HIST1H1C). Disruption of the hTR-HIST1H1C RNA association resulted in markedly increased telomere elongation without affecting telomerase enzymatic activity. Conversely, over-expression of HIST1H1C led to telomere attrition. By using a combination of mutations to disentangle the effects of histone 1 RNA synthesis, protein expression, and hTR interaction, we show that HIST1H1C RNA negatively regulates telomere length independently of its protein coding potential. Taken together, our data provide important insights into a surprisingly complex hTR-RNA interaction network and define an unexpected non-coding RNA role for HIST1H1C in regulating telomere length homeostasis, thus offering a glimpse into the mostly uncharted, vast space of non-canonical messenger RNA functions.Telomerase RNA (TR) provides the template for DNA repeat synthesis at telomeres and is essential for genome stability in continuously dividing cells. We mapped the RNA interactome of human TR (hTR) and identified a set of non-coding and coding hTR-interacting RNAs, including the histone 1C mRNA ( Most human cells display progressive telomere shortening during cell divisions, ultimately resulting in replicative senescence or apoptosis . In the While all TRs contain a short internal template for telomeric DNA repeat synthesis , vertebrInterestingly, while hTERT expression is silenced in most human somatic cells, hTR is broadly expressed in normal tissues . In addiBesides serving as flexible scaffolds for protein binding and RNP assembly, most non-coding RNA classes engage in complementarity-driven base-pairing with other RNAs or DNA . In addiWhile the protein composition  and chro[FA] RNA-seq) (-/hTERT- VA13 cells transiently transfected by hTR (VA13-hTR) and hTR+/hTERT+ HeLa cells for hTR antisense purification , as well as mock pull-down from untransfected (hTR-negative) VA13 cells was carried out in parallel. RNA fragments co-purifying with hTR were identified by determining their enrichment in pull-down vs input samples. To build a high-confidence set of hTR interacting RNA molecules, only highly (>4 fold) enriched, reproducibly identified peaks were considered further, resulting in 80 RNA species in VA13-hTR cells. Unfiltered peak calling results produced by the JAMM universal peak finder  . Since tRNA-seq)  and can RNA-seq) , we usedfication . Controlk finder  are prov+ HeLa cells , nuclear structures formed at the sites of histone gene transcription and concentrating factors involved in histone pre-mRNA recognition and maturation  of HIST1H1C (nts 334–348) complementary to the terminal stem-loop sequence of the P6b region of hTR  determine the interaction.In order to verify this RNA-RNA interaction, various mutations disrupting the TRIAGE-P6b complementarity were introduced in hTR . For theciencies . Importa mutants , includisequence  but was  paralog , suggestHIST1H1C RNA or hTR. As H1.2 protein levels were not significantly affected by hTR or variant hTR expression  length analysis to follow the mean telomere length (MTL) over time in HT1080 cell lines stably expressing the hTR variants. Expression of hTR variants did not change the cell growth characteristics  method that facilitates the identification of critically shortened telomeres . HIST1H1elomeres . In agreelomeres , suggestHIST1H1C ORF  of hTR. Strikingly, functional annotation analysis  has been previously reported to interact with hTR in transcriptome-wide screens of cellular RNA-RNA interactions , or a frameshift at the start of the HIST1H1C ORF (abolishing protein production without limiting RNA expression), we demonstrated that the telomere elongation phenotype is mediated by HIST1H1C acting as a non-coding RNA, and not through H1.2 protein expression. As neither HIST1H1C over-expression, nor mutations in the P6b stem-loop of hTR disrupting HIST1H1C association had an effect on telomerase enzymatic activity, we propose that HIST1H1C regulates telomere length homeostasis acting at a step downstream of RNP assembly, possibly by sequestering the active telomerase complex and preventing its proper intranuclear targeting or recruitment to telomeres. As both hTR and replication-dependent histone (pre)mRNAs accumulate in Cajal bodies  are located – along with a number of core histone genes – in the large HIST1 gene cluster on chromosome 6, and are expressed in a cell cycle-dependent fashion in somatic cells , we consistently detected a preferential enrichment of HIST1H1C mRNA over the other variants. The reason for this specificity is currently unclear, but could potentially be explained by different intramolecular RNA secondary structures involving the TRIAGE sequence in HIST1H1B and HIST1H1E, or by the involvement of an RNA-binding protein – preferentially associating with the HIST1H1C mRNA – facilitating the TRIAGE-P6b RNA duplex formation. Nevertheless, it remains possible that under different conditions or in different cell/tissue types HIST1H1B and/or HIST1H1E might more efficiently interact with hTR and regulate its function(s).The human genome encodes 11 histone 1 (H1) subtypes, five of which  remains to be determined. Alternatively, the potential lack of this interaction might reflect divergent evolutionary strategies employed by these short-lived, small species with excessively long telomeres and somatic telomerase activity .[FA] experiments in VA13 cells were carried out using transient pU1-hTR(451) transfections, achieving hTR expression levels comparable to endogenous amounts in HeLa cells . The pBles/cell  containsThe HIST1H1C genome region, containing 735 nucleotides upstream of the HIST1H1C start codon, the HIST1H1C ORF, and 452 nucleotides downstream of the stop codon was amplified from HT1080 genomic DNA and inserted into the pPGK-puro plasmid. For control transfections, the HIST1H1C ORF was deleted (mock_HIST1H1C). The 3xFLAG sequence was inserted in-frame at the start codon of HIST1H1C, resulting in pPGK-puro-3xFLAG-wtHIST1H1C. Silent mutations in the TRIAGE sequence were introduced into this plasmid to generate pPGK-puro-3xFLAG-silentHIST1H1C . In ordeAll plasmid constructs were verified by sequencing.Telomerase-negative WI38 VA13-2RA ALT cells were cultured in minimum essential medium (MEM) supplemented with 1 mM sodium pyruvate, 2 mM L-glutamine, 1x MEM NEAA, 10% fetal calf serum, 100 U/ml penicillin, and 100 μg/ml streptomycin. HT1080 and HeLa cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 2 mM L-glutamine, 10% fetal calf serum, 100 U/ml penicillin, and 100 μg/ml streptomycin. All cell culture reagents were purchased from Thermo Fisher Scientific. All cell lines used in this study were authenticated by short tandem repeat (STR) genotyping . The absence of mycoplasma contamination was routinely verified by the Mycoplasma PCR detection kit .Transfections were carried out using Lipofectamine 2000 reagent (Thermo Fisher Scientific), according to the manufacturer’s instructions. For the establishment of stably transfected polyclonal or clonal HT1080 cell lines, transfections were carried out using linearized plasmids, and selection with 3 μg/ml puromycin dihydrochloride (Thermo Fisher Scientific) was initiated 48 hr after transfection.[FA] essentially as described in  and cross-linked with 2% methanol-free formaldehyde (Thermo Fisher Scientific) at 37°C for 10 min, followed by quenching with 500 mM final concentration of glycine at 37°C for 5 min. Cells were rinsed three times with ice-cold PBS and collected in ice-cold scraping buffer . Cell pellets were collected by centrifugation (1000 g at 4°C for 5 min), and washed again in ice-cold scraping buffer. Aliquots of cell pellets were flash frozen in liquid nitrogen and stored at −80°C until further use.2, 0.5 mM EDTA, 1 mM TCEP [tris(2-carboxyethyl)phosphine], 0.5 mM PMSF (phenylmethylsulfonyl fluoride), and 0.1% NP-40) on ice for 10 min, followed by 20 dounces in a 2 ml glass dounce homogenizer. Nuclei were enriched by centrifugation at 4°C, 3300 g for 7 min, and resuspended in 1 ml GuSCN hybridization buffer . RNAs were fragmented using the Bioruptor sonication system (Diagenode) at high-intensity setting for a total of 10 min (30 s ON – 30 s OFF) for VA13 cells and 15 min for HeLa cells, resulting in RNA fragments of ~150–300 nucleotides. Lysates were cleared by centrifugation at 4°C, 16,000 g for 10 min.Pellets from ~20 million cells were lysed in 1 ml lysis buffer ; hTR_pd2 – 5’-[Biotin]CCCCGGGAGGGGCGAACGGGCCAGCAGCTGACATTTTTTGTTTGCTCTAG (complementary to position 160–209 of hTR); and hTR_pd3 – 5’-[Biotin]GCGAGAAAAACAGCGCGCGGGGAGCAAAAGCACGGCGCCTACGCCCTTCT (complementary to position 59–108 of hTR)] and U2 snRNA [U2_pd1 – 5’-[Biotin]GGGTGCACCGTTCCTGGAGGTACTGCAATACCAGGTCGATGCGTGGAGTG (complementary to position 139–188 of human U2 snRNA); U2_pd2 – 5’-[Biotin]GACGGAGCAAGCTCCTATTCCATCTCCCTGCTCCAAAAATCCATTTAATA (complementary to position 89–138 of human U2 snRNA); and U2_pd3 – 5’-[Biotin]TATTGTCCTCGGATAGAGGACGTATCAGATATTAAACTGATAAGAACAGA (complementary to position 39–88 of human U2 snRNA)] pull-downs.Equimolar mixes of three biotinylated ssDNA ODNs (Sigma-Aldrich) were used for both hTR rArGrArUrCrGrGrArArGrArGrCrGrUrCrGrUrG[3ddC]) (20 pmol RiL-19 RNA adapter (G[3ddC])  was ligaG[3ddC]) , accordiG[3ddC])  was liga-m narrow -r peak. To increase the stringency of our analysis, peaks were further filtered by requiring a minimum peak score of 50,000 (obtained using the JAMM software), a minimum 4-fold enrichment  over the peak region, and a minimum number of 20 reads supporting the peak. Regions enriched in any of the control pull-downs from hTR-negative, mock-transfected VA13 cells were excluded from further analysis. Peaks passing these filtering criteria, together with the unfiltered output files, are listed in Illumina HiSeq RNA-sequencing on multiplexed samples was carried out to obtain ~20 million 100-nt paired-end reads per sample. Adaptor sequences and low quality bases were trimmed from raw sequencing reads using cutadapt . Reads w[FA] section. For measuring cellular RNA expression levels, total cellular RNA was extracted using Trizol reagent (Thermo Fisher Scientific) and treated by RQ1 DNase (Promega). qRT-PCR reactions were carried out using QuantiNova SYBR Green RT-PCR kit (Qiagen), according to the manufacturer’s protocol. Amplification conditions were as described in For qRT-PCRs verifying the enrichment of selected transcripts upon hTR pull-down, RNAs were purified as described in the RAP-RNAqRT-PCR primer sequences are listed in Telomere restriction fragments were analyzed using the TeloTAGGG Telomere Length Assay kit (Roche). Briefly, cells were harvested by trypsinization, washed in PBS and collected by centrifugation at 400 g for 4 min. Genomic DNA was isolated using DNeasy Blood and Tissue Kit (Qiagen), digested with HinfI and RsaI restriction enzymes (New England Biolabs) and separated by gel electrophoresis either on 0.8% agarose gels at 50V overnight in 1X TBE buffer or (to resolve elongated telomeres at later time points) on 1% megabase agarose gels (Bio-Rad) using a CHEF DRII equipment (Bio-Rad) under the following conditions: 120° field angle, 5 to 30 s switch times, 5 V/cm and 14°C for 14 hr in 1X TAE. Following the resolution of DNA fragments, DNA was transferred to a positively charged nylon membrane (Roche) by Southern blotting and hybridized with a digoxigenin-labelled telomeric probe. Membranes were exposed to X-ray film (Carestream) and developed in X-OMAT 2000 Processor (Kodak). Mean telomere lengths were calculated as described in The TeSLA procedure was carried out as described in Briefly, 50 ng of genomic DNA was ligated with TeSLA-T oligonucleotides and then digested with CviAII, BfaI, NdeI, and MseI restriction enzymes (New England Biolabs), followed by shrimp alkaline phosphatase (New England Biolabs) treatment. The digested DNA was ligated with double-stranded TeSLA adapters, and 30 pg of the ligated DNA was subsequently used for long-range PCR amplifications. Amplification reactions were carried out in 25 μl volume, using 2.5 units of FailSafe Enzyme Mix (Lucigen) with FailSafe buffer H and 0.25 μM primers (AP and TeSLA-TP). After the initial melt at 94°C for 2 min, 25 PCR cycles were carried out . Amplified PCR products were resolved on a 1.2% agarose gel at 50V overnight in 1X TBE buffer. Southern blotting and telomere signal detection was performed using the TeloTAGGG Telomere Length Assay kit (Roche), as described for the TRF assay. The TeSLA-QUANT software was used for image quantification and statistical analysis, as described in 6 cells/ml in CHAPS lysis buffer containing 400 mM NaCl dimethylammonio]−1-propanesulfonate (CHAPS), 10% glycerol, 1x complete protease inhibitor cocktail without EDTA (Roche), 5 mM β-mercaptoethanol), as described in Cells were trypsinized, washed once in ice-cold PBS, and resuspended at 10Protein concentration of the extracts was determined using the Bradford assay (Bio-Rad). Amplification reactions were carried out in 20 μl volume, using SYBR Green Universal Master Mix (Thermo Fisher Scientific), 1 mM EGTA, 80 ng TS primer (5’- AATCCGTCGAGCAGAGTT) , 80 ng AUsing the Bio-Rad CFX96 qPCR instrument, samples were incubated for 30 min at 30°C, for 10 min at 95°C, and amplified in 40 PCR cycles with 15 s at 95°C and 60 s at 60°C. Relative telomerase activities were determined using a standard curve from 5-fold serial dilutions of wild-type hTR-transfected HT1080 cells. Samples were also serially diluted to verify that the measurements were carried out in the linear range of the assay. qTRAP measurements were carried out in triplicates and repeated at least twice for each sample. Heat-inactivated wild-type hTR-transfected HT1080 cell lysates and CHAPS buffer were used as negative controls, verifying that the amplification products were due to telomerase activity.Cells were lysed by sonication in ice-cold RIPA buffer containing 1x Protease Inhibitor Cocktail (Roche). Proteins were then separated by SDS-PAGE and transferred to 0.2 µm PVDF membranes (Bio-Rad). Membranes were blocked with Superblock T20 (TBS) blocking buffer (Thermo Fisher Scientific) and probed with primary  and secondary [ or polyclonal goat anti-mouse ] antibodies or HRP-conjugated p53 antibody . Reactive bands were visualized with Immobilon western chemiluminescent HRP substrate (Millipore) in a luminescence imager .Clonal HT1080 cell lines stably expressing exogenous wtHIST1H1C were seeded in 6-well plates and cultured for 4–5 days prior to staining. Cells were fixed and stained according to the manufacturer’s protocol . Stained cells were imaged with an inverted microscope  with PixeLINK colour megapixel firewire camera (PL-A662). The percentage of SA-β-gal positive cells were determined by manually counting the positive cells across four randomly selected microscope frames and then normalized to the total number of cells. Staining was performed in triplicates between 8 (1 sample) and 10 weeks (two samples) post-transfection, and cell counts were pooled for the analysis. HT1080 cells treated with 10 µM of the topoisomerase type 2 inhibitor Etoposide (Sigma) for 24 hr followed by 24 hr recovery served as positive control. Untransfected HT1080 cells served as negative control.Statistical analyses were performed as described in the figure legend for each experiment. All data are presented as mean ± s.d., unless otherwise noted in the legends. All data shown are representative of two or more independent experiments, unless otherwise indicated.RNA-seq data that support the findings of this study have been deposited in the NCBI Sequence Read Archive (SRA) under the accession code SRP123633. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by James Manley as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Thomas R Cech (Reviewer #2); Marlene Oeffinger (Reviewer #3).Thank you for submitting your article "The RNA interactome of human telomerase RNA reveals a coding-independent role for a histone mRNA in telomere homeostasis" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife for potential publication. As you read above, the paper now has been evaluated by three reviewers and I am pleased to report that they all very much support it. Indeed, they agreed that the manuscript reports an exciting and potentially very important analysis of the "RNA interactome" of human telomerase RNA (hTR). They were impressed by the depth and breadth of the analyses that include pull-down assays, mutational tests, overexpression experiments, telomere length measurements, and cellular senescence assays. Furthermore, the reviewers commented on the fact that the experiments are generally well-controlled, repeated a good number of times, and clearly explained.Thanks for submitting your manuscript to Nevertheless, there were issues for which the reviewers felt that an amelioration would be great. I will subdivide those into one major issue, for which further experimental evidence is required, and some other issues that you may address experimentally and/or by editorial changes.Major issue:All experiments in which mutated RNAs are used and that are aimed at documenting biological effects of a loss of the supposed RNA-RNA interaction were done incorporating mutations in only one of the two partner RNAs. A rather standard experiment for these kinds of approaches is to also mutate both RNAs at the same time in such a way that the stipulated base-pairing interaction is re-established, albeit with the mutated sequences. Such an experiment would go a long way in supporting your claim that the phenotypes you see for the individual mutations are due to the presumed loss of RNA-RNA interaction. You could do this experiment rather easily in the way that you did the individual mutations  to show that the two mutated RNAs do interact and can be pulled down.HIST1H1C RNA specifically interacts with hTR”, the authors conclude that sequence complementarity between the TRIAGE region of the histone mRNA and P6b of hTR is necessary for the interaction. However, mutating one partner in the proposed interaction  only shows that these sequences are necessary, it doesn't indicate that they're necessary because they base-pair with the other partner. Subsection “Disrupting the HIST1H1C-hTR RNA-RNA interaction leads to increased telomere elongation”, and also the title of Figure 4, these words claim a causal relationship between disrupting the interaction and increased telomere length. But again, it's the mutation in hTR that causes both disruption of interaction and increased telomere length, and it's a plausible MODEL that it does so via disruption of an RNA-RNA interaction. (1) "the TRIAGE sequence is responsible for the observed phenotype" is a correct conclusion from the data, but (2) "telomere length is regulated by the.…RNA-RNA interaction" was not shown, because only one partner was mutated . "80 RNA species interacting with hTR" should be qualified to say "80 RNA species interacting directly or indirectly with hTR."As one reviewer put it: "In the last paragraph of the subsection “The qualifications requested in the above paragraph would go away if the authors could perform a "killer experiment" to directly test for RNA-RNA base-pairing. This is the classic requirement in the RNA field: mutate sequence 1 and lose activity, mutate sequence 2 and lose activity, make the compensatory double-mutant and regain activity. The authors have set themselves up beautifully to do this experiment: in the subsection “HIST1H1C regulates telomere length as a non-coding RNA”, they express a mutated mRNA "silentHIST1HC" with half of the TRIAGE nucleotides mutated. This mutant does not show the telomere elongation phenotype seen with the WT HIST1H1C mRNA. If they were to simultaneously mutate P6b in hTR so that it matched the sequence of silentHIST1HC, their model would predict that the double mutant would regain telomere elongation and regain pull-down." Major issue:[…] As one reviewer put it: "In the last paragraph of the subsection “HIST1H1C RNA specifically interacts with hTR”, the authors conclude that sequence complementarity between the TRIAGE region of the histone mRNA and P6b of hTR is necessary for the interaction. However, mutating one partner in the proposed interaction  only shows that these sequences are necessary, it doesn't indicate that they're necessary because they base-pair with the other partner. Subsection “Disrupting the HIST1H1C-hTR RNA-RNA interaction leads to increased telomere elongation”, and also the title of Figure 4, these words claim a causal relationship between disrupting the interaction and increased telomere length. But again, it's the mutation in hTR that causes both disruption of interaction and increased telomere length, and it's a plausible MODEL that it does so via disruption of an RNA-RNA interaction. (1) "the TRIAGE sequence is responsible for the observed phenotype" is a correct conclusion from the data, but (2) "telomere length is regulated by the.…RNA-RNA interaction" was not shown, because only one partner was mutated . "80 RNA species interacting with hTR" should be qualified to say "80 RNA species interacting directly or indirectly with hTR."The qualifications requested in the above paragraph would go away if the authors could perform a "killer experiment" to directly test for RNA-RNA base-pairing. This is the classic requirement in the RNA field: mutate sequence 1 and lose activity, mutate sequence 2 and lose activity, make the compensatory double-mutant and regain activity. The authors have set themselves up beautifully to do this experiment: in the subsection “HIST1H1C regulates telomere length as a non-coding RNA”, they express a mutated mRNA "silentHIST1HC" with half of the TRIAGE nucleotides mutated. This mutant does not show the telomere elongation phenotype seen with the WT HIST1H1C mRNA. If they were to simultaneously mutate P6b in hTR so that it matched the sequence of silentHIST1HC, their model would predict that the double mutant would regain telomere elongation and regain pull-down."HIST1H1C distinct from the TRIAGE sequence . In that case, complementarity in itself was not sufficient to ‘rescue’ the hTR-HIST1H1C association. We speculated that the HIST1H1C region complementary to the RS-hTR variant might not be accessible for hybridization. Alternatively, P6b-TRIAGE duplex formation might be facilitated by an RNA-binding protein with some sequence-specificity/preference.We carried out a conceptually similar experiment, incorporating mutations in the P6b stem-loop of hTR that introduced complementarity to a region of HIST1H1C, and confirm the requirements for the TRIAGE and P6b regions for duplex formation. Unfortunately, the mutant hTR does not support the assembly of catalytically active telomerase RNPs, possibly due to the disruption of the P6b stem-loop structure, which might also interfere with the correct folding of the adjacent P6.1 region.Now, based on the reviewers’ recommendations, we have introduced mutations in the P6b stem-loop of hTR that re-establish complementarity to the silentHIST1H1C mRNA. As shown in a new Figure 5—figure supplement 1, this hTR variant specifically interacts with and pulls down the silentHIST1H1C mutant mRNA, but not wtHIST1H1C. Similarly, wild-type hTR only pulled down the wild-type HIST1H1C mRNA (either endogenous or exogenous), but not the silentHIST1H1C variant. These results clearly demonstrate a direct RNA-RNA interaction between hTR and


Dynamic self-organisation far from equilibrium is a key concept towards building autonomously acting materials. Here, we report the coupling of an antagonistic enzymatic reaction of RNA polymerisation and degradation to the aggregation of micron sized DNA coated colloids into fractal structures. A transient colloidal aggregation process is controlled by competing reactions of RNA synthesis of linker strands by a RNA polymerase and their degradation by a ribonuclease. By limiting the energy supply (NTP) of the enzymatic reactions, colloidal clusters form and subsequently disintegrate without the need of external stimuli. Here, the autonomous colloidal aggregation and disintegration can be modulated in terms of lifetime and cluster size. By restricting the enzyme activity locally, a directed spatial propagation of a colloidal aggregation and disintegration front is realised. The resulting properties can be tuned by the choice of the single building blocks and their interactions17. DNA coated colloids have proven to be well suitable, as the programmability and specificity of their interactions allow a fine control of the formation of a diverse set of structures23. At high interaction energies amorphous gel like structures can be tightly controlled by fine tuning the kinetic pathways leading to the dynamic arrest27. In multicomponent systems, the interactions can be activated at different stages, which leads to the formation of scaffolds, able to guide the subsequent aggregation processes and thereby to the formation of higher hierarchical structures28. The DNA functionalised colloidal structures realized until now have in common that the finally formed structures are stable and static in time.Self-assembly of colloidal particles is a powerful concept to build functional materials29 or temperature shifts31. The dynamic transition from colloidal aggregation to disintegration in time without external intervention, requires a precise control of the underlying reaction kinetics. Such transient and autonomous control of colloidal aggregation has been realized by tuning the hydrophobicity of single building blocks with a simultaneous limited energy supply32. In this promising approach, the transient switch between colloidal aggregation and disintegration was achieved without external stimuli, yet lacks the selectivity of DNA hybridisation. For polymeric gels external control and autonomous structure formation processes have already been shown to be a promising route for the formation of active materials with unique properties35. The coupling of the selectivity and the dynamic control of DNA hybridisation of colloidal interactions would open up a new set of autonomously responding material classes, with the potential of hierarchical structure formation starting from the micron-scale.One approach to control the time course of colloidal aggregation is the use of external stimuli, which has been realized by controlling phosphorylation/dephosphorylation reactionsT7 Polymerase is used to synthesize RNA-linker strands to induce the specific aggregation of micron sized colloidal particles . Above this speed, the polymerase produces a sufficient amount of RNA (>50 nM) to enable optimal aggregation conditions in just a few minutes. Here, the speed of the aggregation process is only limited by the diffusion of the colloids and further increase of RNA-linker does not result in enhanced aggregation speed. Additionally, we used confocal microscopy to prove the selectivity of the RNA-linker caused by the DNA/RNA hybridisation . AggregaFinally, we show that the aggregation of the colloids is also able to tune optical properties of the solution. Therefore, we illuminated a sample with a green laser before and after the enzymatic induced clustering. The monodisperse colloids absorb and scatter the laser light, which can be seen by the outline of the sample holder. After the aggregation of colloids takes place, the colloidal solution gets transparent for the laser light. Here, huge volume fractions arise, where almost no colloid is present and the light is able to pass Fig. .Figure 3RNaseH. Pre-built RNA-linker (100 nM) were added to a colloidal solution containing 0.1 U/μl of RNaseH. After the structure formation was completed (20 min), the disassembly of the structures was observed within 30 min , the double-stranded DNA template (Temp) and the substrate NTP, while the degradation depends on the concentration of RNaseH and the total amount of RNA. By that, the enzymatic reaction can be described by Michaelis-Menten-Kinetics.Finally, we aimed to create an autonomously aggregating colloidal system, which exhibits a transient cluster aggregation without external intervention. Therefore, we had to identify the underlying kinetic parameters of the enzymatic reaction precisely. We assume that the system consists of a linear production and a linear degradation reaction. The speed of production depends on the concentration of k1 and k2 the rates of production and degradation, respectively. The rate of production was obtained experimentally from fluorescence measurements in the absence of RNaseH  according to eq. RNaseH does lead to an effectively slower production rate, which equilibrates to a steady state, but does not show transient behaviour. In this setup, an autonomous transient RNA “pulse” can only be realised by the depletion of the NTP pool. Here, the consumption of the substrate reduces the production rate continuously. This is achieved, once the T7 enzyme concentration is sufficiently higher than the substrate concentration. Experiments were performed to identify the optimal relation between T7 polymerase and NTP concentration, which shows the reduction of RNA production . As a result, we were able to observe the transient aggregation of the colloids without external stimuli  and T7 RNA Polymerase (200 U/μL). The colloidal aggregation experiments were performed with a total concentration of 30 mM MgCl2 for the optimal activity of T7 RNA Polymerase and 450 mM sucrose to avoid the sedimentation of the colloids. The colloids were used at 0.15 vol.-% and 5 mg/ml BSA was used to avoid the stickiness of the glass surface. The working temperature was T = 37 °C for all experiments. Therefore, an IBIDI heating-chamber and a copper objective warmer was used. For the diffusion experiments, parafilm was cutted in the shape shown in Fig. Fluorescent, streptavidin coated microspheres (Polysciences) with a diameter of 1 μm were functionalized with 1.7 μM of biotinylated DNA strands (biomers) for at least 6 hours and washed by centrifugation (6000 rcf) afterwards. The sequences of this constructs are: Docking A: 5′-CCACACCAACCAAC-3′- biotin and Docking B: biotin -5′-ACTTACTATATAAC-3′. The sequence of the dsDNA Template is: 5′-CACCCACCCACACCAACCAACAAAACTTACTATATAACCCCTATAGTGAGTCGTATTAG-3′ and the complementary part: 5′-CTAATACGACTCACTATAGGGGTTATATAGTAAGTTTTGTTGGTTGGTGTGGGTGGGTG-3′ – (biotin) with an optional biotin modification for the functionalisation of the coverslides used in the diffusion experiments. The linker Bright-field optical images and videos were obtained using a Leica DMI6000B and an HCX PL APO 40x/1.25–0.75 oil CS objective. For the confocal microscopy, a Leica TCS SP5 was used and a HCX PL APO 63x/1.40–0.60 oil CS objective. The fluorescence measurements were performed using a Jasco Spectrofluorometer FP-8500 and the DNA/RNA intercalator SyBr GreenII. The fluorescence signal (a.U.) is transferred to the total RNA concentration. To this end, the minimum of the signal during the RNA production was determined and computed to be the DNA template concentration in solution, because at this time point no RNA was produced so far. This calibration allows a direct conversion of the fluorescence signal to the DNA concentration. In control measurements we confirmed the linear relation between fluorescence signal and DNA concentration up to 10 μM.The average cluster size of the colloidal aggregates is determined by thresholding the bright field images to create a binary image. The obtained masks of the colloidal aggregates were analysed in terms of size and total amount. The simulations were performed in rectangular matrices using periodic boundary conditions for the colloids and repulsive boundary conditions for the RNA. Differential equation eq.  were useSupplementary Informations


While RNA is often created from linear splicing during transcription, recent studies have found that non-canonical splicing sometimes occurs. Non-canonical splicing joins 3’ and 5’ and forms the so-called circular RNA. It is now believed that circular RNA plays important biological roles such as affecting susceptibility of some diseases. During the past several years, multiple experimental methods have been developed to enrich circular RNA while degrade linear RNA. Although several useful software tools for circular RNA detection have been developed as well, these tools are based on reads mapping may miss many circular RNA. Also, existing tools are slow for large data due to their dependence on reads mapping.In this paper, we present a new computational approach, named CircMarker, based on k-mers rather than reads mapping for circular RNA detection. CircMarker takes advantage of transcriptome annotation files to create the k-mer table for circular RNA detection.Empirical results show that CircMarker outperforms existing tools in circular RNA detection on accuracy and efficiency in many simulated and real datasets.We develop a new circular RNA detection method called CircMarker based on k-mer analysis. Our results on both simulation data and real data demonstrate that CircMarker runs much faster and can find more circular RNA with higher consensus-based sensitivity and high accuracy ratio compared with existing tools.The online version of this article (10.1186/s12864-018-4926-0) contains supplementary material, which is available to authorized users. In most eukaryotic genes, coding regions (exons) are separated from noncoding regions (introns)[1]. During RNA splicing, introns are removed and exons are joined to form a contiguous coding sequence called messenger ribonucleic acid (mRNA). This “mature” mRNA is ready for translation, and those contiguous coding sequences are called transcripts . SplicinSince circular RNAs do not have 5’ or 3’ ends, they are resistant to exonuclease-mediated degradation and are presumably more stable than most linear RNAs in cells. Based on this feature, some benchmark experimental methods have been developed to degrade the linear RNA while enriching the circular RNA. For example, one method is treating samples with RNase R, an enzyme which degrades linear RNAs but not circular RNAs. This treatment can enrich circular RNAs , 11.Computational tools for circular RNA detection have been developed. Currently, there are several existing tools for circular RNA detection, such as Find_circ , CIRCexpAll of these methods mentioned above depend on reads mapping. These mapping based methods have some inherent issues. The first issue is computational efficiency: the existing tools use BWA, Bowtie or TopHat for reads mapping. Although BWA and Bowtie are widely used in sequence analysis, reads mapping is still time-consuming for circular RNA detection. This is because reads mapping tries to map every read, even when the read is not relevant for circular RNA detection. In addition, since some new short sequences may be created in the middle by circRNA detection tools for the second round mapping, reads mapping can become very slow when TopHat-fusion is used, due to the large length of sequences. Moreover, these tools may miss circular RNA in some cases due to errors in reads mapping. For example, some reads related to circular RNA may be un-mapped due to reads error.In this paper, we develop a new computational method, called CircMarker, for circular RNA detection. The objective of CircMarker is finding the presence of circular RNA (in particular the join of two known exons). CircMarker doesn’t reconstruct the complete sequence of circular RNA. The key idea of CircMarker is that it doesn’t rely on reads mapping. Instead, CircMarker analyzes short sequence segments, called k-mers, for circular RNA detection. The main advantage of using k-mers is efficiency: finding k-mers from reads is much faster than reads mapping. Another advantage is that k-mer tolerates more errors in reads and carries useful information about the presence of circular RNA, which may be missed by reads mapping. Empirical results show that CircMarker is more accurate than (or as accurate as) existing methods on simulated and real datasets in calling circular RNA. CircMaker runs much faster than existing methods.out of order relative to the reference, CircMarker considers this as an evidence for the existence of circular RNA.The overall approach of CircMarker is shown in Fig. CircMarker creates a table for storing the k-mers within the reference genome that are near the exon boundaries as specified by the annotations. The k-mer table is designed to be space-efficient. We only record the following five types of information for each k-mer, including chromosome index, gene index, transcript index, exon index and the part tag as shown in Fig. LB is defined as below: K is the length of the k-mer. LR is the length of reads, and Rc represents the percentage of reads that should be covered in each boundary. Since we expect more than half reads to be considered, we set the default value of Rc as 30% (2∗30%=60%>50%). Le is the length of current exon. If When extracting k-mers from annotated exons in the reference genome, we only consider the exons with circular splicing signal in either head or tail part. And we only consider the k-mers which come from the left and right boundaries of the exon. The length of the boundary region Nh specifies the number of k-mers which supports all of hit exons in one hitting status. The PreBestHitNum means the Nh of previous best hitting status. PreBestHitLen means the summary length of the hit exons in previous best hitting status. If Nh is larger than PreBestHitNum+5, the current hitting status will be set as the previous best hitting case, which means we prefer the hitting status with conditional larger number of k-mers supporters. Otherwise, the hitting status with the shorter total length of the hit exon will be chosen. We set the previous best hitting status as the final best hitting case when all of hitting status been processed. Finally, the Nh of best hitting case should be at least 5. Otherwise it is discarded.Once the k-mer table of the annotated exons is created, we now process each sequence read. Here we examine k-mers contained in a read and search for a match in the k-mer table. This way, we obtain the “hitting status”, which means which transcript can be hit by current reads. “hit exon” means the exon that is hit by the k-mer in the reads in the k-mer table. Each read may be related to more than one hitting status. Each hitting status contains at least one hit exon, and each hit exon should be supported by at least one k-mer. We scan all reads to check their hitting status. In order to skip irrelevant reads, we first perform basic check by sampling eight k-mers from 10% to 80% position of the current read. The read passes the sampling check only if at least two k-mers can find a hit in the k-mer table. If so, we examine all of k-mers from start to end, collecting all hitting status in this order. Since each hitting status is contributed by multiple k-mers, the “best hitting case” is defined as: The previous step identifies best hitting cases. Due to the inherent noise in the data (e.g. read errors and duplications), we perform the following filtering step to improve the accuracy. There are two main filtering procedures.Filtering procedure one. The first filtering procedure is checking the hitting number. The minimum hitting number Nh is smaller than Filtering procedure two. Based on the number of hit exons in best hitting case, we divide all cases into two types: the case of self-circular if the number of hit exons is equal to 1, and the regular-circular case otherwise.S(n) as n continuous tag “S” in one exon (similar for E(n)). If we have S(1) and E(10) in one exon, then we condense them to E(10). This may help us to filter some random hits. We consider a candidate a valid self-circular RNA only if there are two tags that are arranged from E to S sequentially (i.e. going backward at the circular RNA join junction).For self-circular case, only the exon containing the circular splicing signal in both sides will be considered. Then, the best hitting case will be considered as the self-circular RNA candidate if For the regular-circular case, the best hitting case will be considered only if it contains two exons. First, an exon will be skipped if its hitting time is at most 3 in order to remove some random hits. Then, we try to condense the tags. Here the method described in the self-circular case will be applied at first. After that, for the first exon we condense SE to E while condensing SE to S for the second exon. This condensation logic may help for the case where some of exons are fully covered by current reads. The best hitting case will be kept only if the number of condensed tag in both exons is equal with 1 and the tags arranged from E to S sequentially.There are two cases for calling circular RNA: the self-circular case and the regular-circular case.Self-circular RNA. First, a self-circular RNA candidate will be discarded if the length of current exon is shorter than the read length while the Nh is smaller than Le−K+1. Otherwise, the best hitting case will be considered to be a valid self-circular RNA candidate if it contains circular splicing signals in both sides.Regular-circular RNA. For the direction “+”, the candidate will be dropped if the exon index increases monotonically. Otherwise, we try to identify the breakpoint at the position of the first deceasing and set it to be the joint junction of circular RNA. We call the exon with large index as the head exon while another one as the tail exon. Based on this definition, the head exon is located in the later part of the reference, while the tail exon is located in the earlier part, and the circle should connect the head exon back to the tail exon. The candidate will be viewed as a valid regular-circular RNA candidate only if the head exon and tail exon have the tail and head circular splicing signal respectively. We set the end position of the head exon and the start position of the tail exon as the position of this called regular-circular RNA.For the direction “-”, the procedures is almost the same as the direction “+”. The only difference is how to choose the joint junction. In this case, the candidate will be dropped if the exon index decreases monotonically. Otherwise we try to identify the breakpoint at the the first increasing and set it to be the joint junction of circular RNA. The exon with small index is viewed as the head exon while the big index exon is set as the tail exon.Refining circular RNA candidates. We count how many reads support each circular RNA candidate. Only the candidate with support number smaller than the predefined threshold will be viewed as the correct one. Since the maximum coverage of circular RNA is unknown in most cases, we set the default value to be a large number to allow all of valid circular RNA candidates.Since the study of circular RNA is still at an early stage, there is no widely accepted benchmark data for evaluating the circular RNA calling at present. Recently, there are some public circular RNA databases which collect different types of circular RNA from published papers. Some databases come with the recommended circular RNA detection tool, such as CircBase . Others In this paper, we use both simulated and real data to compare CircMarker with three existing tools, including CIRI, Find_circ, and CIRCexplorer in terms of the number of called circular RNA, accuracy, consensus-based sensitivity, bias and running time. We note that all three tools we compare have customizable parameters. In addition, since all these methods depend on the mapping results coming from different mapping tools, including BWA, Bowtie, TopHat and Tophat fusion, mapping results may impact the accuracy as well. Some tools, such as CIRI, discussed how to optimize the parameters, while other didn’t. None of them provided explicit guideines on how to set parameters for different types of genomes. Therefore, we use the default parameters for these tools including CircMarker in comparison, and we notice that this may lead to some biases to comparision. When comparing the genomic positions of circular joint junction, we allow up to five bp tolerance. Since CircMarker is based on k-mers and each chromosome has its own k-mer table, the running time can be reduced significantly by parallelization .We compare the performance of these tools on the first three chromosomes individually. Because some existing tools do not support parallelization, we use a single core to run each program for circular RNA detection, and use 10 to 12 cores to run the reads mapping programs such as BWA, Bowtie and TopHat.Nh: the number of called circular RNA that are true, (2) accuracy:N is the total number of called circular RNAs by a method, (3) running time.We first use simulated data for evaluation. To generate simulated data, we use the simulation script  released by CIRI. The reference genome is the chromosome 1 in human genome (GRCh37). The annotation file is the version 18 (Ensembl 73). Two different cases are simulated as follows: (1) pair-end reads with 13,856,032 sequences, which roughly lead to 10X coverage for circular RNA and 100X coverage for linear RNA, and (2) pair-end reads contains with 9,400,036 sequences, which lead to us 50X coverage for both circular and linear RNA. The goal of the case 1 simulation is simulating the regular RNA-seq, while the case 2 focuses on the situation when the coverage of circular RNA is higher. The reads length is 101 bp and the insert size is 252 bp in both cases. The total number of simulated circular RNA in benchmark is 8033 and 8071 for those two cases respectively. Note that the true circular RNA is known in simulated data, which can be used in comparison. Since the coverage of circular RNA is known in simulated data, we set the “maximum support reads” to be 10 and 50 in CircMarker respectively. We use the following three statistics for comparison: (1) hit number The results of the four tools being compared are shown in Fig. 3We use two types of real data to evaluate the performance of the four tools.As described before, some public databases contain circular RNA called by published papers. In those papers, the authors usually only validate parts of the computationally detected circular RNA using biological experiments. The final result will be released only when the accuracy of those randomly chosen candidates meets certain standard. Therefore, we consider those released circular RNAs in these databases are reliable in this paper.Data collection We choose CircBase [homo sapiens. We use the circular RNAs recorded in this database as “benchmark”. The reference genome and annotation file come from homo sapiens GRCh37 version 75. The RNA-Seq reads are from SRR901967. These RNA-Seq reads are used to examine circular RNAs from RNase R treated poly(A)-/ribo- RNAs in human embryonic stem cells. There are total 41,342,095 single-end reads in this data.CircBase  as the sN. (4) Running time. The best tool is expected to have large intersection with other tools (low bias), large number of reliable circular RNA with high reliability ratio and fastest running time.We use the first three human chromosomes for comparison and use four statistics for comparison. (1) Hit number The number of circular RNAs in CircBase from chromosome 1 to chromosome 3 is 9142, 7530 and 5320 respectively. The results show that CircMarker finds more “benchmarked” circular RNAs and runs much faster than others , and the other from RNase R treated sample. The circular RNA which can be found in both types of reads is considered to be reliable.Data collection. The reference genome and the annotation file are from Mus Musculus GRCm38 Release79. The RNase R treated reads are from SRR2219951 and the untreated reads are from SRR2185851. The library was prepared using the script Seq v2 Kit from Epicentre [Mus Musculus with the two types of reads for comparison. We use the following three statistics. (1) Reliable circular RNA: the reliable circular RNAs are from the intersection of called circular RNAs between the treated and untreated reads. Each tool reports its own reliable circular RNA from chromosomes 1 to 3. (2) Consensus-based sensitivity: we say a called circular RNA to be trusted if this circRNA is called by at least two tools. These trusted RNAs are considered to be “benchmark”. We collect these trusted circular RNA for each chromosome. Then, we calculate the intersection between the reliable circular RNA and the benchmark for each tool respectively from chromosome 1 to 3. The consensus-based sensitivity is calculated by: We use the first three chromosomes of The results are shown in Fig. 55For both two verification experiments described above, the data we used here are randomly picked out without any preference. As a result, “CircMrker” could also be used to predict the circular RNAs in other dataset and species if the corresponding annotation file could be well obtained.CircMarker takes advantage of annotation file to determine the position of the junction point caused by back splicing. Some existing circular RNA calling tools don’t use annotation files. There are several advantages of using annotation file. First of all, annotation file contains the boundary positions of each exon, which may help to identify the junction point more accurate than only use the position where back splicing occur, especially for the case when the reads error is near the junction point. Secondly, it can help to filter some false positive cases if the exons involved in the back splicing do not contain the reasonable splicing signal as expected. Finally, we can choose some parts of sequence from the boundary of each exon identified by the annotation file to build the k-mer table, which may improve the speed significantly. Moreover, the circular RNA which is supported by annotated exons should be considered as more reliable than the de novo one.On the other hand, a major disadvantage is that, since CircMarker depends on annotation file, it may miss the de novo circular RNAs which occur in unannotated exons. As a result, CircMarker cannot handle the case or perform as well as expected if the annotation file is not given or the quality of annotation file is not good. In addition, some circular RNAs with back splicing junction points within intron may not be detected by CircMarker.% circular RNA recorded in circBase that could hit the boundary of exons recorded in annotation file. In addition, when we perform comparison, the benchmark dataset includes both de novo circRNA and annotation based circRNA. The results show that CircMarker still outperforms other tools even the results predicted by some of other tools include both de novo and annotation based circRNAs.In order to evaluate the limitation of annotation dependence in regular datasets, we made some statistical calculations for the circular RNAs recorded in circBase. We find there are 91.2Based on our experiments described above, a large number of circular RNAs exactly occur in annotated exons.In this paper, we develop a new circular RNA detection method called CircMarker based on k-mer analysis. CircMarker runs much faster than other tools because it doesn’t perform reads mapping. Moreover, k-mers contain useful information about circular RNA detection. Our results on both simulation data and real data demonstrate that CircMarker can find more circular RNA. It has higher consensus-based sensitivity and high accuracy/reliable ratio compared with others. In addition, the circular RNAs called by CircMarker often contain most circular RNAs called by other tools in the real data we tested. This implies that CircMarker has low bias.https://github.com/lxwgcool/CircMarker.CircMarker is easy for use. CircMarker is a stand-alone tool (implemented by C++) and does not depend on any third party tools. The source code is available under the GPLv3 licence at Project name: CircMarker.Project home page:https://github.com/lxwgcool/CircMarkerhttps://github.com/lxwgcool/CircMarkerOperating system(s): Unix.Programming language: C++.Other requirements: GCC.License: GPLv3.Additional file 1Table S1. Contains the detailed results presented in this paper. These include seven tables: one for the results on simulated data, three for public database validation on real RNase R treated reads on the first three chromosomes respectively, and three for the consensus-based validation on real RNase R treated and untreated reads on the first three chromosomes respectively. (XLSX 18 kb)


The catalytic strategies of small self-cleaving ribozymes often involve interactions between nucleobases and the ribonucleic acid (RNA) backbone. Here we show that multiply protonated, gaseous RNA has an intrinsic preference for the formation of ionic hydrogen bonds between adenine protonated at N3 and the phosphodiester backbone moiety on its 5′-side that facilitates preferential phosphodiester backbone bond cleavage upon vibrational excitation by low-energy collisionally activated dissociation. Removal of the basic N3 site by deaza-modification of adenine was found to abrogate preferential phosphodiester backbone bond cleavage. No such effects were observed for N1 or N7 of adenine. Importantly, we found that the pH of the solution used for generation of the multiply protonated, gaseous RNA ions by electrospray ionization affects phosphodiester backbone bond cleavage next to adenine, which implies that the protonation patterns in solution are at least in part preserved during and after transfer into the gas phase. Our study suggests that interactions between protonated adenine and phosphodiester moieties of RNA may play a more important mechanistic role in biological processes than considered until now. Cells transcribe far more RNA than they accumulate, and maintain tightly regulated degradation systems for cleavage of damaged or redundant RNA . The actatalysis ,7–11. Riatalysis , the majatalysis ,13. The eriments ).Unfolded, single-stranded RNA also degrades in solutions that do not contain appreciable concentrations of metal ions , and sevn+ and (M-nH)n− ions of RNA produced by electrospray ionization (ESI)  based approaches for RNA characterization that involve cleavage of multiply protonated or deprotonated RNA ions ,28–34, mon (ESI) . CAD is s Scheme  that cors Scheme , respect 5′ side . Here wen+ or (M-nH)n− ion generation and a collision cell through which a flow of Ar gas was maintained for CAD. RNA was electrosprayed (flow rate 1.5 μl/min) from 0.5–1.0 μM solutions in 1:1 H2O/CH3OH with 20 mM ammonium acetate (for production of (M+nH)n+ ions) or 20 mM piperidine (for production of (M-nH)n− ions) as additives, and polyethylene glycol 1000  was used as internal calibrant for accurate mass measurements , ammonium acetate  and piperidine (≥99.5%) were from Sigma-Aldrich (Austria). The (M+nH)n+ or (M-nH)n− ions under study were isolated in a linear quadrupole prior to dissociation by CAD; for a more detailed description of the experimental setup, see reference n+ ions), and data reduction utilized the SNAP2 algorithm .Experiments were performed on a 7 T Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer  equipped with an ESI source for (M+nH)nterions  such thamoieties ,41. Solueference . For sta3A), 1-deazaadenosine (c1A) and 1,3-dideazaadenosine  were synthesized as described in references (1–14 (Table 2O) was added to ∼100 μl RNA solution (2.5–10 nmol in H2O) and concentrated to ∼50 μl using Vivaspin 500 centrifugal concentrators . The process was repeated six times, followed by six cycles of concentration and dilution with H2O, after which RNA concentration was determined by UV absorption at 260 nm using a NanoPhotometer . CAD of (M-nH)n− ions was used to confirm the sequence of RNAs 1–14.Nucleoside phosphoramidite building blocks of dA, A, C, G and U were purchased from ChemGenes , and those of 3-deazaadenosine (cferences  and 43)3A), 1-de14 Table ,45 and p4+ ions of RNA 1 (Table 2 (c3A at position 8). At first glance, the spectra are highly similar, with y143+, y133+ and (M+3H-guanine)3+ ions (the latter from loss of protonated guanine) as the most abundant products. The high yield of y143+ and y133+ fragments from phosphodiester backbone bond cleavage  ratio including c1+ and c2+. Detectability and ion discrimination was not an issue with the c and y fragments from backbone cleavage on the 5′-side of A7, A8 and A9 (sites 6–8) that all carried a net charge of 2+, including those that showed H2O and/or nucleobase loss (∼13%). Although more pronounced at sites 1 and 2, we therefore investigated the ‘A-effect’ at sites 6–8 by introduction of c1A, c1,3A, c3A and c7A , 7  and 8  in CAD of (M+4H)4+ ions of RNA 1 was significantly higher than that from cleavage at sites 3–5 and 9–14 n+ ions of RNA to ionic hydrogen bonding between adenine protonated at N3 and a nonbridging oxygen of the adjacent phosphodiester group (5′-side) which was thought to facilitate nucleophilic attack of the 2′-oxygen onto the phosphorus (c7 and y8 fragments in CAD of (M+4H)4+ ions of RNAs 2 and 3 compared to that of RNA 1 , and replacing A with c3A or c1,3A at positions 8  or 9 (RNA 9) reduced the yield of c and y fragments from phosphodiester backbone bond cleavage at sites 7 and 8, respectively, without significantly affecting that of other sites  and 9 (RNA 14) did not significantly alter the fragmentation patterns except that no c and y fragments from cleavage on the 3′-side of dA  were observed  and 8  in CAD of RNA 2  was significant, we plotted the signals of products from CAD of RNA 2–5 versus those of the reference RNA 1 2+and y82+ from backbone cleavage at site 8 clearly stood out. The Pearson correlation coefficients for the linear fits in Figure 2; c72+, (c7-guanine)2+, and y82+ excluded), 0.96 2+, and y82+ excluded), 0.84 (RNA 4), and 0.82 (RNA 5). For RNA 2, the signal of y72+, but not those of c62+ and c82+ (the complement of y72+), deviated from the linear fit 2+ and y82+ in CAD of RNA 2 and 3 2+, y82+, (y8-H2O)2+ and c82+, y72+, (y7-H2O)2+ signals in CAD of RNA 8 and 9, respectively  also correlated linearly (Pearson coefficient of 0.995) with those of RNA 1 (AAA at positions 7–9), except for c62+, (c6-guanine)2+ and y92+ from backbone cleavage at site 6, which again deviated by a factor of ∼7 n+ ions observed here, and the lack of it when A is replaced by c3A or c1,3A but not c1A or c7A  of a syn, C2′-endo conformation in which the protonated N3 forms an ionic hydrogen bond with a nonbridging oxygen of the uncharged monophosphoester moiety (N3H+···O = P motif)  . This isdenosine , whereasdenosine ,55 and idenosine . ApparenK values can shift toward neutrality  is shifted toward neutrality, which is consistent with its proposed role in general acid base catalysis by acting as proton donor in the twister cleavage reaction + ions of adenosine monophosphoric acid  4+ RNA ions studied here were electrosprayed from denaturing solutions in 50% CH3OH to prevent the formation of secondary structure , and if this would be reflected in the ESI or CAD spectra.Secondary structure calculations (e.ac.at)  predict tructure ,82. Althrmations  are mutuas phase ,84, but as phase ,29,85–88n+ ions of RNA 1, i.e. ∼95% (M+4H)4+ and ∼5% (M+3H)3+ ions, but their overall abundance substantially decreased with decreasing pH 4+ ions of RNA 1 electrosprayed from solutions at pH 6.8 and 3.0 was similar, but that the yield of c and y fragments from cleavage at sites 1–2 and 6–8 was affected by pH. Because the variation in signal of y133+ and y143+ from cleavage at sites 1–2 was stronger than for other fragments as a result of Coulombic interactions (Lowering the pH of the ESI solution from 6.8 to 3.0 by addition of acetic acid did not significantly affect the relative abundances of (M+nH)asing pH . This obasing pH . Neverthasing pH  such tharactions , we alsoractions . The eff4+ ions of RNA 1, the data in Figure c and y fragments from cleavage at sites 6–8 with decreasing pH, and the corresponding decrease in yield of c and y fragments from cleavage at sites 1–2 4+ ions of all other RNAs studied here 2 Figure  B and C,s Figure , with pr8 Figure . Moreovere Table  and 6.c and y fragments from CAD of (M+4H)4+ ions of RNAs 1–14 n− ions of 14 different, 13–22 nt RNAs in our previous study 4+ ions from ESI. We conclude that the effect of pH illustrated in Figure 4+ ions into c and y fragments by CAD , ∼3.6 (N1) and ∼1.6 (N7), respectively n+ ions of RNA, thereby facilitating nucleophilic attack of the 2′-oxygen onto the phosphorus on its 5′-side and dissociation into c and complementary y fragments. CAD of (M+4H)4+ ions electrosprayed from solutions at different pH shows that the protonation patterns in solution are at least in part preserved after transfer into the gas phase and during nucleophilic attack. Gas phase basicity and pK values suggest that protonation of A at neutral pH is energetically favorable in RNA structures that allow for ionic hydrogen bonding between adenine and the phosphodiester group on its 5′-side. This interaction involves protonation at N3 instead of N1 and syn conformation of adenine, which is generally incompatible with typical RNA secondary structure elements based on nucleobase pairing and stacking interactions, i.e. helix structures. However, in RNA loop and bulge regions that frequently act as binding and/or active sites, the intrinsic preference of adenine for interactions with the adjacent phosphodiester moiety and its contribution to RNA function may be more prevalent than previously anticipated.Our experimental study of 15 nt RNAs and their deaza-derivatives with site-specific incorporation of cgkz574_Supplemental_FileClick here for additional data file.


As the applications of CRISPR-Cas9 technology diversify and spread beyond the laboratory to diagnostic and therapeutic use, the demands of gRNA synthesis have increased and access to tailored gRNAs is now restrictive. Enzymatic routes are time-consuming, difficult to scale-up and suffer from polymerase-bias while existing chemical routes are inefficient. Here, we describe a split-and-click convergent chemical route to individual or pools of sgRNAs. The synthetic burden is reduced by splitting the sgRNA into a variable DNA/genome-targeting 20-mer, produced on-demand and in high purity, and a fixed Cas9-binding chemically-modified 79-mer, produced cost-effectively on large-scale, a strategy that provides access to site-specific modifications that enhance sgRNA activity and in vivo stability. Click ligation of the two components generates an artificial triazole linkage that is tolerated in functionally critical regions of the sgRNA and allows efficient DNA cleavage in vitro as well as gene-editing in cells with no unexpected off-target effects. For CRISPR-Cas9 genome editing, Cas9 protein is guided to its target by single guide (sg) RNA. Here, the authors synthesised sgRNAs via convergent ‘click’ ligation of variable 20-mer RNAs that target the genome and a Cas9-binding 79-mer chimeric RNA/2´-OMe RNA of fixed sequence in a single tube. The resulting double-stranded breaks are recognised by the cell and repaired imperfectly, thus enabling the function of the cleaved sequence to be determined3. By partially inactivating the nuclease activity of Cas9 or creating dead Cas9 (dCas9) fusion proteins, it is even possible to image genomic loci in live cells4, reprogramme the transcriptome6, and create point mutated genomes8. At the core of these innovative applications, and a reason for CRISPR’s far greater adoption than zinc-finger nuclease and TALEN systems, is the fact that the (d)Cas9 protein is guided to its target by a sgRNA that is designed using simple Watson–Crick base-pairing rules.CRISPR-Cas9 genome editing has transformed our ability to manipulate genomes at the single-nucleotide level. The system is composed of a single-guide (sg) RNA that programmes a nuclease (Cas9) to cleave genomic DNA sequence specifically9, profiling single-cell phenotypes10 and studying epigenetic regulation11 have used ~4500, ~2300 and ~450 arrayed sgRNAs, respectively, and many applications are likely being hindered by limited access to sgRNAs14. Enzymatic methods for the preparation of sgRNAs can be complex and time-consuming, and in the case of viral plasmid delivery, raise safety concerns. Methods for direct chemical synthesis of sgRNAs are therefore important; they can provide access to chemical modifications that enhance sgRNA stability21 and reduce off-target effects23. However, 100-mer sgRNAs remain at the limit of solid-phase synthesis and the cost of oligoribonucleotides is far higher than deoxy variants, significantly increasing the barrier to their use. Efforts have been made to address these problems by using a bimolecular guide RNA system (a DNA-targeting ~42-mer crRNA that hybridises to a fixed ~80-mer tracrRNA) and incorporating 2′-F, 2′-OMe or deoxyribonucleotides into the crRNA/tracrRNA components, but this has come at the cost of larger constructs compared to the sgRNA design24.As the questions posed by researchers using CRISPR become more complex, the number of sgRNAs required has substantially increased. For example, high-content screens examining viral infectionHere we synergise and refine these approaches, and use chemical ligation to create a simple method for preparing individual or pools of sgRNAs. Importantly, we demonstrate that a genomic DNA-targeting RNA bearing an alkyne, prepared on demand and in high purity, can be efficiently ligated to an invariant Cas9-binding RNA bearing an azide, made cost-effectively on a large-scale, by simple untemplated copper-catalysed azide-alkyne cycloaddition (CuAAC) chemistry. The resultant sgRNA contains an artificial triazole backbone at the point of ligation that enables effective Cas9-mediated DNA cleavage in vitro and in cells, with a comparable off-target profile to in vitro transcribed sgRNA.26 chemical ligation, a reaction that has been reported to work well for RNA–RNA ligation29. A 37-mer crRNA was synthesised on solid phase with a terminal 3′-O-propargyl nucleotide introduced via a commercially available solid support, and a 66-mer tracrRNA was prepared with a terminal 5′-amino group that is post-synthetically labelled using a C6-azide NHS ester. The oligonucleotides were then chemically ligated using the CuAAC reaction, which after optimisation gave very efficient conversion to the clicked sgRNA  but might affect CRISPR activity, we produced an equivalent system that cannot be contaminated with copper. To achieve this the 37-mer crRNA was prepared with a terminal 3′-amino group, labelled with DBCO-NHS ester and reacted with the azido-tracrRNA under copper-free strain-promoted azide-alkyne cycloaddition (SPAAC) conditions  should facilitate CuAAC35. To access this linkage, the 5′-hydroxyl group of unmodified TBDMS-synthesised tracrRNA was converted to an azide via an iodo intermediate on the solid support prior to oligonucleotide deprotection36. Subsequent deprotection and ligation of the 5′-azide tracrRNA with the 3′-O-propargyl crRNA proceeded efficiently  is essential to control DNA target specificity. However, such purification may not be needed for clicked sgRNAs as both the ~20-mer and 79-mer RNAs are already purified by RP-HPLC prior to ligation. In order to explore this option to the extreme, the effect of residual starting material on Cas9 activity was first evaluated. Taking a purified clicked sgRNA-DNA construct and titrating in either the 79-mer tracrRNA-DNA or the ~20-mer RNA  confirmed that the Cas9-binding azido-79-mer inhibits target DNA cleavage but not the alkynyl-20-mer Fig. . Consequ′-sectionOne of the potential benefits of our chemical approach is the ease of mixing and matching DNA-targeting ~20-mers and reacting them with the invariant 79-mer to form pools of sgRNAs under denaturing conditions without fear of enzymatic bias that can occur during transcription. Therefore, to test this, CuAAC ligation was performed with a ~20-mer RNA containing either a 5′-C6-Cy3 or 5′-C6-ATTO 647N fluorescent dye. These fluorophores are well separated spectrally enabling quantification of CuAAC-mediated ligation via denaturing PAGE. Similar coupling efficiency was observed for both dye-labelled oligonucleotides , which is known to be recognised by Cas9 (ref. 42) and has a mixed A-/B-character in hybrid duplexes43. Promisingly, the introduction of 2′-OMe modifications synergistically offsets this effect, possibly through changes in sgRNA folding stability (note that only the 2′-OMe modified 79-mer required a heated HPLC column for purification).The individual sgRNAs assayed at 1 h Fig. , where cEMX1 target46 were prepared bearing a 5′-C6-amino group with and without deoxy- or 2′-OMe ribonucleotides, and IVT sgRNA was used as a control, a true gold standard for RNA integrity. Cells were transiently transfected with a Cas9-expressing plasmid and sgRNA, the genomic DNA was extracted and the target region amplified by PCR. Indel formation was then assessed using the T7E1 assay.Evaluating the on-target activity of the clicked sgRNAs in live cells was a key priority. sgRNAs for the established n = 6, biological replicates); however, upon the introduction of site-specific 2′-OMe modifications indel formation was significantly improved , and was comparable to IVT sgRNA. This is consistent with in vitro results  relative to the purified construct. The key difference in the crude preparation is that the excess ~20-mer RNA and unreacted 79-mer used during the click reaction are not removed. This may influence the lipid–RNA ratio in the transfection complex formed and hence transfection efficiency.Clicked sgRNAs with all ribonucleotides were functional in cells Fig. . The actlts Fig.  for shorlts Fig.  despite 46. Statistically enriched reads .Cautious of unintended changes in local base-pairing specificity and therefore potential off-target activity caused by the Tz2 linkage, we assayed for this possibility by comparing IVT sgRNA with the all-RNA clicked variant. The CIRCLE-seq protocol was chosen due to its NGS read-efficiency using genomic DNA, its sensitivity and its preference for over- rather than under-estimation of off-target effectsO-methyl-3′-phosphonoacetates that enhance target specificity or backbone and other 2′-sugar modifications that improve sgRNA stability23.In summary, we have demonstrated that the sgRNA can be fragmented, even at functionally critical regions, into smaller components, which after chemical ligation using the CuAAC reaction gives a sgRNA that performs efficient gene editing in cells comparable to IVT sgRNA. Our approach reduces the synthetic burden of sgRNA synthesis by allowing: (1) a fixed 79-mer component to be produced cost-effectively on a scale far higher than current enzymatic routes, with the potential for even larger-scale synthesis upon further development and (2) a highly pure variable DNA-targeting ~20-mer component to be produced on demand. Importantly the site-specific incorporation of modified nucleotides such as deoxy- or 2′-OMe ribonucleotides into our design is feasible and advantageous, as could be many other modifications such as bridged nucleic acids or 2′-24 or sequence modifications. Notably, the greatest strength of our split-and-click approach lies in screening numerous individual sgRNAs for function rather than preparing a single sgRNA. As the number of sgRNAs needed increases, the costs and time associated with repeated full-length sgRNA synthesis become greater and the merits of our approach become more evident. Moreover, clicked sgRNAs provide easier access to bespoke pools of modified sgRNAs (cf. full length sgRNA synthesis). Libraries of ~20-mer RNAs could be archived and custom libraries generated economically on demand with short turnaround times between sgRNA design and application.The successful use of the biocompatible triazole linkage, Tz2, allows radical fragmentation of the sgRNA. For example, the sgRNA could be split into multiple parts and combinatorially reassembled to access diverse sgRNA libraries containing chemicalO-propargyl G-ib 2′-lcaa CPG 1000 Å (Chemgenes) or 3′-amino-modifier C7 CPG 1000 Å (Link Technologies) were packed into a twist column (Glen research) for synthesis. 2′-O-TBDMS RNA phosphoramidites  were dissolved in anhydrous acetonitrile (0.1 M) immediately prior to use. Coupling, capping and oxidation reagents were 5-benzylthio-1H-tetrazole , fast deprotection Cap A (5% tert-butylphenoxyacetyl acetic anhydride in tetrahydrofuran)/Cap B (16% N-methylimidazole in tetrahydrofuran) and iodine , respectively. The coupling time during RNA synthesis was 10 min. Stepwise coupling efficiencies were determined by automated trityl cation conductivity monitoring and in all cases were >97%.RNA synthesis was performed on an Applied Biosystems 394 automated DNA/RNA synthesiser using a standard phosphoramidite cycle of detritylation, coupling, capping, and oxidation on a 1.0 µmole scale. Either nucleoside SynBase™ CPG 1000/110 (Link Technologies), 3′-After automated solid-phase synthesis, the fully protected resin-bound 5′-OH RNA was treated with methyltriphenoxyphosphonium iodide in anhydrous DMF  for 1 h at room temperature. The solid support was washed with dry DMF (3 × 1 mL) and dried with argon. A saturated sodium azide solution was then prepared by heating sodium azide (100 mg) resuspended in dry DMF (2 mL) for 10 min at 70 °C. After cooling to room temperature, the resin was treated with the solution for 5 h at 55 °C. The resin was then washed with DMF (3 × 1 mL), acetonitrile (3 × 1 mL) and dried with argon. The 5′-azido-RNA was cleaved from solid support and deprotected as described below.Oligonucleotides bearing primary aliphatic amines were treated with diethylamine (20% in anhydrous acetonitrile) for 20 min at room temperature to suppress the formation of cyanoethyl adducts. The resin was then washed with acetonitrile (3 × 1 mL) and dried with argon.g, 30 min, 4 °C), the supernatant discarded and the pellet washed twice with 70% ethanol (750 µL). The pellet was then dried in vacuo, dissolved in water and desalted using a NAP™-10 column before further purification.The solid support was exposed to concentrated aqueous ammonia:ethanol (3:1 v/v) in a sealed vial for 2 h at 55 °C. The solution was filtered and the ammonia removed in vacuo. The ammonia-free solution was then freeze-dried, re-dissolved in a 1:1 mixture of dry DMSO (300 µL) and triethylamine trihydrofluoride (300 µL) and heated for 2.5 h at 65 °C. After cooling down to room temperature, sodium acetate  and butanol (3 mL) were added and the RNA was stored for 30 min at −80 °C. The RNA was then pelleted by centrifugation  were used according to the manufacturer’s instructions. Typically, 3–5 washes were performed.Oligonucleotides were purified using a Gilson HPLC system with ACE® C8 column  with a gradient of buffer A  to buffer B  and a flow rate of 4 mL/min. For unmodified oligonucleotides , the gradient was 20–30% buffer B over 21 min. For other oligonucleotides, the gradient was suitably adjusted. Note that for 2′-OMe-modified oligonucleotides, the column was heated to 55 °C to disrupt secondary structures.2O) and 5′-azido-RNA (500 pmol in 1 µL H2O) were mixed with MgCl2 , triethylammonium acetate buffer , DMSO (5 µL) and fresh ascorbic acid . While degassing the oligonucleotide solution with argon in a 0.5 mL eppendorf, a solution of CuSO4-tris(3-hydroxypropyltriazolylmethyl)amine  was added and the reaction  was left for 1–2 h at room temperature. The sample was then diluted with water and desalted using an Amicon Ultra Centrifugal Filter.3′-Alkyne RNA (750 pmol in 1 µL H3′-DBCO RNA (1250 pmol) and 5′-azido-RNA (500 pmol) were mixed in a NaCl solution  containing EDTA  and HEPES  and were either left for 1–2 h at room temperature or heated for 5 min to 95 °C then cooled to 25 °C by 1 °C/min and left for 1–2 h at room temperature.Oligonucleotides were mixed with formamide (50% v/v) and loaded onto a denaturing 8–10% polyacrylamide gel  and separated at 20 W for 2–3 h. DNA bands were visualised under UV, excised, crushed, soaked in water  or buffer  overnight at 37 °C with vigorous shaking. After gravity filtration to remove the gel, the oligonucleotide solutions were concentrated in vacuo and desalted using two consecutive NAP™-25 columns according to the manufacturer’s instructions.2. The cell line was not mycoplasma tested or authenticated.U2OS cells (kindly gifted by Prof. G. Dianov) were cultured in DMEM  supplemented with fetal bovine serum  in a humidified incubator at 37 °C with 5% CO5 cells were seeded in six-well plates. After 48 h, the media was replaced with fresh media and the cells were transfected using Lipofectamine 3000 (3.75 µL), P3000 (10 µL) and pSpCas9(BB)-2A-Puro-v2-Broccoli (5 µg) according to the manufacturer’s protocol. After 12 h, the media was replaced with fresh media and the cells were transfected using Lipofectamine RNAiMAX (7.5 µL) and the appropriate sgRNA  according to the manufacturer’s protocol. After 12 h, the media was replaced with fresh media. Cells were then allowed to recover and grow for a further 72 h with media replaced when necessary.1.5 × 10Further information on experimental design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting SummarySource Data


The target RNAs are varied to form different sizes  and sequences in the bulge formed upon binding to the PNAzyme. PNAzyme-promoted cleavage of the target RNAs was observed and variation of the substrate showed a clear dependence on the sequence and size of the bulge. For targets that form 4-nt bulges, we identified systems with an improved efficacy  as well as systems with an improved site selectivity (up to over 70% cleavage at a single site as compared to 50–60% with previous targets sequences). For targets forming 3-nt bulges, the enhancement compared to previous systems was even more pronounced. Compared to a starting point of targets forming 3-nt AAA bulges , we could identify target sequences that were cleaved with half-lives three times lower (ca 7–8 h), i.e., at rates similar to those found for the fastest 4-nt bulge system. In addition, with the 3-nt bulge RNA target site selectivity was improved even further to reach well over 80% cleavage at a specific site.In this report, we investigate the efficiency and selectivity of a Zn Therapeutic oligonucleotides (ONs) provide an opportunity for treating serious, life-threatening diseases with limited options using traditional low molecular weight molecules and antibody drugs . AlthougMoreover, antisense oligonucleotide (AON) action based on a 1:1 binding stoichiometry may require a relatively high dosage of ON to silence the target mRNA transcript effectively . EfficacON-based artificial nucleases (OBANs) ,9,10,11 2+ as a co-factor were shown to act as RNA restriction enzymes with high site selectivity and half-lives of cleavage down to 15 to 30 min, at a 1:1 ratio of PNAzyme to RNA +4 calcd 3539, found 3539. RP-HPLC purification and analysis was carried out on a Ascentis Express Supelco Peptide ES-C18 column  with a linear gradient elution of 27% B over 30 min at 60 °C, using a flow rate of 1.0 mL/min and UV detection at 260 nm. The following solvent system was used: solvent A, water containing 0.1% TFA; solvent B, 50% MeCN: water containing 0.1% TFA , resulting in 100 μM accessible zinc ions). PNAzyme1 is known to cleave phosphodiester bonds efficiently in the presence of copper ions −2 calcd 1528, found 1527, Fragment2  C96H118N40O68P10 [M]−2 calcd 3230, found 3229. RNA13 (-AUAA-) Fragment1 C47H61N19O32P4 [M]−2 calcd 1528, found 1527, Fragment2  C96H117N39O69P10 [M]−2 calcd 3231, found 3230. RNA24 (-AUA-) Fragment1 C47H61N19O32P4 [M]−2 calcd 1528, found 1527, Fragment2  C86H105N34O63P9 [M]−2 calcd 2902, found 2901. RNA32 (-GUA-) Fragment1 C47H61N19O32P4 [M]−2 calcd 1528, found 1527, Fragment2  C86H105N34O64P9 [M]−2 calcd 2918, found 2917.RNA cleavage reactions were performed as above. The main RNA fragments were collected from IE-HPLC runs and desalted as described earlier. Mass spectrometry of the collected fractions was performed in negative ion mode using a solution of MeCN–water  and increased site selectivity (from 50–60% to over 70%). Three-nt bulges in previous studies were cleaved less efficiently by Zn2+-based PNAzymes than 4-nt bulges [2+ PNAzyme is still less efficient than the most active Cu2+-dependent PNAzymes, but the present study demonstrates that for Zn2+-dependent PNAzymes carrying novel chelates, not only the “cleaving unit” is important, but it is also essential to investigate different potential RNA targets in order to find optimal PNAzyme action. This should be even more important as PNAzymes are developed for use in cell assays and eventually in vivo. We, for the first time, report on how the sequence of an RNA bulge formed upon hybridization to a Znt bulges . However


This approach has significant advantages over existing technologies, particularly in its versatility in regards to binding partner character. Indeed, proof-of-principle application of RNA arrays to both RNA–small molecule and RNA–RNA pairings is demonstrated, highlighting their potential as a platform technology for mapping RNA-based networks and for pharmaceutical screening. Furthermore, the simplicity of the method supports greater user-accessibility over currently available technologies. We anticipate that functional-RNA arrays will find broad utility in the expanding field of RNA characterization.We are just beginning to unravel the myriad of interactions in which non-coding RNAs participate. The intricate RNA interactome is the foundation of many biological processes, including bacterial virulence and human disease, and represents unexploited resources for the development of potential therapeutic interventions. However, identifying specific associations of a given RNA from the multitude of possible binding partners within the cell requires robust high-throughput systems for their rapid screening. Here, we present the first demonstration of functional-RNA arrays as a novel platform technology designed for the study of such interactions using immobilized, active RNAs. We have generated high-density RNA arrays by an innovative method involving surface-capture of  Understanding the molecular interactions that underlie biological processes is a fundamental aspect of basic biological research and pharmaceutical drug development. The last two decades have seen the development of high-throughput gene expression profiling technologies, including DNA microarrays and RNA-sequencing (RNA-seq), leading to an increased focus on RNA and its interacting partners. Indeed, increasing numbers of RNA–protein , RNA–smaSome progress has been made towards the development of high-density RNA surfaces that are suitable for probing RNA–protein interactions. The Greenleaf ,12 and LA major drawback of the existing technologies is the reliance on specialist equipment, not always available to the everyday RNA researcher. Here, we present a simple, innovative method for the production of the first high-density functional-RNA arrays, programmed by DNA arrays, that would be suitable for high-throughput analysis of RNA interactions. We demonstrate proof-of-principle application of these arrays to both RNA–small molecule and RNA–RNA interactions, exemplifying the unique capability of our RNA array platform with regard to the range of binding partners that can be investigated. We highlight the potential utility of RNA arrays as a platform technology in the development of novel antimicrobial strategies and RNA therapies.in vitro transcription templates for preparation of DNA template arrays contained a biotinylated 5′ linker (5′–biotin–ctc gag–3′) to facilitate surface-immobilization, immediately followed by a T7 promoter (5′–taa tac gac tca cta tag–3′) and then the sequence encoding the RNA of interest  DNA template array, were generated by standard PCR using the MicA/MicAsa PCR templates prepared above, sense primer: 5′–biotin–ctc gag taa tac gac tca cta tag g–3′ and antisense primers 5′–Alexa647–aaa agg cca ctc gtg agt–3′ or 5′–Alexa647–gca tgc atc ccg gcc cgc gac tat ctt acg ca–3′ for MicA and MicAsa, respectively.5′ biotin- and 3′ fluorescently-labelled DNA in vitro transcription templates were purified using a standard spin column-based PCR-clean-up kit and their size was confirmed by agarose gel electrophoresis.All PCR and 2O (wash-cycle A). They were then submerged in 45 ml 50 mM ethanolamine–HCl pH 8.5 and incubated at room temperature for 30 min, with rocking, to block unreacted NHS functional groups. Wash-cycle A was repeated and the slides were dried by centrifugation at 500 × g for 5 min at room temperature.For low-density surfaces, NHS-activated microarray slides  were spotted with 10 μl aliquots of 16.7 μM (1 mg/ml) streptavidin in phosphate-buffered saline . For high-density surfaces, NHS-activated slides were coated with 90 μl 1μM streptavidin in PBS and covered with a LifterSlip (Thermo Fisher Scientific). Spotted or coated slides were incubated at 37°C for 1 h in a humidified chamber. Where present, LifterSlips were removed and slides were washed, with rocking, once for 5 min with 45 ml PBS containing 0.05% (v/v) Tween 20 (PBS-T), once for 5 min with 45 ml PBS and once for 5 min with 45ml Hg for 5 min at room temperature.For low-density arrays, between 0.2 μl and 10 μl of 200 nM 5′ biotinylated DNA template in PBS was manually pipetted onto the streptavidin on the prepared streptavidin-spotted slides. For high-density surfaces, an automated arrayer (Genetix Qarray2) fitted with a 200 μm pin-head was used to spot 200 nM 5′ biotinylated DNA template onto prepared streptavidin-coated slides at the spot separations indicated in the Figure legends. The DNA-spotted slides were incubated at 20°C and 50% relative humidity for 30 min, washed using wash-cycle A and dried by centrifugation at 500 × sa–Alexa647 in vitro transcription templates were visualized at 670 nm, following excitation at 650 nm, using a microarray slide scanner .DNA-template arrays containing 3′ fluorescently-labelled MicA–Alexa647 and MicAStreptavidin surfaces, for use as RNA capture surfaces, were prepared as described above for DNA template arrays, except that streptavidin was used at a concentration of 16.7 μM (1 mg/ml) in PBS to produce coated (high-density RNA capture) surfaces.Spotted (low-density) tobramycin RNA capture surfaces were prepared as described above for streptavidin surfaces, except that 5 mM tobramycin in PBS was used instead of streptavidin.in vitro transcription mixture ), 1X Enzyme Mix (MEGAscript T7 Transcription Kit) and 0.5 mM of ATP, CTP, GTP and UTP) were assembled into a ‘sandwich’ arrangement  and the transcription reaction was performed in the dark. Arrays containing Cy3- or Cy5-labelled RNAs were visualized at 570 or 670 nm, following excitation at 550 or 650 nm, respectively, using a microarray slide scanner.For preparation of Cy3/Cy5-labelled RNA arrays, the ompA and Qrr1 RNA probes were generated by in vitro transcription. The relevant PCR template (prepared above) was used as the template in an in vitro transcription reaction using the MEGAscript T7 Transcription Kit supplemented with 0.05 mM Cy3/Cy5-UTP. The probes were purified using the MEGAclear Transcription Clean-up Kit (Thermo Fisher Scientific).Internally Cy3/Cy5-labelled 2, 20 mM NaCl) were pipetted over the functional-RNA array. This was covered with a LifterSlip and incubated at 20°C, in a humidified chamber, in the dark for 16 h, unless stated otherwise. The LifterSlip was removed and the probed array was washed with 45 ml hybridization buffer for 1 min and 45 ml water for 10 s (wash-cycle B) before being dried by centrifugation at 500 × g for 5 min at room temperature. Simultaneous probing was carried out as described for single probes using concentrations of 3 μM for each individual RNA probe and 20 μM for malachite green. Probed arrays were visualized at 570 nm (Cy3-labelled RNA probes) and/or 670 nm , following excitation at 550 nm and/or 650 nm, respectively, using a microarray scanner.For single probes, 90 μl of 30 nM–3 μM  labelled RNA probe or 20 μM malachite green (Sigma) in hybridization buffer (40 mM Tris (pH 7.8), 6 mM MgClin vitro transcribed RNAs. A surface consisting of an array of immobilized double-stranded-DNA in vitro transcription templates is positioned opposite to an RNA capture surface, in a ‘sandwich’ arrangement, with the two surfaces connected by a solution of in vitro transcription reagents  functionality of the RNAs on a generated RNA array and (iii) generation of multi-RNA arrays, were performed using manually spotted, low-density, DNA template arrays.Initial proof-of-concept studies to demonstrate (i) surface-capture of RNA aptamers with nanomolar affinity for small-molecules (e.g. the aminoglycoside tobramycin ) or prottob) conjugates and the corresponding unconjugated RNAs. The RNAs of interest included three bacterial sRNAs and the 5′ UTR of a bacterial mRNA. Following transcription using the ‘sandwich’ arrangement described in Figure tob conjugates were preferentially captured on a tobramycin capture surface relative to unconjugated RNAs (DNA template arrays were prepared encoding a variety of RNA–tobramycin aptamer (RNAted RNAs . Howeverted RNAs .sa) conjugates and the corresponding unconjugated RNAs. In this case, the RNAs of interest included bacterial sRNAs, the 5′ UTRs of bacterial mRNAs, and a synthetic RNA aptamer. As shown in sa conjugates were present on the generated RNA array, the nonconjugated RNAs were not captured. There was no evidence of non-specific binding of RNA to the capture surface. Therefore, we decided to focus our efforts on RNAsa conjugates.Due to the non-specific binding of RNA to the tobramycin surface, we next decided to evaluate the use of the streptavidin aptamer for RNA-capture. As above, DNA template arrays were prepared encoding a variety of RNA–streptavidin aptamer (RNAompA mRNA interaction (ompA by binding to the 5′ untranslated region (5′ UTR) of ompA mRNA, inhibiting translation and targeting the RNA for degradation , was prepared  and mald Figure . The RNAd Figure . The spein vitro transcription templates onto glass slides. Spotting of 10 μl DNA template produces spots of ∼8 mm diameter, allowing for a density of four templates per standard 25 × 75 mm glass slide. By reducing the spotting volume to 0.2 μl, spots of ∼2 mm diameter were produced and a density of 24 templates per slide was achieved. These low- to medium-density arrays may be useful for addressing direct mechanistic questions as part of a basic research project. However, much higher density arrays would be required for most pharmaceutical applications.Our initial validation experiments were carried out using DNA template arrays generated by manual spotting of the DNA sain vitro transcription templates    Figure . This wa) Figure . The spo surface . Further surface  and gene surface . TherefoHaving successfully generated a high-density RNA array of identical RNA molecules, we wanted to expand our method to the fabrication of high-density multi-RNA arrays. Furthermore, we wanted to demonstrate the potential of these arrays as a high-throughput screening platform for RNA-based interactions.ompA or Qrr1, an sRNA that binds to and regulates the expression of hapR mRNA  of the interaction and the fluorescence intensity of the probe. To demonstrate this, we selected the interaction between hapR and Qrr1. The theoretical ΔG of the interaction between the wild-type hapR and Qrr1 was calculated using Mfold , known to disrupt hapR:Qrr1 pairing sa and hapR(C64G)sa, a hapR variant resulting in a theoretical hapR:Qrr1 ΔG of –14.2 kcal/mol , with several in the 75–200 nucleotide range . This size range encompasses many prokaryotic, eukaryotic and viral RNAs of functional interest, as well as biotechnologically relevant synthetic RNAs such as aptamers, and we envisage that longer RNAs could be produced, if required. The functional-RNA arrays were probed with either a small-molecule that fluoresces upon RNA-binding or a fluorescently-labelled RNA binding partner. However, a wide-range of probes could be used, including protein, DNA, RNA and small molecules.Here we have demonstrated an innovative method for the production of high-density arrays of functional RNAs that involves the surface-capture of s Figure . Each ofgical pH . Using tin vitro transcription templates in their photolithography-based method, limited the size of the RNAs that could be generated. An additional benefit of our method over both of the currently available platforms is the 3′-tethering of the RNA to the capture surface. The conjugation of an aptamer to the 3′ end of the RNA of interest, to facilitate surface-immobilization, ensures that only full-length, correctly folded RNAs are captured.Our production method has significant advantages over previously reported technologies. Firstly, the physical separation of the DNA template and RNA capture surfaces simplifies the final RNA surface, relative to those in the methods based on the Illumina Next Generation Sequencing platform . The SmiThe advantage of RNA-based tethering, to produce a simpler RNA surface, combined with the ability to immobilize full-length functional RNAs expands the utility of multi-RNA platforms. To-date, applications have focused solely on RNA–protein interactions. The Illumina-based approaches, which retain the DNA template on the ‘RNA’ surface, are unsuitable for studying primarily sequence-driven RNA–RNA interactions and the length-limitations of the photolithography method exclude the investigation of interactions relying on a global structure such as RNA–small molecule interactions. We have now demonstrated the proof-of-principle application of high-density functional-RNA arrays to the investigation of RNA–RNA and RNA–small molecule interactions. Furthermore, our method is more accessible than the existing technologies, both of which require specialist equipment. Preparation of the high-density RNA arrays does require access to an automated arrayer. However, low/medium-density arrays can easily be prepared and probed using standard laboratory equipment.ompA . sRNAs act by imperfect base-pairing to an mRNA target, typically within the 5′ UTR, modulating translation initiation and/or transcript stability ,32. ThroompA  and Qrr1pA  thatIn addition, by using protein synthesis as a read-out, the RNA array platform has scope for use in post-transcriptional regulation assays. These assays would be applicable to the non-coding RNA systems discussed above, small molecule–RNA-based systems (e.g. riboswitches ) and theOverall, we have demonstrated a simple, novel method for the production and application of the first functional-RNA arrays that has significant advantages over previously reported technologies improving accessibility of RNA array platforms to the RNA research community. More importantly, our method is uniquely suitable for the investigation of RNA–RNA and RNA–small molecule interactions. Our RNA arrays have true platform technology capabilities, with broad potential applications in the non-coding RNA, post-transcriptional gene regulation and biosensor fields.Supplementary DataClick here for additional data file.