RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein 3D complex structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases.Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore  with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structural similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Take the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME.Based on a size-independent scoring function RMscore, a novel RNA 3D structural alignment approach RMalign is developed and integrated into PRIME2.0, which could be useful for the biological community in modeling protein-RNA interaction.The online version of this article (10.1186/s12864-019-5631-3) contains supplementary material, which is available to authorized users. RNA plays important roles in many biological processes such as gene regulation, subcellular location and splicing. High-throughput global mapping of RNA duplexes with near base-pair resolution reveals that RNA interacts with RNA and RNA-binding proteins using higher order architectures in living cell . Most ofIn this manuscript, we introduce an RNA structural alignment approach based on RMscore, which is a size independent scoring function to measure RNA structural similarity. Firstly, we reveal the liner relationship between the logarithmic length of RNA and the logarithmic radius of gyration (Rg) of RNA. At the same time, the aligned correlation coefficient (ACC) describing the relationship between RMSD and Rg also has a complex function relation with the RNA length. Combining these function relations, a length slightly independent scoring function RMscore is determined (the RMscore only slightly decreases as the length increases). Then RMscore is applied to two randomly selected independent datasets to optimize parameters and determine the transition point from similar to dissimilar. With the transition point, RMscore performs better than SARAscore , 13, 22 We download RNA structure coordinates from PDB  website Total 2557 RNA structures and their complexes from PDB are separated by chains. 3775 RNA chains are kept expect the RNA structures in mmcif format. PDB-3775 represents all RNA structures in PDB. The relationships between the Rg of RNA and the RNA length are explored in this dataset.ACC in proteins describing the relationship between RMSD and Rg is reported in . In ordeWe randomly selected 0.3 million RNA pairs from all-to-all alignment of RNA chains in PDB-3775 to optimize parameters in RMscore. The alignment of the paired RNA is generated by needle . This daWe randomly chose 0.1 million RNA pairs from all-to-all pair of RNA chains in PDB-3775 to determine the phase transition. The alignment of the paired RNA is aligned by SARA , which ihttp://ndbserver.rutgers.edu/). We extract one RNA chain from one structure or its complex. This process outputs 175 RNA chains. tRNA pairs are then constructed through all-to-all pairwise alignment by SARA.We downloaded all tRNA structures from NDB  (http://http://structure.biofold.org/sara/datasets.html), which is constructed from SCOR [FSCOR , 13 is drom SCOR  to benchwww.rnabinding.com/RMalign/RMalign.html. The vary cut-offs are tried, because it is still unknown which value is appropriate to cluster the RNA structures.Structural similarity is used as evaluation in protein structural alignment protocol. However, RNA function is used as the metric in benchmarking in balance-FSCOR. So, we construct balance-x-FSCOR to benchmark RNA structural alignment approach employing the RNA structural similarity RMScore as a metric. Firstly, FSCOR is clustered by RMalign with different RMscore cut-offs  to construct the x-FSCOR. Then 1000 positive and negative pairs are randomly selected from all-to-all pairs of x-FSCOR. These datasets are named as the balance-x-FSCOR. If the number of positive or negative pairs is less than 1000, the dataset contains less pairs. The structural classes with various cut-offs of 419 RNA chains in FSCOR can be downloaded from The unbound set is used to compare the performance of PRIME  and PRIM0.4 where N is the number of residues in a protein. Adopting a similar strategy with protein, we investigate the relationship between Rg of the RNA and its length. Simply, all RNA structures are represented with C3’ atoms. The average log Rg located in the same length bins is calculated. After calculating Rg of all RNAs in PDB-3775, we observe that a scaling law Rg ∝ N0.39 for RNA.The Rg of protein is an important metric to describe the compactness of protein. Previous studies , 31 abouA (RB) is the Rg for structure A (B), rAi(rBi) is the coordinate vector after superposition.The ACC has a function correlation with the Rg of protein and RMSD . The Rg In order to reveal the relation between the ACC and aligned nucleic acids, we take a similar strategy used in TMalign . FirstlyN is the length of fragment in the fragment-pairs.Where LInspired by a protein scoring function TM-score, we introduce a size-independent scoring function RMscore describing the similarity of RNA structure. For an RNA alignment, RMscore is defined as:N is the length of average of target and query RNA, LT is the length of aligned nucleic acids to the target structure, di is the distance between the ith pair of aligned nucleic acids and d0 is a scale to normalize the length effect. ‘Max’ denotes the maximum after optimal superposition. For different scoring strategies, a different d0 is adopted. In RMscore, a length dependent d0 is adopted. Similarity to TM-score, d0 is estimated by multiplying 0 and the length can be estimated from Rg ∝ N0.39 and eq. (2).Where L0 is optimized to smooth the curve).Here the constant compensation (set as 0.6) is introduced to smooth the curve when the RMscore is optimized in random pairs-0.3 M , gapless structural alignment (GSA) and alignment combining SSA and GSA, respectively.1.1 RNA SSA. We totally consider five secondary structural states of RNA calculated by X3DNA . They ar1.2 RNA GSA. The secondary initial structural alignment is GSA. In TMalign, the TM-score is used as the comparison metric. In RMalign, we employ the RMscore to select the best alignment.1.3 Alignment combining SSA and GSA. In the third initial alignment, we combine the SSA and GSA with the scoring matrix that is a half/half combination of secondary score matrix  and distance score matrix .We obtain the alignments based on the step 1. In this step, the alignment is scored by the RMscore. First, the alignment is divided into fragment of length  where LN is the length of alignment. The complete alignment fragment is then rotated by the convergent rotation matrix which is obtained by continuously superposing the nts of fragment by Kabsch algorithm with distance less than 5 Å. Secondary, the RMscore is calculated with eq.We obtain the RMscore rotation matrix from step 2. In this step, the aligned RNA structure is rotated by the RMscore rotation matrix. And then a scoring similarity matrix S is calculated according to the Eq.In this step, the alignment is scored with all the aligned nts. And the result is output.ij is the distance of the ith nt in RNA 1 and the jth nt in RNA 2 under the RMscore superposition. The value of d0 is determined by eq. min is the length of smaller RNA.Comparing with TMalign, two processes are modified. Firstly, in the secondary type of initial alignment, RNA secondary structure is calculated by X3DNA . SecondlTo test the performance of RMalign in RNA function classification and compare with ESA-RNA, a balance-FSCOR based on FSCOR is constructed. The same structures may have different functions. And the purpose of RNA structural alignment approach is to detect the structural similarity. So, we also benchmark RMalign in balance-x-FSCOR. The AUC value is used as the metric to measure the performance.We previous developed an approach PRIME  to predi0 is assigned to 5 in the definition of RMscore) changes with the length. To overcome the shortcoming of length-dependent scoring function for comparing RNA structures, we propose a size-independent scoring function RMscore  to measure the RNA structural similarity. In order to obtain the formula of RMscore like TM-score, firstly, we reveal that log Rg of RNA has liner relation (R2 = 0.91) with the logarithmic RNA length  with the number of aligned nucleic acids  is 10% improved than before. Benchmarking on RNA function prediction, RMalign also shows a very high precision with AUC 0.95, which is as good as ESA. The study provides a foundation for novel RNA structural alignment approach in a size-independent way, applicable to the protein-RNA complex structure modeling and RNA function and fold classification. On the basis of the results we designed and implemented an RNA alignment tool, which should be useful for the biological community interested in RNA structural studies.Additional file 1:Figure S1.RNA structural similarity vs the length of RNA. The length of RNA is plotted against RMscore with a different compensation  to smooth the average RMscore at a smaller length, in random pairs 0.3 M. RNA alignment is accomplished by needle in EMBOSS package. Raw-RMscore means that d0 is assigned to 5 Å in RMscore definition. The compensation is chosen as 0.6 that will result in the smoothest curver. (PDF 227 kb)Additional file 2:Figure S2.RMSD vs RNA structural similarity. SARAscore (A) and RMscore are plotted against RMSD in 0.1 million randomly selected pairs, RNA structural alignments are accomplished by SARA. The insets show the fraction of RNA-RNA pairs with RMSD <= 5 Å are plotted with 0.05 bins to show the phase transition from dissimilar RNA pairs to the similar pairs. (PDF 413 kb)Additional file 3:Figure S3.Distribution of RMScore (B) and SARAscore (A). All-to-all pairs are categorized into Low-RMScore (5 Å < RMSD <= 10 Å), High-RMscore (RMSD <= 5 Å) or Non-RMscore (RMSD > 10 Å). The same category criterion is applied for SARAscore. The RMscores corresponding to the peak value are separated clear, which can be used to distinguish similarity or dissimilarity RNA pairs, but SARAscores corresponding to the peak value are close. (PDF 178 kb)Additional file 4:Figure S4.Distribution of RMscore benchmarking in balance-FSCOR. Positive pairs are RNA pairs with the same functions. Negative pairs are RNA pairs with different functions. The figure shows that most negative pairs have lower RMscore and most positive pairs have higher RMscore. (PDF 196 kb)Additional file 5:Figure S5.F-measure, ACC, MCC vs RMscore cut off. F-measure, ACC (accuracy) and MCC are plotted against RMscore cut off selected to predict the positive or negative pairs in benchmarking on balance-FSCOR. (PDF 359 kb)Additional file 6:Figure S6.A false positive example of RMalign benchmarking in balance-SCOR. 5msf:S (yellow) is superposed on 2iz8:R (cyan) by RMalign. These two RNA structures are very similar (RMscore = 0.837). But they are belonged to two distinct function classes in FSCOR . (PDF 236 kb)Additional file 7:Figure S7.Benchmarking of RMalign and SARA in balance-x-FSCOR. (PDF 143 kb)


Although group II intron ribozymes are intensively studied the question how structural dynamics affects splicing catalysis has remained elusive. We report for the first time that the group II intron domain 6 exists in a secondary structure equilibrium between a single- and a two-nucleotide bulge conformation, which is directly linked to a switch between sugar puckers of the branch site adenosine. Our study determined a functional sugar pucker equilibrium between the transesterification active C2′-endo conformation of the branch site adenosine in the 1nt bulge and an inactive C3′-endo state in the 2nt bulge fold, allowing the group II intron to switch its activity from the branching to the exon ligation step. Our detailed NMR spectroscopic investigation identified magnesium (II) ions and the branching reaction as regulators of the equilibrium populations. The tuneable secondary structure/sugar pucker equilibrium supports a conformational selection mechanism to up- and downregulate catalytically active and inactive states of the branch site adenosine to orchestrate the multi-step splicing process. The conformational dynamics of group II intron domain 6 is also proposed to be a key aspect for the directionality selection in reversible splicing. Oceanobacillus iheyenis/Azotobacter vinelandii (Oc19) group II intron from the IIC subclass, in which the branching and not the hydrolysis pathway was activated  were used in combination with in-house synthesized 13C-, 15N- and 2H-modified RNA phosphoramidites. The synthetic access to the stable isotope labelled phosphoramidites was earlier described  with an average loading of 40 μmol g−1 was used to synthesize the RNAs on an ABI 391 PCR Mate using a self-written RNA synthesis cycle. Amidite (0.1 M) and activator  solutions were dried over freshly activated molecular sieves (3 Å) for at least 48 h. The following reagent mixtures were used: Cap A: acetic anhydride/lutidine/tetrahydrofuran 1/1/8 (v/v/v). Cap B: tetrahydrofuran/N-methylimidazole 86/16 (v/v). Oxidation solution: 500 mg iodine dissolved in a mixture of 70 ml THF, 20 ml pyridine and 10 ml water. Detritylation solution: 4% dichloroacetic acid in anhydrous toluene. After complete RNA synthesis, the solid support was dried in 1 h in high vacuum. Standard alkaline deprotection: 650 μl aq. methylamine solution (40%) and 650 μl aq. ammonia solution (28–30%) were added to the solid support. The reaction tube was shaken vigorously and incubated at 37°C for 6 h. The solid support was pelleted via centrifugation and the supernatant was transferred to a 10 ml round bottom flask. The remaining solid support was washed three times with a mixture of THF/water (1/1), the liquid phases were combined with the first filtrate and evaporated to dryness. The residual white precipitate was dried in high vacuum for at least 1 h. 2′-O-TBDMS deprotection: The residue from the previous step was dissolved in 300 μl anhydrous dimethylsulfoxide, and triethylamine trihydrofluoride was added (50 eq. per TBDMS group) and the deprotection mixture incubated at 37°C for at least 16 h. Then, the deprotection mixture was quenched with 2 ml quenching buffer  and directly applied to a HiPrep 26/10 desalting column  using a ÄKTA start system . The crude RNA was eluted using HPLC grade water and the RNA containing fractions (UV detection at 254 nm) were collected in a 50 ml round bottom flask. After evaporation, the crude RNA was dissolved in 1 ml HPLC grade water and transferred to a 1.5 ml reaction tube. The crude RNA was stored at −20°C. The quality of the crude RNAs was checked via anion exchange chromatography on an analytical Dionex DNAPac PA-200 column  and at elevated temperature (80°C). Purification of the RNA sequences was achieved in a single run by applying the crude RNA on a preparative Dionex DNAPac PA-200 column . The fractions containing the desired RNA were pooled and loaded on a C18 SepPak catridge  to remove HPLC buffer salts. The RNA sodium salt form was then eluted from the C18 column with water/acetonitrile , concentrated and transferred to a 1.5 ml reaction tube for concentration determination and mass spectrometric analysis. Sample concentrations were determined by measuring UV absorption at 260 nm on a NanoPhotometer .Standard 2′-escribed . Controlo-nitrophenyl (NPE) protecting group was attached to the 2′-hydroxyl of adenosine, which was then transformed into a phosphoramidite building block suitable for solid phase RNA synthesis. In the first step, the NPE group was attached to adenosine 2′-OH and 3′-OH under alkaline conditions. The resulting mixture of 2′- and 3′-substituted products was successfully separated via silica column chromatography after the introduction of a phenoxyacetyl moiety at the exocyclic amino group. Subsequently, the 2′-and N6-protected nucleoside was transformed to its 5′-O-4,4′-dimethoxytrityl derivative under standard conditions before the 2-cyanoethyl-N,N-diisopropyl phosphoramidite function was introduced to yield the 2′-O-photolabile protected adenosine building block 4 for RNA solid phase synthesis. A detailed description for the preparation can be found in the supplementary information (The photolabile α-methyl-ormation .−1), followed by washing with 50 ml acetonitrile and drying the solid support in high vacuum for 1h. This step is crucial to avoid a strand cleavage after the removal of the photolabile NPE protecting group. The solid support bound RNA was suspended in 2 ml methanol in a UV cuvette and constantly shaken while exposed to UV light (wavelength 260 nm) for 5 h at room temperature. Then, the solid support was washed 3× with 5 ml methanol, and 3× with 10 ml dry acetonitrile, before it was again connected to the RNA synthesizer. The additional nucleotides were added to the A23 2′-oxygen using commercially available reverse direction RNA phosphoramidites  giving the desire 2′-5′-phosphodiester linkage in the branched RNA. Concentrated reverse amidite solutions (0.3 M) were dried over freshly activated molecular sieves (3 Å) for at least 48 h. The coupling time for the reverse amidites was increased to 8 min. For capping, oxidation and deblocking the standard reagents described in the sample preparation section were used. The branched RNA was deprotected and purified according to the previously described protocol.For branched RNA synthesis, the 2′-photolabile NPE protected adenosine building block was used in combination with standard RNA phosphoramidites as described in the sample preparation section. Prior to the attachment of nucleotides at the 2′-oxygen of the branch site A23, the 2-cyanoethyl protection groups of the phosphodiester backbone were removed from the solid support bound RNA with 10% diethylamine in acetonitrile (20 ml with a flowrate of 3 ml min2O (pH 8.0); eluent B: methanol; gradient: 0–100% B in A within 30 min; UV detection at 260/280 nm. The correct assembly of all RNAs used in this study was confirmed by the mass data.All RNAs were analyzed on Finnigan LCQ Advantage MAX ion trap instrumentation connected to a Thermo Scientific UHPLC . RNA mass spectra were acquired in the negative-ion mode with a potential of −4 kV applied to the spray needle . LC: 250 pmol RNA dissolved in 30 μl of 20 mM ethylenediaminetetraacetic acid (EDTA) solution; average injection volume: 30 μl; column: Waters xBridge C18 2.5 μm column (1.0 × 50 mm) at 30°C; flow rate: 100 μl/min; eluent A: 8.6 mM triethylamine (TEA), 100 mM 1,1,1,3,3,3-hexafluoroisopropanol in H3, pH 6.9) and transferred into restricted volume Shigemi tubes giving 0.7–1 mM sample concentrations. NMR experiments on 13C- and 15N-modified RNA sequences were conducted either on a Bruker 600 MHz Avance II+ NMR with a Prodigy TCI probe or on Bruker 700 MHz Avance Neo system with a QXI (1H/13C/15N/31P) room temperature probe. The 2D 1H–13C correlation spectra were acquired using either a 1H–13C-BEST TROSY pulse sequence or a standard HMQC Bruker pulse program (hmqcetgpsi.2). The 2D 1H–15N correlation spectra were acquired using a 1H–15N-SOFAST HMQC pulse sequence. The fold-specific Watson Crick base pairs were determined using a HNN–COSY experiment from the Bruker pulse sequence library (na_hnncosygpphspwg). The 13C-longitudinal exchange data for the D56 13C1′-modified RNA was determined using an alternative approach combining a T1 and a longitudinal exchange NMR experiment  and eleven ZZ exchange (42 h) experiments, respectively.RNA samples were lyophilized as sodium salts and dissolved in 280 μl NMR buffer  data points using the nmrpipe software package . AM(0)/BM(0) and MA(t)/BM(t) are the peak intensities of fold A and B at zero mixing time or the mixing time t from the T1 experiment. The eigenvalues of the spin density matrix with magnetization transfer effects from chemical exchange are given byija defined as:Peak intensities at the various mixing periods were obtained by summing over 1 × 1  of the D56 RNA, while R1B1 and R1B2 are the longitudinal relaxation rates in conformation 1B and 2B, respectively. Experimental uncertainties were estimated from 1000 Monte Carlo runs in which synthetic data sets were generated from the best-fit values of kB→2B1, kB→1B2 and R1B1, R1B2 by adding random errors based on the signal-to-noise ratio to the best-fit curves.Parameters 13C version—termed H1′(C1′)H2′ experiment—on the two 13C1′-labelled D56 RNA mutants. In detail, the experiment was run on 1 mM 1B and 2B mutant RNA samples in restricted volume Shigemi tubes in a 2D manner without 13C chemical shift evolution. The data matrix for each spectrum was 1024 × 96 complex data points with 12 ppm spectral width in both proton dimension, the number of scans was 384 and the inter-scan delay was 1 s to yield a total measuring time of 12 h per spectrum. The cross peak/diagonal peak ratio of U12 in both folds and A23 in the 1B fold provide a direct measure of the 3JH1’H2’ according to the equation:Scross crosspeak intensity, Sdiag crosspeak intensity, JHH the 3-bond H1′–H2′ scalar coupling constant and the ζ delay set to 13.05 ms. The cross- and diagonal peak intensities were obtained by using the nmrpipe nlinLS peak fitting procedure using Lorentzian peak fitting (To address the sugar pucker in the 1B and 2B fold we adapted the H(N)HA experiment originally proposed for the determination of the three bond scalar coupling constant between the amide proton and the α-proton in proteins . The exp fitting .α versus temperature and fitting the experimental data in KaleidaGraph (Synergy Software) using the following equation:R ideal gas constant to give the enthalpy ΔH° and the entropy ΔS° of the melting transition at 298 K. For all RNAs- the wildtype D56 RNA, the 1B mutant RNA and the 2B mutant RNA-concentration independent melting points indicative for monomolecular folds were observed (The RNAs were lyophilized and then dissolved in 800 μl (1 cm cuvettes) or 300 μl (1 mm cuvettes) melting curve buffer  to give RNA concentrations of 2 and 5 μM, respectively. Absorbance versus temperature profiles were recorded at 250 and 260 nm on a Cary-100 spectrophotometer equipped with a multiple cell holder and a Peltier temperature-control device. Data were collected for five heating-cooling cycles at a rate of 0.7°C/minute. Melting transitions were essentially the same with respect to the two different wavelengths and heating-cooling cycles. Melting point temperatures are reported as mean value of the five measurements. The thermodynamic parameters of the monomolecular melting processes of the D56 1B and 2B mutants were obtained by plotting the association degree observed .in trans to a D1 RNA  spectrum nine peaks with strongly varying intensities were observed  basal stem and the 2B fold with a A23-U24 two nucleotide bulge and a 3-bp basal stem could be verified. All the residues, which were replaced by the 13C or 15N labeled counterparts in the course of this study, are shown in the supporting information  and backward (kB) exchange rate, was found for residue U24 with 20.89 ± 0.91 s−1, followed by A23 (15.24 ± 0.57 s−1) and the exchange rate decreases steadily approaching the cUUCGg tetraloop . The same rate differentiated kinetics was found for a D56 construct with aromatic nucleobase 6–13C- and 8–13C-labels on U12, U13, U24, A4 and A23  with respect to the strand molarity (0.75 mM) of the D56 RNA. As the physiological Mg2+-ion concentration can be up to 1 mM in cells (Two magnesium (II) ions are essential to form the active site catalytic triad in D5 and are involved in exon ligation catalysis . Thus, wm Figure  and alsoA Figure . By usin% for 2B . The equin cells , 20 equiin cells . Two bin13C-A4 modified 27nt Oc19 D6 RNA was synthesized and relying on a 2′-photolabile protected adenosine building block and reverse amidites an RNA tetramer was attached to the 2′-OH of the branch-site adenosine A23 . Th. Th13C-AA Figure  and F. IA Figure . The braTo get further insights into the function of the bulge conformation in the branching reaction the sugar pucker of A23 in both states was investigated. First, the D56 RNA construct was trapped in either one of the two bulge conformations by nucleotide mutations. The G6C and C21G mutations lead to a strong preference of 1B as in the competing 2B fold a C6–C20 and a G5–G21 mismatch will occur. The NMR data is fully in support of a switching towards 1B Figure . Similar13C version of a H(N)HA-NMR experiment, . We. WeOceane regime . Our resOc19 D56 RNA. This conformational heterogeneity of D6 leads to a sugar pucker change of the branch site adenosine from the C2-’ to C3′-endo state, thereby modulating the transesterification activity. The findings suggest that the group II intron ribozyme utilizes a conformational selection mechanism to vary the population of reaction competent and incompetent states to orchestrate the various steps—branching and exon ligation—of group II intron catalysis. The secondary structure equilibrium is further proposed to be a key aspect for the reversibility in group II intron splicing. The findings of our work are summarized in a revised schematic representation of the branching pathway integrating the secondary structure and sugar pucker equilibrium in the splicing process  ions. The added divalent cations lead to a preference of 2B, thus preparing the group II intron for the second exon ligation step. Magnesium (II) ions are essential co-factors for this step as catalytically relevant Mgond step . This suond step . Then, tond step .The structural heterogeneity could be recapitulated in five other D6 RNA constructs from various classes suggesting that we indeed observe functional secondary structure and sugar pucker dynamics in D6 with an important role in catalysis.Taken together, our findings strongly suggest that conformational dynamics is a crucial aspect in group II intron self-splicing. So far, the conformational selection concept was mainly discussed in context of biomolecular recognition and in protein catalysis but not as a key aspect in ribozyme activity . The dyngkz965_Supplemental_FileClick here for additional data file.


In addition to the protein coding information, viral RNA genomes code functional information in structurally conserved units termed functional RNA domains. These RNA domains play essential roles in the viral cycle . Understanding the molecular mechanisms behind their function is essential to understanding the viral infective cycle. Further, interfering with the function of the genomic RNA domains offers a potential means of developing antiviral strategies. Aptamers are good candidates for targeting structural RNA domains. Besides its potential as therapeutics, aptamers also provide an excellent tool for investigating the functionality of RNA domains in viral genomes. This review briefly summarizes the work carried out in our laboratory aimed at the structural and functional characterization of the hepatitis C virus (HCV) genomic RNA domains. It also describes the efforts we carried out for the development of antiviral aptamers targeting specific genomic domains of the HCV and the human immunodeficiency virus type-1 (HIV-1). The biological function of RNA has, for long time, been considered to be restricted to its role in the transmission of genetic information from DNA to proteins. The discovery in the early 1980′s that the RNA is capable of catalyzing chemical reactions  led to tDeciphering the mechanisms that underlie each of the functions performed by functional genomic RNA domains and understanding the molecular strategies used by RNA genomes to contain all the information necessary for each of them are scientific problems of great relevance and interest. Elucidating these mechanisms would provide information of enormous importance to address the control of infections caused by RNA viruses. The current information indicates that structural RNA domains play out their different biological roles  by directly recruiting viral and/or cellular factors and/or by forming high-order structures via the establishment of long-range RNA–RNA interaction networks .The essentiality of these genomic RNA domains for the virus makes them excellent candidates as targets for the development of antiviral strategies. Drugs aimed at interfering with their function either by blocking their interactions with viral or cellular factors or by impeding their proper folding may seriously compromise the viral viability. Therefore, they should be considered promising antiviral agents. Aptamers specifically recognize and efficiently bind to structural elements within a target molecule, being among the most promising molecules for interfering with the function of structural domains of viral RNA genomes.Flaviviridae bear a positive single-stranded RNA genome. They are responsible of worldwide public health problems. The family comprises three genera: Flavivirus, that includes important human pathogens like Dengue virus (DENV), West Nile virus (WNV), yellow fever virus (YFV), or Zika virus (ZIKV), among many others; Pestivirus, represented by the virus responsible of the Bovine viral diarrhea (BVDV) or the classic swine fever (CSFV); and Hepacivirus, with the hepatitis C virus (HCV) as the most significant member. Their genome contains multiple highly conserved structural RNA domains, which store essential information for the completion of the viral cycle. This review focuses on the HCV genome, paying special attention to the work carried out in our laboratory aimed at the structural and functional characterization of genomic RNA domains. The second part of the review summarizes the main strategies we have developed seeking aptamers targeted against specific genomic RNA domains of the HCV and HIV genomes. Members of the family Flaviviridae that has attracted major efforts to understand the molecular mechanisms that govern its infective cycle. Its genome is a 9600-nt-long single-stranded RNA molecule, which codes for a single open reading frame (ORF) flanked by highly conserved untranslated regions (UTRs). Although the UTRs comprise numerous conserved structural/functional RNA elements that play essential roles for the consecution of the viral cycle, these RNA elements are also distributed throughout the coding region  has provided a good overview of the HCV genome folding ,21,22,23Specific structural elements of the 5′ and 3′ ends of the HCV genome are involved in the viral replication and translation. This implies the existence of a communication between the two genomic ends. It was commonly accepted that the acquisition of a genomic circular conformation was mediated by the recruitment at both ends of the cellular and viral proteins, as it was demonstrated for the recruitment of IRES-activity stimulating proteins by the genomic HCV 3′ UTR ,28,29,30The 5BSL3.2 domain is also involved in an Apical loop–Apical loop (ALAL) long-distance interaction with the highly conserved 3′SLII domain included in the 3′X tail region at the 3′ UTR ,34 Figu. This inA functional RNA–RNA interaction seems to take place between domain VI, at the 5′ end of the protein coding region, and the highly conserved linker sequence between domains I and II within the 5′ UTR. This interaction has been proposed to participate in the regulation of both the viral replication and translation processes ,38,39,40All together, these results probe the existence of a network of long-range RNA–RNA interactions. This network involves essential structural genomic RNA domains of at least the IRES, CRE, and the genomic 3′ UTR X-tail region and governs the regulation of the essential viral processes -based technology analysis and accessibility using oligonucleotide microarrays, was used. The results of this analysis allowed us to conclude that interactions between elements of both ends of the viral genomes promote to each other their structural tuning at the secondary and tertiary levels, which undoubtedly leads to the mutual modulation of their functionality ,44 Figu. Each onBesides intramolecular genomic RNA–RNA interactions, intermolecular ones have also been described. Thus, the dimerization of the HCV genome is initiated at a highly conserved palindromic sequence named DLS (Dimerization Linkage Sequence) located at the 3′SLII element of the 3′X tail region ,46 Figu. The 3′XAll together, these data demonstrate the existence of a dynamic network of RNA–RNA contacts that tune the structural conformation of essential distal RNA elements, resulting in the regulation of the different stages of the viral cycle. Our data support that the 5BSL3.2 domain operates at the core of this regulatory system.Interfering with the function of viral genomic structural domains either by modifying their folding or by impeding the interactions they are involved in offers a potential means to compromise their viral viability and in the consequence of fighting viral infections. Much effort has been devoted to the development of nucleic acid-based strategies aimed to interfere with the functioning of viral RNA genomes. Several types of RNA molecules have been shown to be efficient antiviral agents, and some of them have even entered clinical trials ,51,52. O12–1015. The winner molecules are known as aptamers. Since it is an entirely in vitro process, the selection conditions are controlled by the researcher and can be modified along the process. Aptamers are short single-stranded DNA or RNA oligonucleotides that recognize and bind efficiently and specifically to a target molecule ,54. AptaHerein, we summarize the attempts performed at our laboratory to identify aptamers with a potential antiviral activity targeting highly conserved structural RNA elements within viral genomes, paying special attention to the work we have performed to develop RNA inhibitors targeting the HCV RNA genome. 15 variant RNA molecules with lengths of 84 nt. The original population resulted from the randomization of 25 consecutive nucleotides linked at the 3′ end of the catalytically active hammerhead ribozyme targeted against position 363 of the HCV IRES. The resulting population was subjected to a two-sequential-selection-steps procedure [We developed an innovative SELEX strategy that was applied to a theoretical population of more than 1 × 10rocedure . The firrocedure   of the most efficient in vitro inhibitors yielded efficient antiviral activities showing inhibitions up to 60% and 70% of the viral translation and replication, respectively ,62,63,64The highly conserved genomic CRE element codes essential information for the regulation of the viral processes. It has been shown to be an essential partner in the viral replication ,25, and 18 variants) was synthesized and introduced in a standard SELEX procedure. With this strategy, we searched for binders to a 194-nt-long viral genomic RNA fragment containing the CRE structural element [For this assay, a starting population of RNA molecules resulting from the randomization of 30 contiguous nucleotides (yielding a theoretical heterogeneity of more than 1 × 10 element  (Figure  element ,67. To t element . FurtherTaken together, the results of this study support the potential use of the HCV CRE element as a therapeutic target. More importantly, they provide evidences that aptamers are also useful molecular tools for understanding the function of RNA elements and the molecular mechanisms underlying the RNA-mediated regulation in viral genomes. Reports describing the successful targeting of different functional RNA elements in several viral RNA genomes by inhibitor RNA molecules can be found in the literature. Besides HCV, we have also targeted RNA aptamers against the 5′ UTR of the HIV-1 RNA genome. For this purpose, we developed an innovative approach in which we combined a standard SELEX strategy with an in silico analysis of the selected aptamers, allowing the design of more efficient ones .15 variants of 64-nt-long RNA molecules. They were binding-challenged against a HIV-1 genomic RNA fragment comprising the first 308 nt of its 5′ UTR. After 11 selection rounds, we observed that the specific 8-nt-long sequence 5′-GGCAAGGA-3′ appeared in high frequency in the RNA population reaching 89.2% from the total analyzed sequences after cycle 14 [The SELEX procedure was applied to a starting population of more than 1 × 10cycle 14 . InteresA comparison study of the inhibition of HIV particles production in the cell culture by the RNApt16 and the two most represented selected aptamers XIV22 and XIV26 demonstrated that the three of them efficiently inhibited viral particle production reaching a maximum inhibition value of 85% for the RNApt16  Figure b. Thus, Viral RNA genomes have developed an information storing system beyond the protein coding one, which stores essential information for the completion of the viral cycle. This system uses structural RNA elements to perform essential functions. The high conservation of these elements among different viral isolates and their important roles in the viral cycle make them potentially good therapeutic targets for new antiviral strategies, which has been confirmed by the reported examples of interfering with their activity by the use of RNA aptamers or other RNA inhibitors. The approach of RNA aptamers targeting highly conserved structural genomic RNA elements offers a potential means of developing new and efficient antiviral strategies. In addition, aptamers are excellent molecular tools to study the functionality of structural RNA domains in viral RNA genomes.


Negative air ions (NAIs) have been discovered for more than 100 years and are widely used for air cleaning. Here, we have carried out a comprehensive reviewing on the effects of NAIs on humans/animals, and microorganisms, and plant development. The presence of NAIs is credited for increasing psychological health, productivity, and overall well-being but without consistent or reliable evidence in therapeutic effects and with controversy in anti-microorganisms. Reports also showed that NAIs could help people in relieving symptoms of allergies to dust, mold spores, and other allergens. Particulate matter (PM) is a major air pollutant that affects human health. Experimental data showed that NAIs could be used to high-efficiently remove PM. Finally, we have reviewed the plant-based NAI release system under the pulsed electric field (PEF) stimulation. This is a new NAI generation system which releases a huge amount of NAIs under the PEF treatment. The system may be used to freshen indoor air and reduce PM concentration in addition to enriching oxygen content and indoor decoration at home, school, hospital, airport, and other indoor areas. The coverage period of the database search is around 100 years from 1918 to 2018 (the updated search date is 10 September 2018). Three keywords “negative”, “air”, and “ion” were used to search all collected articles in these databases. The database searches harvested a total of 335, 681, and 221 articles that contain all of the three keywords in either titles or abstracts when the PubMed database, ScienceDirect, and IEEE Xplore databases were employed, respectively. Only peer-reviewed and English-language articles were considered. We then screened these articles by manually reviewing titles, which led to 170, 279, and 117 studies selected (duplicated references were excluded); the remaining were excluded due to no relationship to the NAI topic. We further carried out abstract screening to exclude more unrelated articles. Based on the abstract reviewing, 93, 113, and 57 references were selected, which were subjected to full-length reviewing. During full-length article reviewing, some cited references, which were not included in the full-length review list, were also selected for additional reviewing.The systematic review of studies on NAIs initiated by literature searches. Three databases were selected including PubMed database . In this patent, an ultraviolet lamp was used to irradiate metal materials, which photo-electrically eject electrons. The electrons then collide with air molecules and generate NAIs.3 within one hour. The NAI concentration was increased to 825 ions/cm3 under UV light condition, significantly higher than the control  can be used to directly ionize air molecules to generate NAIs ,8. Actua control A. Furthe control . After tThe atmosphere surrounding the earth is subjected to a natural electric field and its intensity is continuously fluctuating under both local and global influences . The locArtificial corona discharge is an efficient way to generate NAIs. When a high negative voltage is applied to a conductor/electrode and generated electric field is high enough, corona discharge occurred ,13. If a2−) which was bound to clusters of water molecules to form the structure O2−(H2O)n [The considerable numbers of NAIs are found under waterfalls or in the seashores. These NAIs are generated by Lenard effect. Lenard effect was also called spray electrification or waterfall effect and was first systematically studied by Philipp Lenard , who won2−(H2O)n , and was2−(H2O)n . NAIs ge2−(H2O)n .3, 3 n by the Lenard effect is around 60 seconds, but the NAIs generated by corona discharge could survive only several seconds [As described above, we have reviewed the generation of various sources of NAIs and various factors affecting NAI generation. Here, we further explored the fate of generated NAIs. Generally, generated NAIs are not stable and will be gradually decayed. When NAIs combined with water molecules and form negative-ion clusters, their lifetime will be longer. For example, the half-life of negative oxygen ions O seconds . Such a  seconds . In the  seconds  and refe seconds . This ma2•− was a kind of NAIs [2•− are the major negative ions [2•− is mainly produced in the thylakoid membrane of photosystem I (PSI) [2•− within PSII has also been reported [2•− steadily by univalent reduction of MV or paraquat and subsequently transferring their electrons to oxygen [Nicotiana tabacum) suspension culture [Negative oxygen ions are the most commonly recognized NAIs. Reports showed that superoxide O of NAIs ,36,37,38 I (PSI) . Generatreported ,41,42. Sreported ,44. Diffreported . In addio oxygen ,46,47. I culture .Arabidopsis leaf extracts [PsbS1 gene encodes a 22-kDa Photosystem (PS) II protein involving in nonphotochemical quenching (NPQ) of chlorophyll fluorescence. Disfunction of this gene by either transfer DNA (T-DNA) knockout or RNAi led to increased superoxide production [Furthermore, the generation of superoxide in plant leaves can be improved by regulating gene expression. For instance, expression of the gene encoding Rho-related small G protein (ROP) 2 promoted superoxide generation in extracts . Interesoduction . The resAloe arborescens, Clivia miniata, Chlorophytum comosum, Opuntia brunnescens, Crassula portulacea) [Aloe arborescens. However, for the remaining 4 plant species, less effect was observed and NAI release was not significantly affected by light intensities. Skromulis and Noviks (2012) surveyed the effects of meteorological factors on NAI release in a city and found that the highest level of NAI concentration was observed in morning sections and less air pollutants were detected in the duration [Crinum asiaticum, Narcissus tazetta, and Zephyranthes carinata, under different light intensities and found that the stronger light might stimulate the plants to release more NAIs [In addition to superoxide, other negative ions also contribute to the composition of plant NAIs. However, little is known on how these negative ions are generated and released in plants. Studies mainly focused on several factors that affect the release of NAIs in plants. Wang and Li (2009) investigated the effects of light intensities on NAI release in five plant species (tulacea) . Their dduration . Wu et aore NAIs .3−(H2O)n, O−(H2O)n, and O3−(H2O)n. [2−, CO3−, or NO3− and their (H2O)n clusters as well as HSO4− core ions [−, NO2−, HCO3− and their water clusters [Generally, NAIs are composed of multiple negatively charged molecules and these negative ions combine with several or up to 20 or 30 water molecules and form negative-ion clusters such as CO−(H2O)n. ,52,53. M−(H2O)n. ,55,56. Eore ions . Studiesclusters ,57. Figuclusters ,64,65,663− and other negative ions include O−, O3−, NO3−, and so on, which consist of less than 10% [3− is the major ion followed by HCO3− and others. Both results from Shahin (1969) and Nagato et al. (2006) indicated that O2− is not dominant products of negative ions produced by corona discharge [Negative ion species generated by corona discharge have been identified through mass spectrometry by several experiments ,66,67. Tthan 10% ,72,73,742− and O− [− concentration was significantly increased near waterfalls. These three ions, O2−, O−, and OH−, further evolved into other types of ions. As a result, the following 5 types of ions were regarded as the major negative air ions generated by waterfalls. They were listed in Waterfalls-induced negative ions evolved from both O− and O− . The OH−− is formed when an oxygen molecule O2 obtains an electron  reviewed key reports published in that period on the biological effects of NAIs 976 revie. Subsequ3 has been regarded as the threshed value for fresh air and the concentration should be higher for boosting the human immune system  were exposed to NAIs and only the growth Mycobacterium parafortuitum was inhibited [Similarly, there are lots of research articles to present the effects of NAIs on the growth of microorganism . The majrcescens . Exposurorescens ,99,100 aii cells . NAIs prii cells . Except  notatum ; the use notatum . Howeverd apples . In anotnhibited .Avena sativa, the fresh and dry weight were increased [Besides animals/humans and microorganisms, plants might also be affected by NAI exposure. After NAI treatment of ncreased  and meanncreased . Oxygen ncreased . Plant hncreased . Lettucencreased . NAI trencreased . NAIs hancreased . Howeverncreased .2 is a biradical with two unpaired electrons in different orbitals and is capable of accepting up to 4 electrons [2•− is formed by accepting one electron. At least 4 oxidation states of oxygen have been identified including O2 (dioxygen), O2+ (dioxygen cation), O2•− (superoxide) and O22− (peroxide dianion) [2 gives birth to O2•−, which serves as both a radicle and a negative ion; thus, the radicle sign “•” is added [2•− is a kind of negative oxygen ions, which was documented by several reports [2•− was also detected in those NAIs that were generated by stimulating plant roots using PEF [The oxygen molecule Olectrons . Superoxdianion) . The monis added . Therefo reports ,38,127.  reports ,38,128 a reports ,127. SupStaphylococcus albus [Staphylococcus albus by NAIs was protected by adding superoxide dismutase. However, reports showed that many other factors might also lead to the observed lethal effect of NAIs on bacteria [An early study showed the involvement of superoxide in the bacterial killing of us albus . As commus albus , the resbacteria . Inhaledbacteria ,129. Golbacteria , but it bacteria ,130,131.bacteria ; thus, t2.5, seriously affected human health. Based on the data from Lim et al. (2012), around 3.2 million people died in 2010 due to exposure to PM [2.5 [1.0 and PM2.5 penetrate deeply into the lung, irritate and corrode the alveolar wall and, as a result, affect lung functions [PM is the major pollutant during haze episodes. Evidence showed that PM, especially PMre to PM . Annuall PM [2.5 ,134. PM unctions  and mighunctions . Howeverunctions .https://www.epa.gov/indoor-air-quality-iaq/guide-air-cleaners-home).NAIs are unipolar ions and they will electrically charge PM, which will be deposited/precipitated more rapidly than uncharged ones as charged PM can attach to nearby surfaces, attach to each other and settle faster, or sink faster under the gravity ,141,142 3) [Tanaka et al. (1996) reported that NAIs reduced respirable and inhalable dust counts by 46% . PM conc3) . Many ot3) ,151,152)3) ,150,153)3) . Studies3) . In the 3) , which i3) ,156,157)3) .https://patents.google.com/patent/CN203313745U/en?oq=CN+203313745+U). In a word, studies on the mechanism of plant-based release of NAIs by PEF stimulation are still under way. More efforts should be carried out on the comparison between these plant-based NAIs and other sources of NAIs and their difference in biological effects and PM reduction.As mentioned above, plants could release a huge amount of negative air ions under PEF stimulation. This is an interesting topic and many more works should be carried out to figure out the mechanisms for plants to help generate NAIs. Evidence showed that light intensity significantly increased the plant-based NAI release by PEF stimulation . During Some studies have suggested that NAIs had multiple health benefits on humans/animals, might inhibit the growth and/or kill some of microorganisms and promote plant development , but som


In trypanosomes, in contrast to most eukaryotes, the large subunit (LSU) ribosomal RNA is fragmented into two large and four small ribosomal RNAs (srRNAs) pieces, and this additional processing likely requires trypanosome-specific factors. Here, we examined the role of 10 abundant small nucleolar RNAs (snoRNAs) involved in rRNA processing. We show that each snoRNA involved in LSU processing associates with factors engaged in either early or late biogenesis steps. Five of these snoRNAs interact with the intervening sequences of rRNA precursor, whereas the others only guide rRNA modifications. The function of the snoRNAs was explored by silencing snoRNAs. The data suggest that the LSU rRNA processing events do not correspond to the order of rRNA transcription, and that srRNAs 2, 4 and 6 which are part of LSU are processed before srRNA1. Interestingly, the 6 snoRNAs that affect srRNA1 processing guide modifications on rRNA positions that span locations from the protein exit tunnel to the srRNA1, suggesting that these modifications may serve as check-points preceding the liberation of srRNA1. This study identifies the highest number of snoRNAs so far described that are involved in rRNA processing and/or rRNA folding and highlights their function in the unique trypanosome rRNA maturation events. Ribosomal RNA (rRNA) processing in eukaryotes is a complex, multi-step process process that starts in the nucleolus, proceeds in the nucleoplasm and culminates in the cytoplasm ,2. The pO- methylation (Nm) and H/ACA RNAs, which guide pseudouridylation , Leishmaniasis  and Chagas’ disease or American trypanosomiasis . In addition, this family of organisms serves as an important model system to study the role of post-transcriptional regulation, RNA splicing and RNA editing  is divided into three RNA species , TB11Cs2C3 (snR30), TB11Cs2C1 and TB11Cs2C2 species, in addition to 22 other snoRNAs imbedded within the known snoRNA clusters. Among these, three snoRNAs  were shown to affect trypanosome-specific processing steps  architecture of . brucei , T. cruzT. cruzi  and L. ddonovani ,54 ribosdonovani . The remdonovani . AnalysiIn this study, we determined the role of ten abundant snoRNAs by examining how their depletion affects rRNA processing. We show that RNAi silencing of each individual snoRNA resulted in rRNA processing defects that were mostly trypanosome-specific processing events and were characterized by accumulation of rRNA precursors and reduction in the level of distinct srRNA populations. The study reveals that these snoRNAs affect specific cleavage events and are present in distinct processing complexes that are involved in either early or late LSU rRNA processing steps. In agreement with the recent Cryo-EM studies , our resT. brucei strain 29–13, which carries integrated genes for the T7 polymerase and the tetracycline repressor, was grown in SDM-79 medium supplemented with 10% fetal calf serum in the presence of 50 μg/ml hygromycin and 15 μg/ml G418. Cells were transfected as previously described (Procyclic escribed .® recombination cloning system (®8/GW/TOPO® vector (Invitrogen) and subjected to LR-recombination with the pTrypRNAiGate vector  was generated by cloning Streptococcus pyogenes Cas9 from plasmid pX330  into plasmid SK91-NLS-CerFP . This plasmid was transfected into procyclic 1313 T. brucei cells . For gene tagging, PPOT v4 plasmid  was used as a template for PCR amplification, adding coding sequences for eYFP and hygromycin resistance. Tif6 flanking sequences (Tb11.v5.0246) were amplified using specific primers as described in LeishGEdit . RNA extracted from the post-ribosomal supernatant (PRS) was used for library preparation, essentially as described  and reads of 15 bases or less were discarded from subsequent analysis. The remaining reads were mapped to the T. brucei genome (http://tritrypdb.org/common/downloads/release) using SMALT, v0.7.5  with the default parameters, allowing non-unique reads to be mapped randomly to their best match in the genome. Raw read counts for each snoRNA were obtained using Multicov from the Bedtools suite, v 2.17.0  sucrose gradient at 35 000 rpm for 3 h using a Beckman SW41 rotor at 4°C. RNA and protein from each fraction were analyzed by northern and western analyses.Sucrose gradient fractionation was performed as described . Whole cT. brucei and L. donovani ribosomes  were used for the validation of modified residues in the LSU. SSU-modified residues were validated using L. donovani SSU model 6AZ1. Figure preparation was performed using PyMol and Chimera. L. donovani models and maps used for figure preparation were: 6AZ3 (EMD-7025), and 6AZ1 and correspond to L. donovani LSU and SSU, respectively.The cryo-EM atomic models of 8 cells were washed in transcription buffer , 1 mM dithiothreitol (DTT), 10 μg/ml leupeptin [Sigma]) and incubated in 0.4 ml TB containing 0.2 mg lysolecithin  on ice for 1 min. Non-induced cells, or cells after 3 days of silencing were permeabilized. Transcription was performed in the presence of 100 μg/ml amanitin. Total labeled RNA was extracted from the cells and fractionated on a 6% denaturing gel.Nascent transcription run-on analysis was carried out using a permeabilization protocol described by Tschudi and Ullu . The onlT. brucei cells were harvested and resuspended at 5 × 107 cells/ml and washed twice with phosphate- buffered saline (PBS). Cells (∼109) were concentrated and incubated on ice. 4′-Aminomethyl-trioxsalen hydrochloride (AMT) (Sigma) was added to the cells at a concentration of 0.2 mg/ml. Cells treated with AMT were kept on ice and irradiated using an UV lamp at 365 nm at a light intensity of 10 MW/cm2 for 30 min. Next, the cells were washed once with PBS and deproteinized by digestion with proteinase K (Roche) (200 μg/ml in 1% sodium dodecyl sulphate for 60 min). RNA was prepared using TRIzol (Sigma) reagent. Approximately 250 μg of RNA was used for affinity selection, essentially as described  were incubated with AMT-psoralen, as described above. Total RNA was fragmented, dephosphorylated using alkaline phosphatase and purified. The RNA was then ligated using RNA ligase (Thermo Scientific) at 25°C overnight and again purified, and cross-linking was reversed by irradiation at 254 nm  was performed using either 1 mM final deoxynucleoside triphosphate (dNTP) (high dNTP sample), or 0.004 mM final dNTP (low dNTP sample), exactly as described in (http://imagej.nih.gov/ij/).The primer extension analysis for mapping nucleotide 2′-ribed in ,67. In tribed in . Primer T. brucei cells was treated with 50 μl of CMC (N-cyclohexyl-N′ -β -(4-methylmorpholinium) ethylcarbodiimide p-tosylate) in buffer  at 37°C for 20 min. Following ethanol precipitation, the pellet was dissolved in 80 μl of Na2CO3  at 37°C for 4 h to remove all CMC groups from Us and Gs. The treated RNA was used as a template in primer extension analysis. In this method, the RT stops 1 nt before the modified base  from ied base ,69. PrimT. brucei, based on samples derived from RNPs fractionated on an FPLC sizing column (FPLC library)  . The stulibrary) . We wishlibrary) . The comlibrary) . The RPKlibrary)  (Supplemlibrary) . Notablylibrary)  are indelibrary) .Trypanosome-specific snoRNA, TB11Cs2C1, was shown to be involved in SSU processing  from the 5.9 Kb LSU precursor (27SA2 in yeast). The 5.1 Kb trypanosome LSU precursor (like 26S in yeast) is generated by cleavage in ITS2 to release the 5.8S species from LSU rRNA. However, trypanosomes have unique additional processing steps to remove the srRNAs from 5.1 Kb LSU precursor. A very distinct trypanosome-specific precursor is the 3.9 Kb pre-rRNA species, which separates the large LSU after removal of srRNA2 (sr2), srRNA 4 (sr4) and srRNA 6(sr6). The 3.9 Kb precursor RNA contains the two LSU large fragments and srRNA1 (sr1). All three 5.9 Kb, 5.1 Kb and 3.9 Kb LSU precursors were accumulated in cells defective in rRNA processing upon depletion of NOP1  nucleotide by primer extension under different dNTP concentration. Nm residues induce a specific reverse transcription (RT) stop one nucleotide downstream of the methylated site under low dNTP (0.004 mM). The results based on two biological independent replicates demonstrate as expected that under low dNTP, the corresponding methylation-dependent stops on rRNA (1 nt before the modification site) at position Gm102 guided by TB9Cs2C5 was specifically reduced upon silencing. . Previously it has shown that minimum 10 bp duplex around the modification site modify target site efficiently . To idenThe TB10Cs4C3 snoRNA was distributed between early complexes (fractions 17–23) but also accumulated in later pre-40S complexes (fractions 9–11) Figure . BioinfoTo determine whether TB10Cs4C3 snoRNA involved in pre-rRNA processing, silencing of the TB10Cs4C3 snoRNA was verified  but also exhibits a distinct peak on the pre-40S/pre-60S complexes (fractions 9–13) Figure . The basThis snoRNA co-sedimented with both large and smaller rRNA processing complexes Figure . The intTB10Cs1C4 snoRNA was silenced and efficient silencing was observed (The TB10Cs1C1 snoRNA was found not only in early complexes (fraction 19–21) but also in late pre-60S ribosomal complexes (fractions 11–15) Figure . The proT. brucei snoRNAs  are found mainly in a single population in the smaller complexes as a monoparticle cosedimenting with the ∼20S–30S. These snoRNPs have been predicted to guide modification on rRNA but does not base-pair with the precursor rRNA. We therefore examined the possibility that whether these additional snoRNAs may also be involved in rRNA processing.Sedimentation shown in Figure Density gradient sedimentation analyses demonstrated that TB8Cs1C1 snoRNA is associated with late pre-60S complexes   Figure  and sileAll together, these data demonstrate that TB9Cs2C3 snoRNA silencing has a severe impact on both pre-LSU rRNA processing and methylation.T. brucei H/ACA snoRNA examined in the current study and guides Ψ on LSUα-939  and Cbf5 (H/ACA pathway) depletion  and those that guide modifications on mature rRNA segments and do not interact directly with srRNA1 . Altogether, our data indicate that liberation of srRNA1 is the most tightly controlled processing event identified in this study. It is possible that different snoRNAs could assist in cleavage reaction at same ITS3 site.et al.  is anchored by uL3 (previously L3), and srRNA6 (sr6) anchored by eL6 (L6) and eL33 (L35A), all early-acting proteins  implicated in directing cleavages at specific and unique sites. In addition, LSU processing factors may be replaced by trypanosome-specific snoRNP proteins. The Dbp6p and its associated factors, which was shown to function in releasing the 5.8S rRNA in yeast , are misThe study raises the question why such an elaborate processing pathway exists in trypanosomes. It was suggested that rRNA fragments are excised to shorten the rRNA and to compensate for the long expansion segments, which are longer than in other eukaryotes . HoweverO-methylation along the protein exit tunnel near the PTC as a check-point for ribosome biogenesis. The unique properties of the rRNA processing mechanism and machinery could be harnessed for designing future drugs to inhibit the ribosome function in trypanosomes, in ongoing attempts to fight the devastating diseases caused by these parasites.In summary, this study identifies key events in trypanosome rRNA processing controlled by snoRNAs and highlights the importance of snoRNAs guiding 2′-The RNA-seq data reported in this article has been deposited in the Sequence Read Archive (SRA) database (accession number: SRP133196).Supplementary DataClick here for additional data file.


Our system thus provides a methodology for explorations across human tissues, diseasestates, and throughout all domains of life.RNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. Althoughrecent studies have systematically identified individual RNA-binding proteins, their higher-order assembly into ribonucleoprotein(RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification ofRNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that more than 20% of knownhuman protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes thatdissociate, change composition, or form stable protein-only complexes in the absence of RNA. We use our method to systematicallyidentify cell-type-specific RNA-associated proteins in mouse embryonic stem cells and finally, distribute our resource, rna.MAP,in an easy-to-use online interface ( Ribonucleoprotein (RNP) complexes carry out many essential biological processes. Mallam et al. developed differentialfractionation (DIF-FRAC), a proteomics method to systematically discover RNP complexes. Using their method, they discoveredpreviously unknown RNP complexes, classified complexes by their RNA-dependent stability, and identified previously unknown rolesfor RNA in known protein complexes.  RNA-binding proteins (RBPs) play essential roles in diverse biological processes and in most cases act within higher ordermulti-protein complexes called ribonucleoprotein (RNP) complexes .UnderstStrikingly, however, we still lack any systematic characterization of the assembly of individual proteins which associate withRNA in higher order RNP complexes, leaving a crucial gap in our knowledge. Here we define an RNA-associated protein as a protein thatphysically interacts directly with RNA or indirectly with RNA through a secondary interacting molecule. A worldwide effort iscurrently under way to systematically identify multi-protein complexes using high-throughput mass spectrometry techniques , but nonHere, we present differential fractionation (DIF-FRAC) for interaction analysis, which measures the sensitivity of proteincomplexes to a given treatment  using native size-exclusion chromatography followed by mass spectrometry. DIF-FRAC isbased on a high-throughput co-fractionation mass spectrometry (CF-MS) approach that we developed and applied to a diverse set oftissues and cells types en route to generating human and metazoan protein complex maps . DIF-FRAAnalysis of DIF-FRAC data answers important questions as to the role of RNA plays in macromolecular complexes. Specifically, weidentify RNP complexes that (1) dissociate, (2) form stable protein-only complexes also in the absence of RNA, and (3) changecomposition in the absence of RNA, suggesting specific roles for RNA in each of these cases. The technical flexibility of DIF-FRAC indiscovering RNA-associated interactions can potentially be expanded to virtually any tissue and organism. To demonstrate theversatility of our method to additional non-human samples, we apply DIF-FRAC to mouse embryonic stem cells (mESCs), identifying 1,165RNA-associated proteins, to show that the method is highly adaptable and can be extended to discover RNP complexes in diversesamples.rna.proteincomplexes.org.Finally, we created a system-wide resource of 1,428 RNP complexes, many of which are previously unreported as having an RNAcomponent, representing 20% of known human protein complexes. We provide our resource, rna.MAP, to the community as a fully searchableweb database at The DIF-FRAC strategy builds upon our previous strategy of CF-MS for identifying protein complexes in cellular lysate.CF-MS cWe applied DIF-FRAC to human HEK293T cell lysate using size-exclusion chromatography (SEC) to separate the cellularproteins in a control and an RNase A-treated sample into 50 fractions . DuringWe next used mass spectrometry to identify and quantify the resident proteins in each fraction for both the control andRNase A-treated chromatographic separations, resulting in 8,376 protein identifications . Using these abundance measurements, we compared elution profiles  between the control and RNase A-treated experiments for each protein. A shift in a protein’s elutionprofile between experiments is indicative of a protein-RNA interaction. For example, the known RNA helicase DDX21 shows asubstantial shift in its elution profile upon RNase A treatment , consistr ~ 550 kDa)  , as wellIn order to systematically identify RNP complexes in a DIF-FRAC experiment, we first developed a computational frameworkto identify statistically significant changes in individual proteins’ elution behavior to identify RNA-associated proteins.We observed a variety of changes in elution behavior of known RNA-binding proteins upon RNase A treatment, including decrease inmolecular weight , increase in molecular weight , decrease in observed abundance , andincrease in observed abundance  . To captDIF-FRAC identifies 1,012 proteins with significant elution profile differences in HEK293T cells with a false discoveryrate (FDR) cutoff of 5% . To valiTo expand on previous systematic studies of RNA-binding proteins, we exploited the unique features of DIF-FRAC to identifywhich RNA-associated proteins are assembled into higher order RNP complexes. Specifically, we searched for protein complexes whosesubunits co-elute in the control experiment in addition to being sensitive to RNase A treatment  complex  Figure  is a train vivo, we looked in enhanced cross-linking andimmunoprecipitation (eCLIP) data from the ENCODE project  and identify 1,428 complexes that contain a majority of RNA-associated proteins see . This anTo validate RNP complexes identified by our DIF-FRAC method, we reanalyzed an orthogonal proteomics dataset on the basisof a pull-down approach using microRNA (miRNA) hairpins as bait. RNA performs a variety of roles in macromolecular complexes. For example, it can bind as a substrate, function as anintegral structural component, or act as a regulator of a complex’s composition. Mirroring these roles, DIF-FRAC datareveal that upon RNA degradation, the proteins in RNP complexes can remain in an intact complex , become The first category, which we term “apo-stable,” defines protein complexes that remain stable after RNase Atreatment. These include the exosome, RNase P, and the multi-synthetase complex .ElutionThe second category, which we designate as “structural,” refers to complexes for which RNA is essential forthe RNP complex structure and/or subunit solubility. These include, for example, the 60S and 40S ribosomal subcomplexes . UpondeThe third category, “compositional” complexes, refers to those in which RNA promotes different stablecombinations of protein-complex subunits, perhaps in a regulatory role . ForexaWe also identified an uncharacterized compositional RNP complex containing the cell growth regulators DRG1 and ZC3H15(DRFP1)  that areA further example of a compositional RNP complex is the transcription factor (TF) IIIC-TOP1-SUB1 complex, which isinvolved in RNA polymerase III pre-initiation complex (PIC) assembly .DIF-FRAFinally, we identified the chromatin remodeling BRG/hBRM-associated factors  and polybromo-associated BAF  complexes as compositional RNP complexes, which is significant becausethese are some of the most frequently mutated protein complexes in cancer  Figures–S5G. BAFAlthough our efforts focused primarily on higher order RNP complexes, it is important to note that DIF-FRAC is also apowerful complement to existing methods for characterizing individual RNA-associated proteins. Indeed, DIF-FRAC identified 196human RNA-associated proteins not previously identified in the many previous studies discussed in the Introduction . These DAs we described above, inspection of elution profiles for the individual proteins revealed at least four distinct DIF-FRACsignals  and 4. TAnalysis of all identified RNA-associated proteins shows 796 (79%) decrease in molecular weight, while 216 RNA-associatedproteins (21%) increase in size . Aside fLooking specifically at individual proteins provided insights that could affect our understanding of human disease. Forexample, we found that BANF1, a chromatin organizer, appears insoluble under our experimental conditions without RNA . InteresE.coli, γ complex, is known to load the DNAclamp onto RNA-primed template DNA (S. cerevisiae coulddirectly bind different species of nucleic acids. We observed that RFC not only binds double-stranded DNA (dsDNA) but also bindsdouble-stranded RNA (dsRNA) with surprisingly tight binding constants in the nanomolar range  as apositive regulator of RNP granule formation , 10–100 times less than required using anaffinity purification strategy. Regardless of the nature of binding (direct versus indirect), we show the DIF-FRAC method stronglyrecapitulates previously identified RNA-binding proteins –S2F. ForA further advantage of our method is the ability to discriminate between protein interactions that are and are notmediated by RNA. As mentioned above, most protein complex maps available do not consider protein RNA interactions, and moreovermost experiments used to identify protein-protein interactions are done without an explicit step of removing nucleic acid. We havehighlighted previously important protein interactions that are mediated by RNA  and the There are several potential limitations of our method that arise from the lysis requirement in our experimental procedure.First, during lysis there is a potential for gain of interactions of molecules that do not encounter each other in normalphysiological settings . Our experimental procedure does, however, mitigate thispossibility by greatly decreasing the concentration of protein during lysis as well as during chromatography (~100-fold dilution).Second, our lysis conditions provide an environment that allows only stable interactions to be observed making transientinteractions increasingly unlikely to be observed. Finally, our lysis conditions are optimized for soluble proteins and do notspecifically enrich for membrane-bound proteins. Because of this bias, we likely miss potential RNA-associated membrane-boundproteins.One final limitation of our approach involves the variation of protein abundance observed between the control sample andexperiment sample. Other groups have cleverly approached this problem using a SILAC (stable isotope labeling by amino acids incell culture) strategy . UnfortuHere, we report the design, development, and application of a robust fractionation-based strategy to determine RNPcomplexes on a proteome-wide scale. We successfully used DIF-FRAC to identify 115 stable RNP complexes throughout the humaninteractome and applied DIF-FRAC to multiple cell types and species. Combining this with previous data, we generate a resource ofthe RNA-bound human proteome and demonstrate that upward of 20% of protein complexes contain an RNA component, highlighting theprevalence of RNP complexes in the cellular milieu. Together our results provide a valuable tool for researchers to investigatethe role of RNPs in protein function and disease.The DIF-FRAC methodology offers important advances over previous techniques to examine RNA-protein interactions.Specifically, interactions are probed proteome-wide in a native, whole-lysate sample using a strategy that is not reliant onlabeling or cross-linking efficiency. We show that DIF-FRAC can be applied effectively to multiple cell types and organisms andhas the potential to provide information on protein-RNA interactions in disease states. Furthermore, DIF-FRAC is a broadlyapplicable framework that can be extended to examine other large-scale proteomic changes in a system of interest.We also introduce three classifications of RNP complexes  that provide a usefulframework to organize the roles of RNAs in macromolecular complexes. Additionally, DIF-FRAC provides information on thebiochemical characteristics  of RNP complexes in the presence and absence of RNA that offerclues to disease pathophysiology. We anticipate this technique to be a powerful tool to uncover the molecular mechanisms ofRNA-related diseases. Overall, the DIF-FRAC method described and demonstrated here charts new territories in the cellularlandscape of RNA-protein interactions. We have used DIF-FRAC to provide the first system-wide resource of human RNPs, providing abroadly applicable tool for studying cellular interactions and responses in multiple cell types and states.kdrew@utexas.edu). This study did not generate new reagents.Further information and requests for resources and reagents should be directed to and will be fulfilled by the LeadContact, Kevin Drew  cultured in DMEM (GIBCO) supplemented with 10% (v/v) FBS (Life Technologies)were continually split over 7 days to give four 10-cm dishes of adherent cells. For the control fractionation sample, two10-cm dishes of cells were harvested at 80%–100% confluence without trypsin by washing in ice cold phosphate bufferedsaline (PBS) pH 7.2  and placed on ice. Cells (approximately 0.1 g wet weight) were lysed on ice (5 min) byresuspension in Pierce IP Lysis Buffer  containing 1x protease inhibitor cocktail III . The lysis step results in approximately a 10-folddilution. The resulting lysate was clarified  and left at room temperature (30 min). The samplewas filtered (Ultrafree-MC filter unit (Millipore); 12,000 g, 2 min, 4°C) to remove insoluble aggregates. RNase Atreated samples were prepared on the same day in an identical manner, except RNase A  was added after lysate clarification and the sample left at room temperature (30 min) beforefiltration.2. Cells were passaged every 2 days. Lysis and RNase A treatment were done as described in the HEK293Tprotocol.Gelatin adapted mouse J1 ES cells  were cultured in Dulbecco’s ModifiedEagle’s Medium  containing 18% fetal bovine serum , 50 U/mL ofpenicillin/streptomycin with 2 mM L-glutamine (Life Technologies), 0.1 mM non-essential amino acid (Life Technologies), 1%nucleosides (Sigma-Aldrich), 0.1 mM β-mercaptoethanol (Sigma-Aldrich), and 1,000 U/mL recombinant leukemia inhibitoryfactor . ES cells were plated on 15-cm dishes coated with 0.1% gelatin and incubated at 37°C and 5%COLeukocyte-reduced red blood cells (RBCs) were obtained from an anonymous female donor and purchased from Gulf CoastRegional Blood Center . The RBCs used were kept at 4°C for either 7 days or 54 days depending on samplebefore lysis to ensure reticulocytes mature into RBCs. Prior to cell lysis, RBCs were washed with ice cold PBS for 3 times at 600 g for 15 min at 4°C. RBCs were then lysed in hypotonic solution  containingprotease and phosphatase inhibitors  with a ratioof 1 volume packed RBC: 5 volumes hypotonic solution. Hemolysate (soluble fraction of RBC lysate) was collected bycentrifuging white ghosts (membrane fraction of RBC lysate) at 21,000 g for 40 mins at 4°C. Hemolysate was collectedand stored at −80°C until further use. On the day of experiment, hemolysate was thawed and treated withHemoglobind (Biotech Support Group) in order to remove hemoglobin from hemolysate. A total of 4–5 mg of total proteinswere split into control and RNase A treated samples. The RNase sample was treated with RNase A as described in the protocol ofRNase A treatment of lysate from HEK293T cells. Both samples were filtered (Ultrafree-MC filter unit (Millipore); 12,000 g, 2min, 4°C) to remove insoluble aggregates prior to fractionation.−1. Fractions were collected every 0.375 mL. The resulting dilution from input lysate isapproximately 100-fold. The elution volume of molecular weight standards (thyroglobulin (Mr = 669 kDa); apoferritin(Mr = 443 kDa); albumin (Mr = 66 kDa); and carbonic anhydrase (Mr = 29 kDa); Sigma) wasadditionally measured to calibrate the column (All lysates were subject to size exclusion chromatography (SEC) using an Agilent 1100 HPLC system  with a multi-phase chromatography protocol as previously described . Solublee column .2, and digested with trypsin . Digestion was quenched (1% formic acid), and the sample volume reduced to ~100 μLby speed vacuum centrifugation. The sample was washed on a HyperSep C18 SpinTip (Thermo Fisher), eluted, reduced to neardryness by speed vacuum centrifugation, and resuspended in 5% acetonitrile/ 0.1% formic acid for analysis by LC-MS/MS.Fractions were filter concentrated to 50 μL, denatured and reduced in 50% 2,2,2-trifluoroethanol (TFE) and 5 mMtris(2-carboxyethyl) phosphine (TCEP) at 55°Cfor45 minutes, and alkylated in the dark with iodoacetamide . Samples were diluted to 5% TFE in 50 mM Tris-HCl, pH 8.0, 2 mM CaClPeptides were separated on a 75 μM × 25 cm Acclaim PepMap100 C-18 column (Thermo) using a 3%–45%acetonitrile gradient over 60 min and analyzed online by nanoelectrospray-ionization tandem mass spectrometry on an OrbitrapFusion or Orbitrap Fusion Lumos Tribrid (Thermo Scientific). Data-dependent acquisition was activated, with parent ion (MS1)scans collected at high resolution . Ions with charge 1 were selected for collision-induced dissociationfragmentation spectrum acquisition (MS2) in the ion trap, using a Top Speed acquisition time of 3 s. Dynamic exclusion wasactivated, with a 60 s exclusion time for ions selected more than once. MS from HEK293T cells was acquired in the UT AustinProteomics Facility.Fractions from a biological replicate SEC separation corresponding to higher molecular weight species  ArcticExpress (Agilent) E. coli co-transformed with pLant2b-RFC-AE (pIF117) and pET11-RFC-BCD(pIF116). RFC was subsequently purified by SP and Q  ion exchange chromatography. Protein concentration wasdetermined by comparison to a BSA titration curve using Coomassie-stained SDS-PAGE.RFC was purified as previously described . Briefly32P]-ATP usingT4 Polynucleotide Kinase (NEB). Free nucleotide was removed using G-25 MicroSpin columns . Oligonucleotideswere subsequently heated to 75°C and slowly cooled to room temperature to allow proper annealing. 1 nM oligonucleotideand various concentrations of RFC (0 to 256 nM) were incubated for 15 minutes at room temperature in a buffer containing 25 mMTris-HCl [pH 7.5], 50 mM NaCl, 2 mM MgCl2,2 mM DTT, and 0.1 mg/mL BSA. Reactions were quenched with 6x loading dye and subsequently separated by native acrylamide gelelectrophoresis. Gels were dried on Zeta-Probe Membrane (Bio-Rad) at 80°C for two hours. Bands were visualized by aTyphoon FLA 7000 phosphorimager . Binding was quantified using FIJI (D for oligonucleotidebinding.Oligonucleotide constructs were based on an earlier description . Each ofing FIJI . SubsequD from fitting to hyperbolic equation (Bound = (v*[E])/(kD+[E])), where“[E]” is the concentration of the enzyme, and “v” and “kD” are solved bylinear regression.Calculated khttp://proteowizard.sourceforge.net/tools.shtml) and then processed using MSGF+  respectively. We next normalize Dp by the total abundance seen forprotein p in both the control and experiment Dp=∑periment .(2)Dpnoterature . See FigWp, is a mixture ofunannotated RNA-associated proteins as well as non-RNA-associated proteins. In order to evaluate significance of aprotein’s Dnorm being greater than what is expected by non-RNA binders, wemodel the distribution Wp using a two component Gaussian mixture model (GMM). Toensure an accurate model fit we evaluate our GMM fit using three criteria GMMp ofGMMp . (6)Zp=DZp using the normal distributionsurvival function and then false discovery correct p values across all proteins using the Benjamini/Hochberg correction  and processed as described above using MSBlender.Distributions in www.bioinformatics.babraham.ac.uk/projects/fastqc/), 3′ adaptor contamination was removed usingCutadapt (v1.10)  . Alignme (v1.10) ,against (v1.10)  annotate (v1.10) was usedProteomics data are deposited in Pride with accessions PRIDE: PXD015406, PRIDE: PXD014820, and PRIDE: PXD014607.RNA-seq data are deposited in Gene Expression Omnibus (GEO: GSE137651).https://github.com/marcottelab/diffracSource code is freely available on GitHub: http://rna.proteincomplexes.org/Website providing online access of RNP complexes can be found here: 1234


This paper considers the task of finding the smallest circle into which one can pack a fixed number of non-overlapping unit squares that are free to rotate. Due to the rotation angles, the packing of unit squares into a container is considerably harder to solve than their circle packing counterparts. Therefore, optimal arrangements were so far proved to be optimal only for one or two unit squares. By a computer-assisted method based on interval arithmetic techniques, we solve the case of three squares and find rigorous enclosures for every optimal arrangement of this problem. We model the relation between the squares and the circle as a constraint satisfaction problem (CSP) and found every box that may contain a solution inside a given upper bound of the radius. Due to symmetries in the search domain, general purpose interval methods are far too slow to solve the CSP directly. To overcome this difficulty, we split the problem into a set of subproblems by systematically adding constraints to the center of each square. Our proof requires the solution of 6, 43 and 12 subproblems with 1, 2 and 3 unit squares respectively. In principle, the method proposed in this paper generalizes to any number of squares.The online version of this article (10.1007/s10898-018-0711-5) contains supplementary material, which is available to authorized users. This paper deals with the problem of finding the smallest value of r such that one can pack Let Packomania  Let mentclass2pt{minimn due to symmetries in the search space. This section introduces a tiling method to split ..\documenA and B be the vertices in C be the vertex in The triangle entclass1pt{minimaentclass1pt{minimay axis of Proposition The tiling produced by Algorithm 2 suffices if one wants to use a complete global optimization approach for the packing problem. On the other hand, it is not suitable for a rigorous approach since the elements in s in the Problem (The elements in the Eqs. –11) are are and  Problem  by the iMarkót and Csendes  propose Friedman  gives anLet mentclass2pt{minimcore i7 processor with a frequency of 2.6 GHz, 6 GB of RAM and Windows 10. We compiled the code using the g++ 7.3 compiler with the option http://www.mat.univie.ac.at/~montanhe/publications/n3.zipWe perform the computational part of the proof in a i-th iteration, we consider instances of form (We prove the theorem in three phases. At the  of form  and defi of form –11) acc acci-th s replaced by the interval The proof considers the scaled version of the problem to ensure the mathematical certainty of our statements. Therefore, the CSPs in this section are of form  with thementclasspt{minimaWe also assume the labeling scheme for the triangles introduced in Sect. Phase 1 In this iteration, we are interested in reducing the search domain of each subproblem and finding triangles which can contain the center of squares with no rotation. The tiling has 36 triangles, but the symmetries in Instances Phase 2 This phase aims to discard as many instances as possible to reduce the number of hard subproblems in the last iteration. There are 630 possible combinations of 36 triangles taken 2 by 2. After eliminating symmetric and previously discarded cases, we obtain 43 instances. We also propagate any reduction in the search domain in the first phase to the subproblems in the second phase. Again, we remove the condition We run the Algorithm 1 with entclass1pt{minimaWe conclude the second phase with 22 infeasible subproblems. Again, any case in the next phase containing a combination found infeasible in this step can be discarded without any processing.Phase 3 In this phase we set the full model in Problem Below is the link to the electronic supplementary material.


We developed a ribonuclease for site-specific targeting and cleavage of single-stranded RNA. The engineered RNase protein was constructed by incorporating two independent functional domains, an RNase HI domain that could cleave the RNA strand in a DNA-RNA hybrid, and a domain of the RHAU protein that could selectively recognize a parallel DNA G-quadruplex (G4). The newly designed RNase first recruits a DNA guide oligonucleotide containing both a parallel G4 motif and a template sequence complementary to the target RNA. This RNase:DNA complex targets and efficiently cleaves the single-stranded RNA in a site-specific manner. A major cleavage site occurs at the RNA region that is complementary to the DNA template sequence. The newly designed RNase can serve as a simple tool for RNA manipulation and probing RNA structure. Many RNA transcripts are of several thousand nucleotides long and understanding the structure and function of full-length RNA is not always possible. Thus, fragmentation of the RNA molecules is an attractive approach for RNA research5. RNA-based enzymes (ribozymes), which can catalyse RNA cleavage in a sequence-specific manner, include artificially selected ribozymes and natural ribozymes, such as those involved in RNA splicing and RNA processing7. In addition, development of DNAzymes for RNA cleavage has also been reported8. However, the use of protein enzymes (ribonucleases or RNases) for fragmentation of the RNA molecules is a wide-spread method5. Unlike Type II restriction enzymes that cleave DNA in a site-specific fashion9, most of RNases exhibit non-specific cleavage activity10.RNA plays a central role in various biological processes including gene expression, gene regulation and catalysis11. RNase H enzymes, consisting of a RNA-DNA hybrid binding domain and a catalytic domain, can cleave the RNA strand in a RNA-DNA hybrid12. Fusing the catalytic domain RNase HI with a sequence-specific zinc-finger protein resulted in an enzyme that cleaved the RNA strand in a RNA-DNA hybrid at 5 nt from the recognition sequence5. However, this technique will require the specific nucleotide sequence to be present at the desired location of the RNA molecule. DNA oligonucleotide-targeted RNA cleavage provides an alternative approach for fragmentation of RNA molecules19. Hybridization of a short DNA oligonucleotide was early proposed to direct the RNase H-mediated cleavage at a specific site of a RNA molecule13. Later, RNase H was used for site-specific RNA cleavage using the complementary chimeric 2′-O-methyl20 DNA or 2′-O-methyl RNA-flanked DNA21. To enhance the RNA site-specific cleavage efficiency, a short antisense DNA oligonucleotide was covalently conjugated to RNase H to generate a hybrid ribonuclease19. Especially, the RNase H-antisense oligonucleotide conjugate was shown to efficiently cleave target RNA transcripts and inhibit gene expression in vitro and in living cells19. In this approach, the RNase H-antisense oligonucleotide chemical conjugation is the limiting step for varying target sites5.Development of RNase systems for site-specific RNA cleavage would greatly help the studies of RNA structure and function. An artificial site-specific RNA endonuclease was generated by incorporating an RNA cleavage domain with a series of Pumilio/fem-3-binding factor domains that could specifically recognize an 8-nt RNA sequence and cleave near the binding site23. Recently, powerful CRISPR-Cas systems have been applied for RNA recognition and cleavage. The crisprRNA-Cas protein complex can directly recognize and cleave target RNAs24. The type VI effector Cas13 acts as a crRNA-guided ribonuclease function which can degrade RNAs25. The type III-associated RNase (Csm3 and Csm6) targets and cleaves the viral DNA and its transcriptional RNAs26. Programmable RNA recognition and site-specific cleavage by Cas9 has been reported28. By using specifically designed DNA PAMmers, the Cas9 system can selectively bind and cleave RNA targets in a site-specific manner in vitro27. Without addition of DNA PAMers, recognition of a natural PAMer of RNA target by NmeCas9 also allows site-specific cleavage of the RNA target28. Despite these advances, it is not simple to manipulate these CRISPR-Cas systems for programmable RNA recognition and cleavage due to the protein complexity and a need for guide RNA, additive DNA-PAMer or rPAM presence. This study aimed to establish a simple ribonuclease approach for programmable site-specific RNA cleavage, based on a DNA guide and protein-DNA structure-specific recognition.It has been established that RNA can act as a template for a ribonucleoprotein to selectively recognize and knockout complementary messenger RNA (mRNA) including the miRNA and siRNA machineries30. G4 structures are highly polymorphic: four strands of the G-tetrad core can be parallel (oriented in the same direction) or non-parallel32. In cell, the formation of G4 structures has been implicated in many biological processes such as telomere maintenance, replication, transcription and translation34. G4 structure provides a potential target site for recognition by small molecules and proteins36. The specific interaction between the N-terminal domain of the RHAU protein and a G4 has been characterized38. Incorporating fluorescent protein to this RHAU peptide motif provided a useful probe for discrimination between different G4 topologies39.G-quadruplexes (G4) are four-stranded structures formed by stacking of multiple G-tetradsIn this study we generate a novel RNase, when formed a complex with a guide DNA strand containing both a parallel G4 motif and a template sequence, is capable of site-specific cleavage of a single-stranded RNA at a predetermined position, providing a powerful tool for RNA manipulation.Kd) in the nanomolar range39 possibly due to the packing of several helices formed at the N-terminal of RHAU40. We designed a ribonuclease RHAU140-RNase HI by incorporating a catalytic domain of RNase HI from B. halodurans to the RHAU140 peptide. The newly designed RNase is expected to be capable of recruiting a DNA strand containing both a parallel G4 motif and an antisense DNA template sequence. The RNase:DNA complex can selectively target and cleave a single RNA strand at the predetermined position that is complimentary to the template DNA  is sufficient for specific recognition of a parallel G4, longer RHAU peptides exhibit higher binding affinity with RHAU140 (aa 53–192) displaying a dissociation constant  at the 5′ end Table . It took4) and 8 thymines (T8), respectively, between the G4 motif and the template sequence  have also been studied , leading to the formation of a complex of RHAU140-RNase HI:G4-DNA. This complex can directly bind and cleave an RNA target at a predetermined position.12. However, the antisense DNA oligonucleotide and RNase components need to co-localize at the desired cleavage site of the RNA molecule for the reaction to happen, hence, limiting the efficiency of site-specific RNA cleavage. Pre-loading of the DNA template on the enzyme, such as in a DNA oligonucleotide-conjugated RNase H, was shown to enhance the direct binding and site-specific cleavage of the RNA target19. However, this approach requires chemical conjugation between the DNA template and the RNase H enzyme. On the other hand, the systems of a zinc-finger protein conjugated RNase5 and an artificial RNA endonuclease11, while having the advantage of being protein-only sequence-specific RNases, would require particular RNA sequences at the desired locations of the RNA substrate. Sequence-specific ribonuclease systems using non-covalently bound RNA templates including miRNA, siRNA and crRNA are powerful and have been exploited both by the nature and in biotechnology41. Our approach is similar regarding the use of a nucleic acid template. However, while the specificity and efficiency might not meet that of the natural systems, its simplicity and the use of a more robust and inexpensive DNA template could make our approach attractive.In fact, RNase H family itself is able to target and cleave the RNA strand of a DNA-RNA hybrid43. All of major cleavage sites in our assays were at a 5′-guanine, consistent with a previous report on multiple nucleotide preferences of the cleavage sites of RNase H43. The linker between RHAU140 and the RNase HI domain may also affect the position of the cleavage site(s)5. However, the effect of the linker between the G4 motif and the template region was not observed. The site-specific cleavage position can be further restrained and optimized by using a DNA guide strand containing modified nucleotides20.The complex of RHAU140-RNase HI:G4-DNA acts as a nucleoprotein enzyme to catalyze the site-specific RNA cleavage. Major cleavage sites occur inside the RNA region that is complementary to the DNA template and depend on nucleotide sequence of the DNA-RNA hybrid43, a RNA target sequence with highly-efficient cleavage site(s) can be chosen for programmable RNA cleavage. The cleavage site can be further restricted with a chimeric design of the DNA template segment20. In our approach, the DNA guide can be easily constructed by adding a DNA template sequence, complementary to the RNA target segment, to a stable parallel G4 motif, e.g. TT(G3T)4, not complementary to any part of the RNA molecule. This G4-DNA guide, forming a complex with RHAU140-RNase HI, can direct an efficient cleavage of the single-stranded RNA target in a site-specific manner. The cleavage of the RNA strand can be readily monitored and demonstrated by gel electrophoresis.Generally, RNase HI can cleave non-specifically any RNA sequence in a DNA-RNA hybrid. Thus, in principle any RNA segment that is unique in the RNA molecule can be chosen as the cleavage target in our approach. On the other hand, the cleavage efficiency and specific cleavage site(s) depend on the sequence of the chosen target segment. Based on previous studiesKd < 100 nM) to parallel G4s. In principle, a full-length catalytic dead mutant of RHAU with much higher affinity to G4s can also be used. However, the large size of the full-length RHAU (>1000 aa) might limit its applications. On the other hand, a short G4-recognition motif of RHAU can be further engineered to have a higher affinity to G4s. The newly developed RNase can also be used to probe the formation of G4 structures in RNA, when using DNA templates not containing a G4.This study takes advantage of a G4-recognition motif of RHAU to construct an RNase that can recognize a G4-containing DNA template for programmable site-specific RNA cleavage. We have demonstrated the concept using a G4-binding motif of RHAU with a 140-aa length and a nanomolar-range affinity  and ON2 (5′-accaactcgagctactttcgcccgtaatcggccttaatttcc-3′). The PCR product was then cloned into treated pET-Duet1 (Merck) at BamHI and XhoI, resulting in plasmid RHAU140-RNase HI. The wild-typeRNase HI was generated by PCR using a pair of primer ON3 (5′-agccaggatccgatgggcgcaaaagaggag-3′) and ON2 (5′-accaactcgagctactttcgcccgtaatcggccttaatttcc-3′). This PCR product was then cloned into treated vector pET-Duet1 (Merck) at BamHI and XhoI, resulting in plasmid RNase HI  for protein expression. The bacteria were cultured in LB medium containing 100 µg/ml of ampicillin and the cells were grown at 37 °C, shaking at 220 rpm to an OD600 of 0.6–0.8. Then, IPTG was added to a final concentration of 0.5 mM. The cells were incubated for 10 hours at 16 °C, shaking at 180 rpm before being harvested. The pellet was re-suspended into the BugBuster protein extraction reagent consisting of benzonase nuclease. The insoluble material was removed by centrifugation at 20,000 rpm, for 1 hour at 4 °C. The soluble fraction was applied to a column filled with Ni-agarose beads. The column was washed with 30 volumes of buffer . The proteins were then eluted by buffer  and the high concentration of imidazole was removed by dialysis buffer . The purity of desired proteins was analysed by SDS-PAGE , 20 mM potassium phosphate, 100 mM potassium chloride, pH 7.0. The DNA oligonucleotides, B1 containing a G4 motif (10 µM) and B0 without a G4 motif (10 µM) (Table 3T)4, which is not complementary to the RNA target, was introduced to B1, C1, D1, E1 and F1 . A specific cleavage site occurred inside of the complementary region between RNA and DNA. Reaction was stopped by adding equal volume of formamide. For cleavage site mapping, T1 RNase and alkaline hydrolysis (Fermentas) cleavage was performed as RNA ladders. The samples were resolved on a 15% polyacrylamide-7 M urea gel and imaged by the Gel Doc (Alpha Innotech).The RNA target R1), derived from the EGFP gene and consisting of 30 nt, was labelled with FAM at the 5′ end. DNA oligonucleotides B0, B1, C1, D1, E1 and F1 having 20 nt that are complementary to different parts of the RNA sequence. A G4-forming motif TT(G, derivedSupporting Information