated overnight  as a control for the exposure experiments. If the expected growth phenotypes  were not observed for any single sample in the plating control, the exposure experiment was repeated for the set of samples. From the 50 total isolates available from the N. gonorrhoeae panel of the CDC Antimicrobial Resistance Isolate Bank, 49 were used in this study. One isolate was excluded from this study because we suspected that it had been contaminated; we did not detect porB primer amplification using qPCR.Antibiotic susceptible and resistant clinical isolates were obtained from the N. gonorrhoeae (NCBI Reference Sequence: NC_002946.2) using Bowtie2 (version 2.2.5) and quantified using the Subread package (version 1.5.0-p1). A pseudocount of 1 was added to the gene quantification; gene expression was defined in transcripts per million (TPM). In this study, tRNA was excluded from the sequencing data.RNA was extracted using the Enzymatic Lysis of Bacteria protocol of the Qiagen RNeasy Mini Kit and processed according to the manufacturer’s protocol. DNA digestion was performed during extraction using the Qiagen RNase-Free DNase Set. The quality of extracted RNA was measured using an Agilent 2200 TapeStation . Extracted RNA samples were prepared for sequencing using the NEBNext Ultra RNA Library Prep Kit for Illumina  and the NEBNExt Multiplex Oligos for Illumina. Libraries were sequenced at 50 single base pair reads and a sequencing depth of 10 million reads on an Illumina HiSeq. 2500 System  at the Millard and Muriel Jacobs Genetics and Genomics Laboratory, California Institute of Technology. Raw reads from the sequenced libraries were subjected to quality control to filter out low-quality reads and trim the adaptor sequences using Trimmomatic (version 0.35). The reads were aligned to the FA 1090 strain of 2(C:T) against the −log2(expression in TPM) for all genes. To identify genes that were differentially expressed between control and treated samples, we defined a threshold of significance. The threshold of significance was calculated from the C:T ratios at t = 0 min for the biological replicates that were sequenced (three susceptible and three resistant isolates). For each of the six gene expression datasets (one for each isolate), we fit a negative exponential curve to the outer edge of each plot and then averaged the curves from all six datasets. Finally, we added a 90% confidence interval to the average curve by assuming a Gaussian fit for the error distribution, which is our threshold of significance. Genes with a −log2(C:T) value above or below the upper and lower thresholds were identified as differentially expressed. Genes that were differentially expressed consistently  among the three susceptible isolates and were not differentially expressed among the three resistant isolates were defined as candidate markers.For each gene, we defined the C:T ratio as the gene expression (TPM) in the control sample divided by the gene expression (in TPM) in the treated sample. We plotted the −log2(ERCC copies added) against log2(gene expression in TPM) and performed a linear regression in the region of linearity. We used the linear regression to convert TPM values to total RNA copies in each sample. Finally, using the CFU measured for each sample from plating (described in the “Antibiotic exposure for RNA sequencing” section), the total RNA copies were converted to copies per cell.To measure copies per cell using sequencing data, we added 2uL of (1/1000 dilution) ERCC RNA Spike-In Mix  to the lysis buffer in the RNeasy Mini Kit to each individual sample. We calculated the number of copies of each ERCC transcript in the sample, by accounting for dilution and multiplying by Avogadro’s number (manufacturer’s concentrations were reported in attomoles/µL). We plotted the relationship between log40 and primer alignments were verified using SnapGene. Expression of candidate markers was quantified using the Bio-Rad QX200 droplet dPCR system . The concentration of the components in the dPCR mix used in this study were as follows: 1 × EvaGreen Droplet Generation Mix (Bio-Rad), 150 U/mL WarmStart RTx Reverse Transcriptase, 800 U/mL RiboGaurd RNase Inhibitor, 500 nM forward primer, and 500 nM reverse primer. The RNA extraction comprised 5% of the final volume in the dPCR mix. The remaining volume was nuclease-free water. For each isolate, candidate marker expression was quantified in the control and treated samples and the fold-change difference (C:T ratio) was calculated. To account for potential differences between the control and treated samples that could arise from experimental variability and extraction efficiency, we used ribosomal RNA (rRNA) as an internal control because from our sequencing data, we found that rRNA was not affected by antibiotic exposure in the time frame of this study. To normalize by rRNA, we quantified the 16S rRNA in the control and treated samples by dPCR and calculated an rRNA C:T ratio. We then divided the C:T ratio of each marker by the rRNA C:T ratio. All dPCR C:T ratios reported in this paper are the normalized C:T ratios.Primers were designed for candidate markers using Primer-BLASTN. gonorrhoeae and sequences for the primers used in this study are provided in the Supplementary file available. The complete sequencing data generated during this study are available in the National Center for Biotechnology Information Sequence Read Archive repository under the study accession number SRP150785.Data on candidate markers for Supplementary Information


Metastasis is responsible for the vast majority of all cancer-related deaths . While mOur group has recently demonstrated the efficacy of a small molecule inhibitor of ezrin in blocking metastatic progression, using a novel intravital imaging model of lymph node (LN) metastasis . LNs areLN status remains a gold standard for assessing relapse risk, however not all node-positive breast cancer patients will relapse and greater than 25% of apparent LN-negative patients eventually develop recurrence . This stInterestingly, targeting ezrin in our LN metastasis model principally affected metastatic dissemination with minimal changes to primary tumor growth , consistpos/CD62Lneg) in TDLN of mice treated with the ezrin inhibitor, suggesting that ezrin-targeted therapy, in addition to inhibiting metastasis, may also enhance host T cell anti-tumour activity (unpublished results).Cancer cells within LNs are also capable of manipulating host immune cell activity to create a microenvironment conducive to tumor outgrowth. In addition to tumor-cell specific expression, ezrin is also expressed by cytotoxic T-lymphocytes (CTLs) and is implicated in regulating T-cell activity . In thisIn summary, ezrin is involved in multiple aspects of metastasis , 5. Our


The distribution of m6A peaks within HIV transcripts in HIV Tg rats largely corresponded to the ones observed for HIV transcripts in cell lines and T cells. Host transcripts were found to be differentially m6A methylated in HIV Tg rats. The functional roles of the differentially m6A methylated pathways in HIV Tg rats is consistent with a key role of RNA methylation in the regulation of the brain transcriptome in chronic HIV disease. In particular, host transcripts show significant differential m6A methylation of genes involved in several pathways related to neural function, suggestive of synaptodendritic injury and neurodegeneration, inflammation and immune response, as well as RNA processing and metabolism, such as splicing. Changes in m6A methylation were usually positively correlated with differential expression, while differential m6A methylation of pathways involved in RNA processing were more likely to be negatively correlated with gene expression changes. Thus, sets of differentially m6A methylated, functionally-related transcripts appear to be involved in coordinated transcriptional responses in the context of chronic HIV. Altogether, our results support that m6A methylation represents an additional layer of regulation of HIV and host gene expression in vivo that contributes significantly to the transcriptional effects of chronic HIV.Internal RNA modifications have been known for decades, however their roles in mRNA regulation have only recently started to be elucidated. Here we investigated the most abundant mRNA modification, N It has long been known that transfer RNA (tRNA), ribosomal RNA (rRNA) and both mRNA and noncoding RNA (ncRNA) contain multiple modifications –5. At leg others . Of thesne (m6A) , 2. The  in 1974 , 12. Whis of m6A . mRNAs a6A-modified RNA sites has been suggested [6A methylated, while highly regulated mRNAs often contain m6A residues [6A methylated within the consensus motifs G(m6A)C and A(m6A)C [6A appears to contribute to the regulation of gene expression and is increasingly being implicated in disease. Evidence suggests that RNA m6A methylation contributes to the regulation of splicing, nuclear RNA export, mRNA stability, and translation [6A methylation is at least in part dynamically regulated and potentially reversible [6A RNA methylation and the fate of m6A methylated RNA [6A demethylase, has been linked to a variety of biological processes including dopaminergic neuron regulation [6A contents in the RNAs of T2DM patients and diabetic rats are significantly lower than controls, while the level of FTO in T2DM patients was significantly higher [6A content in mRNA and impaired fertility [Reminiscent of DNA epigenetics, the occurrence of both stable and dynamically regulated muggested , 14–16. residues , 10. RNA A(m6A)C . RNA modnslation –23. RNA versible . Multiplated RNA . The fatgulation  and a vagulation –31. The ertility .6A modified. Classic studies by Shatkin and associates in the 70s showed specific patterns of m6A modification in transcripts from both DNA and RNA viruses such as influenza virus, adenovirus, papovavirus, and flavivirus, among others [6A modified [6A modification in HIV-1 transcripts contributes to regulate HIV-1 mRNA in vitro [6A modification of viral RNAs appears to be antiviral innate immune evasion, since the presence of m6A on viral RNA has been shown to reduce its activation of toll-like receptor 3 signaling [Multiple viral RNAs have long been known to be mg others –35. Retrmodified , 37. Themodified –41. Recein vitro –44. Anotignaling .6A methylation of RNA in the hippocampus of HIV Tg rats. HIV-1 Tg rats harbor a non-replicating HIV-1 transgene and express multiple HIV-1 proteins under the viral LTR in macrophages, lymphocytes and disease-relevant glial cells, such as microglia and astrocytes, but not in neurons [Here we investigated m neurons , 47. HIV neurons  and reve neurons , 50, bef neurons . This is neurons .6A RNA methylation in HIV transcripts in HIV Tg rats largely corresponds to the ones reported for HIV transcripts in cell lines and T cells in vitro [6A methylation of genes involved in several pathways related to neural function, suggestive of synaptodendritic injury and neurodegeneration, consistent with our previous observations in both HIV Tg rats and humans with HIV [6A methylation and expression are also involved in immune response and inflammation, also consistently with gene expression changes in HIV Tg rats and humans with HIV [6A methylation in transcripts of pathways involved in RNA splicing and processing, which are processes previously implicated in the role of m6A RNA methylation [We observed that distribution of peaks of min vitro –44. Furtwith HIV , 53. Trawith HIV , 53. We hylation .6A. As shown in 6A methylation was similar, although a slightly larger number of genes with down-regulation of m6A methylation was observed than genes with up-regulated methylation in the hippocampus of HIV Tg rats  in the distal CDS and 3’UTR of the mRNAs and especially in the immediate vicinity of the stop codon , which partially overlaps with the 3’ LTR  algorithm  to ident Tg rats . A total Tg rats  of whichhylation . The topc injury –60, Fig  Tg rats , 53, Fig6A pathways related to neural function include “Reactome Neuronal Systems”, “KEGG Axon Guidance”, and “Reactome Trafficking of Glur2 containing AMPA Receptors”  vivarium on a 12h/12h light/dark cycle with ad libitum access to food and water. Rats were sacrificed by decapitation under deep CO2 anesthesia. All procedures adhered to the Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee of The Scripps Research Institute.Adult male HIV Tg rats in Sprague Dawley background (Harlan Sprague-Dawley) 5–6 months of age were housed in groups of two per cage in a temperature-controlled (20–26 6A (Synaptic Systems), which was pre-immobilized to M-270 Epoxy using the Dynabeads Antibody Coupling kit (Thermo Fisher Scientific) for easy capture of the m6A-modified RNA in subsequent elution with a Proteinase K-containing buffer. Eluate with the anti-m6A captured RNA was extracted by phenol/CHCl3 followed by ethanol precipitation.Total RNA was extracted from dissected hippocampi of the wildtype and HIV Tg rats with RNEasy kit (Qiagen) and poly-A selected with Dynabeads Oligo(dT)25 (Thermo Fisher Scientific) following manufacturer’s instructions. Poly-A enriched RNA was recovered by ethanol precipitation overnight, fragmented using RNA Fragmentation Reagents (Thermo Fisher Scientific) and ethanol precipitation overnight. An aliquot of fragmented RNA was set aside serving as input or control for RNA sequencing. Twenty μg of fragmented RNAs were denatured at 75°C for 5 min, cooled on ice for 3 min, and then incubated for 2h at 4°C with 3 μg of pre-equilibrated anti-mLibraries for the anti-m6A-captured and control  RNAs were generated for next gen sequencing, following the protocol recommended in the ScriptSeq v2 RNA-Seq Library Preparation Kit (Illumina), and uniquely barcoded with the ScriptSeq Index PCR Primers (Illumina) to run a 16-library sample multiplex in a single flowcell on a NextSeq500 platform (Illumina) to generate ~30M 1x75 bp reads/sample.Sequences were first trimmed using Trimmomatic with default setting (version 0.33). All the samples passed the quality control by fastQC (version 0.11.3). Fastq files were aligned to a combined rat genome (Rnor_6.0) and HIV-1 genome (NC_001802) using STAR (v2.4) with default setting. The overall mapping rate range from 79.78% to 85.79% 6A peaks within host mRNAs, per base coverage was generated for all the m6A treated samples using R package “RiboProfiling” for a fixed interval of 400 bases around the start codon and stop codon.RNA methylation sites were detected using exomePeak  in HIV infected rat samples and control samples separately. Aligned bam files of m6A treated samples and non-treated samples were directly passed to exomePeak to test for statistically significant enrichment. A total of 9696 and 9051 consist RNA methylation sites with at least 100 bp were identified in HIV infected samples and control samples respectively . 201 genes were identified with significant changes of methylation   . Differe < 0.05) . A set o < 0.05) .A total of 1,452 well-known gene sets from the MSigDB collection was tested with the GSEA algorithm. 166 gene sets were significantly changed in expression   and 74 gS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.


Seven human isoforms of importin α mediate nuclear import of cargo in a tissue- and isoform-specific manner. How nuclear import adaptors differentially interact with cargo harbouring the same nuclear localisation signal (NLS) remains poorly understood, as the NLS recognition region is highly conserved. Here, we provide a structural basis for the nuclear import specificity of W proteins in Hendra and Nipah viruses. We determine the structural interfaces of these cargo bound to importin α1 and α3, identifying a 2.4-fold more extensive interface and > 50-fold higher binding affinity for importin α3. Through the design of importin α1 and α3 chimeric and mutant proteins, together with structures of cargo-free importin α1 and α3 isoforms, we establish that the molecular basis of specificity resides in the differential positioning of the armadillo repeats 7 and 8. Overall, our study provides mechanistic insights into a range of important nucleocytoplasmic transport processes reliant on isoform adaptor specificity. Importin α isoforms regulate the nuclear import of different cargo proteins but the mechanisms conferring isoform specificity are not fully understood. Here, the authors study the interactions of importin α1 and α3 with two viral cargos, elucidating the structural basis for isoform-specific cargo recognition. The classical nuclear import pathway is best understood, and is initiated by recognition of proteins that contain a classical nuclear localisation sequence (NLS) by importin α4. This complex is transported through the nuclear pore complex by importin β, involving interactions with FG repeat regions contained within nucleoporin proteins6. Once the complex has traversed the nuclear envelope, RanGTP dissociates the complex, and the import receptors are recycled back to the cytoplasm to perform further rounds of transport10. Importin α is constructed from an N-terminal importin β-binding (IBB) domain and a C-terminal NLS binding domain featuring ten armadillo (ARM)-repeat motifs11. Most commonly, the cargo NLS binds on the concave site of the ARM repeats and involves interactions at either the major site, through ARM repeats 2–4 in the case of classical monopartite NLSs , the minor site involving ARM repeats 6–8  or both the major and minor sites in the case of classical bipartite NLSs . Although this process has been well characterised for the importin α1 adaptor protein, many nuclear proteins exhibit specificity for other importin α isoforms, and the molecular basis for this specificity is understood poorly. Complicating our understanding of how importin α isoforms exhibit specificity, the seven importin α isoforms are highly conserved in the regions that mediate NLS binding. Establishing how nuclear cargo are recognised in an isoform-specific manner is important for understanding many key regulatory processes including cell differentiation, cancer and viral infection. For example, both RCC1  and HIV-1 integrase (responsible for integrating the HIV-1 genome into the DNA of an infected cell) bind specifically to importin α316. STAT1, a signalling molecule in the innate immune system response, binds specifically to the convex C-terminal surface of importin α5, α6 and α718. The avian influenza PB2 viral polymerase subunit, which is a major virulence determinant, has isoform specificity for importin α3 in avian hosts and importin α7 in mammalian hosts, providing a kinetic advantage owing to lower importin α autoinhibition by the importin beta binding domain19.Active transport of proteins from the cytoplasm to the nucleus is mediated by a family of nuclear transport receptors known as importins (or karyopherins), together with a number of ancillary proteins including nucleoporins and Ran21, however, the basis of this specificity remains unclear. HeV and NiV are recently emergent, zoonotic pathogens of the Henipavirus genus in the Paramyxoviridae family. These viruses use bats of the Pteropus genus as reservoir hosts but cause infections in humans with a high rate of fatality (~ 60%)22. The viruses are non-segmented, negative-sense RNA viruses and encode six genes, five of which encode a single protein, whereas, the sixth gene, P, encodes four proteins: P, V, W and C. Of these, P, V and W share a common N-terminal domain, but differ within the C-terminal domain owing to frame-shifting that results from insertion of non-template encoded nucleotides into P gene transcripts25. This results in proteins that have different functions and cellular locations, with only the W protein displaying steady-state nuclear localisation owing to a C-terminally located NLS26. In the context of NiV infection, the nonstructural W protein plays an important role in virulence29. It has been demonstrated to antagonise innate antiviral defences by blocking interferon-induced gene expression and by preventing expression of type I IFNs, with nuclear localisation shown to be important for the latter function30. A recent study has identified additional host targets of NiV W, including the PRP19 complex, for which the nuclear localisation of W appears to be required for the interaction31.The W protein of Nipah virus (NiV) is recognised specifically by importin α3, which occurs through an NLS in the unique C-terminal domain32. Here, we establish key differences in the binding interface of W proteins with importin α1 and α3, providing insights into adaptor specificity. We use mutagenesis and chimeras of importin α1 and α3 to confirm the importance of these differences and show that the C-terminal ARM repeats 7 and 8 of importin α3 are important for specificity as their conformation in importin α3 allows for an extensive binding interface not possible in importin α1. These insights extend our understanding of adaptor specificity and establish how important nuclear cargo proteins are imported in an isoform-specific manner.Previous reports have demonstrated specificity of the importin α3 adaptor for the NiV W protein, however, like most cargo, the basis for this specificity is unknown20. To confirm this, and assess W binding against a more extensive range of importin α isoforms, we performed co-immunoprecipitation assays against respective importins and probed for the presence of HeV and NiV W. We found that both HeV and NiV W bound preferentially to importin α3 and α4, but not importin α1, α5, α6 or α7 , rather than the better characterised importin α1 KPNA2) Fig. 20. To co Fig. 20. α7 Fig. , confirmTo determine the binding affinities between importin α3 and Henipavirus W proteins, as well as confirm a direct interaction, the C-terminal NLS domain of the W protein from both HeV and NiV were cloned as a glutathione S-transferases (GST) fusion protein and assessed for importin α binding by isoforms from representative subfamilies. From enzyme-linked immunosorbent assay (ELISA) measurements, we found that importin α3 bound with high affinity to the W protein from both HeV and NiV , whereas importin α1 and α7 bound with much lower affinity  Fig. . Togethe12121 symmetry and diffracted to 2.2 Å resolution. The asymmetric unit (ASU) contained one importin α1 chain bound to a single HeV W chain, in which residues 434–441 were bound to importin α1. The interaction was mediated by 15 hydrogen bonds, one salt bridge interaction, and buried a surface area of 683.5 Å2 ;  bound residues 436–443 of the NiV W C-terminal domain. In comparison, the importin α3:NiV W complex had P1211 symmetry, diffracted to 2.3 Å resolution, and showed more extensive binding, with residues 421–446 bound to importin α3. The binding interface was also similar to HeV W, with the importin α1:NiV W complex mediated by 15 hydrogen bonds, one salt bridge interaction, and a buried surface area of 688.7 Å2. The importin α3:NiV W interface was mediated through 31 hydrogen bonds, seven salt bridge interactions, and buried 1591.8 Å2 of surface area. These results indicate that the interaction of the henipavirus W proteins with importins is highly conserved.To test whether the same binding patterns were present in the NiV W protein, we examined the binding determinants of importin α:NiV W complexes using the same approach. We observed binding patterns that were very similar to those seen with HeV W for both importin α1 and α3 , the positioning of ARM repeats 1–3 could play a role in the binding of proteins harbouring an NLS at the N terminus, such as RCC1 (see Discussion).To extend on the structural repertoire of importin α structures, we tested whether these ARM repeats are similar in the structures of importin α isoforms that do not contain bound cargo. Importin α1 and α3, crystallised in P2ged Fig. . Superpoged Fig. , consistARM1-5:α3ARM6-10) bound W, whereas the reverse chimera comprised of importin α3 IBB ARMs 1–5 and importin α1 ARMs 6–10 (importin α3ARM1-5:α1ARM6-10) did not pull-down W  behind NLS adaptor specificity has remained elusive. Recently, one study described the specificity of the RCC1 factor for importin α3, highlighting that additional residues outside the NLS were important for maintaining specificity15. A comparison between the mechanism presented in the RCC1 study and our study, highlights a number of important differences. The study by Sankhala et al. 15 indicated that the NLS of RCC1 binds in the major binding site of ARMS 2–4 of both importin α1 and α3, with additional interactions occurring at the N terminus of importin α3 ARM repeats 1–4 and the β-propeller region of RCC1, mediated by flexibility and rotation of importin α3 in ARM repeats 1–2. This mechanism of isoform specificity is well suited to cargo containing NLSs at the N terminus, and our structural data of the unbound and NLS-bound forms of importin α3 supports this model. This mechanism is distinct, however, from that governing specificity for cargo such as the W proteins in this study, where the NLSs are located at the C terminus because the protein interface would not extend past the N terminus of ARM repeats 2–4. Thus, it may be possible that isoform specificity of cargo containing N-terminal NLSs may reside through differential interaction of the N-terminal ARM repeats, whereas cargo bearing C-terminal NLSs may show specificity through differential interaction with the C terminus of importins, as demonstrated in this study. Although further work will be needed to establish how extensive these rules are, it is likely that different mechanisms may govern isoform specificity, dependent upon the position of the NLS within the cargo.Although numerous studies have reported specificity of nuclear adaptors for a wide range of cargo19. However, our results identify clear differences in the structural positioning of the C-terminal ARM domains 7–8 of importin α1 and α3, accompanied with a significant increase in the binding interface at this region in importin α3. In addition, the chimeric protein importin α1ARM1-5:α3ARM6-10 binds to W, whereas importin α3ARM1-5:α1ARM6-10 does not interact, confirming that the C-terminal domain of the importin α is the differentiating factor, rather than the N terminus. The importance of the C-terminal ARM repeats has been demonstrated for importin α5 binding of nonclassical NLS cargo, involving a distinct mechanism and ARM repeat 1038. It has also been hypothesised that that flexibility of importin α3 may mediate increased specificity and/or affinity, and indeed, this was described as a contributing factor to the binding of RCC1. However, this is unlikely to have a role in the specificity of importin α3 for the W protein, because the difference in binding interactions occur outside of the hinge region. As well, if the hinge region was critical for mediating specificity, the α3ARM1-5:α1ARM6-10 chimera would have interacted with W if the hinge region was the critical region for mediating specificity. In addition, we show through structural comparisons between apo- and W bound-importin α3, that structural changes in the N terminus were not associated with direct binding interactions in this region. Importantly, these results do not discount the previous work of RCC1 binding, but rather, complement and extend it by highlighting that different mechanisms are likely to exist in mediating specificity.The findings of our study are distinct from mechanisms that have been previously hypothesised. The IBB domain has been shown to auto-inhibit differentially across isoforms39, and the maintenance and lineage determination of embryonic stem cells40. Differential expression of importin α3 has been demonstrated to occur following rabies infection, with overexpression observed only in paralytic rabies, suggesting a possible prognostic marker41. The inhibition of importin α3 has also been shown to attenuate prostate cancer mestastasis42. The expanded rules for isoform specificity outlined here will provide important insights into our understanding of nuclear transport adaptor cargo specificity and their function in cellular processes.Our study defines a basis to explain the importin α specificity of henipavirus HeV W and NiV W virulence factors for importins, demonstrating that differences in the ARM repeats in the C terminus of importin α3 mediate specificity. Nuclear transport of cargo in an isoform and tissue-specific manner is critical for health and disease. Subtype switching of importin α1 to α5 in embryonic stem cells results in the initiation of neural differentiationEscherichia coli and synthesised . These constructs were cloned into pGEX4T-1 vector at BamHI and EcoRI sites. Importin α3 and α7 lacking the auto-inhibitory IBB domain (residues 64–521 and 73–536) were codon optimised for E. coli expression, synthesised and cloned into pET15b vector at NdeI and EcoRI sites. The importin α chimeras lacking the auto-inhibitory IBB domain (for recombinant expression) were comprised of α1aa71–279:α3aa271–521 and α3aa64–270:α1aa280–529, with numbering according to UniProtKB P52292 and UniProtKB O00629 for importin α1 and importin α3, respectively. Importin α1ΔIBB, encoding residues 71–529, was cloned in the pET30 vector as described previously44.The C-terminal domain of HeV  and NiV  with an N-terminal TEV cleavage site (ENLYFQS) were codon optimised for expression in 45. In brief, starter cultures were inoculated into 2 L baffled flasks containing 500 mL of expression media consisting of 1% (w/v) tryptone, 0.5% (w/v) yeast extract, 0.5% glycerol, 0.05% glucose, 0.2% (w/v) α-lactose, 0.025 M NH4SO4, 0.05 M KH2PO4, 0.05 M Na2HPO4, 1 mM magnesium chloride and either 100 µg/mL ampicillin, or 50 µg/mL kanamycin. Cells were harvested via centrifugation at 6500×g and 18 °C for 30 min and resuspended in phosphate buffer (PB)  or Tris-buffered saline (TBS)  buffer with complete ethylenediaminetetraacetic acid (EDTA)-free protease inhibitor. Cells were lysed using two freeze–thaw cycles and addition of 20 mg lysozyme and 0.5 mg DNase.Plasmids were transformed into BL21 (DE3) pLysS cells and expressed using the Studier auto-induction method46.Affinity purifications of 6xHis-tagged importin α were performed by injecting clarified cell lysate onto a GE HisTrap 5 mL column using PB, washing the column with 15 column volumes and then eluting over 5 column volumes using a gradient elution with high imidazole PB . Affinity purifications of GST-tagged proteins were performed on a GST Trap 5 mL column using TBS, and elution buffer containing 10 mM glutathione. All size exclusion purifications were performed on a Superdex 200 pg 26/600 column using TBS pH 8.0, and eluted proteins were pooled and concentrated using 10 kDa MW centrifuge filters. Complex formation was performed as described previously47. In brief, 96-well clear plates were coated with GST-NLS fusion proteins using bicarbonate/carbonate buffer pH 9.6 for 2 h at room temperature. The plates were washed using TBS containing 0.05% v/v Tween20 (TBST). Blocking was achieved using 5% (w/v) skim milk in TBST for 2 h at room temperature. Wells were washed three times in TBST buffer and incubated with decreasing concentrations of 6xHis-tagged importin αΔIBB  diluted in TBS for 2 h. The plate was washed three times, blocked for 2 h, washed a further three times and then incubated for 2 h with 100 µL a 1/5000 dilution of anti-6xHis 4HRP conjugated rabbit polyclonal antibody (Abcam ab1187). The plate was washed a further three times before addition of 100 µL of TMB substrate (Sigma T4444). The colorimetric reaction proceeded for 20 min before being stopped with 100 µL of 2 M H2SO4, and the absorbance measured at 450 nm using an Epoch microplate spectrophotometer (Biotek). One-site specific binding analysis using least squares fit was performed using GraphPad Prism version 7.00 for Mac, GraphPad Software, La Jolla California USA, www.graphpad.com.The method was based on previously published microtiter plate assays48. Purified importin α1, α3 and α7 in 20 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), 150 mM NaCl, pH 8.0 were labelled using the NHS RED NanoTemper labelling kit according to the manufacturer’s instructions. For the assay, 5 μL of labelled protein was mixed with 10 μL of the unlabelled NiV W at various concentrations and 5 μL of 0.05% (w/v) Tween20. All experiments were incubated for 10 min before applying samples to Monolith NT Standard Treated Capillaries (NanoTemper Technologies). Thermophoresis was measured at 25 °C with laser off/on/off times of 5 s/30 s/5 s. Experiments were conducted at 20% light-emitting diode power and 20–40% MST infra-red laser power. Data from three independently performed experiments were fitted to the single binding model via the NT. Analysis software version 1.5.41 (NanoTemper Technologies) using the signal from Thermophoresis + T-Jump.Affinity measurements using MST were carried out employing a Monolith NT.115 instrument (NanoTemper Technologies)4SO4, 0.01 M DTT, 0.1 M sodium HEPES pH 7.0. The importin α1ΔIBB:NiV W and importin α1ΔIBB:HeV W complexes were crystallised in 0.01 M DTT, 0.7 M sodium citrate and 0.1 M sodium HEPES pH 7.0, with rod-shaped crystals forming within 2–3 days. Importin α3ΔIBB was crystallised using 0.2 M sodium nitrate, 0.1 M Bis-Tris propane pH 6.5 and 25% (w/v) PEG 3350. Plate-like crystals formed within 2–3 weeks. The importin α3ΔIBB:NiV W complex crystallised in 0.2 M lithium nitrate, 20% (w/v) PEG 3350 conditions with a rod morphology that diffracted to 2.3 Å. The importin α3ΔIBB:HeV W complex crystallised in three forms. Crystal form 1 was obtained using 0.1 M sodium citrate pH 5.0 and 20% (w/v) PEG 2000, crystal form 2 was obtained using 0.1 M sodium HEPES pH 7.5, 25% (w/v) PEG 200 MME and crystal form 3 obtained using 0.2 M sodium chloride, 0.1 M MES pH 6.5, 10% (w/v) PEG 4000 conditions. Diffraction of the importin α3ΔIBB:HeV W complex crystal forms ranged from 1.6–2.3 Å.All crystals were obtained using the hanging drop vapour diffusion method over a 300 µL reservoir solution. Importin α1ΔIBB was crystallised with 1 M NH49 and MX250 macromolecular beam lines using an ASDC Quantum 210r, ASDC Quantum 315r detector and Eiger 16 M detector, respectively. Data reduction and integration was performed using iMosflm51 for data collected using ADSC 210r and ADSC 315r detectors, whereas reduction and integration of data collected on Eiger 16 M was performed using XDS52. Merging, space group assignment, scaling and selection of 5% reflections for Rfree calculations was done using Aimless54 and the CCP4 suite55. Phasing was performed using molecular replacement in Phaser MR56, with PDBID 5FC8 used as a search model for importin α1 cargo-free, importin α1:HeV W complex and importin α1:NiV W. The importin α3:HeV W complex was phased using 4UAE as a search model, from which the solution was then used as a search model for the importin α3:NiV W complex. The cargo-free importin α3 structure was phased by placing the N terminus and C terminus domains separately. Models were refined using iterative cycles of manual real space coot57 and maximum likelihood phenix refine58.X-ray diffraction data were collected at the Australian Synchrotron on the MX12.HEK293T (ATCC—CRL-3216) and Hela cells (ATCC—CCL-2) were maintained in Dulbecco's Modified Eagle Medium, supplemented with 10% fetal bovine serum and cultured at 37 °C and 5% CO20. NiV W was subcloned with a Flag-tag into pCAGGS. Overlapping PCR was used to clone HeV W R422A/R423A, HeV W R422D/R423D, HeV W K437A/K438A, HeV W K437D/K438D, NiV W R424A/R425A, NiV W R424D/R425D, NiV W K439A/K440A, NiV W K439D/K440D and importin α3 ARMs 7/8 mutant (N348 A/N352A/E387A/N394A), which were then cloned with an HA- or Flag-tag into pCAGGS. pCAGGS Flag importin α1, α3, α4, α5, α6 and α7 were previously described36. Importin α3 was subcloned with an HA-tag into pCAGGS. Overlapping PCR was used to generate the chimeric importin α1ARM1-5:α3ARM6-10 (residues 1–279 of importin α1 and residues 271–521 of importin α3) and importin α3ARM1-5:α1ARM6-10 (residues 1–270 of importin α3 and residues 280–529 of importin α1), which were cloned with Flag tags into pCAGGS. For additional information regarding primers used in this study, please see Supplementary Table The sequence for HeV W (NCBI: JN255804.1) was synthesised  and cloned with N-terminal Flag- and HA-tags into the mammalian expression plasmid pCAGGS. pCAGGS HA NiV W was previously described6) were transfected with the indicated plasmids using Lipofectamine 2000  and at 24 h post transfection, cells were lysed in NP-40 lysis buffer -N,N,N',N'-tetraacetic acid, 10% glycerol, protease inhibitor ). Anti-FLAG M2 magnetic beads or EZview Red anti-HA Agarose affinity gel  were incubated as indicated with lysates for 1 h at 4 °C, washed five times in NP-40 lysis buffer, and eluted using 3X FLAG or HA peptide  at 4 °C for 30 min. Whole cell lysates and co-precipitation samples were analysed by western blot.HEK293T cells (1 × 104) grown on glass coverslips were transfected with indicated HeV and NiV W plasmids (300 ng) using Lipofectamine 2000 . At 24 h post transfection, cells were fixed using 4% paraformaldehyde and permeabilised using 0.1% Triton X-100. Cells were stained using mouse anti-HA  (dilution 1:500) and secondary antibody conjugated to Alexa Fluor 594  (dilution 1:2000). Images were taken using a Zeiss LSM 800 confocal microscope at × 64. To determine the ratio of nuclear to cytoplasmic fluorescence signal (Fn/c), Hela cells (1 × 104) were plated in a 96-well plate  and transfected with the indicated HeV and NiV W plasmids (300 ng) using Lipofectamine 2000. At 24 h post transfection, cells were fixed and permeabilised as above and stained with anti-HA  (dilution 1:500) and Alexa Fluor 488  (dilution 1:2000). Images were taken at × 10 using a BioTek Cytation 5 Cell Imaging Multi-Mode reader and were analysed using Gen5 Image Prime software to determine Fn/c, using the calculation Fn/c = (Fn-background)/(Fc-background), where Fn is nuclear fluorescence and Fc is cytoplasmic fluorescence. Fn/c was determined for 50 cells in each condition; error bars indicate the standard error of the mean (SEM).Hela cells , polyclonal rabbit anti-HA (H6908), and monoclonal mouse anti-HA (H3663) were purchased from Sigma-Aldrich . Monoclonal rabbit β-actin antibody was purchased from Cell Signalling (4967 S) .Lysates were run on 10% Bis-Tris Plus polyacrylamide gels  and transferred to polyvinylidene difluoride membrane . Membranes were probed with the indicated antibodies and were developed using Western Lightning Plus ECL  and imaged using a ChemiDoc MP Imaging System .g and analysed by SDS-PAGE (165 V for 30 min on a 4–12% Bis-Tris plus gel (Novagen)).Each binding experiment was comprised of 100 µL of 30 µM GST or GST-SV40Tag, combined with 100 µL of 30 µM of each importin α variant, and incubated at room temperature for 2 h with 50 µL of glutathione agarose beads (Sigma G4510). The beads were centrifuged and washed three times with 1 mL Tris wash buffer . Samples were centrifuged, the supernatant discarded, and 50 µL sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) loading buffer containing 100 mM DTT was added to each tube. Samples were heated for 10 min at 95 °C, vortexed for 5 min, centrifuged for 10 min at 17 000 × Supplementary InformationPeer Review File


Circular RNAs (circRNAs) are a class of non-coding RNA that were previously thought to be insignificant byproducts of splicing errors. However, recent advances in RNA sequencing confirmed the presence of circRNAs in multiple cell lines and across different species suggesting a functional role of this RNA species. CircRNAs arise from back-splicing events resulting in a circular RNA that is stable, specific and conserved. They can be generated from exons, exon-introns, or introns. CircRNAs have multifaceted functions. They are likely part of the competing endogenous RNA class. They can regulate gene expression by sponging microRNAs, binding proteins or they can be translated into a protein themselves. CircRNAs have been associated with health and disease, some with disease protective effects, some with disease promoting functions. The widespread expression and disease regulatory mechanisms endow circRNAs to be used as functional biomarkers and therapeutic targets for a variety of different disorders. In this concise article we provide an overview of the association of circRNAs with various diseases including cancer, cardiovascular and kidney disease as well as cellular senescence. We conclude with an assessment of the current status and future outlook of this new field of research that carries immense potential with respect to diagnostic and therapeutic approaches of a variety of diseases. Cellular ribonucleic acids are a family of coding and non-coding sequences. Merely 1–2% of the human genome is transcribed into RNA transcripts, which are translated into protein . Aside fCircular RNAs are diverse RNA species that are found in all live forms from archaea to humans . AlthougIt is imperative to distinguish valid circular RNA from artifact circular RNA. Several methods have been described to separate linear from circRNAs. In general circRNAs do not have a 3′end as compared to linear exonic RNA and they migrate slower in a two-dimensional polyacrylamide gel . RNA degSimilarly, circRNAs must be distinguished from RNA lariats which are generated through canonical RNA splicing and resemble exonic circRNAs. However, lariat RNAs are distinguished by the presence of a 2′–5′junction in contrast to the 3′–5′linkage present in exonic circRNAs. Due to this linkage difference RNA lariats can be depleted by treatment with RNA lariat debranching enzyme followed by exonuclease digestion . CircRNAGenome-wide analyses establish a strong evidence of circRNA presence in various cell types . The expThe evidence on the molecular and cellular functions of circRNAs is emerging. However, regarding the vast majority of circular RNA species their biogenesis, targets and regulation are still not well understood . The celThe circRNA-sponging function of miRNA is well characterized in the literature (more examples are provided in this review in the following sections). However, it should be noted that merely binding of circRNA to a miRNA does not always result in miRNA suppression. For example, when ciRS-7 is bound to miR-671 it becomes susceptible to cleavage by Argonaute 2 (AGO2), thus releasing miR-7 in the process. Therefore, circRNAs may act as a miRNA reservoir or mediate their transportation .6-methyladenosine (m6A) RNA base modification, the initiation factor eukaryotic translation initiation factor 4 gamma (elF4G2), methyltransferase-like 3 (METTL3), and methyltransferase-like 14 (METTL14). Mass spectrometry and computational prediction analyses demonstrated that there are hundreds of endogenous circRNAs with the potential to be translated into protein  by using the technique photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation or PAR-CLIP (muscleblind (MBL/MBNL1) in both drosophila and humans carries highly conserved binding sites for the RNA binding protein MBL in both the flanking introns and within the circularized exon 2. The authors further demonstrated a strong and specific binding of MBL to the circRNA, and this interaction was essential for increased circMbl expression by exogenous expression of MBL  was shown to particularly sponge miR-138 and regulate its function in an in vitro study using HEK293 cellls (HIPK3) gene, circHIPK3 is thought to have the potential to sponge up to 9 different miRNAs with an estimation of 18 binding sites. There was a significant inhibition of cell growth upon circHIPK3 silencing in the human hepatocyte cell line HuH-7. Moreover the authors demonstrated direct binding to and sponging of miR-124 thus regulating its activity (BCL2-associated transcription factor (BCLAF1) (There is a breadth of evidence to support the sponging effect (as competing endogenous RNA) regarding miRNAs of circRNAs in a tissue or organ specific manner. The following section describes several selected examples that demonstrate the specific sponging role of circRNAs in various tissues. The circRNA sponging function was elegantly demonstrated by Memczak et al. in their seminal study. They described the sponging function of a circRNA CDR1as or ciRS-7. This circRNA was shown to harbor 74 miR-7 seed matches suggesting a strong interaction between this circRNA and its miRNA target. The interplay between CDR1as and miR-7 was demonstrated to be specific to neuronal tissue in adult and embryonic mouse brain. CDR1as overexpression and the resulting inhibtion of miR-7 by morpholinos in zebrafish resulted in reduction of midbrain size . Similar3 cellls . The ciractivity . The expactivity . In skel(BCLAF1) . Lastly,(BCLAF1) .Since the identification of circRNA sponging of miRNA that are involved in cancer pathogenesis, a multitude of studies have been forwarded regarding the association of circRNAs with various human cancers . Given tfilamin-binding LIM protein 1 (circFBLIM1) reduced cellular proliferation and invasion, and concomitantly induced apoptosis in human HCC cell lines HepG2, 7402, and 97H. CircFBLIM1 acts as a competitive endogenous RNA to filamin-binding LIM protein 1 (FBLIM1) and by binding to miR-346 which targets FBLIM1 mRNA (UV radiation resistance-associated gene (circUVRAG) expression suppressed proliferation and metastasis of bladder cancer in vitro using the human bladder cancer cell line UM-UC-3 and in vivo using Balb/c nude mice injected with lentivirally stabilized circUVRAG UM-UC-3 into the right flank. The effects of circUVRAG are attributed to a mechanism that involves miR-223 sponging and downregulation of fibroblast growth factor receptor 2 (FGFR2) . Suppression of circ-U2AF1 resulted in decreased expression of NOVA2 and an increase in expression of miR-7-5p both in vitro and in vivo. As a result, silencing of circ-U2AF1 decreased cellular proliferation, migration and invasion, and increased cell death by apoptosis in the human glioma cell lines U87MG, and U251. Luciferase assay demonstrated that miR-7-5p is a direct target of circ-U2AF1 (metastasis-associated gene (MTA1), was found to be a direct target of circ-UBAP2. Suppression of circ-UBAP2 induced apoptosis and decreased proliferation and migration of the human TNBC cell lines BT-20 and MDA-MB-231  cell lines HepG2 and SMMC-7721 by sponging miR-31, increasing the expression levels of histone deacetylase 2 (HDAC2) and cyclin-dependent kinase 2 (CDK2) . In anotIM1 mRNA . Circ_00IM1 mRNA . The oncIM1 mRNA . Circ_00IM1 mRNA . The dow (FGFR2) . Similar (FGFR2) . In a hurc-U2AF1 . In tripA-MB-231 .itchy E3 ubiquitin protein ligase (circ-ITCH) was demonstrated in human bladder cancer tissue and in human bladder cancer cell lines EJ and T24. The overexpression of circ-ITCH inhibited cellular migration, proliferation, and metastasis in vitro and also suppressed cancer formation of xenografts in Balb/c nude mice in vivo. The mechanism of circ-ITCH function is demonstrated by the up-regulation of the tumor suppressor genes p21 and phosphatase and tensin homolog (PTEN) expression due to the sponging effect of circ-ITCH to both miR-17 and miR-224  was found to be significantly downregulated. Further analysis showed that suppression of circMTO1 led to increased proliferation and invasion of the human HCC cell lines HepG2 and SMMC-7721. CircMTO1 exerts its anti-tumor activity by sponging oncogenic miR-9 and thus preserving the expression of p21  was found to be an independent predictor of 28 days survival of patients with acute kidney injury (AKI). In this study miR-126-5p was downregulated in AKI patients and in hypoxic endothelial cells. This effect is attributed to the potential sponging function of ciRs-126 of its target miR-126-5p , an important 60S-preribosomal assembly factor, and protects against atherogenesis by modulating ribosomal RNA (rRNA) maturation in human smooth muscle cells and macrophages, thereby inducing apoptosis and inhibiting cellular proliferation  targets miR-652-3p specifically that leads to mitochondrial fission inhibition and a reduction of cardiomyocyte apoptosis by decreasing the expression of mitochondrial protein 18 (MTP18). In a mouse model of myocardial infarction (MI), suppression of MFACR in cardiomyocytes decreased mitochondrial fission and notably myocardial infarction (Autophagy-related circular RNA (ACR) has a protective role in the heart against ischemia/reperfusion injury in a mouse model of myocardial infarction by repressing autophagy through a process that involves PTEN induced putative kinase 1 (Pink1) activation, family with sequence similarity 65, member B (FAM65B) phosphorylation, and the blocking of Pink1 promoter DNA methylation mediated by DNA methyltransferase 3 beta (Dnmt3B) (circRNA rno-circ_0056717 (circDiaph3) was identified in a microarray and the suppression of this circRNA increased insulin-like growth factor 1 receptor (Igf1r) expression and stimulated vascular smooth muscle cell proliferation and migration . Overexpression of circRNA_000203 in mouse cardiac fibroblasts resulted in a promotion of the expression of collagen-1-α2 (col1a2), collagen-3-α1 (col3a1) and αSMA via a mechanism of sponging miR-26b-5p. Additionally, the downregulation of circRNA_010567 in cardiac fibroblasts resulted in suppression of TGFb1 expression, which is a target of miR-141, and the decreased expression of fibrosis related genes (The role of circRNA in the pathogenesis of cardiovascular diseases is a heavily reviewed topic . Therefoferation . In a moferation . The cirfarction . The fun(Dnmt3B) . In a raigration . In an eer of EH . The cired genes .cyclin-dependent kinase 2 (CDK2) and the cell cycle inhibitor p21  was overexpressed in peripheral leucocytes of patients with type 2 diabetes mellitus (T2DM). The expression of circANKRD36 positively correlated with higher glucose and glycosylated hemoglobin levels. In addition, the expression levels of interleukin-6 (IL-6) and tumor necrosis factor alpha  were significantly increased in the peripheral blood of patients with T2DM. The authors suggest an involvement of circANKRD36 in the increased inflammatory response in T2DM patients through a mechanism that involves the regulation of miRNAs, hsa-miR-498, hsa-miR501-5p, and hsa-miR-3614-3p . In diabitor p21 , 2017a. itor p21 .The elucidation of the function of circRNAs is an emerging field of science with a tremendous potential after previously being dismissed as RNA artifacts. They are ubiquitously expressed and thousands of members have already been identified. This fact only expands their potential to possibly enhance our knowledge to understand the difference between health and disease. Owing to their structure stability and their presence in exosomes circRNAs may also exert their function in an autocrine, paracrine and possible endocrine fashion. In addition, the fact that circRNAs are widely distributed in the cellular compartment as well as in the extracellular space in various body fluids affords them to become ideal candidates as reliable biomarkers for various human diseases. As in any emerging field caution is advised in interpreting new findings. To date studies are merely descriptive in nature. More in depth analysis about the function and regulation of identified circRNAs is needed to understand their mechanism of action. These RNA molecules hold great promise as biomarkers of disease because of their high resistance to exonuclease activity and might even be more highly expressed than their linear counterparts. Especially their putative role as miRNA sponges makes them interesting targets for future investigations. In the future circRNA-based therapies might be introduced. The concept of establishing circRNAs as therapeutics in the future relies on the stability and expression in distinct organs and their specificity for a certain disease. The more specific circRNAs are to a specific organ or disease, the less likely it is to encounter off-target effects, while therapeutically modulating a specific circRNA in a disease setting. As is known for microRNA the likelihood of ensuing significant and undesired off-target effects greatly limits the use of these transcripts as therapeutics. The elucidation of the role of specific circRNAs is the prerequisite for a targeted RNA–based therapy for a specific disease. Tissue/cell specificity might be achieved by coupling ncRNAs to tissue-specific antibodies and/or peptides, thereby reducing off-target effects.GH and JL contributed equally to the review manuscript. Both authors approved the final manuscript for submission and publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.