Post-transcriptional mechanisms play a predominant role in the control of microRNA (miRNA) production. Recognition of the terminal loop of precursor miRNAs by RNA-binding proteins (RBPs) influences their processing; however, the mechanistic basis for how levels of individual or subsets of miRNAs are regulated is mostly unexplored. We previously showed that hnRNP A1, an RBP implicated in many aspects of RNA processing, acts as an auxiliary factor that promotes the Microprocessor-mediated processing of pri-mir-18a. Here, by using an integrative structural biology approach, we show that hnRNP A1 forms a 1:1 complex with pri-mir-18a where both RNA recognition motifs (RRMs) bind to cognate RNA sequence motifs in the terminal loop of pri-mir-18a. Terminal loop binding induces an allosteric destabilization of base-pairing in the pri-mir-18a stem that promotes its downstream processing. Our results highlight terminal loop RNA recognition by RBPs as a potential general principle of miRNA biogenesis and regulation. hnRNP A1 is an auxiliary factor that promotes the Microprocessor-mediated processing of pri-mir-18a, of the oncomiR-1 cluster. Here the authors employ an integrative structural biology approach and provide insights into the molecular mechanism of how hnRNP A1 facilitates pri-mir-18a biogenesis. Mature miRNAs are generated by two RNA cleavage steps involving nuclear and cytoplasmic RNase III enzymes . Primary miRNA  transcripts are cropped by the Microprocessor complex (comprising Drosha and DGCR8) in the nucleus giving rise to ~70 nucleotide (nt) stem-loop precursor miRNAs , which following export to the cytoplasm, are further processed by Dicer into mature miRNAs2. In higher organisms, many miRNA genes are transcribed together as a cluster3. A prototypical example is the miR-17–92 cluster that is encoded as an intronic polycistron comprising six individual miRNAs that are highly conserved in vertebrates 5. This cluster is frequently amplified and overexpressed in human cancers; hence, it is also referred to as oncomiR-1.MicroRNAs  are a class of highly conserved small non-coding RNAs that play a crucial role in the regulation of gene expression. They are involved in a variety of biological processes including cell growth, proliferation, and differentiation6. A number of specific RNA-binding proteins (RBPs) have recently emerged as important post-transcriptional regulators of miRNA processing. However, very little is known about their mechanism of action. Previously, we identified heterogenous nuclear ribonucleoprotein A1 (hnRNP A1) as an auxiliary factor that positively regulates the processing of miRNA-18a primary transcript (pri-mir-18a) by making specific contacts to the terminal loop of the RNA8 -mediated translation12, mRNA stability13, and telomere maintenance15.The biogenesis of miRNAs is tightly regulated and results in tissue-specific and developmental-specific expression patterns of miRNAsNA8 Fig. . HnRNP AHere, we have combined an integrative structural biology approach with biochemical and functional assays to provide mechanistic insights into the role of hnRNP A1 in stimulating pri-mir-18a processing. We show that hnRNP A1 forms a 1:1 complex with pri-mir-18a in solution, with the recognition of two UAG motifs by the tandem RRM domains of hnRNP A1 revealed by a high-resolution crystal structure. NMR and biophysical data show that high-affinity binding involves recognition of two UAG motifs in the pri-mir-18a terminal loop and the proximal stem region. Notably, binding to the terminal loop induces an allosteric destabilization of base-pairing in the pri-mir-18a stem that promote its processing. These findings may serve as a paradigm for the regulation of miRNA processing by the recognition of the terminal loop by RBPs.7. HnRNP A1 has two RNA recognition motif (RRM) domains, each harboring conserved RNP-1 and RNP-2 submotifs, and a C-terminal flexible glycine-rich tail, which includes the M9 sequence responsible for nuclear import and export16. The RNA-binding region of hnRNP A1, comprising the tandem RRM1-RRM2 domains, is referred to as UP1 (Unwinding Protein 1)17  or with Asp or Ala in both RRM1 and RRM2 domains  is sufficient to abolish the activity of UP1-M9 in our in vivo pri-mir-18a processing assay without affecting the nuclear localization of the protein constructs 17 Fig. . To iden17  with RNA variants corresponding to the terminal loop and stem of pri-mir-18a Fig.  and UP1,17 Fig. . We obse17 Fig. . AltogetKD) of 147 nM  corresponding to the terminal loop of pri-mir-18a harbors an UAG motif, which is known to be recognized by hnRNP A118. This 7-mer single-stranded RNA binds to the individual RRM1 and RRM2 domains with low micromolar affinity  forming 1:1 complexes (Table KD of 3.4 µM (Table UAGAUUAGCA 3′) derived from the pri-mir-18a terminal loop and flanking sequences  to UP1   experiments. Pri-mir-18a 71-mer RNA binds to UP1 with a dissociation constant  Fig. . Binding19  in RRM1 and RRM2 constructs, thus demonstrating that each of the RRMs can interact with the 7-mer RNA Fig. . The CSP20. As this peculiar 2:2 stoichiometry most likely does not represent the UP1:RNA complex in solution, we analyzed UP1, RNA and the protein–RNA complexes by static light scattering (SLS)  Fig. . Both UPLS) Fig. .Table 2C20 and recently reported structures with RNA22. Two conserved aromatic residues in RRM1, Phe17 (RNP-2 motif residue located on β1) and Phe59 (RNP-1 motif residue located on β3), are involved in stacking interactions with the bases of A4 and G5, respectively   Fig. . SAXS anUUUAUUAGCA and 12-mer-mut2: AGUAGAUUUUCA show 10-fold and 20-fold (KD = 154 nM and 330 nM) reduced binding affinity, respectively, compared to the wild-type sequence  in this interface, which is remote from the RNA-binding surface, decreases the binding affinity to the 12-mer RNA by ≈3-fold (KD ~15.5 nM vs. ~40 nM)  binding of UP1 to both UAG motifs.The observation of dynamic base-pairing in the loop-proximal region is consistent with melting of the stem region to enable recognition of both UAG motifs by UP1. In fact, NMR titrations of UP1 with the 17-mer stem-loop RNA show virtually identical CSPs to those seen with the single-stranded 12-mer RNA Fig. . As NMR NA Table . These f1H, 15N imino correlations of the pri-mir-18a 71-mer RNA in the free form and bound to UP1  and maximum pairwise distance (Dmax) values of 37.7 Å and 130 Å, respectively, consistent with a 1:1 complex . The SHAPE reactivity reflects the average intensity of the NMIA-treated RNA primer extension products, normalized to the corresponding untreated RNA, in the presence or absence of UP1/hnRNP A1 proteins.To assess the RNA structure of pri-mir-18a and potential effects of flanking regions in the context of the pri-mir-17-18a-19a cluster, we performed selective 2′-hydroxyl acylation analyzed by primer extension . As indicated by NMR  become protected by protein binding Fig. . This reChanges in SHAPE reactivity observed for the pri-mir-17-18a-19a cluster in the presence of hnRNP A1/UP1 also revealed the protection of nucleotides spanning the terminal loop of pri-mir-18a (nts 170–186). Thus, most of the highly reactive residues in pri-mir-18a display a similar behavior as the pri-mir-17-18a-19a transcript upon addition of UP1/hnRNP A1 proteins Fig. . ImportaIn summary, relative to the free RNA the presence of hnRNP A1/UP1 leads to a decreased SHAPE reactivity around the terminal loop of pri-mir-18a, indicating that this region is the major binding site for the RRMs of hnRNP A1.7. Indeed, footprinting and site-directed mutagenesis of pri-mir-18a suggested that hnRNP A1 alters the local conformation of the stem in the vicinity of Drosha cleavage sites7, although the molecular mechanism was unclear. Indeed, UP1 (unwinding protein 1) can unwind secondary and higher order structures of DNA and RNA33. We constructed a series of mutants in the terminal loop region of pri-mir-18a. These include single and double nucleotide mutants, where an A residue within the UAG motif in the terminal loop or within the second UAG motif, were mutated to C , and a triple mutant (pri-mir-18a[U21A/U29A/U34A]), where all UAG motifs were mutated to AAG. We also designed a mutant, in which the terminal loop was stabilized by five G:C base pairs (pri-mir-18a[5GC]). The wild-type sequence was efficiently processed in the context of the pri-mir-17-18a-19 cluster  leads to partial opening/melting of the terminal loop, which can lead to destabilization of the stem region and thus render it more accessible for processing by Droshater Fig.  and isolter Fig. . The sinter Fig.  and the ter Fig. . The triter Fig. . Howeverter Fig. . The priter Fig. . This laNext, we hypothesized that destabilization and unfolding of the upper stem region close to the terminal loop in pri-mir-18a induced by hnRNP A1 binding can spread along the RNA stem and thus lead to destabilization of the RNA near the Drosha cleavage site. To examine this hypothesis, we tested a different mutant pri-mir-18a, in which the terminal loop was clamped by 5 G:C base pairs . Note, that this RNA variant is different from the pri-mir-18a[5GC] mutant described above, in which the terminal loop region was stabilized by 5 G:C base pairs. Remarkably, the processing of this RNA was impaired in the context of pri-mir-17-18a-19 cluster Fig.  and isolTo rule out the possibility that the internal 5GC clamp affects Drosha processing irrespective of the requirement for hnRNP A1 as an auxiliary factor, we examined the processing of pri-mir-16 with and without the 5GC clamp. Both wild-type pri-mir-16 and pri-mir-16[5GC_internal] were processed by Drosha, indicating that the internal 5GC clamp is not sufficient to impair Drosha processing Fig. . Further22, but reminiscent of recognition of two RNA motifs in single-stranded cis regulatory elements in alternative splicing regulation by hnRNP A121. A common feature for all structures is that the overall domain arrangement and interface in free and nucleic acid bound hnRNP A1 tandem RRMs is conserved  made its processing more efficient and completely independent of hnRNP A18.How does binding of a regulator to the terminal loop of pri-miRNA affect Drosha cleavage at the opposite end of the RNA? We propose that binding of hnRNP A1 to the terminal loop of pri-mir-18a leads to destabilization of base pairs and  melting of the loop-proximal stem region, with subsequent spreading of destabilization of the stem region towards the Drosha cleavage site Fig. . In supp44. Recognition of specific sequences  and/or structural features at these two opposite regions of a pri-miRNA are proposed to guide the binding orientation and conformation of the Drosha/DGCR8 complex and define the cleavage site. Thus, various RBPs that recognize specific sequence motifs in terminal loops may further regulate and enhance processing of specific pri-miRNAs.We propose three non-mutually exclusive mechanisms by which hnRNP A1 may enhance the processing of pri-mir-18a. hnRNP A1 binding may 1) promote Drosha processing by stem destabilization and thereby facilitate the recruitment and/or assembly of an active Microprocessor substrate complex, 2) enhance the assembly of pri-mir-18a with the correct orientation onto the Microprocessor by generating a thermodynamic or conformational asymmetry in the stem, 3) increase the accessibility of the Microprocessor to the pri-mir-18a hairpin in the context of the complete oncomiR-1 cluster. Options 1 and 2 are consistent with recent biochemical, structural and computational studies that have shown that the Microprocessor recognizes two regions at either end of pri-miRNA, i.e., near the ssRNA/stem basal junctions at the bottom and near the terminal loop junction41. A similar role may thus extend to RBPs such as hnRNP A1 that bind to terminal loops of specific pri-miRNAs.Here, we found that strengthening the upper part of pri-mir-18a stem by GC base pairs blocks miRNA processing, whereas disruption of the base pairs enhances Microprocessor cleavage efficiency. It is noteworthy, that the destabilization of the apical RNA helix by binding of hnRNP A1 induces a structural and thermodynamic asymmetry in the stem region (stably base-paired vs. dynamic RNA helix) that may help define, in which orientation pri-mir-18a is recognized by the Microprocessor. It was recently proposed that heme binding by DGCR8 is required to ensure the correct Microprocessor assembly8. Interestingly, several studies have shown that the miR-17–92 cluster adopts a compact tertiary structure, in which individual miRNAs have different expression levels depending on their accessibility46. Notably, a recent SHAPE analysis of the miR-17–92 cluster revealed that the terminal loop of pri-mir-18a in the cluster is partially solvent inaccessible32. As the terminal loop corresponds to the sequence that we have identified as the main hnRNP A1-binding site in pri-mir-18a, it is tempting to speculate that binding of hnRNP A1 to the miR-17–92 cluster is associated with a conformational change in the RNA, which can facilitate Microprocessor assembly and Drosha cleavage. We propose that the tertiary structure of pri-mir-18a in the context of the miR-17–92 cluster, as well as sequences in the stem and loop region, are important determinants of miRNA processing by Drosha. The recognition of the pri-mir-18a stem-loop by hnRNP A1 thus adds an additional layer for the regulation of pri-miRNA processing by an RBP, beyond features that have been recently identified47.It is also likely that the processing efficiency of pri-mir-18a is context-dependent, suggesting that the sequence and/or structure of pri-mir-18a as part of the miR-17–92 cluster is not optimal for Drosha processingIn conclusion, our data demonstrate that recognition of a conserved terminal loop RNA sequence in pri-miRNAs by an RBP can strongly modulate miRNA biogenesis by conformational changes and dynamic destabilization induced by RNA binding. This suggests that recognition of pri-miRNAs by RBPs is a general paradigm for context-dependent regulation of miRNA biogenesis and function.E. coli BL21(DE3) cells (Novagen) in standard media or minimal M9 media supplemented with 1 g/L 15N-ammonium chloride and 2 g/L 13C-glucose. Protein expression and purification was done as described previously10. After growth of bacterial cells up to an OD600 of 0.7–0.8, protein expression was induced by 0.5 mM IPTG and continued overnight at 20 °C. Cells were resuspended in buffer A  glycerol) supplemented with protease inhibitors, and lysed by sonication. The cleared lysate was loaded on Ni‐NTA resin and after several washing steps with buffer A and buffer A containing 25 mM imidazole the protein was eluted with elution buffer containing 300 mM imidazole. After cleavage of the tag by His‐tagged TEV protease at 4 °C overnight, samples were reloaded on Ni‐NTA resin to remove the tag, TEV protease and uncleaved protein. All protein samples were further purified by size-exclusion chromatography on a HiLoad 16/60 Superdex 75 column  equilibrated with NMR buffer .The sequences encoding RRM1 (amino acids 1–97), RRM2 (amino acids 94–196), and UP1 (amino acids 1–196) were subcloned into the pETM11 vector (EMBL) encoding a His tag followed by TEV cleavage site  for 1 h at room temperature, washed with PBS, and further incubated with 1:200 fluorescein-conjugated goat anti-mouse IgG (Cappel Laboratories) (1 h at room temperature). Samples were observed on a Zeiss Axioskop microscope and the images were acquired with a Photometrics CH250 cooled CCD camera using Digital Scientific Smartcapture extensions (IP Lab Spectrum software). The immunofluorescence figures show representative data, and each experiment was reproduced in multiple independent transfections.49. Mouse monoclonal anti-T7 tag HRP conjugate  was used to detect T7-tagged proteins. Mouse monoclonal anti-tubulin  was used as loading control. miScript qRT-PCR kit (Qiagen) was used to detect miRNAs in total RNA isolated with TRIzol reagent (Life Technologies). Each sample was run in duplicate. To measure the levels of the corresponding miRNAs, values were normalized to 5 S RNA. For each measurement, three independent experiments were performed.HeLa cells were cultured in standard DMEM medium (Life Technologies) supplemented with 10% FBS (Life Technologies). Plasmids were transfected into HeLa cells using Lipofectamine 2000 reagent, as described beforeUnlabeled 7-mer, 10-mer, 12-mer, and 17-mer RNA oligonucleotides were purchased from IBA in double-desalted form . All other RNA samples were made by in vitro transcription and purified by denaturing PAGE. RNA samples were heated to 95 °C for 3 min and snap-cooled on ice before use to promote proper folding.E. coli. Gel-purified RNAs (50 × 103 c.p.m. (counts per minute), ~20 pmol) were incubated in 15 μL reaction mixtures containing the specified amounts of proteins in Roeder D buffer  glycerol, 0.2 mM EDTA, 100 mM Tris at pH = 8.0, 0.5 mM DTT, 0.2 mM PMSF) complemented with 0.5 mM ATP, 20 mM creatine phosphate, and 3.2 mM MgCl2. Reactions mixtures were incubated at 4 °C for 1 h followed by electrophoresis on a 6% (w/v) non-denaturing gel. The radioactive signal was captured with radiographic film or was exposed to a phosphoimaging screen and scanned on a FLA-5100 scanner (Fujifilm).Electrophoretic mobility shift assays (EMSA) were performed with internally labeled RNAs and proteins produced in 32P]-UTP. Gel-purified substrates (20 × 103 c.p.m. (counts per minute), ~20 pmol) were incubated in 30 μL reaction mixtures containing 50% HeLa cell extract in Roeder D buffer, 0.5 mM ATP, 20 mM creatine phosphate, and 3.2 mM MgCl2. Then the reactions were incubated at 37 °C for 30 min. The reactions were stopped with 2 × Urea Dye and followed by 8% (w/v) denaturing gel electrophoresis. The signal was registered with a radiographic film or by exposure to a phosphoimaging screen and scanning on a FLA-5100 scanner (Fujifilm).Pri-miRNA substrates were obtained by in vitro transcription with [α-49. In brief, protein extracts from HeLa cells were incubated with in vitro-transcribed RNAs, coupled to adipic acid dihydrazide-agarose beads (Sigma). Next, the beads were washed three times with buffer G  glycerol, 1 mM EDTA, 1 mM DTT, and 0.2 mM PMSF). After the final wash, the proteins associated with the beads were resolved and identified by SDS–PAGE and western blotting, respectively. The following antibodies were used: rabbit polyclonal anti-DHX9 ; rabbit polyclonal anti-hnRNP A1 antibody .RNA pull-down was performed as previously described50. In summary, the RNA substrates were synthesized by in vitro transcription and were 5′ labeled with PKA. A formamide ladder and ribonuclease T1 ladder were generated to assign the nucleotide position. Ribonuclease T1 at 0.5 U/μL was added to 1 μL of RNA (50 × 103 c.p.m.) and 7 μL of 1 × structure buffer . RNAs substrates were unfolded at 90 °C for 1 min and left at RT for 5 min to refold. The footprinting reactions were incubated at 37 °C for 10 min. Reactions were run in the presence and absence of the recombinant UP1 protein and ribonuclease T1. Reactions were run on 10% polyacrylamide gel. The signal was registered with a radiographic film or via exposure to a phosphoimaging screen and then scanned on a FLA-5100 scanner (Fujifilm).The assays were performed as described before51 and analyzed using Sparky . Samples were measured in NMR buffer with 10% D2O added as lock signal. Backbone resonance assignments of UP1 alone and in complex with RNA were obtained from a uniformly 15N,13C-labeled UP1  in the absence and presence of saturating concentrations of RNA. Standard triple resonance experiments HNCA, HNCACB and CBCA(CO)NH52 were recorded at 600 MHz.15N relaxation experiments were recorded on a 600-MHz spectrometer at 25 °C. 15N T1 and T1ρ relaxation times were obtained from pseudo-3D HSQC-based experiments recorded in an interleaved fashion with 12 different relaxation delays  for T1 and 8 different relaxation delays  for T1ρ. Two delays in each experiment were recorded in duplicates for error estimation. Relaxation rates were extracted by fitting the data to an exponential function using the relaxation module in NMRViewJ53.NMR experiments were recorded at 298 K on 900, 800, and 600 MHz Bruker Avance NMR spectrometers, equipped with cryogenic triple resonance gradient probes. NMR spectra were processed by NMRPipe54.Paramagnetic relaxation enhancements (PREs) were recorded at 600 MHz using a sample with concentration of ~200 µM. UP1(Glu66Cys) was spin-labeled by adding 10-fold excess of 3-(2-Iodoacetamido)-PROXYL to the protein solution  and incubating at 4 °C overnight. Unreacted spin-label was removed by size-exclusion chromatography. Completion of the spin-labeling reaction and attachment of a single spin-label on the protein was confirmed by LC/MS. Control spin-labeling reactions using wild-type UP1 protein showed that the native cysteines at positions 43 and 175 are inaccessible. PRE data were recorded and analyzed as described previously1HN-15N bonds were measured on a ~200 µM sample of the UP1/12-mer RNA complex aligned in Pf1 phage (~10 mg/mL) using the IPAP experiment55. RDCs were best-fitted to the structure by singular value decomposition (SVD) using PALES56.Residual dipolar couplings (RDCs) for 57. The CCP4 package58 was used for all subsequent data analysis. The structure of the UP1/12-mer RNA complex was solved by molecular replacement with the program Phaser59 using the coordinates of UP1 bound to modified telomeric DNA (PDB code: 1U1R)60 as search model. Model building was performed manually in Coot61 and refinement was done using Phenix62.Screening for crystallization conditions was done using commercial screens (QIAGEN and Hampton Research) at 20 °C. UP1  was mixed with 12-mer RNA in a 1:1 molar ratio (0.95 mM protein–RNA complex concentration) and incubated on ice for 1 h. Crystallization drops were set-up by mixing 100 nL of complex and 100 nL of reservoir using the sitting drop vapor diffusion method. Crystals with hexagonal plate morphology were obtained in 0.2 M sodium citrate and 20% PEG 3350 after one week. The crystals were cryoprotected in reservoir solution supplemented with 20% (v/v) ethylene glycol and flash frozen in liquid nitrogen. X-ray diffraction data were collected at the European Synchrotron Radiation Facility (ESRF). All diffraction images were processed by XDS63 and PLUMED64. The systems were prepared using the AMBER99SB-ILDN force field65 in tandem with the TIP3P water model66. After solvation in a cubic box , a short initial energy minimization of 100 steps was performed to resolve steric clashes between solvent atoms and the complex. Structures were refined using simulated annealing simulations. Bonds were constrained using LINCS67 and water was kept rigid using SETTLE.The temperature was adjusted with a period of 20 ps between 300 K and 100 K. The SAXS data set of the UP1/12-mer RNA complex encompassed data points with scattering wavenumbers between 0.025 Å−1 and 0.685 Å−1. Intensities up to 0.5 Å−1 were fitted with a polynomial of 16th degree. From this fit, 43 intensities were calculated for wavenumbers between 0.03 and 0.45 Å−1 in steps of 0.01 Å−1. These representative intensities were utilized as restraints. For the UP1/pri-mir-18a complex representative intensities between 0.08 Å−1 and 0.22 Å−1 were calculated and used as restraints as described above. An approximate scaling factor relating calculated and measured SAXS intensities was estimated by the average ratio between the experimental SAXS intensities as taken from the fit and the first round of calculated SAXS intensities. SAXS intensities were calculated using the Debye formula and standard atomistic structure factors corrected for the effect of the solvent68.MD simulations were performed using GROMACS69 with a Gaussian likelihood per data point on the representative SAXS intensities was applied every 10th step. Note, that metainference in the approximation of the absence of dynamics as used here (without replicas and a standard error of the mean set to zero) is equivalent to the Inferential Structure Determination approach70. The attributes of the uncertainty parameter were initially set to large values to allow a slow increase of the restrain force. Additionally, an additional scaling factor between the experimental data was sampled using a flat prior between 0.9 and 1.1.Metainference71 from the crystal structure were restrained using an upper-wall potential on the rmsd of the backbone atoms of residues involved with an energy constant of 10000 kJ/mol, centered at 0 Å.The protein–RNA interface was restrained by harmonic upper-wall potentials centered at 3.5 Å applied on the distances between the centers of the respective rings  with energy constants of 1000 kJ/mol. Furthermore, two crystallographic salt bridges  between the two RRM domains were restrained with similar potentials centered at a distance of 4 Å applied on the distances between their charged groups. Secondary structures identified by STRIDE72. To take into account for the multiple possible alignments of the molecule with the phage, RDCs were calculated as averages over two replicas and a linear restraint with a slope of −20,000 kJ/mol was applied on the correlation between the average and experimental RDCs. Each replica was independently restrained with all the formerly introduced restraints.For the UP1/12-mer complex RDCs restraints were applied using the θ-method73 and the SAXS/SANS profiles and RDCs were independently confirmed using CRYSOL74 and PALES56, respectively.After 300 preliminary annealing cycles , the refined structure was chosen as the one with the lowest metainference energy among those sampled at 300 K in the latest 30 cycles. The quality of the structure was then further assessed using PROCHECKITC  measurements were carried out at 25 °C using an iTC200 calorimeter . Both protein and ligand were exchanged into NMR buffer without DTT. Protein concentrations in the range of 300–1000 µM, depending on the affinity of the interaction, were injected into the sample cell containing RNA with a concentration of 20–100 µM. Titrations consisted of 20 injections of 2 µL or 26 injections of 1.5 µL with a 3-min spacing between each injection. After correction for heat of dilution, data were fitted to a one-site binding model using the Microcal Origin 7.0 software. Each measurement was repeated at least three times.n/dc) values of 0.185 mL/g and 0.17 mL/g for protein and RNA samples, respectively75.Static light scattering (SLS) experiments were performed on a S75 10/300 size-exclusion column  connected to a Viscotek Tetra Detector Array (TDA) instrument equipped with refractive index (RI), light scattering, viscosity and photo diode array (PDA) detectors . Sample volume of 100 µL was injected onto the column pre-equilibrated with NMR buffer and the flow rate was set to 0.5 mL/min. Calibration was done with BSA  at a concentration of 4–5 mg/mL. Data were analyzed by the OmniSEC software using refractive index increment  equipped with a Pilatus detector using 20 µL of sample in a capillary tube. All SAXS data were processed using the ATSAS software package76. The radius of gyration (Rg) and the maximum dimension (Dmax) values were obtained from the GNOM program, which evaluates the pair-distance distribution function, P(r)77. For ab initio modeling of the protein–RNA complex, three scattering profiles corresponding to individual components and the complex were used as input for the multiphase modeling program MONSA78. Theoretical scattering curves were calculated using CRYSOL74.SAXS data of UP1, pri-mir-18a, and UP1/pri-mir-18a complex were recorded at the X33 beamline of the European Molecular Biology Laboratory (EMBL) at Deutsches Elektronen Synchrotron  at 15 °C. The scattering curves were measured with 120-s exposure times  for concentrations in the range of 1–10 mg/mL. The scattering intensity was measured covering the momentum transfer range 0.007 < q < 0.35 Å−1. Sample and buffer volumes were 200 μL and exposed for 60 min. The 2D detector signals of the samples were corrected for detector efficiency, empty cell scattering, directly calibrated (against water), and azimuthally averaged using ILL in-house software . The final 1D scattering curves were further analyzed using the ATSAS software.SANS data were recorded at the large dynamic range diffractometer D22 at the Institut Laue-Langevin (ILL) Grenoble, France, using a neutron wavelength of 6 Å and a detector-collimator set-up of 2 m/2 m. The scattering intensity was measured covering the momentum transfer range 0.02 < 2) using 160 nM RNA prior to treatment with N-methylisatoic anhydride NMIA (Invitrogen), as the modifying agent, as recently described79. RNA was phenol-extracted and ethanol-precipitated and then subjected to primer extension analysis. For primer extension, equal amounts of NMIA-treated and untreated RNAs (10 µL) were incubated with 0.5 µL of antisense 5′-end 32P-labeled primers  in complex with purified hnRNP A1 or UP1 proteins (125 nM) were assembled in folding buffer  with accession code 6DCL. Coordinates for the structural models of the 1:1 complexes of UP1/12-mer and UP1/pri-mir-18a are available upon request. Uncropped gels are presented in Supplementary Fig. Atomic coordinates and structure files for the UP1/12-mer RNA crystal structure have been deposited in the Protein Data Bank (Supplementary InformationPeer Review File


RNA recombination is a major driving force in the evolution and genetic architecture shaping of enteroviruses. In particular, intertypic recombination is implicated in the emergence of most pathogenic circulating vaccine-derived polioviruses, which have caused numerous outbreaks of paralytic poliomyelitis worldwide. Recent experimental studies that relied on recombination cellular systems mimicking natural genetic exchanges between enteroviruses provided new insights into the molecular mechanisms of enterovirus recombination and enabled to define a new model of genetic plasticity for enteroviruses. Homologous intertypic recombinant enteroviruses that were observed in nature would be the final products of a multi-step process, during which precursor nonhomologous recombinant genomes are generated through an initial inter-genomic RNA recombination event and can then evolve into a diversity of fitter homologous recombinant genomes over subsequent intra-genomic rearrangements. Moreover, these experimental studies demonstrated that the enterovirus genome could be defined as a combination of genomic modules that can be preferentially exchanged through recombination, and enabled defining the boundaries of these recombination modules. These results provided the first experimental evidence supporting the theoretical model of enterovirus modular evolution previously elaborated from phylogenetic studies of circulating enterovirus strains. This review summarizes our current knowledge regarding the mechanisms of recombination in enteroviruses and presents a new evolutionary process that may apply to other RNA viruses. Picornaviridae family. This viral family represents one of the widest groups of human and animal viruses and it contains several mammal pathogens, like hepatitis A virus, foot and mouth disease virus, rhinoviruses (RVs), and poliovirus (PV), the etiological agent of poliomyelitis and prototype of EVs. Among the 15 species constituting the Enterovirus genus, seven contain human viruses: Enterovirus A to D (EV-A to -D) and Rhinovirus A to C (RV-A to C) ,7928,79]The first experimental evidence supporting the copy-choice mechanism was provided in 1986 by Kirkegaard and Baltimore, despite being initially proposed by Cooper et al. as a model of recombination in PV , who demNevertheless, the exact molecular mechanism of the copy-choice remains to be elucidated. For that matter, different mechanisms may exist according to the features of the viral polymerase and replication complex, or else, several mechanisms might be possible for a single polymerase. Distinct possible mechanisms have been postulated ,135,136:Finally, for some positive-strand RNA viruses, including PV, it was postulated that template switching preferentially occurs during negative-strand RNA synthesis ,138,139,Many factors are supposed to favor template switching, by having an effect on the pausing of the synthesis on the donor strand, which destabilizes the replication complex and promotes its dissociation, or by playing a role in the association of the nascent strand and the polymerase to the acceptor RNA.The high replication speed of RdRps, as optimized by natural selection, results in a high incorporation of incorrect nucleoside triphosphates (NTPs), which is associated with an increased number of pause events that may favor elongation complex dissociation ,146,147.After dissociation from the donor template, the replication complex and the nascent strand need to bind the acceptor template to re-initiate nucleic acid synthesis. The first feature identified as a factor that affects recombination frequency and location is the sequence identity level between the nascent strand and the acceptor RNA ,129. IndHost and environmental factors are also supposed to be implicated in the copy-choice mechanism. Studies in tombusvirus and hypovirus led to the identification of various cellular pathways and factors that are involved in viral replicative RNA recombination ,169,170.An alternative recombination model that is fundamentally different from the copy-choice was first advanced on the basis of experimental data obtained in a cell-free system that employed purified bacteriophage Qβ replicase to detect replicable RNAs that were generated from nonreplicable RNA fragments ,172. HowFollowing these studies, the existence of a recombination mechanism not involving the viral polymerase was unambiguously demonstrated in vivo in PV while using pairs of defective complementary genomic RNA fragments ,136,173.This hypothesis was confirmed even more rigorously with another pair configuration, in which the two RNA fragments corresponded to the PV genome with a break in the RdRp-coding region . Since eFlaviviridae family, while using similar co-transfection systems with defective RNA partners, which suggests that the mechanism of nonreplicative recombination could be common to several positive-strand RNA viruses [Subsequently, the generation of infectious recombinant genomes in the absence of functional RdRp was also demonstrated in Bovine viral diarrhea virus (BVDV) and Hepatitis C virus (HCV), from the  viruses ,176,177.The details of the mechanism(s) that lead to the generation of recombinant genomes in the absence of viral RdRp still remain unknown. The studies previously mentioned, performed in PV, BVDV, and HCV, suggested the existence of a common mechanism of breakage-ligation, very likely involving cellular factors ,176,177.Other mechanisms of RNA recombination have been proposed to explain the recovery of complete viral genomes from defective RNA fragments in the absence of viral RdRp. In particular, it was demonstrated that the RNA molecules are able to undergo spontaneous nonenzymatic intermolecular transesterification reactions ,182,183.In addition, the fact that RNA recombination can occur in the absence of a functional RdRp does not completely exclude a replicative mechanism of primer extension that would be mediated by cellular polymerases . So far,Several recent studies have investigated the intra- and intertypic EV recombination process in experimental settings in order to understand the rules governing genetic exchanges between EVs, that can lead in particular to the emergence of pathogenic recombinant cVDPVs ,187,188.All of these experimental studies of intra- and intertypic recombination in EVs provided similar results regarding the features of the generated infectious recombinant genomes, in particular the ratio of homologous to nonhomologous recombinants and the location of recombination sites, regardless of the tested RNA partner pair and the genomic region targeted for recombination. No significant differences in the genomic structure of the obtained recombinants were observed whether the defective EV genome was rescued with a replicable or nonreplicable RNA fragment, i.e., whether the initial step in the generation of the recombinants involved a replicative or nonreplicative RNA recombination event ,122.Both homologous infectious recombinant genomes and nonhomologous ones, showing deletions or insertions at the recombination site, were recovered from the recombination cellular systems previously described. When both RNA partners belonged to the same type, homologous recombinant genomes were mostly isolated ,122. On Most of the isolated nonhomologous recombinant genomes displayed inserted sequences of a variable number of additional nucleotides, which usually created duplications of homologous parental sequences on each site of the recombination site, and could reach a length of around 570 nucleotides in the 5’ UTR  and 148 To conclude, these recent experimental studies of recombination in EVs led to the elaboration of a new model for the generation of homologous recombinant EV genomes. Homologous recombinant EVs that are observed in nature could have directly arisen through replicative homologous recombination, according to the previously proposed copy-choice model, or as the result of successive nonhomologous recombination events. In the latter scenario, initial nonhomologous recombinants, which could have been generated by either replicative or nonreplicative recombination, would then evolve into homologous recombinants over one or more subsequent replicative recombination events. Nonhomologous recombinant genomes would function as precursors and recombination intermediates in a multi-step process that leads to the emergence of a diversity of homologous recombinants . This moAnother common feature of the viable recombinant genomes that were isolated from the recombination cellular systems is that recombination sites appeared not to be randomly distributed within the targeted genomic region, instead being located in recombination “hotspot” regions. Experimental studies of intertypic RNA recombination between PV and EV genomes enabled defining six recombination hotspots throughout the EV genome: three in the 5’ UTR and three in the non-structural region ,121,122.Given that the recombinants that were isolated from the in vitro recombination systems were viable viruses replicating in cultured cells, it was also interesting to investigate their ability to replicate in vivo and their pathogenicity following the inoculation of transgenic homozygous PVR-Tg21 mice, which constitutively express the human PV cellular receptor CD155 ,199. In The existence of recombination hotspots in the 5’ UTR and the region encoding nonstructural EV proteins was demonstrated in intertypic recombinants that were obtained from recombination cellular systems ,121,122.In conclusion, the different recent studies that are presented in this review, which investigated the genetic exchanges between a panel of EV genomes in cellular systems, enabled experimentally demonstrating the theoretical model of EV evolution through modular recombination that had been proposed from phylogenetic studies of EV strains (see §2.4). These studies provided experimental evidence supporting phylogenetic data that EV genomes should be considered as combinations of genomic fragments, or recombination modules. These recombination modules would evolve independently and combine through RNA recombination during co-infections. The possible combinations of recombination modules generating viable EV genomes could be determined by preferred mutual functional compatibility, which was shown to be strikingly conserved between co-circulating EVs of the same species ,97. A reDespite the importance of RNA recombination in the evolution of EVs ,106,111,Moreover, these experimental studies highlighted a novel aspect of the organization of the EV genome. The genome of EVs could be defined as a combination of genomic modules that can be exchanged through RNA recombination. These results provided the first experimental evidence that supported the theoretical model of EV modular evolution previously elaborated from phylogenetic studies ,111. TheCoronaviridae family, by recombination at the VP1-2A junction [The structural and functional constraints that limit the exchanges of functional recombination modules also require further evaluation. For instance, recombination within the coding regions of human EV genomes has been thought to only occur among members of a given species . Howeverjunction ,46. ThesFlaviviridae family [Finally, the concept of genome evolution through exchanges of recombination modules might apply to other RNA virus families. Indeed, similar recombination models have been suggested for viruses from the e family , for core family  and BMV e family . At a lae family . Thus, t


Liver Hepatocellular Carcinoma (LIHC) is one of the major cancers worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting the stage of LIHC from the genomic profile of samples.The Cancer Genome Atlas (TCGA) dataset of 173 early stage (stage-I), 177 late stage  and 50 adjacent normal tissue samples for 60,483 RNA transcripts and 485,577 methylation CpG sites, was extensively analyzed to identify the key transcriptomic expression and methylation-based features using different feature selection techniques. Further, different classification models were developed based on selected key features to categorize different classes of samples implementing different machine learning algorithms.In the current study, in silico models have been developed for classifying LIHC patients in the early vs. late stage and cancerous vs. normal samples using RNA expression and DNA methylation data. TCGA datasets were extensively analyzed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early vs. late stages and cancer vs. normal samples of LIHC with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC (Matthew’s correlation coefficient) 0.58 with an accuracy of 78.87% on the validation dataset in discrimination of early and late stage. Additionally, the prediction models developed based on 5 RNA transcripts and 5 CpG sites classify LIHC and normal samples with an accuracy of 96–98% and AUC (Area Under the Receiver Operating Characteristic curve) 0.99. Besides, multiclass models also developed for classifying samples in the normal, early and late stage of cancer and achieved an accuracy of 76.54% and AUC of 0.86.http://webs.iiitd.edu.in/raghava/cancerlsp/).Our study reveals stage prediction of LIHC samples with high accuracy based on the genomics and epigenomics profiling is a challenging task in comparison to the classification of cancerous and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP ( The features are selected from 440 CpG sites, and 236 RNA transcripts that are significantly differentially methylated and differentially expressed respectively . Here, prediction models developed based on 33 CpG sites (multiclass-CpG) using various techniques of WEKA. Naïve Bayes model is the top performer with an accuracy of 77.43 and 76.54 and weighted average AUC 0.88 and 0.86 on the training and independent validation dataset, respectively . BesidesMT1E, CNDP1, C7, GMNN, and EEF1A2 are among the 5 RNA transcripts identified in multiclass classification. Enrichment analysis indicated upregulated genes such as GMNN and EEF1A2, are enriched in various growth promoting processes and pathways, while downregulated genes like CNDP1 and MT1E enriched in negative regulators of growth and C7 enriched in innate immunity pathways respectively. Multiclass-RNA signature with their biological importance is tabulated in Table P in Enrichment analysis of multiclass-RNA is represented in Figures  using WEKA and also employed 51 hybrid features (which were chosen to distinguish early and late stage). Further, prediction models developed using these features implementing WEKA.classifiers.meta.MultiClassClassifier of WEKA. The Naïve Bayes prediction model based on 51 features attained moderate performance to classify all three stages with an accuracy of 59.13% and 66.19% with weighted average AUC 0.79 and 0.84 on the training and validation dataset, respectively; while, other models and the models based on 25 features have lower performance , a web server that executes in silico prediction models developed in the present study for prediction of cancer status, i.e. cancerous or normal and stage of cancer of sample using methylation beta values of CpG sites and FPKM quantification values of RNA transcripts. Here, the user required to submit FPKM values of RNA markers (RNA transcripts) and methylation beta values of marker CpG sites of every subject. The output result displays a list for patient samples and corresponding predicted status of cancer or normal and early or late stage. Moreover, the user can select among the models developed from LS-CpG-WEKA, LS-RNA-WEKA LS-CpG-RNA-hybrid, LCN-5-CpG, LCN-5-RNA, multiclass-RNA, and multiclass-RNA which have been identified as putative markers sets for stage progression of LIHC.This module permits the users to predict the disease status, p-value for every feature that indicates whether there is a significant difference in the methylation pattern of CpG site or RNA expression in early and late stage. Furthermore, it also provides threshold-based ROC of each feature along with mean methylation of CpG site and mean expression of that RNA transcript in the early and late stage of LIHC. This web server is available from URL (http://webs.iiitd.edu.in/raghava/cancerlsp/) for the scientific community.This module is beneficial in assessing the role of each RNA transcript and CpG site to distinguish early stage from the late stage. In addition, it provides a LIHC has become a major threat worldwide owing to its high morbidity and mortality rate. Furthermore, the appropriate treatment options are affected not only by the degree of liver dysfunction but also by tumor stage . Therefoi.e. cg07132710 and cg11232136 (LS-CPG-AUC signatures) are associated with GATA2 and ZNF566 genes respectively. While, four hypermethylated CpG sites in early stage  are associated with TRIM27, TOX3, NOLC1, and ATP1B1. In the literature, it has been shown that these genes are epigenetically regulated and associated with cancer progression in different malignancies [Two highest scoring hypomethylated CpG sites in early stage gnancies .NCAPH, CYP4A, GLYATL1, HILPDA, CANT1, SLC2A2, ALDOA, CNFN, A1BG, CYP2B6, HN1, and ITIH1 were among top-ranking genes based on AUC. Previously, different reports uncover their involvement in a variety of malignancies [HN1 and SLC2A2 with the survival of LIHC patients and ITIH1 with the progression of LIHC [ALDOA, EFNA3, and FTCD was also observed in LIHC [On ranking the gene expression of all the transcripts, we found gnancies –71. Evid of LIHC –74. Aber in LIHC –78.NOLC1, ZNF566, SMAD7 and UBASH3A respectively. Previously the aberrant methylation pattern of these genes has been observed in different types of cancers [As cancer is a complex disease for which a single feature is not enough to define its progression. Therefore, we used state of the art machine learning techniques to elucidate key features associated with LIHC stage and further improve the performance of the models. We found 21 CpG (LS-CPG-WEKA) methylation sites that achieved the AUC of 0.78 on validation data to discriminate early stage from the late stage. Among LS-CPG-WEKA signature CpG sites such as cg16657244, cg11232136, cg11023721, and cg27111890 are associated with  cancers .DCK, CDCA5, RPP25, FUT11, NECAB1, FNTB, ZNF576, NETO2, and ELOVL3 are observed as upregulated genes in late stage and are involved in cell division processes, transcriptional regulation, while the genes downregulated in late stage such as MAT1A, LCAT, CFHR3 are involved in normal functioning of liver including lipid and lipoprotein metabolism [MAT1A in LIHC [CDCA5 and RAMP3) in this biomarker panel have already been identified as prognostic markers in LIHC in the literature [etc. They are observed as significantly upregulated in the late stage of LIHC samples in our study.We also report 30 RNA transcripts (LS-RNA-WEKA) that achieved the AUC of 0.77 on the validation dataset. Among the 15 protein-coding transcripts in LS-RNA-WEKA signature, tabolism   distinguish cancer samples from normal with AUC 0.98 and 0.97 respectively. Earlier studies have shown the association of downregulation of CLEC4G expression with the progression of LIHC [PITX1 correlated with the tumor progression HNSCC [PITX1 with the progression of LIHC.In addition to stage classification, we have also elucidated vital features that are differentially expressed in LIHC as compared to normal samples. Using a simple threshold-based approach, we have identified the top 10 CpG sites and top 10 RNA transcripts that discriminate cancer samples from adjacent normal with AUC greater than 0.9.  of LIHC ,90. Our  of LIHC . Previouon HNSCC  and ESCCon HNSCC . SimilarLCAT gene could classify cancer and normal samples with AUC 0.92, is also identified as one of the important markers (LS-RNA-WEKA) to distinguish early stage samples from the late stage. Thus, our report suggests the role of LCAT as an important signature marker in the progression of LIHC. Previously its association was observed with the poor survival of LIHC patients [Additionally, prediction models developed using multiple features filtered by different techniques to distinguish cancer from control samples. Interestingly, our study has shown that the model based on 5 features i.e. 5 RNA transcripts , or 5 CpG sites  performed quite similar to that of models based on much large dimension of features as indicated by the performance to discriminate cancer and normal samples with 96.47% accuracy and AUC 0.97 on the independent validation dataset. Interestingly in the current study, we observed that patients .MT1E, CNDP1, C7, GMNN, and EEF1A2, we achieved an accuracy of 72.84% with weighted AUC of 0.81. Further using 33 CpG sites  accuracy of 77.54% and weighted AUC 0.86 is obtained. Their enrichment analysis shows that upregulated genes are enriched in various growth enhancing processes and pathways such as positives regulation of different kinase signaling, GTPase; while downregulated genes are enriched in negative regulators of growth processes and innate immunity pathways. This might lay insight towards their involvement in the progression of the cancerous condition.Our next aim was to enhance our model for the stage classification, so we included the normal samples along with early and late stage samples to develop multiclass prediction models that can separate normal, early stage and late stage tissue samples. The advantage of this model as compared to the binary model is that it also captures the gene expression differences between adjacent normal and early stage samples. Based on the 5 RNA transcripts include In summary, we have achieved only reasonable performance (AUC 0.82) using the hybrid model based on 51 potential markers (include 21 CpG sites and 30 RNA transcripts) to classify early stage from the late stage via our systematic analysis of methylation and gene expression profiles of LIHC tissue despite implementing different approaches. In addition, 5 RNA and 5 CpG sites based prediction models distinguish cancer vs. normal samples with quite high precision. Furthermore, multiclass prediction models based on 5 RNA transcripts and 33 CpG sites attained reasonable performance in categorizing normal, early and late stage samples. Our findings suggest that the stage classification is a quite challenging task in comparison to cancer vs. normal samples classification from their expression and methylation profiling. Moreover, LIHC is a complex disease with various pathological bases; thus, our results raise the hypothesis that the exploration of these potential biomarker combinations might offer more accurate and precise diagnosis LIHC at the early stage.We anticipate this study would be beneficial to recognize these new important epigenetic and genomic-based candidate markers for early diagnosis of liver cancer. Their potential as a prognostic marker of LIHC can be further investigated by clinicians and researchers involved in the respective research area. We have integrated all our models based on these potential markers in the form of web server named CancerLSP for the use of scientific community working in the respective research area.In the current study, we have identified potential biomarkers based on genomic and epigenetic profiles to differentiate the early and late of HCC with reasonable performance. One of the limitations associated with these biomarkers is that they are derived from the tissue samples; which required invasive approaches for their isolation. But, one can employ a similar strategy if sufficient data is available for blood, serum, urine, cell-free DNA etc. to develop non-invasive biomarkers. Furthermore, our study mainly based on TCGA high throughput datasets for both internal and independent validation. These potential biomarkers further need to be validated on other external datasets derived from various other studies to elucidate their clinical utility.S1 File(DOC)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


Circular RNAs (circRNAs) from back-splicing have been found in every cell and tissue. By binding to miRNAs and proteins or even by encoding small proteins, circRNAs are now emerging as important regulators in modulating transcription, RNA splicing and interference. The highlighted discovery reports an important role of circRNAs in antiviral innate immunity by binding PKR as PKR inhibitors. Rapid degradation of circRNAs by activated RNase L from virus infection or poly I:C stimulation is required to free PKR for its activation. Systemic lupus erythematosus patients remark with circRNA reduction and aberrant PKR activation. Intracellular foreign RNAs are recognized by two groups of intracellular RNA sensors. Immune sensing receptors recognize 5′-triphosphate (ppp) ssRNA and short dsRNA to induce immune responses via activation of transcription and production of cytokines. This group includes RIG-I (retinoic acid-inducible gene I), MDA-5 (melanoma-differentiated gene 5) and TLRs (Toll-like receptors). Nucleic acid receptors directly recognize and act on dsRNAs in different size to execute antiviral activities by blocking translation and inducing degradation and modification of pathogenic dsRNA. The latter group includes PKR (dsRNA-activated protein kinase R), OAS (oligoadenylate synthetase), and ADAR1 (adenosine deaminase acting on RNA 1).A viral RNA genome in a circular form was initially recognized in viroids in 1976 and in human hepatitis delta virus in the early 1980s. A large number of circular RNAs (circRNAs) produced from primary mRNA (pre-mRNA) splicing in eukaryotic cells were also subsequently noticed to be the splicing intermediate intronic lariats . AlthougFurther investigation to identify the circRNA-associated proteins revealed that the circRNAs, both circPOLR2A and circCAMSAP1, preferentially bind to nucleic acid receptors PKR, NF90 (ILF3 isoform-2) and OAS, whereas their linear RNA forms bind to immune-sensing receptors TLR3, RIG-I and MDA-5. Interestingly, this binding of circPOLR2A to PKR could be blocked by a short 33-bp dsRNA of which binding doesn’t activate PKR, but the same short RNA in linear form had no effect. These observations led the investigators looking into the question whether the circRNAs could form intramolecular RNA duplexes to bind and activate PKR. Surprisingly, they discovered that each HeLa cell may contain ~ 9000–10,000 copies of circRNAs and each circRNA bears at least 1–4 intra-dsRNA regions in size of 16–26 bps, leading the authors to hypothesize that the short dsRNA region in a circRNA binds PKR in normal cell condition, but not activates PKR because of its short size and thus functions as a PKR suppressor. Further experimental approaches by ectopic expression of circRNAs or by stimulation of RNase L KO cells with poly I:C confirmed this important function of circRNAs in suppression of PKR activation and in innate immunity against EMCV infection.More strikingly, the authors in this report  discoverTogether with the findings of circRNA binding PKR and sensitive to RNase L digestion, the study  proposes


Drosophila melanogaster testis. Recent advances in RNA Sequencing (RNA-Seq) technology help us to understand the complexity of eukaryotic transcriptomes by dissecting different tissues and developmental stages of organisms. To gain a better understanding of cellular differentiation of spermatogenesis, we applied RNA-Seq to analyse the testis-specific transcriptome, including coding and non-coding genes.The formation of matured and individual sperm involves a series of molecular and spectacular morphological changes of the developing cysts in We isolated three different parts of the wild-type testis by dissecting and cutting the different regions: 1.) the apical region, which contains stem cells and developing spermatocytes 2.) the middle region, with enrichment of meiotic cysts 3.) the basal region, which contains elongated post-meiotic cysts with spermatids. Total RNA was isolated from each region and analysed by next-generation sequencing. We collected data from the annotated 17412 Drosophila genes and identified 5381 genes with significant transcript accumulation differences between the regions, representing the main stages of spermatogenesis. We demonstrated for the first time the presence and region specific distribution of 2061 lncRNAs in testis, with 203 significant differences. Using the available modENCODE RNA-Seq data, we determined the tissue specificity indices of Drosophila genes. Combining the indices with our results, we identified genes with region-specific enrichment in testis.Drosophila melanogaster spermatogenesis to our dataset, we were able to describe transcript composition of different regions of Drosophila testis, including several stage-specific transcripts. We present searchable visualizations that can facilitate the identification of new components that play role in the organisation and composition of different stages of spermatogenesis, including the less known, but complex regulation of post-meiotic stages.By multiple analyses of our results and integrating existing knowledge about The online version of this article (10.1186/s12864-018-5085-z) contains supplementary material, which is available to authorized users. A Drosophila testis is a blind-ended tube, where consecutive stages of spermatogenesis are presented in the developmental order from the apical end to the basal end. The apical tip of the testis is enriched with somatic hub cells, germ-line stem cells enclosed by cyst progenitor cells and the dividing gonialblast cells. Four synchronous divisions result in cysts with 16 primary spermatocytes. Two somatic cyst cells support the germline cells throughout the entire spermatogenesis process. Meiotic cysts, consisting of 64 haploid spermatids in each one, are found in the middle region of the testis. The basal region contains mainly the post-meiotic elongated, individualizing cysts. After meiosis, the cells change shape and size dramatically and develop specialized cellular organelles, such as the basal body, axoneme and acrosome , 3.Enzymes and structural components of the mitochondria are synthesized mainly from the nuclear genome. Mitochondrial enzymes frequently contain duplicates with testis-biased expression patterns for one of them, based on microarray analysis of Drosophila males and females , 5. SperA testis region-specific microarray experiment proved the presence of X chromosome-linked gene inactivation during male meiosis and suggested that meiotic sex chromosome inactivation could be a general mechanism which contributes to the evolution of some testis-biased genes , howeverDrosophila melanogaster testis using RNA-Seq. Our work highlights the molecular composition of different regions of Drosophila testis. By integrating our dataset and the publicly available databases, we determined and visualized expression patterns of functionally related genes and correlated their known and predicted functions to different parts of the testis.Here we report the transcriptomic profiling of functionally and anatomically different parts of the To gain a better understanding of cellular differentiation during spermatogenesis, we decided to compare transcript composition of different parts of the Drosophila testis. We cut testes into three parts: apical, middle and basal regions, which represent the proceeding stages of spermatogenesis  values of individual genes, we compared transcript levels between apical, middle and basal regions of the testis.We performed RNA Sequencing of poly(A)D. melanogaster genome release. The microarray experiments reported of 13150 genes [D. melanogaster reference genome and corresponding transcript annotation that contains 33869 records of transcripts and 17412 gene records.Previously, Vibranovski et al. published transcriptome data containing 18082 records, based on GeneChip® Drosophila Genome 2.0 Array (Affymetrix) experiments. The probes were 25 nt length oligos designed based on the 3.1 50 genes . The prep-value< 0.05) between regions based on Cuffdiff analyses , while in the case of genes that are expressed in other tissues, transcript levels are more likely (61.3%) lower towards the basal region of the testis Fig. . By compC. elegans and D. melanogaster was reported by Li et al. in 2014 [Drosophila melanogaster genes based on the modENCODE tissue expression database and identified the testis-enriched genes , we collected four sets of proteomic data and filtered out 263 proteins that appeared at least three times in the DmSP datasets [To test the spatial distribution of transcripts that contribute to the formation of the datasets , 13. We Analysis of our data showed that testis-enriched genes generally have higher transcript levels and accumulate in the later stages of spermatogenesis. We need to highlight the fact that in spite of these tendencies, there are ubiquitously expressed genes with high transcript levels in later stages and also with considerable transcript level differences between the apical and basal regions. Nevertheless, our data suggest that the formation of the unique structures and molecular composition of sperm relies on various testis-specific gene products, accumulating in the later stages of spermatogenesis.bam expression is necessary for the differentiation of gonialblast cells, however, in the ranked list, bam expression is at the 7174th position out of 17409, which means that bam is not in the top 40% of the list, despite its important function in cell fate regulation  is responsible the regulated degradation of cellular proteins and protein turnover . ComponeDrosophila. The transcripts of the five ubiquitin-coding genes, CG11700, RpL40, RpS27A, Ubi-p5E and Ubi-p63E, show differential distribution in the examined regions of testis. Two of them, CG11700 and Ubi-p63E, have high and specific transcript accumulation in the testis. The CG11700 transcript accumulates in the apical and in the middle regions, while Ubi-p63E is enriched in the basal region  is substantially elevated towards the basal region, however, the precise function of Neddylation during late spermatogenesis has yet to be elucidated  accumulate at the late stages, suggesting late function of these proteins  and basally-  accumulating members  show accumulation in the basal region of the testis, while several of them are restricted to the spermatogonial stages of the apical region  by in situ hybridization  revealed them as major players in a large number of pathways across species . Out of Drosophila melanogaster genome is differentially expressed to generate a wide spectrum of protein-coding and non-coding RNAs at different developmental stages, which produces a transcriptome of unexpected complexity through the lifetime of the fly [The field of transcriptomics using RNA-Seq has developed rapidly with the introduction of next-generation sequencing technologies and has replaced the microarray analysis method for gene expression profiling. RNA-Seq has enabled the detailed profiling of samples from very small amounts of starting material, such as single early embryonic stem cells . The hig the fly . Importa the fly .Microarray data and also whole testis RNA-Seq analysis are available to identify the testis-specific genes , 57. OurTowards the basal end of the testis, we detected elevated transcript levels of the testis-specific subunits of almost every catalytic and regulatory particle of the proteasome, which provides candidates to identify as new members of the ubiquitin-proteasome system that play role in spermatogenesis. Based on the predominantly nuclear localisation of Prosα6T, a testis-specific subunit of the catalytic part of the proteasome, a major requirement of the proteasome for nuclear degradation, was suggested, which results in normal nuclear condensation and histone-to-protamine transition . It was Cysts are covered by two somatic cyst cells, which grow simultaneously with the elongation of the spermatids. One of them covers the nuclear part of the cyst and the other one covers the 64 elongated 1,8 mm long tails, resulting an extremely large cell. Cyst cells and germ cells have an intimate connection, but there is relatively little information available about the transcript composition of the huge cyst cells. Our analysis cannot distinguish between the transcriptome of cyst cells and germ cells, therefore transcripts from cyst cells are represented in the dataset too. The results of a cyst cell-specific RNAi screen demonstrated that there is a cyst cell-specific gene network for each main stage of spermatogenesis. It was shown that a specific set of microtubules and the dynein-dynactin complex contribute to the morphological changes during elongation of the tail cyst cell . We alsocut up, a dynein light chain coding gene is transcribed in early stages, but the protein localises to the actin cones of the individualisation complex of spermatids [The lifetime of RNAs and proteins influence their function, so the protein-coding genes with low transcript levels in the late stages could be important when the protein remains stable into the late stages of spermatogenesis. For example, ermatids . However, [Increasing evidence suggests that gene duplication can contribute to the specialized function of the post-meiotic mitochondrial proteins in Drosophila testis . Our ana, . FurtherWe found a surprisingly high number of lncRNAs with high testis specificity and basal enrichment. Recently, it was shown that the knock down of lncRNAs in the testis frequently resulted in late spermatogenesis defects, which may indicate their regulatory function at the final stages of spermatogenesis . The facOur analysis provides a more detailed transcript composition of the coding and non-coding genes in the main stages of spermatogenesis and we were able to highlight the very special transcript composition of the basal region of testis, which contain the post-meiotic stages, where specialized protein degradation, cytoskeletal, nuclear or mitochondrial rearrangements and organelle specialization happen. Our searchable dataset and visualization method also offer a list of functionally related genes for further study, to gain a better understanding of different aspects of Drosophila spermatogenesis.Sample preparation followed Vibranovski et al.  were regarded as differentially expressed. The false-discovery rate corrected p-values and the log2 fold changes of the expression data are provided in Additional file RNA quality was verified by capillary gel electrophoresis in a Bioanalyzer 2100 instrument (Agilent) using the Agilent RNA 6000 Nano Kit, then RNA concentrations were determined by fluorometric measurement with a Qubit 2.0 fluorometer (Thermo Fisher Scientific) using the Qubit RNA HS Assay kit. Indexed RNA-Seq libraries were prepared from 800 ng total RNA using the TruSeq RNA Library Prep Kit v2 (Illumina) following the TruSeq RNA Sample Prep v2 LS protocol provided by the manufacturer. This, in short, includes purification of poly(A) mRNA with oligo-dT magnetic beads, RNA fragmentation, synthesis of double-stranded cDNA using SuperScript II reverse transcriptase (Invitrogen), ligation of indexed Illumina adapters and amplification with limited-cycle PCR. Sequencing libraries were validated by capillary electrophoresis using the Agilent DNA 1000 kit in a Bioanalyzer 2100 instrument, then quantitated with the Qubit dsDNA HS Assay Kit in a Qubit 2.0 instrument and with the KAPA Library Quantification Kit (KAPA Biosystems) in a Piko-Real Real-Time PCR System (Thermo Fisher Scientific). 4 nM sequencing libraries were pooled, denatured with 0.1 M NaOH and, after dilution, paired-end sequencing was done with an Illumina MiSeq sequencer using the MiSeq Reagent Kit V3–150. Primary sequence analysis was done by BaseSpace cloud computing environment. FastQ files were aligned to the Dmr6.05 n = (xi-x̄)/σ where xn is the normalised value, xi the original value, x̄ is the average of the dataset and σ is the standard deviation of the dataset. For each comparative analysis, we updated the related gene IDs from FlyBase. GO enrichment analysis was conducted on GOrilla [P value threshold was 10^− 3. Ranked lists were created based on testis specificity index, FPKM in apical, middle, and basal regions with higher values at the top. We created target lists based on the resolved testis specificity, differences in transcript levels between the apical and the basal regions. Target lists of genes were searched for GO enrichment on the background of the whole dataset. Graphs were created in Microsoft Excel. Networks were built in Cytoscape 3.5.1 [Tissue enrichment was calculated based on the work of Li et al. . We calc GOrilla , 71. P vpe 3.5.1 . Line chpe 3.5.1 .− 1 tRNA (Sigma) [cDNA was produced with RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific) based on the manufacturer’s instructions and used to amplify ~ 500 bp long templates. Probes were synthesized with DIG RNA Labelling Kit (SP6/T7) (Roche) according to the manufacturer’s instructions. In situ hybridization was performed as described by White-Cooper with the following differences: hybridization buffer contains the additional 100 μg ml (Sigma) . Images − 1 final concentration. Samples were mounted in Slowfade diamond antifade mountant (Thermo Fisher Scientific). Fluorescent images were taken by using Olympus Fluoview Fv10i Confocal microscope.Genomic DNA was purified from 30 flies and was used for PCR reaction with Phusion high fidelity DNA polymerase (New England Biolabs). CG10748-GFP and CG10749-mCherry constructs were designed and constructed by using NEBuilder Hifi DNA assembly master mix (New England Biolabs) based on the manufacturer’s protocol. The constructs contain the 5′ regulatory element, the 5’ UTR region, the coding sequence of each gene and C-terminal GFP or mCherry sequence in a pUASTattB vector. Transgenic constructs were injected into flies containing a source of φ-31 integrase and an attP landing site (Bloomington Stock Center No: 25709). Testis preparation and staining were performed as earlier described by White-Cooper 2004 . 4′,6-diAdditional file 1:Figure S1. Testis sample preparation and analysis of RNA-Seq results. Figure S2. Transcript distribution of the member of the ubiquitin system E3, and Cullin, SKP1 and F-Box genes visualized by Cytoscape. Figure S3. Distribution of transcripts of kinases visualized by Cytoscape. Figure S4. Distribution of transcripts of phosphatases visualized by Cytoscape. Figure S5. Distribution of transcripts of genes involved in citrate cycle and the sugar metabolism visualized by Cytoscape. Figure S6. Distribution of transcripts of genes of OXPHOS visualized by Cytoscape. Figure S7. Distribution of transcripts of long non-coding RNA genes visualized by Cytoscape. (PDF 12098 kb)Additional file 2:Differential gene expression analysis, and testis specificity. Results of the differential gene expression analysis of different regions and the testis specificity index of Drosophila genes. (XLSX 2979 kb)Additional file 3:GO analysis. Summary of different GO term analyses. (XLSX 257 kb)Additional file 4:http://www.cytoscape.org/). (CYS 1139 kb)Full network of the analysed FlyBase Gene Group data visualised by Cytoscape. Cytoscape session file contains the whole gene group network of FB2017_05 FlyBase release, and the numeric values of each node related data. Cytoscape 3 session file (.cys). Appropriate viewer: Cytoscape 3 (


The viral RNA (vRNA) genome of influenza viruses is replicated by the RNA-dependent RNA polymerase (RNAP) via a complementary RNA (cRNA) intermediate. The vRNA promoter can adopt multiple conformations when bound by the RNAP. However, the dynamics, determinants, and biological role of these conformations are unknown; further, little is known about cRNA promoter conformations. To probe the RNA conformations adopted during initial replication, we monitored single, surface-immobilized vRNA and cRNA initiation complexes in real-time. Our results show that, while the 3′ terminus of the vRNA promoter exists in dynamic equilibrium between pre-initiation and initiation conformations, the cRNA promoter exhibited very limited dynamics. Two residues in the proximal 3′ region of the cRNA promoter (residues absent in the vRNA promoter) allowed the cRNA template strand to reach further into the active site, limiting promoter dynamics. Our results highlight promoter-dependent differences in influenza initiation mechanisms, and advance our understanding of virus replication. Influenza is one of the world's major uncontrolled diseases. Annual influenza epidemics are estimated to cause up to 650 000 deaths worldwide, while intermittent pandemics have led to the deaths of many millions of people. Influenza is caused by the influenza virus, an enveloped virus with a single-stranded, negative-sense, segmented viral RNA (vRNA) genome. Each vRNA segment is bound by the viral RNA-dependent-RNA-polymerase (RNAP) and viral nucleoprotein (NP) to form a ribonucleoprotein complex (vRNP) ,2. In vRReplication of the vRNA is carried out by the RNAP, a multi-functional complex that is an attractive target for antiviral drug development. High-resolution structures of the RNAP have become available in recent years, providing intriguing static snapshots of how these enzymes function . The RNAStructures of the RNAP-bound vRNA promoter have been resolved by x-ray crystallography ; in the Similar to the vRNA, the 5′ and 3′ ends of each cRNA segment are conserved and form the cRNA promoter. As cRNAs are produced, they are bound by newly-made RNAP and NP to form cRNP complexes, which in turn synthesize new vRNAs . Only limited structural information on the conformation of the cRNA promoter is available; however, it is known that the conformation of the proximal 5′ cRNA (nucleotides 1–12) is virtually identical to that of the proximal vRNA 5′ terminus . HoweverIn this study, we have used single-molecule Förster resonance energy transfer (smFRET) to study the conformations and dynamics of the influenza virus vRNA and cRNA promoters during replication initiation. Building upon our previous solution-based smFRET studies ,17, we pRNAs corresponding to the 3′ and 5′ conserved ends of the neuraminidase vRNA or cRNA gene segments were custom synthesized and labeled with Cy3 and ATTO647N by IBA . The single-stranded RNAs were annealed in annealing buffer . RNA sequences are as follows: 5′ vRNA 5′-AGUAGUAACAAGGAGUUXAAA-3 where X = U-Cy3, 3′ vRNA 5′-UUUAAACUCCUGCUUUUGCX-3′ where X = U-Atto647N, 3′ vRNA 5′-UUUAAACUCCUGCUUUXGCU-3′ where X = U-Atto647N, 5′ cRNA 5′-AGCAAAAGCAGGAGUUX-3′ where X = U-Cy3 and 3′ cRNA 5′-AAACUCCUUGUUUCUACX-3′ where X = U-Atto647N. Recombinant influenza A/WSN/33 (H1N1) histidine-tagged RNAP (with a deca-histidine tag and a protein-A tag on the C-terminus of the PB2 subunit) was expressed in mammalian cells and purified using IgG-sepharose as previously described .in vitro ApG primed replication assay was used to show that the decahistidine tag had no effect on protein activity, as previously described  for 15 min at 24°C before the complexes were diluted to a final RNA concentration of 0.5 nM for imaging. Neutravidin-coated glass coverslip chambers were prepared as described  glucose was added to the observation chamber prior to imaging. Experiments were performed at 22°C unless indicated otherwise. Solution-based confocal experiments were carried out as previously described (RNAP-promoter RNA replication complexes were formed by incubating 10 nM dsRNA with influenza RNAP in binding buffer (50 mM Tris–HCl (pH 8), 500 mM NaCl, 10 mM MgClescribed . A 10 nMescribed ,23. RNAPescribed ,17.Single-molecule total internal reflection fluorescence (TIRF) experiments were performed on a custom-built objective type TIRF microscope, as previously described ,25. GreeE*, was calculated as described  E*, or kymographs showing multiple FRET traces in one field of view, were constructed using selected molecules. We also manually classified the intensity time trajectories of individual molecules into groups. Static complexes were defined as time trajectories showing a stable E* signal with no fluctuations to higher or lower FRET states during our observation period. Dynamic complexes were defined as time-trajectories showing anti-correlated changes in the DD and DA intensities during the observation period (50 s).Histograms of HMM analysis was performed on time-trajectories displaying dynamic FRET fluctuations using a modified version of the ebFRET software ,29. Eachx, y and z, respectively) (A model which incorporated the influenza B polymerase (PDB code 5MSG), residues 1–14 of the 5′ RNA (PDB code: 5MSG), residues 1–6 of the 3′ RNA in the pre-initiation state (PDB code 4WRT) and residues 1–13 of the 3′ RNA in the initiation state (PDB code 5MSG) was created in PyMol, and the duplex RNA was extended to positions 18 for the 5′ and 17 for the 3′ RNA using the DuplexFold module of the RNAstructure webserver  and the ctively) ,33. Herein vitro activity assay confirmed that the decahistidine tag had no effect on RNAP activity  and an acceptor dye at position 1 on the 3′ strand (3U1) Figure  and C. Won assay . Dyes aton assay  and prevon assay ,17.E*). An initial visual inspection of time-traces revealed that the 3′ vRNA within individual replication complexes was highly dynamic, exhibiting clear transitions between high (E* ∼ 0.8) and low (E* ∼ 0.4) FRET states  immobilized RNAP-vRNA promoter complexes containing fluorophores at position 18 on the 5′ strand, and position 1 on the 3′ strand , we measured FRET between dyes located at positions 18 on the 5′ vRNA and 13 on the 3′ vRNA Figure . Here, t0 Figure  and G. I0 Figure . These rWe also examined the dynamics of replication complexes containing promoter vRNA labeled with donor and acceptor fluorophores at positions 18 on the 5′ strand and 4 on the 3′ strand , a labelde novo initiation, to the sample well prior to imaging , suggesting that nucleotide addition decreases the abundance of the pre-initiation conformation. We thus propose that the presence of the initiating nucleotide stabilizes the 3′ end of the vRNA in the active site. This observation was supported by individual time traces, which showed fewer transitions to the high FRET (pre-initiation) state . Si. Side noe Figure . The shie Figure .To further analyze the interconversion of the 3′ vRNA between the pre-initiation and initiation conformations, we used Hidden Markov Modeling (HMM) to assign the states. The two states in each trace were color-coded, binarized, and sorted into a single plot from the shortest to longest in length. This analysis showed that, in the absence of nucleotides, some complexes showed rapid transitions between the initiation (shown in blue) and pre-initiation (shown in gray) states, while others showed slower transitions Figure . To quanin vitro replication assay , weon assay .n = 128) of replication complexes containing a labeled cRNA promoter and obtained a wide FRET distribution, corresponding to replication complexes with the cRNA promoter in multiple conformational states , which likely represents ic replication complexes wherein the 3′ cRNA template has translocated further into the active site to allow internal initiation at position 4.Unlike the vRNA promoter, for which replication initiates terminally at position 1 on the 3′ template Figure , replica2 Figure . Based oE* ∼ 0.5), or static high-FRET conformation (E* ∼ 0.7). We calculated the fraction of replication complexes in each of these conformations, and compared it to the conformations obtained without nucleotides. When ApG was absent, no low-FRET complexes were observed, cRNA in 46% of the complexes was in a mid-FRET state  and cRNA in 54% of the complexes was in a high-FRET state   translocated complexes), with the remaining complexes being either in a mid-FRET state  or high-FRET state   showing no transitions between states Figure . Time tr Figures , F. By c) Figure . Unlike ) Figure ,15 suggein vitro capped primer extension assay  remained static . Seq. Seq6). In summary, our results advance our understanding of influenza virus replication by providing novel insights into the differences in initiation mechanisms for the vRNA and cRNA promoters. Real-time monitoring of promoter binding at the level of single RNAP complexes has revealed mechanistic details unavailable from ensemble transcription assays or static crystal structures, and has opened an avenue to study transitional intermediates as they occur along the initiation pathway. Our methods should also be able to examine other, alternative promoter RNA conformations  and ourgkz313_Supplemental_FileClick here for additional data file.


Leptotrichia shahii (LshCas13a), in Schizosaccharomyces pombe, an important model organism employed by biologists to study key cellular mechanisms conserved from yeast to humans. This approach was shown to knock down targeted endogenous gene transcripts with different efficiencies. Second, we engineered an RNA editing system by tethering an inactive form of LshCas13a (dCas13) to the catalytic domain of human adenosine deaminase acting on RNA type 2 (hADAR2d), which was shown to be programmable with crRNA to target messenger RNAs and precisely edit specific nucleotide residues. We optimized system parameters using a dual-fluorescence reporter and demonstrated the utility of the system in editing randomly selected endogenous gene transcripts. We further used it to restore the transposition of retrotransposon Tf1 mutants in fission yeast, providing a potential novel toolset for retrovirus manipulation and interference.In contrast to genome editing, which introduces genetic changes at the DNA level, disrupting or editing gene transcripts provides a distinct approach to perturbing a genetic system, offering benefits complementary to classic genetic approaches. To develop a new toolset for manipulating RNA, we first implemented a member of the type VI CRISPR systems, Cas13a from  Modulation of gene expression or alteration of gene transcripts provides a critical layer of regulation at the RNA level in living cells. Editing or partially disrupting transcripts serves as a distinct approach for the analysis of gene functions, offering benefits complementary to classic genetic approaches and, possibly, novel insight into the regulation of gene function at the RNA level.Leptotrichia shahii (LshCas13), was found to knockdown target transcripts in Escherichia coli and mammalian cells and has been used in nucleic acid detection and RNA tagging  is a commonly employed, important model system used by biologists to study processes that are conserved from yeast to humans. The use of S. pombe in research has led to many key discoveries related to cell-cycle control  family of enzymes  in fissiS. pombe for targeting and disrupting gene transcripts. We then designed and engineered an RNA editing system by tethering an inactive form of LshCas13a (R1278A mutant) (dCas13a) to the catalytic domain of human adenosine deaminase acting on RNA 2 (hADAR2d). Similar to the mutant Cas9 variant (dCas9) that is capable of binding target DNA but inactive in DNA cleavage (KD ∼ 7 nM) than the wild-type (KD ∼ 46 nM), although it was catalytically inactive (KD ∼ 69 nM) . Escherichia coli DH5α was used for the purpose of molecular cloning.Polymerase chain reaction (PCR) was performed using Taq (Thermo Fisher Scientific) or KOD FX DNA polymerase (TOYOBO). Plasmids and chromosomal DNA were extracted using the Plasmid Mini Kit I and Gel Extraction Kit from OMEGA. Cloning was performed using either restriction endonucleases and T4 DNA ligase (New England Biolabs) or the ClonExpressS. pombe strain FY7652 containing the ura4-D18 and leu1-32 alleles was employed in this study , followed by digestion with DNase I to remove possible contamination by genomic DNA. Reverse transcription was performed on total RNA using the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific) and oligo(dT) primers. Gene transcript levels were quantified via quantitative PCR (qPCR) using the TaKaRa_SYBR®Premix Ex Taq™ II_RR820Q kit (TAKARA) following the manufacturer’s instructions. Corresponding primer sets and tdh1-79-RT-p5/tdh1-79-RT-p3, ade6-1160-RT-p5/ade6-1160-RT-p3 and act1-1566-RT-p5/Act1-1566-RT-p3 were used for the amplification of tdh1, ade6 and act1, respectively . To normalize RNA inputs, ΔCt values for each sample (e.g. a culture of a strain carrying the plasmid pDUAL-HFF1-Cas13a) were computed by subtracting the Ct values for act1 from the Ct values for the targets (e.g. tdh1). The relative transcript abundance  for each target was obtained as the 2−ΔCt value. The relative transcript abundance of the control sample (the strain carrying the plasmid pDUAL-HFF1-Cas13a) was set as standard ‘1’. The normalized expression of tdh1 in the strain carrying the plasmid pDUAL-HFF1-Cas13a-rrk1-crRNA-tdh1, or that of ade6 in the strain carrying the plasmid pDUAL-HFF1-Cas13a-rrk1-crRNA-ade6 was calculated based on comparison against control samples.To measure transcript levels in ectively . Note thSchizosaccharomyces pombe strains expressing dCas13a-hADAR2d transfected with different reporter constructs were grown in EMM medium at 30°C to the exponential growth phase. Cells were harvested via centrifugation for 5 min at 1200 g at room temperature, then washed twice with 500 μl of distilled water and re-suspended in distilled 60% (v/v) glycerol at 0.5–1.0 × 107 cells/100 μl. A 2.5 μl aliquot of the cell suspension was then placed on a 60 × 24 mm cover slip coated with poly-L-lysine and covered with an 18 × 18 mm cover slip. Fluorescent images were visualized using a ZEISS LSM 880 confocal laser scanning microscope (Axio Imager 2) with a C-Apochromat 40 × /1.2 W KorrM27 objective and a PMT detector. The excitation and emission wavelengths for red fluorescence (mCherry) were 543 and 621 nm, and those for green fluorescence (eGFP) were 488 and 523 nm, respectively.S. pombe strains  carrying different plasmid constructs (i.e. the positive control (mCherry-eGFP), negative control (mCherry-eGFP-W58X) or mCherry-eGFP-W58X) with various crRNA/pRNA configurations were grown to late log phase. Cells were harvested, and ∼1 × 107 cells were fixed with 4% (v/v) formaldehyde for 2 h. The cells were then washed five times with phosphate-buffered saline (PBS) buffer and resuspended in 1 ml of PBS buffer. Fluorescence intensities at 488 and 561 nm were measured with a BD LSR II SORP flow cytometer (BD Biosciences) with the following configurations: 488 nm blue laser, 505LP LP filter and 525/50 BP filter with the PMT position on E (for eGFP detection); or 561 nm laser, 600 LP filter and 610/20 BP filter with the PMT position on A (for mCHERRY detection). Raw data were analyzed with Flowjo7.5 software .To measure the fluorescence intensities of yeast cells, 20 ml cultures of To estimate the efficiency of RNA editing based on the ratio of fluorescence intensities between eGFP and mCHERRY (i.e. the ratio of the green fluorescence intensity to the red fluorescence intensity), the ratios of negative control cells and positive control cells were used as the baseline (0%) and maximum (100%), respectively, to normalize and calculate the RNA editing efficiency for samples of different constructs.DNase I to eliminate possible contamination by genomic DNA. cDNA was synthesized using the RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific) with oligo(dT) primers. DNA fragments of target genes were amplified through PCR using specific primers (Dataset S1), prior to Sanger sequencing . The RNA editing efficiency for each edited site was estimated based on the ratio of the signal peak height for the edited base to that of the sum of edited and unedited bases, as previously described (To determine the editing efficiency based on the ratio of edited bases (I) to unedited bases (A), total RNA was extracted using the RNeasy Plus Mini Kit (Qiagen) and then digested with escribed .7, 106, 105 and 104 cells/mL were performed for plating on paired YES plates containing either 5-FOA or both 5-FOA and G418  or together with pre-crRNA sequences (under the control of a separate rrk1 promoter) on the same plasmid  in fission yeast . The transformed yeast cells were first plated in media containing thiamine, which inhibited the expression of LshCas13a under the nmt1 promoter. The strains transformed with the empty plasmid (pDUAL-HFF1)  , or the AL-HFF1) . Howeverd Figure . Slower d Figure ,31, whictdh1 transcript (crRNA-tdh1) caused significantly slower growth of the yeast cells compared with yeast carrying only LshCas13a, the crRNA targeting ade6 (crRNA-ade6) showed no significant effect on the cell growth rate   Figure . It was ll lysis ,33, whicS. pombe. As a proof of concept, we designed and engineered a fusion construct by tethering the deaminase domain of human ADAR2  of LshCas13a was previously found to have lost target RNA cleavage activity but retained the sequence-specific RNA binding capability of the protein  for the formation of the dsRNA substrate required by hADAR2d. The crRNA and pRNA transcription cassette was driven by the promoter , and thrpromoter . These pnmt1 promoter) in the same plasmids containing different crRNA/pRNA constructs  consisted of an integrated dCas13a-hADAR2d fusion gene and a modified episomal vector containing crRNA/pRNA constructs. To facilitate the testing of RNA editing activity on target RNA substrates, we next constructed a dual-fluorescence fusion protein reporter consisting of mCherry and eGFP, as described previously , but witWhile dCas13a-hADAR2d-expressing cells carrying the negative control plasmid (mCherry-eGFP-W58X in pDUAL-HFF1) produced only red fluorescence, cells carrying the positive control (mCherry-eGFP fusion in pDUAL-HFF1) displayed both red and green fluorescence  at various positions along the crRNA–pRNA fusion sequence Figure . It shouS. pombe. Five crRNA–pRNA fusion constructs with varying pRNA lengths of 9, 11, 21, 31 and 37 were generated, with the editing reside (A) fixed within the pRNA region at 6 bp from the crRNA/pRNA boundary . The expression of these genes ranged from low (e.g. fewer than one mRNA molecule per cell for mel1 in the single-cell organism vegetative growth phase) to high (e.g. 560 mRNA molecules per cell for tdh1 during single-cell vegetative growth)  ,41.rrk1 promoter), as described elsewhere . Among the nine target sites, six were found to show detectable RNA editing activity, with the editing efficiency ranging from 7.5 to 54.5% (Table ade6 transcript at position 622A (below detectable levels), such activity was observed at two other sites, 1003A and 1160A, with 7.5 and 9.0% efficiency, respectively. The editing efficiency showed no apparent relationship with either the abundance of transcripts in cells or the G+C content of the sequences flanking the editing sites. Although the tdh1 and nmt1 genes displayed comparable transcript abundance   to the model organism S. pombe, and used it to target and knockdown endogenous gene transcripts in fission yeast cells. To implement LshCas13a in S. pombe, we combined the rrk1 promoter (transcribed by RNA Pol II) with a ribozyme cassette for cis-processing of pre-crRNA. A similar system was previously employed to express the CRISPR-Cas9 system in S. pombe  to the deaminase domain of human ADAR2 (hADAR2d). The dCas13a-hADAR2d fusion protein was reprogrammed to edit fluorescent reporter mRNA and endogenous gene transcripts. Although no investigation of targeted RNA editing tools for yeast has yet been reported, in mammalian cells, a specifically designed guide-RNA has been introduced to recruit endogenous ADAR2 to edit specific mRNAs (KD ∼ 7 nM) than wild-type LshCas13a (KD ∼ 46 nM)  ,12.Lachnospiraceae bacterium (LbaCas13a), the first 13 nucleotides (C1–G13) of the spacer in the LbaCas13a/crRNA complex are well ordered and sequestered within the LbaCas13a protein, whereas the rest 11 (G14–C24) were disordered and exposed in solvent , to mediate RNA editing in human cell lines has also been reported  exist. So it is likely that very few, if any, off-target editing sites exist in S. pombe. Supporting our speculation, in yeast S. cerevisiae only around a dozen editing sites were found in mRNA substrates when transfected with a full-length human ADAR2 vector (The utilization of a different member of the Cas13 family, Cas13b from reported . With anreported ,46–48. Th1 Table ,38. Thush1 Table . In addih1 Table ,42. Furth1 Table ,50, ther2 vector .S. pombe, we foresee the potential improvement and expansion of this system for many other functions, to address fundamental questions regarding the modulation of gene functions at the RNA level using the model system of fission yeast. To improve the efficiency of the CRISPR-Cas13-related tool, it can be enhanced with new functionalities. For example, our dCas13a-hADAR2d system in S. pombe can be further improved by simply using the E488Q variant of hADAR2d. Additionally, we may consider expanding its RNA editing activity by replacing hADAR2d with an RNA-editing enzyme with a different specificity, such as a member of the APOBEC family of enzymes that catalyze cytidine to uridine conversion. The available molecular tools for manipulating RNA sequences are limited, and this RNA manipulation platform is especially valuable for perturbing model organisms such as S. pombe. Our implementation of the CRISPR-13a system in fission yeast serves as a stepping stone toward a much-promised new family of toolsets that target gene transcripts for a wide range of research and biotechnological applications.While we have shown that Cas13a can be repurposed and implemented for RNA interference and RNA editing tasks in Supplementary DataClick here for additional data file.