Escherichia coli, we found that ∼64% of the detected RNA pairs involved known sRNAs, suggesting that yet unknown sRNAs may be included in the ∼36% remaining pairs. To determine the latter, we first tested and refined a set of quantitative features derived from RIL-seq data, which distinguish between Hfq-dependent sRNAs and “other RNAs”. We then incorporated these features in a machine learning-based algorithm that predicts novel sRNAs from RIL-seq data, and identified high-scoring candidates encoded in various genomic regions, mostly intergenic regions and 3′ untranslated regions, but also 5′ untranslated regions and coding sequences. Several candidates were further tested and verified by northern blot analysis as Hfq-dependent sRNAs. Our study reinforces the emerging concept that sRNAs are encoded within various genomic elements, and provides a computational framework for the detection of additional sRNAs in Hfq RIL-seq data of E. coli grown under different conditions and of other bacteria manifesting Hfq-mediated sRNA-target interactions.The genomic revolution and subsequent advances in large-scale genomic and transcriptomic technologies highlighted hidden genomic treasures. Among them stand out non-coding small RNAs (sRNAs), shown to play important roles in post-transcriptional regulation of gene expression in both pro- and eukaryotes. Bacterial sRNA-encoding genes were initially identified in intergenic regions, but recent evidence suggest that they can be encoded within other, well-defined, genomic elements. This notion was strongly supported by data generated by RIL-seq, a RNA-seq-based methodology we recently developed for deciphering chaperon-dependent sRNA-target networks in bacteria. Applying RIL-seq to Hfq-bound RNAs in  Escherichia coli for which it was suggested that their RNA-binding activity is Hfq-independent  have emerged as a major class of post-transcriptional gene expression regulators in bacteria. These are short RNA molecules, 50–400 nucleotides long, which regulate their targets in trans, usually by incomplete base pairing with their mRNAs, affecting translation and/or mRNA stability . sRNAs wependent . In the E. coli, Spot 42, is dated to 1973 . Application of RIL-seq to E. coli grown to exponential phase, stationary phase and exponential phase under iron limitation revealed ∼2800 RNA–RNA interactions, ∼64% of which involved well-established sRNAs and the rest involved RNAs derived from various genomic entities  were derived from intergenic regions or from 3′ UTRs, underpinning their potential as novel sRNAs. Indeed, some of these second RNAs, such as those embedded in the 3′ UTR of cutC and in the 3′ UTR of cpxP were identified in independent studies as sRNAs (The discovery of novel Hfq-bound sRNAs that are encoded within a variety of genomic elements was enhanced by RIL-seq (RNA Interaction by Ligation and sequencing), a high-throughput methodology we recently developed for mapping direct RNA–RNA interactions mediated by Hfq , 2018. Tentities . Interesentities . The posentities . RIL-seqentities , by the entities , compatias sRNAs .E. coli grown under additional cellular conditions and in RIL-seq data generated for other bacteria manifesting Hfq-mediated sRNA-target interactions. It may also inspire the application of similar algorithms for analysis of large-scale data generated by equivalent protocols in other contexts.In total, RIL-seq data comprised ∼1000 RNA–RNA pairs that did not include a known sRNA , suggestWe used three data sets of chimeric fragments corresponding to S-chimeras, obtained in RIL-seq experiments applied to bacteria grown to exponential phase, to stationary phase and to exponential phase under iron limitation . The datWe describe each RNA by features mainly extracted from RIL-seq data  and compi) and an intercept (β0), such that n is the number of selected features and x_i is the value of feature i. The probability of a RNA to be a sRNA is then computed as 1/(1 + exp(−l)). We tested the obtained logistic regression model by applying it to RNAs in the test set. In practice, we conducted 10,000 iterations of this procedure, and recorded the probabilities a RNA obtained when it was included in the test set of an iteration. The final predicted probability of each RNA was computed as the mean of the predicted probabilities across all the iterations in which it was included in the test set. The logistic regression was trained using the default parameters of the sklearn linear_model LogisticRegression class, i.e., using L2 regularization.The development of the predictive scheme was carried out separately for each data set. Each RNA in the data was described by a vector of the selected features. The data set was split in a ratio of 2:1 into a training set and a test set, respectively, where in each set the ratio of known sRNAs to “other RNAs” was maintained . We applper se is not informative. Instead, we can transform the feature values into z-scores, and compare the products of weight and the feature standard deviation:It is common to examine the weights in order to learn on the relative contributions of the various features to the computed probability. However, as the values of the different features span different numeric scales, comparison of the weights im and is are the mean and the standard deviation, respectively, of the RNA’s thi feature values. The equation shows that transforming the data to z-scores is associated by an appropriate change of the intercept by the weighted sum of the mean feature values, and the weights of the features are represented by the products of the original weight and standard deviation of each feature, which are comparable. In practice, we applied this transformation to the average coefficients from the 10,000 logistic regression iterations we conducted per growth condition.where robust_scale function. Then, we applied the PCA transformation for the first two dimensions using sklearn decomposition PCA class.Feature vectors of the RNAs were initially scaled with python’s sklearn preprocessing module using the fadA 3′ UTR we were not able to determine the 5′ end   and RNasTo verify whether the putative sRNAs we report are supported by other data sets, we compared their estimated coordinates to chimeric fragments included in the Hfq-CLASH data set . We consEscherichia coli MG1655 and its isogenic strain MG1655 hfq::Kn were grown over-night in LB medium and then diluted 1:100 in fresh LB medium and grown while shaking at 37°C. Samples of culture were collected throughout growth, and centrifuged at 4°C. The pelleted cells were resuspended in 50 μl of TE buffer , mixed with lysozyme to a final concentration of 0.9 mg/ml and fast frozen in liquid nitrogen. The samples were then subjected to two cycles of thawing at 37°C and freezing in liquid nitrogen.For the verification of novel sRNA expression, cultures of 32P] end labeled DNA probes. For each tested sRNA, the northern blot was repeated at least twice, with a different replicate of total RNA. The probe sequences are listed in Total RNA was extracted from harvested cells using TRI-reagent (Sigma). 30 μg of total RNA were separated in 7 M urea/6% polyacrylamide gels in 44.5 mM Tris-base, 44.5 mM boric acid and 2 mM EDTA pH 8.0, and transferred to Zeta-Probe membrane (Bio-Rad) by electroblotting. The membranes were hybridized with specific [We used compatible RNA-seq data available in the lab, which were generated as following: Three single colonies of MG1655 cells carrying a pJV300 plasmid  were groExamination of the chimeric fragments corresponding to S-chimeras in RIL-seq data hinted at several properties that may aid in the classification of RNAs represented in these chimeras as either sRNA or target RNA . sRNAs wi) features derived from the chimeric fragments the RNA is involved in (first layer), and (ii) features of the RNA interactors (second layer). The incorporation of both layers of features in the analysis is inspired by the acknowledgment that up to date the number of identified sRNA–sRNA interactions is very small and far below the number of identified sRNA interactions with mRNAs. Recognizing first layer features that support the RNA as a sRNA along with second layer features that do not support the interactors as sRNAs should provide stronger support for a sRNA candidate than expected from its first layer features alone. Combining the two layers of traits is expected to enhance the discriminative power of the model and to increase the reliability of predicted sRNAs.While previously these properties were intuitively considered for supporting or rejecting a sRNA candidate , our aimAll analyses described hereinafter were carried out for each RIL-seq data set separately . We describe in the text the results for the stationary phase data and in the Supplementary Material the results for the two other data sets. When relating to the chimeric fragments, we refer to chimeric fragments corresponding to S-chimeras identified in RIL-seq results.Y1,…,Yi,…,Yk and is involved in n1,…,ni,…,nk chimeric fragments corresponding to each RNA, respectively, making up a total of N chimeric fragments. The trait ‘number of chimeric fragments the RNA is involved in’ can be described as N, or as the mean of ni, or as the median of ni. We examined 18 features in total, several of which regard different representations of the same trait . For example, let X be a RNA that was identified as interacting with k RNAs me trait . We assime trait . We thenme trait , and selme trait . The U-tTotal number of chimeric fragments: The total number of chimeric fragments that included the RNA. This value was normalized by the total number of chimeric fragments in the data set. (B) Number of unique interactions: Number of unique interactions the RNA was involved in (k). This value was normalized by the total number of unique interactions in the data set. (C) Second-In-Chimera (SIC) score: A score representing the fraction of chimeric fragments in which the RNA was the second RNA of the chimera, while taking into account the number of unique interactions this RNA is involved in. We defined this score as U-tract length: For each RNA we assigned the length of the longest U-tract that could be identified in a region spanning 50 nucleotides around the segments of this RNA in the chimeric fragments of the RIL-seq data. (E) Median number of interactions of interactors: Each interactor of a RNA is also annotated by feature B. We take the median of these values across all interactors of the RNA. (F) Median SIC score of interactors: Each interactor has a SIC score, as defined above. We take the median of these values across all interactors of the RNA. Note that since the variances of features A, B, and E were extremely large, their values were transformed to Log10 scale for further analysis.Six features were selected, four are first layer features and two are second layer features . It is o– 23 and 10– 11) suggested that they can be used for classifying sRNAs and for the determination of novel, yet unknown sRNAs, which may be hidden in RIL-seq data.The distributions of these feature values differed statistically significantly (after Bonferroni correction for multiple hypotheses testing) between the groups of known sRNAs and “other RNAs” , 3. It iEach RNA in RIL-seq data was represented by a vector of the above six features. Analysis of the vectors by principal component analysis (PCA) further demonstrated the separation of known sRNAs from “other RNAs” by the features and the contribution of the various features to this separation , 5A,B. FTo systematically and comprehensively identify novel sRNA candidates, we applied logistic regression, using these six features as characteristics of each RNA in the data. We applied 10,000 iterations of the logistic regression, where in each iteration we randomly split the data into a training set and a test set. Each training set included 2/3 of the known sRNAs and 2/3 of the “other RNAs” and the test set included the rest of the data. At each iteration we trained a logistic regression model on the training set, resulting in a linear combination of the features, which provides the probability of a RNA in the data to be a sRNA . The model was then used to compute these probabilities for RNAs in the test set. At the end of the process, each RNA had a list of N probabilities, where N is the number of test sets that included this RNA. The final sRNA probability of a specific RNA was the average of these probabilities, considered hereinafter as the sRNA score of the RNA. Determining different probability thresholds above which a RNA is determined as a sRNA, we obtained a receiver operating characteristic (ROC) curve and a precision–recall (PR) curve for each iteration and for the average results , 5C,D, sDue to the low frequency of sRNAs in the data and the inclusion of sRNAs not yet discovered in the training set, we expect the model to output uncalibrated prediction probabilities. We therefore did not determine a probability threshold above which a RNA is predicted as a sRNA, but ranked the RNAs by their sRNA scores, scanned the ranked RNAs from top down and searched for RNAs ranked above or in the vicinity of known sRNAs , 5E. As To verify that our results do not depend on the number of iterations or the selected ratio of 2:1 between the sizes of the training and test sets, we conducted the analyses for different numbers of iterations and different ratios of training to test set sizes. These analyses confirmed that the results are independent of these parameters –9.The logistic regression assigns weights to the features, which are used for the computation of the probability of a RNA in the data to be a sRNA  as a sRNA. To assess this, we examined the sRNA scores of “target hubs”, defined as RNAs interacting with at least four different sRNAs in at least one condition. Indeed, out of 18 “target hubs”, 16 got low sRNA scores . When “tA scores . Interesenerated . In geneenerated . Yet, in of GcvB . SroC sp of GcvB , 5.allR, bhsA, fadA, glpX, malG, ybiJ and ykgH), two in intergenic regions , one in 5′ UTR (kilR), and one in a coding sequence (aceK). To validate the expression of these sRNAs we probed them by northern blotting, which provides information on both the expression pattern of the RNA and on its approximate size. Total RNA was extracted from wild type K-12 and Δhfq strains grown to different growth phases and the expression of the sRNA candidates was tested. As malG 3′ UTR and rbsB-rbsK IGR were in the meanwhile reported by other groups as sRNAs [MalH . The second class comprises sRNAs with very few targets. This group includes sRNAs that are not highly expressed in the conditions studied here and, thus, are lowly ranked at these conditions, but they are likely to be detected under the relevant condition. In addition, this class includes highly specialized sRNAs with specific targets, mostly considered as sponges. While some sponges, with extremely high first-layer properties, such as SroC, are classified as sRNAs, others are not (uhpT and sucD). Re-examination of the proximity of the chimeric fragment coordinates of the CDS-derived candidates to 5′ UTR or 3′ UTR can resolve some of these misclassifications.While most of the previously known sRNAs are properly classified, we do encounter and expect misclassifications emerging mainly from three major RNA classes. The first class comprises “target hubs” that interact with multiple sRNAs . While i are not . As thisbhsA mRNA, encoding a small outer membrane protein that is involved in various stress responses. Oxidative stress induced by hydrogen peroxide or paraquat activates bhsA transcription (ytfK and ompC, and both were shown to be involved in the cellular response to oxidative stress. ytfK, induced by paraquat (Salmonella to facilitate and regulate the diffusion of hydrogen peroxide through the outer membrane (Finding that the targets of the newly revealed sRNAs have functions that are associated with the function of the hosting mRNA would support the functionality of the novel sRNAs as regulatory molecules. It would also suggest that the sRNA and hosting gene affect the same pathways at different regulation levels, and, in case they share targets they may generate regulatory circuits combining multiple regulation levels. However, as RIL-seq data do not provide information whether the sRNA enhances or represses the target expression, it would not be possible at this stage to draw mechanistic conclusions on such possible circuits. Yet, we found for several of the novel sRNAs that the hosting genes and their targets are involved in common pathways . For exacription . Also, Bcription . The RILparaquat , was shoparaquat . OmpC, amembrane . Thus, RIn summary, using our methodology followed by experimental verification we reaffirmed that there is a rich repertoire of sRNAs encoded within various genomic elements and generated under different conditions. The use of our systematic approach has allowed us to identify putative sRNAs that would not have been considered otherwise, such as AllZ, KilS and BhsB. Each of them is involved in only a few interactions and a few hundred chimeric fragments, and they would not have been suspected as sRNAs by examining their individual features alone. Yet, the overall combination of their features has allowed their detection. Especially, their interactors had very few interactions with “other RNAs”, rejecting the interactors as sRNAs and supporting AllZ, KilS and BhsB as sRNAs. We believe that taking into account both the first- and second-layer features empowers our predictions. Hence, taking into account the information extracted directly from RIL-seq data while accounting for the RNA–RNA interaction network inferred from RIL-seq results is highly rewarding. Our approach may be generalized to other RNA-seq-based methodologies, where the results may imply a network structure or hierarchy of the genes. Combining features based on the direct sequencing results with features based on a higher-order structure of the data may prove beneficial to the inference of novel biological insights in other contexts.https://www.ebi.ac.uk/arrayexpress/E-MTAB-9834 and in https://www.ebi.ac.uk/arrayexpress/E-MTAB-3910.The data used in this article can be found in HM and YA initiated and supervised the study. LA contributed to the experimental investigation. AB contributed to the software development and computational analysis. YA, LA, AB, and HM contributed to the writing – original draft and review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


RNA-mediated pathways form an important regulatory layer of myriad biological processes. In the last decade, the potential of RNA molecules to contribute to the control of agricultural pests has not been disregarded, specifically via the RNA interference (RNAi) mechanism. In fact, several proofs-of-concept have been made in this scope. Furthermore, a novel research field regarding extracellular RNAs and RNA-based intercellular/interorganismal communication is booming. In this article, we review key discoveries concerning extracellular RNAs in insects, insect RNA-based cell-to-cell communication, and plant–insect transfer of RNA. In addition, we overview the molecular mechanisms implicated in this form of communication and discuss future biotechnological prospects, namely from the insect pest-control perspective. RNAs exist in a wide variety of structures and sizes, well suited to regulate a multitude of processes. Regulatory RNAs, also referred to as non-coding RNAs, do not contribute directly to protein synthesis but function at various control levels to modulate gene expression. These molecules act both at the transcriptional and post-transcriptional levels, by mediating chromatin modulation, regulating alternative splicing, inducing suppression of translation, or directing the degradation of target transcripts .Eukaryotic regulatory RNAs are broadly classified into long ≥200 nt) and small (≤200 nt). While numerous of the so-called long non-coding RNAs are described to regulate gene expression at various levels, it has recently been shown that some might, in fact, have coding functions ,2. Nonet00 nt andClassification of sRNAs relies on their biogenesis mechanisms, size, complementarity to the target, associated proteins, and main regulatory processes in which they are involved. Based on these, several sRNAs are recognized among eukaryotes, of which two are common to plants and animals: microRNAs (miRNAs) and small interfering RNAs (siRNAs). In broad terms, miRNAs originate from the processing of endogenous stem-loop RNA precursors and act to regulate the expression of endogenous genes. In turn, siRNAs originate from long double-stranded RNA (dsRNA) structures and mainly function in the protection against viruses and transposons ,10,11. WAlthough the mechanisms by which they act are not as extensively investigated as in eukaryotes, regulatory RNAs are also present in Archaea and Bacteria. In this regard, the RNA chaperone Hfq is well described to play a central role in several RNA-based regulatory systems in prokaryotes ,15,16,17While regulatory RNAs and their pathways are mostly investigated at the intracellular level, discoveries regarding a role of extracellular RNAs in cell-to-cell communication are now making the scene. Numerous are the examples of interspecies and even interkingdom RNA-based communication, especially those concerning host–parasite/symbiont interactions. The study of RNA-based transfer of information promises not only to bring fundamental insights into a novel layer of regulatory complexity, but also to open doors to a handful of biotechnological applications ,27,28.Insects are the largest and most diverse group of animals on Earth. They form an important component of the ecosystems and affect many aspects of human life. A major part of insects’ impact occurs via their interaction with plants; for instance, some species are important pollinators, while others are agricultural pests and vectors of plant diseases ,29,30. IThe presence of RNA molecules in extracellular environments, mainly of sRNAs, has been systematically reported. Throughout the past decade, it became evident that RNA molecules mediate an important layer of communication, even between diverse and phylogenetically distant organisms ,33,34. IAllomyrina dichotoma [Drosophila melanogaster flies, and some were demonstrated to differentially accumulate with age [D. melanogaster cell lines S2R+ and D17-c3 [D. melanogaster cells, namely of S2 and Cl8 cell lines, with indications for differential secretion [Drosophila flies, extracellular siRNAs have been shown to contribute to systemic antiviral immunity [Tribolium castaneum cultured cells [Leptinotarsa decemlineata, dsRNA has been identified in the extracellular medium of cultured cells previously treated with dsRNA [In insects, little more than a handful of studies report extracellular RNAs and their  role in intercellular/intraindividual communication, namely in beetles and flies: (i) extracellular sRNA populations have been observed in the hemolymph of the korean rhinoceros beetle, ichotoma ; (ii) miwith age ; (iii) sd D17-c3 ; (iv) execretion ; (v) in immunity ; (vi) exed cells ; and  containing the miRNA Cve-miR-281. On another front, the wasp larva also releases the associated C. vestalis bracovirus, which expresses the Cve-miR-novel22 miRNA in the host. Remarkably, both miRNAs influence the host’s ecdysone cascade to delay its development, hereby granting the parasite additional time to develop [Cuscuta and their hosts, where bidirectional RNA transfer has been demonstrated [Cuscuta miRNAs have been reported to target Arabidopsis genes [Glycine max-Phaseolus vulgaris) grafting system has been recently demonstrated [Functional RNA transfer between different species is also currently described in animals. This is broadly termed interspecies RNA-based communication, and the so-far described examples mainly refer to host–parasite interactions ,75,77. A develop . In addi develop ,60,61,82 develop ,79,80. Tnstrated ,88,89,90is genes . In addinstrated .There are known cases of RNA transfer crossing the three domains of life and several examples of interkingdom RNA-based communication between plants or insects and their microorganisms. Within Eukarya, functional RNA transfer has been reported between plants (Plantae) or insects  and their fungal (Fungi) or protist (Protista) pathogens ,95,96,97Myzus persicae. In this study, reduced aphid fecundity was observed when the insect fed on Arabidopsis mutants for the miRNA pathway. This led to speculation that processing of aphid miRNAs by the plant might mediate aphid infestation [Bemisia tabaci have been identified both in tissue and phloem of the tomato plant [M. persicae aphids were demonstrated to transfer the Ya transcript to their host plants, where it moves to distal leaves. This transcript was identified as a long non-coding RNA virulence factor, with functional studies demonstrating that it promotes aphid fecundity [Until now, very little is known regarding RNA transfer from insects to plants, with only three reported studies. The first indication for a possible insect–plant functional RNA transfer was obtained in the green peach aphid, estation . Later, to plant . Interesecundity .Cucumis melo miRNAs has been shown in the cotton–melon aphid, Aphis gossypii, demonstrating that plant miRNAs are stable in the insect body [Bombyx mori, mulberry miRNAs were identified in both hemolymph and tissues [Schizaphis graminum, and yellow sugarcane miRNAs in the yellow sugarcane aphid, Sipha flava. In silico target prediction indicated the involvement of these miRNAs in processes important for aphid fecundity, suggesting a role in decreasing aphid infestation [Brassica oleracea in the gut of M. persicae, the green peach aphid. The predicted miRNA targets in the insect were mainly carbohydrate transport and metabolism, RNA processing and modification, and nuclear structure genes [P. xylostella, the diamondback moth. Importantly, two of the most abundant identified plant miRNAs were validated to target P. xylostella hemocyanin domain containing genes, that are known to play important roles in the hemolymph of arthropods. Moreover, other highly abundant plant miRNA was demonstrated to influence the pupal development and egg-hatching rate [D. melanogaster larvae [A. mellifera TOR, a key player in cast development [The presence and functionality of dietary RNAs in animals have been a matter of debate over the last decade ,105,106.ect body . In the  tissues . Later, estation . Anotherre genes . Plant-ding rate ,112. Remr larvae . This mielopment ,114. Tab® and was developed by Monsanto and Dow Agrosciences [Adding to the previously described observations, it is relevant that plant–insect functional transfer of RNA can be engineered to occur. In short, host plants can be genetically modified to express specific RNA molecules targeting key insect genes. When the insect feeds on the plant, these RNA molecules enter the insect body, are taken up by recipient cells, and induce gene silencing by RNAi. In the context of insect pest control, there are several examples of transgenic plants designed to induce RNAi in insects, a process commonly designated as host-induced gene silencing. The first successful studies on achieving resistance to a pest–insect via transgenic plants expressing dsRNA date to 2007 ,116. Sinsciences . This ilTransfer of information via extracellular RNAs has been seen to occur in insects. In addition, bidirectional plant–insect RNA transfer has been reported and, in some cases, the RNA signal was shown to be functional. This was demonstrated both in the context of specific (possibly coevolved) plant–insect interactions, as well as of genetically engineered plants, to exert RNA-based insect pest control see . NeverthCaenorhabditis elegans, transmembrane systemic RNA interference defective (SID) proteins act as channels for intercellular RNA movement [Drosophila cells [In plants, naked RNAs, i.e., not protected by RNA binding proteins or encapsulated in EVs, can be transferred over short and long distances ,143,144.movement ,160,161.la cells . HoweverIn both plants and fungi, apoplastic movement of naked sRNA can also occur. In this case, naked sRNAs are secreted from the plasma membrane and move throughout the cell wall to extracellular spaces, where they can then enter both neighboring or distant cells ,146,147.Plants also transfer naked sRNAs via the phloem, using the vascular system to spread these molecules throughout the plant to distant cells ,146,147.In human plasma, naked extracellular RNAs are rapidly degraded . SimilarCucurbita maxima Phloem Small RNA-Binding Protein 1 can bind sRNAs, transferring them between cells, both via the plasmodesmata and the phloem [In plants, RBPs are established to mediate short- and long-range RNA transport. The e phloem ,173. Fure phloem ,175,176.e phloem ,178. In e phloem .Heligmosomoides bakeri, secondary siRNAs are loaded into an extracellular Ago protein, and this complex is subsequently secreted in EVs, suggesting a role of this Ago protein in mediating the selective sorting of sRNAs in EVs in this species [Drosophila-derived cell lines verified the presence of extracellular miRNAs associated with an Ago protein [In 2008, Mitchell and colleagues demonstrated that extracellular sRNAs present in human plasma are protected from degradation due to their association with certain entities . In line species . In the  protein ,65, suggBesides Ago proteins, the association of sRNAs to lipoproteins has been demonstrated as well. Lipoproteins have been shown to be associated with miRNAs, and high-density lipoproteins (HDLs) can functionally transfer miRNAs to recipient cells . FurtherAdditionally to lipoproteins and Ago proteins, other animal proteins have been suggested to bind RNA in the extracellular environments. Particularly, mammalian Nucleophosmin1 was demonstrated to bind miRNAs and protect them from nuclease degradation . In inseEVs form a heterogeneous group consisting of exosomes, microvesicles and apoptotic bodies. Although long viewed as part of cellular waste disposal pathways, it is by now clear that EVs can functionally transfer their content  to recipient cells .Despite previous debate regarding plant cell wall preventing formation and function of EVs, recent evidence shows that EVs are also produced by these organisms ,197,198.C. elegans [Heligmosomoides polygyrus, Litomosoides sigmodontis [Brugia malayi [H. bakeri, and Trichuris muris [Ixodes Ricinus and Haemaphysalis longicornis [Procambarus clarkia [A. dichotoma [T. castaneum [L. decemlineata [Drosophila [D. melanogaster [T. castaneum cell culture, siRNA-containing EVs spread an RNAi-based silencing signal to recipient cells [C. vestalis deliver miRNAs to its host P. xylostella, which target its ecdysone cascade, resulting in a developmental delay [Leptopilina boulardi injects vesicles in its host Drosophila. Although transfer of RNA has not been investigated in this case, these EVs—venosomes—were shown to interfere with the hosts’ defense [In 2007, a first study demonstrating that EVs mediate intercellular communication in mammalian cell lines, by transferring functional RNA from donor to recipient cells, was reported ,38. Sinc elegans ,58,69,76modontis , Brugia a malayi , H. bakeis muris ; in the gicornis ,82; as w clarkia . Also in clarkia ,64 and i clarkia ,65; as wichotoma  and in castaneum  and L. dmlineata . Moreoveosophila ,65. Notent cells . Third, al delay . Likewis defense . Moreove defense . TogetheTwo potential RNA selective sorting mechanisms have been proposed in mammals. First, miRNAs packaged into EVs were demonstrated to contain specific nucleotide motifs with which specific ribonucleoproteins interact to allow selective sorting ,218,219.EVs are produced by all domains of life and are considered part of an ancient mechanism for RNA export ,225. In To sustain the currently growing food demand, agricultural pests are one of the many challenges that are faced. Pest insects destroy 18–20% of the annual global crop production, which is estimated at $470 billion . The claRNA-mediated insect gene silencing is a promising tool to contribute to highly specific pest control strategies. Nevertheless, the effect of RNA is not equally efficient in every insect, with high levels of variability between different species and even between different populations of the same species. RNA degradation by gut nucleases and extreme pH conditions upon ingestion is appointed as a reason for inefficient RNA-based silencing in several insect species. In addition, inefficient or absent functional RNA uptake systems in the gut, as well as the lack of an efficient systemic RNAi spreading mechanism from the gut to the rest of the insect body, are also considered potentially important limiting factors ,242,243.In mammals, both RBPs and EVs have been explored for efficient cellular delivery of nucleic acids. These approaches are mainly explored in humans, in the context of targeted drug delivery therapeutics. Interesting examples are mammalian lipoproteins, which are often proposed for use in human siRNAs delivery ,248,249.Besides RBPs, many studies appoint EVs as promising human drug-delivery vehicles ,251,252.To ensure environment-friendly and biosafe insecticides, specificity is a watchword. The idea of RNA-based pest control strategies is highly popular since high species- and gene-specificity can be obtained at the level of the nucleic-acid sequence ,255. CurD. melanogaster siRNAs are 2′-O-methylated, while this is not the case in the lepidopteran species P. xylostella, B. mori, and Trichoplusia ni [dicer2 or argonaute2) [First, several factors have been shown to influence the loading of sRNAs into Ago proteins, such as their sequence and structure ,263,264.lusia ni ,267. In onaute2) ,269,270.Apis genus and to only be associated with jelly-secreting bees [Second, exhaustively unravelling mechanisms of RNA transfer within and between a wide range of organisms can stimulate the design of highly specific RNA delivery vehicles. It is important to elucidate what EV populations and protein-complexes interact with RNAs in different species, as well as to determine their composition and structure. It is also relevant to determine their specificity and, in this regard, an example has recently been reported in bees. Specifically, the domain of MRJP-3 that provides its RNA binding activity seems to have emerged in the ing bees . Moreoveing bees ,273,274.In summary, three levels of specificity and efficiency could be aimed at, as determined by (1) the cellular delivery vehicle, (2) the recognition by the cellular machinery, and (3) the nucleotide sequence. Importantly, exploring these three levels in distinct contexts of integrated pest management has potential to delay, or avoid, the development of resistance. In addition, it is also relevant that the use of highly efficient and specific RNA delivery methods can contribute to the release of low quantities of the RNA-based insecticide in the environment, reducing potential toxicity concerns.RNA molecules and their related pathways are classically described at the intracellular level. Recently, the existence of a novel RNA-based communication layer, via the spread of extracellular RNAs in biological fluids, became well established and gave rise to a novel research area that is currently booming in biomedical sciences. Although this research field remains underexplored in insects, growing evidence indicates that this route of communication is also functional in these animals. Specifically, extracellular RNAs are present in insects, are contained within EVs and/or interact with RBPs, and are transferred to recipient cells to exert their function. Nevertheless, only a limited number of studies are currently available, which do not cover the dimension and diversity of the class Insecta, or of the wide range of insect–plant interactions. A wider and deeper fundamental understanding of the intercellular RNA transfer mechanisms in these organisms is required. In this scope, there is an urgent need for well described, robust, and reproducible protocols for isolation of extracellular components, such as EVs and RBPs, from complex biofluids. In addition, although the advent of deep sequencing technologies has led to an increase of descriptive studies, these only form a starting point. In fact, functional studies regarding natural RNA-transfer mechanisms, as well as their potential for bioactive delivery of exogenous RNA molecules, are required. Noteworthily, it is critical to not only unravel conserved processes, but also species-specific mechanisms. Thus, besides model organisms, efforts must also be invested into several pest and beneficial systems. At last, on the long run, special attention should be given to the development of production/purification techniques of selected extracellular components bound to RNAs of interest; as well as to biosafety assays, namely concerning the specificity of the developed approaches.Considering the potential of RNA-based insect pest control strategies, understanding the mechanisms by which RNAs are transferred within insects, as well as between plants and insects, promises to give leads to the development of effective strategies for exogenous RNA delivery. This has potential to lead to novel ways to protect crops, to combat insect pests, and to generate high economic, environmental and social value.


Recombinant Leptotrichia wadei dCas13a was expressed using the silkworm-baculovirus expression system and affinity-purified. We found that the CRISPR/dCas13a RNP complex, combined with a chemically synthesized gRNA sequence, could specifically inhibit RT of EGFR and NEAT1, but not nonspecific RNA. Thus, the CRISPR/dCas13a RNP complex can inhibit RT reactions in a sequence-specific manner. RT inhibition by the CRISPR/dCas13a system may be useful to assess target binding activity, to discriminate RNA species retaining target sequences of gRNA, or to suppress RT from undesirable RNA species.The clustered regularly interspaced short palindromic repeats (CRISPR) system is widely used for genome editing because of its ability to cleave specific DNA sequences. Recently, RNA-specific CRISPR systems have been reported. CRISPR systems, consisting of a guide RNA (gRNA) and a nuclease-dead form of Cas13a (dCas13a), can be used for RNA editing and visualization of target RNA. In this study, we examined whether a recombinant CRISPR/dCas13a ribonucleoprotein (RNP) complex could be used to inhibit reverse transcription (RT) in a sequence-specific manner  Streptococcus pyogenes is widely used for genome editing [The clustered regularly interspaced short palindromic repeats (CRISPR) and associated Cas protein system are part of the adaptive immune machinery of bacteria and archaea to defend against foreign DNA elements , 2. In p editing , 4. In tRecently, the CRISPR/Cas13a system was identified to target RNA and has since been used for RNA editing , 6. BecaLeptotrichia wadei dCas13a (Lw-dCas13a) was produced. It was determined that a ribonucleoprotein (RNP) complex, consisting of the recombinant dCas13a protein and synthetic gRNA, can be used as a sequence-specific blocker for RT reactions. This RT inhibition system may be used to estimate whether designed gRNAs can be functional in vitro or suppress RT of undesirable RNA species.In this study, it was examined whether CRISPR/dCas13a can be used as a sequence-specific blocker, by inhibiting reverse transcription (RT) with reverse transcriptases in a test tube . To achiThe human lung cell lines, MRC-5 and NCI-H1299, were purchased from American Type Culture Collection . MRC-5 was cultured in Eagle's minimal essential medium (E-MEM)  supplemented with 10% fetal bovine serum (FBS). NCI-H1299 was cultured in RPMI-1640  supplemented with 10% FBS.®, http://procube.sysmex.co.jp/eng/)  as described previously [To produce a recombinant dCas13a (r3xFLAG-dCas13a-D) protein, an expression construct of Lw-dCas13a  was firseviously . The fingRNAs were synthesized by Gene Design, Inc  . The synTotal RNA was extracted from MRC-5 or NCI-H1299 using Isogen II . Total RNA from either of these cell lines was used throughout this study. RT was performed using ReverTra Ace qPCR RT Kit . Briefly, 1 µl of the total RNA (100 ng for ® SYBR qPCR Mix (Toyobo). Briefly, 2 µl of cDNA, 0.5 µM of each primer, and nuclease-free water were combined with THUNDERBIRD® SYBR qPCR Mix in a total volume of 10 µl following the manufacture’s protocol. The PCR cycling conditions using CFX ConnectTM  were as follows: 1 cycle of 95°C for 1 min; 40 cycles of 95°C for 15 s; and 60°C for 30 s. Primers used in this study are shown in Real-time PCR was performed using THUNDERBIRDStatistical analysis was performed with the Prism software 6  using one-way analysis of variance.L. wadei dCas13a (r3xFLAG-Lw-dCas13a-D) protein was generated using the silkworm–baculovirus expression system. r3xFLAG-Lw-dCas13a-D could be purified without visible degradation   . Howevere below) . TherefoEGFR sequence (gRNA_EGFR) [Fig. 3A and B;EGFR mRNA is expressed in various lung cells, such as a normal lung fibroblast cell line, MRC-5, and a nonsmall cell lung carcinoma cell line, NCI-H1299 [EGFR occurred when the cDNA was synthesized with the dCas13a/gRNA_EGFR RNP complex. Although the dCas13a protein or the gRNA alone showed decreased EGFR PCR amplification, probably in a nonspecific manner, the dCas13a/gRNA RNP complex was most effective to suppress PCR amplification. The dCas13a/gRNA_EGFR RNP complex did not specifically suppress PCR amplification of GAPDH included as a negative control target, compared with the dCas13a protein or the gRNA alone   was usedCI-H1299 , total RNA alone . Normali complex . These rNEAT1 [NEAT1, but no significant differences with EGFR nor GAPDH . As shown in NEAT1 in a sequence-specific manner although moderate nonspecific inhibition effect to GAPDH was also observed. Together, these results showed that the dCas13a/gRNA RNP complex could perform sequence-specific inhibition of RT reactions.To validate sequence-specific inhibition of RT by the dCas13a/gRNA RNP complex, another gRNA was designed to target a noncoding RNA. In this study, we targeted NEAT1  as a modor GAPDH . When no complex . We also complex  and S4. This study showed that the dCas13a/gRNA RNP complex can be used as a sequence-specific blocker to inhibit RT reactions. This system is potentially useful to examine the targeted binding efficiency of CRISPR/Cas13a in a test tube, independent of cleavage activity. In addition, this system could be used to avoid RT of undesirable or redundant RNA species, such as ribosomal RNA. In this context, CRISPR/Cas13a could be used to cleave target RNA. However, CRISPR/Cas13a cleaves collateral as well as target RNA , which mBiology Methods and Protocols online.Not applicable.Not applicable.All data generated or analyzed from the current study are included in this published article.H.F. conceived the project. T.F. designed the experiments. T.F., S.N, and M.Y. performed experiments. T.F. and H.F. wrote the manuscript. All authors read and approved the final manuscript. This work was supported by the Hirosaki University Graduate School of Medicine and Epigeneron Inc. (T.F. and H.F.).T.F. and H.F. filed a patent for applications of the dCas13a/gRNA RNP complexes including those described in this study. Patent names: “Method for detecting target nucleic acid, Method for detecting molecule having nucleic acid binding ability, and Method for evaluating nucleic acid binding ability.” Patent applicant: Hirosaki University. Name of inventors: T.F. and H.F. Patent numbers: Japanese Patent Application No. 2019-191409; PCT/JP2020/039128. Patent status: filed. Specific aspect of manuscript covered in patent application: the patent application covers application of dCas13a/gRNA RNP complexes to inhibit RT reactions in a sequence-specific manner. T.F. and H.F. are cofounders of Epigeneron Inc. and own stock in the company. T.F. is an advisor and H.F. is a director of Epigeneron, Inc. Epigeneron, Inc. owns the right of commercial use of all filed and approved patents.bpab009_Supplementary_DataClick here for additional data file.


The initiation of prostate cancer has been long associated with DNA copy-number alterations, the loss of specific chromosomal regions and gene fusions, and driver mutations, especially those of the Androgen Receptor. Non-mutational events, particularly DNA and RNA epigenetic dysregulation, are emerging as key players in tumorigenesis. In this review we summarize the molecular changes linked to epigenetic and epitranscriptomic dysregulation in prostate cancer and the role that alterations to DNA and RNA modifications play in the initiation and progression of prostate cancer. Prostate cancer (PCa) is the second-most diagnosed cancer in men worldwide. In 2019 it accounted for nearly one in five new diagnoses. It is the first cancer in terms of prevalence and is also a leading cause of male cancer-associated deaths . HoweverERG. This fusion was found in 53% of tumours when the complete sequences of seven primary human prostate tumours and their paired normal counterparts were analysed [CDKN1B (p27) and CDKN2A (p16) genes [APC alterations were enriched in these types of cancer, while alterations in ATM and amplifications in AR were specifically enriched in CRPC. Furthermore, other genes such as PTEN were commonly found altered in all stages of PCa [Over recent decades, a number of other genetic alterations have been identified associated with PCa. The malignancy is generally characterised by frequent androgen-regulated promoters’ fusion with members of the E26 transformation-specific (ETS) family, such as analysed . DNA copanalysed , especiaanalysed . Loss of6) genes . Comparas of PCa . DespiteBRCA1, BRCA2 and ATM (DDR pathway), PTEN, PIK3CA, AKT1 (PI3K pathway), BRAF, HRAS and KRAS (MAPK pathway) [With the advent of high-throughput next-generation sequencing, recurrent point mutations in several genes, including Androgen Receptor (AR), SPOP, TP53, FOXA1 and PTEN, are found associated with PCa incidence . In recepathway) ,12, reveThis emerging era of high-throughput sequencing is revealing a complex scenario of the molecular drivers of prostate cancer, some of which are not associated with permanent DNA changes, but with epigenetic changes such as differential DNA methylation patterns . These fAR alterations, DNA copy number and single point mutations or mRNA expression [Until now, profiling studies of primary PCa have been focused on the most studied alterations of this tumour type, such as pression ,30,31. Hpression . In cancpression . Throughpression . Other spression . Using Tpression . More repression . All thiGSTP1 promoter, a fact which was already described 20 years ago. GSTP1 modulates several signalling pathways involved in proliferation, differentiation and apoptosis [PTEN, which causes its silencing [CDKN2A (which encodes p16) that leads to increased proliferation, thus contributing to carcinogenesis [AR expression is regulated in 30% of CRPC by hypermethylation of its promoter [Mechanistically many studies have shed light on the molecular effects underlying epigenetic dysregulation in PCa. One of the most frequent DNA methylation changes occurs at the poptosis . After tilencing , or the ogenesis . Even thpromoter . More inpromoter . writers, proteins that introduce chemical modifications to DNA and/or histones; readers that identify and interpret those modifications with its domains; and erasers, whose function is to remove the modifications added by the writers [Besides DNA methylation, other epigenetic marks regulate chromatin structure and gene expression. Among the plethora of epigenetic master regulators, we can find  writers . UncontrPTEN, CDKN2A, or AR [YAP1 has also been associated with neuroendocrine prostate cancer (NEPC) [RAD9 transcription, which induces tumorigenicity [DNA methylation is mainly carried out by a family of DNA methyltransferases (DNMTs). Five different enzymes exist in humans, DNMT1, DNMT3a, DNMT3b, DNMT3c, and DNMT3L. From those, DNMT3a, DNMT3b and DNMT3c directly carry out the de novo methylation, while DNMT3L performs it indirectly . DNMT1 pA, or AR ,45,46. Hr (NEPC) . Expressr (NEPC) . In addigenicity . DNMT3a genicity . In sum,genicity , and curHistone modifications are also common epigenetic marks. The most critical marks are catalysed by histone methyltransferases (HMT) and histone acetyltransferases (HATs). Lysine methyltransferases (KMT) are an important family involved in gene transcriptional regulation . SpecifiAlterations in some epigenetic writers such as upregulation of PRMT1 and Coactivator-Associated Arginine Methyltransferase 1 (CARM1) are found in early tumours, suggesting that histone modifications and chromatin remodelling may act as epigenetic drivers at the initial stages of the disease . In addiHistone acetyltransferases (HATs) increased activity can also promote PCa by either acetylating histones or transcription factors, thus inducing transcription . For exaAll this evidence indicates epigenetic writers as promising therapeutic targets, and several molecules targeting p300/CBP, PRMT5, EZH2 are being tested under clinical trials, alone or in combination with other therapies such as AR blockade or immunotherapy in advanced prostate tumours , which wThe bromodomain (BRD)-containing proteins, whose function is to induce transcription initiation through chromatin remodelling, are some of the most studied in prostate cancer cells among other epigenetic readers. More than 70% of NEPC and more than 50% of primary and metastatic PCas show some dysregulation in any of the bromodomain containing proteins . Within Apart from the BET family, several BRD-containing proteins have been associated with mCRPC. TRIM24 is a transcription co-regulator overexpressed in CRPC, and is essential for cancer cell proliferation . BAZ2A bThe critical erasers in PCa are DNA demethylases, histone demethylases (HDMT) and histone deacetylases (HDACs), which cause hypomethylation or deacetylation and consequently the upregulation of gene expression, leading to disease progression and higher cell invasion and metastasis . 5C to 5-hydorxymethylation (hm5C) [To date, the only known DNA demethylases are TET enzymes, comprised of three members, namely TET1-3. DNA demethylation is a three-step process, where TETs participate in the first one, oxidizing mn (hm5C) . Decreasn (hm5C) ,96. TET1n (hm5C) .Within HDMTs, lysine demethylases (KDM) play important roles in cancer. For example, lysine-specific demethylase 1 (LSD1/KDM1A) overexpression has been highly studied for its oncogenic potential. Its overexpression is associated with increased tumour progression and promotion of carcinogenesis in several cancer types, including PCa ,99. In PKDM6A deletion inhibits tumour progression [Many other histone demethylases have been associated to PCa, and similarly to LSD1, their gain or loss of activity seem to have a dual role . H3K4 degression . KDM6B  activity is found associated with PCa too. Contrary to HATs resulting in the increased acetylation of histones or AR, the activity of deacetylases such as SIRT1 or SIRT2 can regulate cellular growth and sensitivity to ADT therapy through AR deacetylation , and theIn conclusion, all this evidence highlights that epigenetic alterations are commonly associated with PCa and, most importantly, can be used to stratify patient risk, predict clinical outcomes, and to determine best therapeutic options or to reveal molecular pathway vulnerability to distinct chemotherapeutic agents ,124. In Similarly to DNA, RNA can also be modified. Despite being known for over 50 years, the study of RNA modifications has suffered a delay regarding epigenetics, probably due to the lack of suitable tools for their study . Thus, t6-Methyladenosine (m6A), with around 0.1-0.4% adenines of all mRNAs being modified [These modifications are found in all types of RNA, from messenger RNA (mRNA) to non-coding RNAs such as ribosomal RNA (rRNA), transfer RNA (tRNA), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) among many others . tRNAs amodified .5C) methylation of tRNAs stabilises their structure and protects them from nuclease-mediated cleavage [In contrast with DNA modifications, which are known to mainly regulate gene expression , RNA modcleavage . HoweverDespite the great diversity of modified nucleotides in RNA and the huge expansion of the field in the past years, little is known about the role of RNA modifications in PCa. In this review, we will summarize the most relevant RNA modifications found to date to have a regulatory role in prostate cancer.6 of adenosine is the most studied modification. This modification mainly occurs in mRNA, but also in non-coding RNAs such as tRNAs, rRNA, miRNA, lncRNAs and circular RNAs (circRNAs) [6A deposition in mRNA is, by far, the best characterised. Recent improvements in high-throughput methods have demonstrated that m6A is not randomly distributed, but is specifically enriched near stop codons in 3’-untranslated regions (UTRs), within long exons, in intergenic regions and introns and at 5’-UTRs [Methylation of position NircRNAs) . Despite 5’-UTRs ,139. 6A deposition is modulated by the dynamic crosstalk between methyltransferases or “writers” and demethylases or “erasers”. In nascent pre-mRNA, m6A is deposited by a multimeric methyltransferase complex. The catalytic core of this complex is constituted by methyltransferase-like 3 (METTL3) and methyltransferase-like 14 (METTL14), which catalyses the formation of m6A and recognises RNA substrates, respectively [6A methyltransferase-associated  [6A methyltransferase, METTL16, which acts as an independent RNA methyltransferase and mainly modifies mRNA and small nuclear RNAs (snRNAs) [6A methyltransferase acting as a heterodimer together with TRMT112 [mectively . This coIAA1429) ,140. Rec(snRNAs) . In addi6A demethylase [6A demethylases is still controversial. In 2017, Mauer et al. found that FTO is able to demethylate m6Am at the 5’ cap, rather than m6A, thereby decreasing mRNA stability. The disparity of this discovery initiates a discussion about m6A detection techniques and concerning the nature of its reversibility [6A can be specifically recognised by a group of RNA binding enzymes or “readers” that recruit downstream complexes mediating the function of this modification [Ten years ago, the discovery of two demethylases, fat mass and obesity-associated protein (FTO) and AlkB homolog 5 (ALKBH5), brought to light the reversible nature of this modification  and receethylase . Howeversibility . Moreovefication . Severalfication   represses the proliferation, tumorigenic invasion, and migration capacity of PCa cells in a catalytic-dependent manner in vitro [6A methylation leads to the alteration of several downstream pathways. For instance, Cai et al. showed that METTL3 alters proliferation by regulating apoptosis through the Sonic Hedgehog-GLI pathway in a catalytic-dependent manner [6A-dependent manner, thus altering PCa cell adhesion and motility [CYCLIN-L1 and CDK19 gene promoters through RNA–DNA interactions in a m6A-dependent manner, leading to increased proliferation and migration both in vitro and in xenografts [USP4 mRNA levels, leading to decreased migration and invasiveness in PCa [6A reader IGFBP2 [6A regulator expression and immune infiltration. In Zhao et al., low METTL3 and HNRNPA2B1 expression correlates with increased immune cell infiltration [Recent computational studies have found a complex scenario in the expression of several writers, readers and erasers in PCa ,148,149,position B. For exhylation ,152,153.in vitro ,152,153,in vitro . The dowt manner . MYC prot manner ,152 and motility . Similarmotility . NEAT-1 nografts . Moreover IGFBP2 . These rr IGFBP2 , suggestltration . In Liu ltration , highligYTHDF2 mRNA [6A writers, downregulation of FTO leads to increased cell invasion and migration capacity by increasing m6A levels [Other members of the methyltransferase complex have been found dysregulated in PCa. VIRMA has been found upregulated in PCa , and itsDF2 mRNA . By contDF2 mRNA . SimilarA levels . 6A deposition has an oncogenic effect in prostate cancer cells and targeting METTL3 could have clinical benefits for PCa patients. With the recent emergence of METTL3 as a promising oncogenic target in several haematological malignancies and solid tumours, a great effort is being made to develop small molecule inhibitors to target m6A deposition. Two recent inhibitors have been developed that target and inhibit METTL3, STM2457 and UZH2. These two inhibitors have shown to decrease m6A methylation activity in vitro and in vivo. In addition, they have shown potent inhibitory potential in several cancer cell lines including PCa, and a promising anti-cancer effect in cell lines and pre-clinical models of acute myeloid leukaemia (AML) [In sum, all this evidence suggests that increased mia (AML) ,163. The5C deposition in RNA is conserved in all life domains [5C methyltransferase that has been demonstrated to be associated with PCa. Increased expression of NOP2 has been considered a marker of bad prognosis for years, correlating with Gleason score, PSA serum levels and recurrence after radical prostatectomy [5C RNA methyltransferases are associated with PCa is also still uncovered.m domains  and has  domains ,165. In  domains ,165. To atectomy ,167. Mecatectomy . This meatectomy . In addiatectomy . NSUN4 tatectomy , yet its5C and f5C have been found in mRNA and tRNAs in vivo [5C into f5C in tRNAs in vivo [5C to hm5C in RNA [Similarly to DNA, hm in vivo . ALKBH1  in vivo . In vitrC in RNA . DespiteC in RNA ,174, yet5C as well as other RNA modifications have been linked to the survival, proliferation and differentiation of tumour cells in several cancers [5C as well as other RNA modifications have been associated with drug resistance regulation [NSUN2 and METTL1, which catalyses tRNA 7-methylguanosine, has shown to sensitize tumour cells to the chemotherapeutic agent 5’-fluorouracil (5-FU) [From a therapeutic point of view, m cancers ,176,177.gulation ,177,178.Pseudouridine (Ψ), the C5-glycoside isomer of uridine, is among the most well-known RNA modifications. It comprises about 5% of the total of all RNA nucleotides and is found in almost all types of non-coding and coding RNAs . Ψ biosySHQ1 was the only gene in the 3p region that harboured tumour associated mutations [DKC1 in metastatic tumours compared to primary tumours [Aberrant deposition of Ψ, dysregulated expression of pseudouridylases and somatic mutations have been linked to PCa. For instance, in an integrative genomic profiling study of prostate cancer, somatic mutations in the chromosomal 3p were identified as tumour suppressors; most importantly, utations ,30, and utations . DKC1 isutations . While, utations . Howeverutations , suggest tumours ,188,189. tumours . Other functional studies have highlighted an oncogenic role for increased Ψ deposition. For example, the stand-alone pseudouridine synthase PUS10 has shown to play a key role in TRAIL-induced apoptosis, participating in the release of cytochrome C and SMAC from the mitochondria in PCa cell lines . MoreoveRNA editing is also a very prevalent post-transcriptional event in human transcriptomes. The most common type of RNA editing results from the conversion of adenosine (A) to inosine (I) in double-stranded RNA, a process catalysed by the adenosine deaminase acting on the RNA (ADAR) family of enzymes. A-to-I editing is highly prevalent within Alu elements, as well as introns, untranslated regions (UTRs) and coding transcripts . In a coTaken together, these studies disclose an important regulatory mechanism in cancer for RNA editing; however, a systematic effort to define the putative roles of edited RNAs in PCa tumorigenesis and progression remains to be established.One of the most abundant ribonucleotides in rRNA is 2′-O-methylation , with up to 112 different positions in human ribosomes. Similarly to Ψ, its deposition is carried out by RNA-guided ribonucleoprotein (RNP) complexes formed by the core proteins NOP56, NOP58, SNU13 and fibrillarin (FBL) and the guiding box C/D snoRNAs . AlteredDespite the initial response to hormone-deprivation treatment, one of the main problems in PCa management is the relapse and progression rate to metastatic tumour, which has limited therapeutic options, none of them completely curative ,202. ThiRecent evidence has highlighted that epigenetic alterations are emerging as potential biomarkers to stratify PCa patients and predict clinical outcomes . EpigeneAs in DNA, RNA modifications are also known to regulate responses to environmental signals ,177 suggSimilarly to epigenetics, RNA modifications are emerging as promising therapeutic targets, and great efforts are now being made to develop small molecule inhibitors to rewire the aberrant cancer epitranscriptomes. However, targeting RNA modifications could be fairly complicated since they are linked to most aspects of RNA biology, and their alteration could involve undesirable toxic effects. Moreover, the role of RNA modifications is context-dependent and could differ between cancers or even between different cell populations ,205. Thu


Mammalian chromatin is the site of both RNA polymerase II (Pol II) transcription and coupled RNA processing. However, molecular details of such co-transcriptional mechanisms remain obscure, partly because of technical limitations in purifying authentic nascent transcripts. We present a new approach to characterize nascent RNA, called polymerase intact nascent transcript (POINT) technology. This three-pronged methodology maps nascent RNA 5′ ends (POINT-5), establishes the kinetics of co-transcriptional splicing patterns (POINT-nano), and profiles whole transcription units (POINT-seq). In particular, we show by depletion of the nuclear exonuclease Xrn2 that this activity acts selectively on cleaved 5′ P-RNA at polyadenylation sites. Furthermore, POINT-nano reveals that co-transcriptional splicing either occurs immediately after splice site transcription or is delayed until Pol II transcribes downstream sequences. Finally, we connect RNA cleavage and splicing with either premature or full-length transcript termination. We anticipate that POINT technology will afford full dissection of the complexity of co-transcriptional RNA processing.  •POINT methodology dissects intact nascent RNA processing•Specificity of Xrn2 exonuclease in co-transcriptional RNA degradation•Splicing suppresses Xrn2-dependent premature termination•Different kinetic classes of co-transcriptional splicing in human genes Sousa-Luís et al. describe tripartite methodology to purify and sequence RNA polymerase II-associated intact nascent transcripts (POINT) from mammalian genomes. POINT-5 distinguishes nascent RNA 5′ end caps from 5′ end cleavage, POINT-seq profiles full-length transcription units, and POINT-nano determines the kinetics of co-transcriptional splicing at a single-molecule level. Transcripts synthesized by RNA polymerase II (Pol II) are extensively co-transcriptionally processed. First a cap structure (7meGppp) is added to the transcript 5′ end soon after its exit from the Pol II complex. This defines all Pol II transcription and is ultimately required for efficient mRNA export and protein translation. As Pol II transcribes into the gene body, introns are removed by splicing through assembly of the spliceosome complex, beginning with recognition of the intron 5′ splice site (SS) by U1 small nuclear RNA (snRNA)-protein complex (U1 snRNP). Once Pol II reaches the intron 3′ end, U2 snRNP identifies the intronic branchpoint and 3′ SS, followed by assembly of a further complex set of U snRNPs  and other associated protein factors. The spliceosome so formed reorganizes the intron into a ribozyme-like structure, leading to intron excision and ligation of upstream to downstream exons. This mechanism implies an intron definition model, whereby appearance of intronic splice signals leads to stepwise assembly of the spliceosome. However, in higher eukaryotes, in which exons are generally much shorter than adjacent introns, splicing factors such as SR proteins initially bind to and define functional exons, known as the exon definition model . This acThese RNA processing and cleavage events are tightly regulated during synthesis of all Pol II transcription units (TUs) from the TSS (transcription start site) to the TES. Several nascent RNA analyses have indicated how these RNA processing events are coupled to transcription . For exain vitro fragmented RNA purified from immunoprecipitated Pol II elongation complexes to profile the nascent RNA across the whole TU (POINT-seq). We also use unfragmented RNA in a 5′ RACE-template switching protocol that maps nascent RNA 5′ ends at single-nucleotide resolution (POINT-5). Notably, POINT-5 precisely maps and distinguishes TSS and RNA cleavage sites on pre-mRNA, pre-miRNA, histone, and U snRNA genes. Xrn2-dependent RNA degradation at pre-mRNA TES is also detected by this technology. We further use the ONT direct cDNA sequencing platform to characterize nascent RNA isolated by POINT technology (POINT-nano), revealing the kinetics of splicing and CPA-mediated RNA cleavage.We describe new technology to dissect the complex Pol II transcription cycle by analyzing intact nascent RNA directly purified from elongating Pol II. This POINT (polymerase intact nascent transcript) methodology involves both Illumina and Oxford Nanopore Technologies (ONT) sequencing platforms. For Illumina we use The analysis of authentic nascent RNA is critical to fully comprehend the mechanisms of Pol II transcription and associated transcript processing. POINT technology allows the isolation of intact nascent transcript from the 5′ cap site through to its 3′ end within the Pol II active site A. This iTARS (We compared POINT-seq data with chromatin-bound RNA (ChrRNA-seq) data from HeLa cells . ChrRNA-TARS F. ImportTARS G.KIAA0513 -derived MIR193A and MIR365B  and cleaves at adjacent RNA sequence . Althougon assay , POINT-5on assay D and S2DGAPDH, significant signal was detected only at the TSS (S peak), not at the TES (C peak), although endonuclease cleavage at PAS by CPSF73 is well established -RNA. In particular, Xrn2 exonuclease activity is specific for 5′P-RNA substrates. We therefore used the 5′ P-RNA-specific nuclease (ExoTerminator [ExT]) to ′ P-RNAs F. Notabl signals G. This cf JARID2 G.ATP5MPL PAS  is cleaved by CPA complex at the PAS and then Xrn2-degraded toward elongating Pol II, resulting in transcription termination J. In conCryptic, inactive PAS are often embedded within mammalian introns, where their use may be restricted by limiting levels of CPA factors or more efficient Pol II elongation . FurtherTBC1D17, showing only splicing inhibition and for much longer MTHFD1L with both splicing and PTT effects induced by PlaB treatment (MTHFD1L (DHX9 shows a clear U1 AMO effect but does not display PTT effects following PlaB treatment (We determined the effect of pladienolide B (PlaB) treatment of HeLa cells on our POINT-seq profiles, as this inhibitor targets the SF3B complex, a component of U2 snRNP  and suppreatment E. Comparreatment  with ourreatment A as for (MTHFD1L B. In conreatment B. The faGPATCH2 (GPATCH2 (GPATCH2). This implies that CPSF73-independent RNA cleavage and Xrn2-independent RNA degradation may also be involved in premature termination.To establish that PlaB-induced PTT is regulated by cryptic PAS activation, we depleted either Xrn2 or CPSF73 proteins for 1 h, followed by PlaB treatment for 3 h in the AID-engineered HCT116 cells. POINT-seq analysis reveals that PTT transcripts are indeed extended as shown in metagene profiles F and S4CGPATCH2 G and S4D transcription with modified ribonucleotides to label and isolate newly synthesized RNA  cells for PC gene introns . InsteadAnother notable feature of our splicing analysis by POINT-nano is that splicing increases to higher levels with longer TUs. Thus, either greater intron or gene size correlates with increased levels of splicing. These correlations imply a simple kinetic model whereby more Pol II elongation time allows more productive spliceosome assembly or splicing completion on upstream introns. However, we also note that a fraction of transcripts appears refractory to both splicing and PAS-mediated 3′ end processing. A similar correlation between inefficient splicing and lack of 3′ end processing has also been made for highly expressed globin genes in MEL cells . We predACTB profile in This uses a direct cDNA sequencing approach by ONT with no prior PCR amplification. Consequently, we used higher RNA input for POINT-nano (>500 ng) than POINT-seq (50 ng), as only the later Illumina-based approach uses amplification. POINT-nano analysis of single cells or small cell number samples is currently not possible. Even with larger cell numbers (as from cell culture), gene read coverage of the human genome was limited in our POINT-nano analysis. Consequently, it was necessary to focus on more highly expressed genes to study complete TUs. Additionally, we currently have a read length limitation to our POINT-nano data. Although input RNA was on average 6,000 nt or greater A, mean rAnother feature of this technique is that 5′ ends of TUs will be under-represented because of removal of the terminal ∼150 nt during Illumina total RNA sequencing library preparation. This will lead to reduced TSS-associated sequence and will therefore underscore the high levels of TSS-associated PTT/pausing observed for most PC genes, which can be detected using the mNET-seq method .Despite these limitations, it is clear that our POINT technology illuminates a deeper understanding of PC gene nascent transcription and coupled RNA processing.taka.nojima@path.ox.ac.uk or taka.nojima@bioreg.kyushu-u.ac.jp).Further information and requests for resources and reagents should be directed to Takayuki Nojima . All code supporting POINT analyses are available on request. The reanalysed published data used in this study can be found at GEO as indicated in the Key Resources Table.The raw and processed data derived from POINT-seq, POINT-5 and POINT-nano analyses as generated in this study are deposited in NCBI GEO (GSE159326). The associated raw image data are available from Mendeley (2.HeLa and HCT116 cells were maintained in high glucose Dulbecco’s Modified Eagle’s Medium (DMEM) with 10% fetal bovine serum (FBS) and penicillin/streptomycin (PS) at 37°C with 5% COIAA  was directly added to Xrn2-AID HCT116 cells in DMEM/10%FBS/PS and incubated for 30 min-4 h as previously published . For CPS7 for POINT-seq and POINT-5, 4x107 for POINT-nano) as in the mNET protocol. Then the chromatin pellet was resuspended in NUN1 (20 mM Tris-HCl (pH 7.9), 75 mM NaCl, 0.5 mM EDTA and 50% Glycerol) and treated with modified NUN2 buffer (20 mM HEPES-KOH (pH 7.6), 300 mM NaCl, 0.2 mM EDTA, 7.5 mM MgCl2, 1% NP-40, 1 M Urea, 3% Empigen, 1x protease inhibitor Complete (-EDTA) and 1x PhosSTOP). Note the Empigen-treated chromatin fraction was gently mixed by tube inversion a few times to avoid chromatin aggregation and incubated on ice for 10 min. The chromatin pellet was then isolated by centrifuged at 400 g for 30 s. This was washed with PBS once and then digested in DNase in the following reaction (10 mM Tris-HCl (pH 7.5), 400 mM NaCl, 100 mM MnCl2, 2 U/μL RiboLock and 0.2 U/μL Turbo DNase) at 37°C for 15 min. After DNA digestion, soluble digested chromatin was collected by 13,000 rpm centrifugation for 10 min. The supernatant was diluted ten-fold in ice-cold NET-2E buffer (50 mM Tris-HCl (pH 7.4), 150 mM NaCl, 0.05 % NP-40 and 3% Empigen BB) and anti-Pol II antibody-conjugated beads were added. 10 or 40 μg of anti-Pol II antibody (200 or 800 μL of Dynabeads anti-mouse IgG) was used for POINT-seq and POINT-5 or POINT-nano methods. Immunoprecipitation was performed at 4°C for 1 h. The beads were washed with 1 mL of ice-cold NET-2E buffer six times. The isolated nascent RNA was then purified using Trizol reagent technology (Direct-zol) twice with one Turbo DNase treatment at 37°C for 10 min. Size of the RNA was analyzed using 4150 TapeStation (Agilent) with high sensitivity RNA kit according to the protocol. For sequencing the isolated RNA, we employed following methods.The POINT method initially followed the previously described mNET-seq protocol  with somThe isolated RNA was fragmented to 150-200 nt at 90°C for 10 min according to a protocol of NEBNext Ultra II Directional RNA library prep kit to prepare PCR library. Therefore small RNAs (up to 150 nt) were removed during the library prep. The library was applied to Illumina NovaSeq6000 (Novogene UK).The template switching approach with random N6 primers was applied to the isolated RNA by following the protocol of the SMARTer Stranded RNA-seq kit. The amplified PCR library was size-selected with SPRISelect beads to 150-800 bp and then applied to an Illumina NovaSeq6000 (Novogene UK). Note that no RNA fragmentation is required in this library prep.in vitro polyadenylation with E.coli PAP. pA+RNA was then size-selected using SPRISelect reagent (x0.6 volume) to remove RNA smaller than 500 nt. The direct cDNA library prep kit was employed.PolyA tails were added to the isolated RNA by Illumina and ONT sequencing (PromethION) were conducted by the high throughput genomics team of the Wellcome Trust Centre for Human Genetics (WTCHG), Oxford and Novogene UK (Europe Cambridge Branch).500 ng of 5′ monophosphate RNA isolated by POINT method was specifically digested with Exoterminator (Cambio) at 30°C for 1 h and then purified with Trizol reagent technology (Direct-zol).BedTools coverage (-s). Splicing patterns were considered for POINT-seq (-split). Furthermore, -counts and -sorted parameters were added to the command. Spearman’s rank-order test was then applied to discover the correlation between samples (ρ). ChrRNA-seq and pA+ RNA-seq were generated as part of GSE60358 and GSE86661; GSE33480, respectively. Their pre-processing was as for POINT-seq. mNET-seq reads were trimmed and aligned as described above for POINT-seq and POINT-5 data. Aligned reads were then transformed into single nucleotide reads by application of script previously created for this purpose  and/or low-quality ends (20 Phred score cut-off). The resultant reads were aligned against the reference human genome (GRCh38) using STAR software , requiricoverage , requiri purpose . Strand-regEx Python package, applying the following rules: i ≤ 3, d ≤ 3, s ≤ 3 and 1i+1d+1 s ≤ 4. Only these reads were preserved with all others discarded, since the Pol II position could not be determined. Barcode and primer sequences in validated reads were trimmed with Porechop, with–discard_middle mode on. Subsequently those were aligned with minimap2 . NanoQC  was usedminimap2  with -axKallisto  was employed. Adaptors were trimmed with TrimGalore using the same parameters as in POINT-seq pre-processing. Then, Kallisto  mapped tNAdb 2.0 , and rRNbamCoverage function. A black list of regions for hg38 assembly has been described (https://www.encodeproject.org/files/ENCFF419RSJ/), and all the reads from these were discarded. Also, RPKM normalization was applied. Then, all expressed genes were scaled to have their TSS and TES overlapping, in a bin size of 10 nt, using the function deepTools computeMatrix . All reads overlapping the gene under PlaB condition were also randomly sampled, and the FPKM for each bin was measured (FPKMsimulation). This simulation was repeated 5000 times for each gene. To obtain more robust results and eliminate potential false negative hits, FPKMsimulation was divided by a 3.5, value discovered by manual curation, giving rise to FPKMT_simulation. For each simulation, FPKMT_simulation was compared with FPKMreal for each bin and counted for how many times FPKMT_simulation was lower than FPKMreal. Starting from the first bin for each gene, whenever FPKMT_simulation was found lower than FPKMreal in at least 90% of the simulations for one bin, the search ended. This led to the conclusion that PTT occurs in that bin region. PTT genes were classified into Early (E), Middle (M), Late (L), according to where the bins were identified. Thus, Early (E) PTT occurred in the first 3 bins, and Middle (M) and Late (L) PTT, occurred between 4-6 bins and 7-9 bins, respectively. If PTT was ascribed to the last bin or was not found for any of the bins then it was considered non-PTT and labeled NC.Premature transcription terminated (PTT) genes were identified by comparison between control DMSO and PlaB POINT-seq data, using a simulation basis approach. Each gene was divided into 10 bins. FPKM of each bin for DMSO was measured . A full overlap (-F 1) and a shared strand between read and exon (-s) was required here. Then, two different windows of 10 nt were created in addition to downstream 3' SS, upstream 3' SS and 5' SS, to analyze splicing patterns. Reads were considered unspliced when detected in the upstream 3′SS window. All reads which were not detected in this window, but only in upstream 5' SS, were considered spliced. Thus, the splicing fraction denotes the number of reads found as spliced divided by spliced and unspliced reads for a given distance or location of Pol II. Importantly, reads spanning several genes were discarded. Only introns with their 3' SS 1500 bp upstream to Pol II were considered, except for POINT-nano and POINT-seq data were used to dissect splicing kinetics. Only internal introns were considered for these analyses. Thus, genes with less than 3 introns were discarded, as well as first and last introns from genes with higher intronic complexity. Intron and exon sizes were extracted from Ensembl annotations GTF files for expressed genes previously identified. The 25% shortest and longest features only were taken to perform splicing comparative analyses. Two-sided Mann-Whitney test was deduced to obtain their significance, followed by a p values adjustment using Holm method. Furthermore, SS strength score was measured with xEntScan  using devast-tools. Exon skipping events were isolated, and exons were considered included or excluded when the Ψ value was higher than 0.75 or lower than 0.25, respectively.Constitutive and alternative splicing event classifications were obtained from Ensembl annotations, as previously described . For casBedTools. BAM files were split by strand with SAMtools according their bitwise flags. In POINT-seq data, forward oriented strand had 83 and 163 flags associated while reverse oriented strand had 99 and 147 flags associated. Oppositely, 99 and 147 flags for POINT-5 correspond to the forward strand, while 83 and 163 to the reverse strand. For POINT-nano, 0 and 16 flags were used to call forward and reverse strand reads, respectively. Data was visualized applying genomeCoverageBed function of BedTools to each strand independently. Trackhubs in the UCSC browser were created by employing the UCSC bedGraphToBigWig tool .Reads were counted for regions of interest with Significance between control and treatment condition was obtained using a two-sided Mann-Whitney test, followed by a p values adjustment using the Holm method. For multiple samples one-way ANOVA comparison was tested, followed by a post hoc analysis using Turkey’s test.


A Watson-Crick G:C base pair in the fluorophore binding site establishes a short hydrogen bond between the N7 of guanine and the phenolic OH of the ligand. Ultrafast excited state proton transfer (ESPT) from the neutral chromophore to the RNA was found with a time constant of 130 fs and revealed the mode of action of the large Stokes shift fluorogenic RNA aptamer.Fluorogenic RNA aptamers are synthetic functional RNAs that specifically bind and activate conditional fluorophores. The Chili RNA aptamer mimics large Stokes shift fluorescent proteins and exhibits high affinity for 3,5-dimethoxy-4-hydroxybenzylidene imidazolone (DMHBI) derivatives to elicit green or red fluorescence emission. Here, we elucidate the structural and mechanistic basis of fluorescence activation by crystallography and time-resolved optical spectroscopy. Two co-crystal structures of the Chili RNA with positively charged DMHBO + and DMHBI+, and they reveal that Chili uses an excited state proton transfer mechanism based on time-resolved optical spectroscopy measurements.Fluorogenic RNA aptamers such as Chili display strong fluorescence enhancement upon aptamer–ligand complex formation. Here, the authors provide insights into the mechanism of fluorescence activation of Chili by solving the crystal structures of Chili with its bound positively charged ligands DMHBO Prominent examples of fluorogenic aptamers include the malachite green aptamer4, Spinach5, Broccoli6, Corn7, Chili8, Mango9, SiRA10, Coral11, and Pepper12 aptamers, which use diverse classes of chromophores that are non-emissive in their free state but display strong fluorescence enhancement upon formation of the aptamer–ligand complex. The class of Spinach, Broccoli, Corn, and Chili are RNA mimics of green fluorescent proteins (GFP) that bind and activate derivatives of 4-hydroxybenzylidene imidazolone (HBI), a small-molecule analog of the tripeptide chromophore in GFP13. Spinach and Broccoli bind the difluorinated ligand DFHBI and its derivatives15, and emulate the deprotonated chromophore and small Stokes shift of the enhanced GFP (eGFP)16. The dimeric Corn aptamer activates DFHO7, a spectrally shifted oxime derivative of DFHBI, which resembles the chromophore of the red fluorescent protein DsRed17. Chili, on the other hand, is a large Stokes shift fluorogenic RNA aptamer, which was engineered from the dimethoxy-HBI (DMHBI)-binding 13-2 RNA aptamer5 by truncation and sequence optimization. The resulting 52 nt Chili RNA preferentially binds and activates the positively charged chromophores DMHBI+ and DMHBO+  that is supported by time-resolved spectroscopy. These insights enabled structure-guided miniaturization of the Chili aptamer and will aid future engineering of fluorogenic modules for sensors and imaging applications.Here we report the co-crystal structures of Chili bound to DMHBO+ and DMHBI+ . The basal stem P1 contains 8 bp (nt 1–8 and 45–52), and the apical 5 bp stem P2 (nt 17–21 and 26–30) is closed by the UUCG tetraloop L2 (nt 22–25). The FBD spans nt 9–16 and 31–44 and accommodates the ligand binding site Fig. . P1 and  P2 Fig. . The G17+ ion , the phenolic hydroxy and one methoxy group of the ligand (2.8 Å) as well as O6 of G15 (3.1 Å). The inter-K+ distance is 3.5 Å.The architecture of the two-tiered G-quadruplex core in the Chili aptamer is characterized by three consecutive guanine steps  with mixed parallel and antiparallel strand orientation, and one nonconsecutive (G9/G32) edge Figs. . All of MA, Fig.  with aveMB, Fig.  is coord+ ligand adopts a nonplanar conformation within the binding site with twist and tilt angles of φ = −27° and τ = 11°, respectively  interaction that is mediated by mutual hydrogen bonds between the NH2 of either guanine and N3/2′-O of the other guanine nucleotide. G31 is further held in place by a hydrogen bond between its own O6 and N4 of C16, which itself is part of a long-range Watson–Crick base pair with G39  and DMHBI+ (KD = 65 nM), and are likely responsible for the higher thermal stability of the fluorescent Chili–DMHBO+ complex compared to the Chili–DMHBI+ complex .At pH 7.5 and in the absence of the Chili RNA, ~80% of DMHBOple Fig. . This pr+ was replaced by Tl+ or by Na+  at position 15. Removal of the proposed proton acceptor abrogated the ability to stabilize the protonated DMHBO+ ligand and resulted in complete loss of fluorescence activation  spectroscopy with fs-time resolution. Upon excitation at 405 nm with 60 fs pulses a fluorescence band at 510 nm with τ < 130 fs was found which is associated with the fluorescence of the protonated species  on the ns timescale revealed multiexponential decays for both Chili–DMHBOies Fig. . The peaies Fig. . Thus, ties Fig. . Fluores+ , and resulted in slower maturation of the fluorescent complex compared to the parent Chili. Interestingly, this Wobble base pair directs the folding kinetics independent of the stem length . The dissociation constants for µChili or P1A-3 and DMHBI+ . Furthermore, the partially unstructured nucleotides U33-G35 are not essential for the function of the aptamer. Breaking the phosphodiester bond between U34 and G35 while connecting the original 5′ and 3′ ends of P1A-3 by a GGAA tetraloop resulted in a circularly permuted Chili aptamer  retained more than 80% of the parent Chili fluorescence intensity with DMHBI+ Fig. . Additiomer Fig.  that ret1, with only few exceptions33. The G-quartets stabilize rigid chromophore conformations by stacking interactions that largely govern the photophysical properties36. Unlike the near-planar fluorinated ligands DFHBI and DFHO in Spinach and Corn21, the fluorophores DMHBO+ and DMHBI+ in Chili adopt moderately twisted conformations, resembling those found in some LSS fluorescent proteins such as LSSmKate119 and mCRISPred37  also contributes to the orientation and stabilization of the ligand by including a methoxy group in the coordination sphere. Interestingly, K+ can be replaced by Tl+ with no change in Stokes shift, but is not easily exchanged by divalent metal ions . For comparison, Spinach places a K+ ion next to the phenolate of DFHBI, which can be replaced by Ba2+20.The structure and fluorescence activation mechanism of the Chili aptamer is distinct from other known HBI-binding RNA aptamers like Spinach and Corn. A common feature of most structurally characterized fluorogenic aptamers is a G-quadruplex in the core of the fluorophore binding site19. Our results suggest that the N7 of G15 in the Chili RNA serves as the proton acceptor. The involvement of this nitrogen atom in stabilizing the protonated ligand in the ground state was unequivocally confirmed by atomic mutagenesis. This adds an additional role to the versatile functions of guanine nucleobases found in riboswitches and ribozymes39.In Chili, the key ligand-RNA hydrogen bond to the Hoogsteen edge of G15 directly controls the protonation state of the ligand in response to photoexcitation. The Chili RNA aptamer mimics the proton transfer mechanism of LSS fluorescent proteins, in which ultrafast ESPT takes place from the chromophore to an acceptor amino acid over a short distance41, but is comparable to what has been found for GFP mutants with short H-bonds and rewired proton transfer pathways44.This work reports the study of ESPT in an RNA environment and allowed probing the RNA-ligand proton transfer dynamics in real time. The proton transfer occurred within 130 fs and is thus much faster than in wt GFP46. Similarly, fluctuations in RNA structures may influence fluorogenic aptamers. The folding kinetics of the Chili aptamer is strongly dependent on the U8:G45 Wobble base pair, as replacement by a Watson–Crick base pair slowed down the maturation of the fluorescent complex. An influence of quadruplex-flanking base pairs on folding and metal ion dependence has also been reported for Spinach and Broccoli aptamers49.Engineered fluorescent proteins harness the interplay of chromophore dynamics and protein environment to fine-tune desirable photophysical properties+ and the orange-red-emitting DMHBO+ ligands unveiled the mechanism for ligand binding and fluorescence activation. The H-bond interaction between the hydroxyphenyl group and the N7 guanine nitrogen together with the coordination of a potassium ion establishes the Hoogsteen side of a G:C base pair on top of a guanine quartet as a structural motif that facilitates ESPT on the 100 fs timescale. These results further strengthen the view of G-quadruplexes flanked by canonical A-form duplexes as versatile and privileged architectures for fluorescence activation of HBI chromophores.In summary, the structures of the Chili aptamer in complex with the green-emitting DMHBI50. The reaction was performed in 8 mL final volume in the presence of 40 mM Tris-HCl, pH 8.0, 30 mM MgCl2, 10 mM DTT, 4 mM of each NTP, and 2 mM spermidine. The reaction mixture was incubated at 37 °C for 4 h, and then stopped by the addition of 100 mM EDTA pH 8.0. The RNA was purified by 10% denaturing PAGE, extracted into TEN buffer, and recovered by precipitation with ethanol.Unmodified RNAs were prepared by in vitro transcription with T7 RNA polymerase. The DNA templates for in vitro transcription were purchased from Sigma Aldrich or Microsynth. The reaction was performed using synthetic DNA template (1 μM), promoter strand (1 μM), and in-house T7 RNA polymerase7G and r4CI were prepared by enzymatic ligation of synthetic oligonucleotides obtained by solid phase synthesis using 2′-O-TOM-protected phosphoramidites under standard conditions51. The c7G phosphoramidite was purchased form ChemGenes, the r4CI phosphoramidite, chemical 5′ phosphorylation reagent, and 3′-phosphate CPG support were prepared as described28. Sequence information for RNAs are given in Supplementary Table RNAs containing c2 was added to a final concentration of 5 mM and the solution was stored at 4 °C for 16 h prior to setting up crystallization drops. The Chili–DMHBI+ complex was concentrated using Vivaspin 3000 MWCO ultrafiltration spin columns to ∼0.5 mM and subjected to crystallization screening at 20 °C. Initial crystals appeared after 1 day. For the crystallization optimization, concentrations of the Chili–DMHBI+  and Chili–DMHBO+  complexes were reduced to ∼0.1 mM. Larger single crystals were grown at 20 °C using the hanging and/or sitting drop vapor-diffusion method by mixing Chili–DMHBI+ and Chili–DMHBO+ complexes with solutions containing 12–17% PEG 400, 0.1 M MES pH 5.4–5.8, and 12 mM spermine tetrahydrochloride in 1:1 ratio. Drops with volumes of 0.5–2 µL produced crystals after 1–2 days and were harvested after 7 days. Prior to data collection, crystals were cryoprotected in the mother liquor containing 30% glycerol and flash frozen in liquid N2.Crystallization complexes were formed by mixing RNA and ligand in a 1:1.2 molar ratio in 10 mM HEPES pH 8.0, 50 mM KCl, and 1.5% DMSO. The sample was heated to 95 °C for 3 min and cooled at 23 °C for 30 min. MgCl+ complex with solutions containing 12–17% PEG 400, 0.1 M MES pH 5.4–5.8, 12 mM spermine tetrahydrochloride, and 1 mM Iridium(III) hexammine (prepared according to ref. 26) in 1:1 ratio. Prior to data collection, Chili–DMHBO+ Iridium(III) hexammine co-crystals  were back-soaked in the mother liquor without Iridium(III) hexammine containing 30% glycerol and flash frozen. Soaking of the crystal was done by transferring native crystal to the mother liquor solution containing 20% glycerol and 0.1 mM Iridium(III) hexammine . After 10 min, the crystal was back-soaked in mother liquor solution containing 30% glycerol and flash frozen in liquid nitrogen.Incorporation of heavy metal ions into the crystals was achieved by soaking and by co-crystallization. The co-crystallization with heavy metal was performed at 20 °C using the sitting drop vapor-diffusion method by mixing Chili–DMHBO52, and reduced with POINTLESS, AIMLESS, and CTRUNCATE within the CCP4 package53. Initial phases were determined by single anomalous dispersion (SAD) using AutoSol Wizard in Phenix54, with the Crystal III data collected at the Iridium L-III peak wavelength. Electron density map from AutoSol was used for automated model building with AutoBuild module in Phenix55. The initial model from AutoBuild was modified by iterative rebuilding in COOT56 and refinement with Phenix.refine57. The partially built model from the Crystal III data were used as a MR search model in Phaser against a high resolution native dataset , previously corrected for anisotropy using the STARANISO server58. . The model was completed by manual rebuilding in COOT and refined to the final Rfree value of 23.7%. The location of the DMHBO+ ligand was confirmed using both unbiased mFo-DFc and polder omit maps59. Ligand restraints were prepared using eLBOW in Phenix. The final structures of Crystal I and Crystal IV were solved by MR using a single 51-nt long RNA model built with Crystal II data. Water molecules were added and inspected manually. Data collection and refinement statistics are summarized in Supplementary Table Diffraction data were collected at 100 K on EIGER16 M and PILATUS 6M detectors at the X10SA (Swiss Light Source) or P11 (DESY) beamlines. Data were indexed, integrated, and scaled with XDS15N labeled nucleotides were purchased from Silantes. RNA folding was achieved by thermal denaturation of the RNA at a concentration of ~0.2 mM followed by tenfold dilution with ice cold water and incubation for 1 h on ice. RNA samples were exchanged with NMR buffer  using centrifuge concentrator devices (Vivaspin 3000 MWCO). RNA concentration in the final NMR samples was ~0.5 mM. The homogeneity of the folding was checked via 15% polyacrylamide native gels  at room temperature and at a power ≤1 W to avoid overheating. All the samples contained 3-(trimethylsilyl)-1-propanesulfonic acid (DSS) as internal NMR reference and were dissolved in 10% D2O/90% H2O. KCl  and 1 equivalent of DMHBI+  were added directly into the NMR tube to the folded RNA.RNA samples used in NMR experiments were prepared by in vitro transcription as described above. 13C/1H cryoprobe or on a Bruker Avance III 600 NMR spectrometer equipped with a BBFO room temperature probe. The HNN-COSY experiment was performed by Dr. Helena Kovacs at Bruker Biospin  on a Bruker 700 MHz spectrometer equipped with a QCI-P cryoprobe. The spectra were acquired and processed with the software Topspin 3.2 . Spectra analysis was performed with the software Sparky 3.114 . The 1H,15N-BEST-TROSY experiments were recorded using a pulse program containing the modifications proposed by Brutscher et al61. and an inter-scan delay of 0.3 s. The HNN-COSY experiment62 was recorded using a soft WaterGATE water suppression scheme63, an HN transfer delay of 2.5 ms, and a NN transfer delay of 15 ms.All the NMR experiments were performed on a Bruker Avance III 600 NMR spectrometer equipped with a DCH Steady-state fluorescence spectra were measured in Hellma ultra-micro quartz cuvettes  with a JASCO FP-8300 spectrofluorometer equipped with an FCT-817S cell changer. Steady-state UV/VIS spectra and melting curves were measured in semi-micro quartz cuvettes (10 mm path length) with a Varian Cary 100 Bio spectrophotometer equipped with a 6 × 6 Multicell Block Peltier Series II cell changer and a Varian Cary Temperature Controller.18. The indicated concentrations of DMHBI+ or DMHBO+ ligand and MgCl2 (5 mM) were added. For screening of metal ion conditions, KCl was replaced by NaCl, LiCl, or TlOAc, and MgCl2 was replaced by MnCl2, BaCl2, or SrCl2. For experiments with Tl+, the RNA was precipitated from NaOAc and desalted via a centrifugal filter (Vivapsin 3000 MWCO), and MgCl2 was replaced by Mg(OAc)2.Samples for UV/VIS and fluorescence spectroscopy were prepared with the indicated concentration of RNA, annealed by heating to 95 °C for 3 min and cooling to ambient temperature for 20 min in binding buffer +) or 456 nm (when using DMHBO+) and fluorescence emission spectra recorded up to 750 nm. Other instrument parameters  were kept constant.Fluorescence spectra of the samples were measured 5 min after addition of the ligand and again after an additional incubation period of 24 h at 4 °C. Samples were excited at 413 nm (when using DMHBIn = 3) were fitted to a one-site binding model in OriginPro 20198.For binding affinity titrations, fluorescence spectra were measured 24 h after preparation of the individual samples and the emission bands were integrated. The resulting values  were recorded, the first ramp served for annealing and was not included in the data analysis. The samples were layered with silicon oil to minimize evaporation during the experiment.2). A sample without RNA and ligand was used to measure the instrument response function (Em wavelength = Ex wavelength). Data analysis was performed with Data Analysis Station 6 (Horiba) by fitting to an appropriate sum of exponentials. Fit quality was judged by the reduced χ2 value  and a random distribution of the residuals.TCSPC measurements were performed on a Horiba DeltaFlex system with a DeltaDiode DD-320 excitation source . The samples were prepared as described above at an RNA concentration of 0.5 and 1 µM ligand . The maximum delay between pump and gate pulse can be up to 1.500 ps. To generate the pump wavelength 405 nm for the FLUPS we used the output from a non collinear parametric amplifier which was set to 810 nm. The 810 nm were guided through a BBO crystal to generate the second harmonic at 405 nm. We used an 800 nm broadband bandpass filter to eliminate the residual 810 nm. The pump pulse energy was 50 nJ and the gate pulse energy was 30 uJ at 1280 nm. For more details on the setup see ref. 2 crystal with some filters to achieve a probe light from 400 to 800 nm. The maximum delay between pump and probe was 8 ns. For a more detailed description of the setup see ref. 65.The pump pulse from the TAS was generated with a traveling wave oscillating parametric amplifier “TOPAS-C” from Light Conversion. The pump pulse energy was set to 40 nJ. The white light was generated with a moving CaF66. This analysis takes the white light dispersion and, in case of the TA measurements, the coherent artifact into account and yields evolution associated difference spectra (EADS) for a consecutive kinetic model and decay associated difference spectra (DADS) for a parallel kinetic model67.For both types of measurement we performed a global analysis of the data using GLOTARAN softwareFurther information on experimental design is available in the Supplementary InformationPeer Review FileReporting summary