Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies. Protein-RNA interactions often play a major role in the cell. They are involved in many processes such as replication, mRNA transcription or regulation of RNA levels and control the operation of key cellular machineries such as the RNA induced silencing complex (RISC). They are thus good candidates for therapeutic studies http://capri.ebi.ac.uk) challenge The structural modelling of large biomolecules and their interactions is a challenging task. A large number of methods for both predicting and evaluating the results have been developed Protein-RNA complexes are especially difficult to predict and model for two reasons: the inherent flexibility of RNA molecules and the electrostatics driving the binding as the RNA molecule is negatively charged. Progress in RNA structure prediction and folding The availability of structural data is essential for machine learning based strategies for scoring in docking experiments. Various machine learning strategies have been developed in the past for protein-protein complexes Protein-RNA native X-ray structures for learning were downloaded from the Protein-RNA Interface Database (PRIDB) We also used the two protein-RNA benchmarks From all the native structures from both the PRIDB and the benchmarks, near-native and decoy conformations are generated using the Rosetta perturbation protocol The RosettaDock protocol is two-level docking search: low resolution and high resolution. The low resolution stage uses a coarse-grained representation of the partners to quickly sample the search space for candidates. The high resolution stage rebuilds the all-atom partners from the low resolution candidates to perform a refined atomic search possibly including rotamer search and loop optimization.ContactS represents the number of interface residues being defined by having a centroid less than 6 Å away from a centroid in the other partner; BumpS is a distance-based penalty for steric clashes; EnvS defines the probability of finding a residue in a specific environment (buried/exposed and interface/non interface); PairS is a pair potential defining the propensity of residues to be found in interaction in given environments and AlignS is an optional term to match a specific alignment pattern (e.g. antibodies).The low resolution scoring function uses the backbone of the molecule and one centroid per residue These five terms of the low resolution score can be computed for protein-RNA complexes in the same way they were for proteins. For RNA, the backbone is chosen to include the sugar ring and the centroid is taken to be the center of mass of the base. All the parameters for the low resolution scoring terms are computed on the PRIDB reference set.VdWS is a Van der Waals term (Lennard-Jones based), ElecS is a Coulomb term, SolvS a solvent term based on the Lazaridis-Karplus model, HbondS is a H-bond 10–12 potential term, SASAS is the solvent accessible surface area term (often omitted), PairS is a pair potential defining the propensity of residues to be found in interaction in given environments and RotamerS is a probability of finding a specific rotamer. Exactly like for the previous low resolution scores, all the terms can be computed for RNA. The rotamer term and loop optimization are switched off for RNA such as in The high resolution scoring function uses all the atoms of the molecules, including the hydrogen atoms, and is made of seven weighted terms:The low resolution representation for each residue/nucleotide is made of the backbone atoms and one pseudo-atom called centroid to represent the side-chain. For the residues, the location of the centroid is taken from RosettaDock (average over a reference set of PDB structures). For RNA nucleotides, the centroid is taken as the averaged position See . The lowWe performed the optimization by supervised learning. To ensure an accurate learning phase, the perturbation was split in two categories for learning labelled near-native (Irmsd<5 Å) and decoy (Lrsmd>8 Å). The assessment was performed using slightly different categories so as to mimic the CAPRI context: near-native (Irmsd<5 Å) and non-native (Irmsd≥5 Å). While these rmsd range are certainly not always likely to accurately represent a correct RNA binding mode, especially considering the variability in size of the RNA molecules, they represent a reachable goal not yet attained by the CAPRI community.Weights for the all atom scoring function described above were optimized in the [0∶1] interval within the ROC-based Genetic LearneR (ROGER) framework using logistic regression and Receiver Operating Characteristic (ROC) based genetic algorithm as previously described for protein-protein docking pdb-out approach. Inspired by the leave-one-out procedure in statistics, we previously used this strategy for machine learning of protein-protein docking scoring functions The first evaluation of the whole scoring procedure is made using cross-validation and a leave-one-To also avoid biasing the samples towards a category while learning, learning is performed with 30 near-native and 30 decoy structures for each of the 120 pdb file leading to a total size of 7,200 structures for the learning set . Test is performed on the 10,000 candidates of each test pdb file. The global procedure flowchart is available in topE10% is the top 10% scoring and topR10% the best 10% rmsd structures. By looking at the degree of overlap between the two categories, the enrichment score provides insight on how good the scoring is ES<1 corresponds to bad scoring, ES = 1corresponds to random scoring and ES = 10 is perfect scoring. Even if what can be considered good scoring is not obvious, the comparison of ES values between 1 and 10 provides good information on how well the strategy performs on different targets.The learning procedure is initially assessed using standard machine learning criteria: analysis of the ROC curve, ROC-AUC in a cross-validation setting and precision for the top 10 structures. CAPRI/Critical Assessment of protein Structure Prediction (CASP) inspired biological criteria are used for the final assessment: Energy vs. Irmsd curve and Enrichment Score (ES). Interface root mean square deviation (Irmsd) is taken from Lensink et al. A data based docking procedure for protein-RNA complexes should first be able to recover the native and close-to-native states for a reference set of complexes. This is assessed by a careful cross-validation setting. In this study we assessed the performance of our learning procedure by plotting Energy vs. Irmsd and checking the enrichment scores of our procedure relatively to the Rosetta CAPRI default. pdb-out setting. The machine learning procedure was also assessed separately by plotting the ROC curves in the leave-one-The strategy was then further evaluated on the Benchmark I and Benchmark II protein-RNA complex structures in a bound setting. The RosettaDock perturbation generation for the conformations ensures that the packing at the interface is relatively correct. Visual inspection shows that the conformations of best energy conformations are relevant from a biological perspective . In a bound setting, for protein-RNA, the relative influence of the parameters shows that the H-bond network is extremely important and must be maintained. st and 3rd quartile ROC performance were assessed in a full docking run unbound setting . Results can be found in The scoring function was then assessed in both bound and unbound (protein and RNA when available) settings. Perturbation runs were performed in a bound setting on the 40 complexes of the benchmarks not in the reference set. Only the 6 pdb files corresponding to median, 1A current limit of our approach is the way RNA flexibility is handled. Handling RNA flexibility for RNA during docking is a very difficult task Protein-RNA complexes are undoubtedly a real challenge for the design of good docking scoring functions. Using a well curated dataset and a well-designed optimization strategy, we show that we could set up of an efficient protein-docking scoring function that can be used in RosettaDock and that can perform better than the existing option in both bound and unbound settings. While scoring can be improved, the nature of RNA makes the prediction experiment still difficult. Electrostatics plays a large role in RNA interactions and ions have to be modelled. Like ours, the data based approaches are limited by the relatively small number of structures available to take ions into account carefully. RNA flexibility modelling for docking is then the next challenge: while some strategies allow for conformation sampling, selection of one or several putative bound states for large cross-docking experiments are still out of reach for both modelling and computational reasons.http://albios.saclay.inria.fr/rosettadockrnaThe source code and files needed to modify RosettaDock 3.4 are available at: Figure S1Model of a nucleic acid (uracile). The phosphate group and the sugar heavy atoms are depicted in gray: (a) coarse-grained level with the centroid atom in red and (b) full-atom level with the base atoms in blue. The centroid is the geometric center of the heavy atoms.(TIFF)Click here for additional data file.Figure S2Energy vs Irmsd for the whole reference dataset in a leave-one-pdb-out setting.(PDF)Click here for additional data file.Figure S3Energy vs Irmsd for the benchmark set in a bound setting. The 10,000 conformations evaluated for our optimized Rosetta scoring function are shown in black. On each plot, the bottom left panel shows the equivalent non-optimized Rosetta result.(PDF)Click here for additional data file.Figure S4Energy vs Irmsd for the unbound test set. The 10,000 conformations evaluated for our optimized Rosetta scoring function are shown in black. On each plot, the bottom left panel show the equivalent non-optimized Rosetta result.(PDF)Click here for additional data file.Figure S5Structure of the mRNA binding domain of elongation factor SelB from E.coli in complex with SECIS RNA (PDB code 2pjp). Mg2+ ions (shown in yellow) are located at the interface and mediate the interaction.(TIFF)Click here for additional data file.Table S1Protein-RNA complexes reference set from the PRIDB. The rightmost column indicates putative redundancy with the docking benchmarks. The Type column refers to the structural family of the RNA molecule: single strand RNA (ssRNA), double strand RNA or single-stranded RNA of helical/paired structure (dsRNA) or transfer RNA (tRNA).(PDF)Click here for additional data file.Table S2Protein-RNA complexes for the test set. For each complex the unbound column for protein and RNA reports the PDB code of the unbound structures when available. The difficulty codes are taken from (PDF)Click here for additional data file.Table S3Leave-one-pdb-out scoring statistics for the reference dataset. Enrichment Score, 10 best energy candidates, 100 best energy candidates, number of near-native structures and Area Under the ROC Curve are reported for each native structure both using the non-optimized RosettaDock scoring function (Default) and our optimized scoring function (ROGER).(PDF)Click here for additional data file.Table S4Scoring results on the bound benchmark test set. Enrichment Score, 10 best energy candidates, 100 best energy candidates, number of near-native structures and Area Under the ROC Curve are reported for each native structure both using the non-optimized RosettaDock scoring function (Default) and our optimized scoring function (ROGER).(PDF)Click here for additional data file.


Paramphistomum cervi were determined for the first time. The five complete rDNA sequences, which included the 18S rDNA, the internal transcribed spacer 1 (ITS1), the 5.8S rDNA, the internal transcribed spacer 2 (ITS2), the 28S rDNA, and the intergenic spacer (IGS) regions, had a length range of 8,493–10,221 bp. The lengths of the investigated 18S, ITS1, 5.8S, ITS2, and 28S rDNA sequences, which were 1,994 bp, 1,293 bp, 157 bp, 286 bp, and 4,186 bp, respectively, did not vary. However, the IGS rDNA sequences had a length range of 577–2,305 bp. The 5.8S and ITS-2 rDNA sequences had 100% identity among the five investigated samples, while the identities among the IGS had a range of 53.7–99.8%. A comparative analysis revealed that different types and numbers of repeats were found within each ITS1 and IGS region, which may be related to the length polymorphism of IGS. The phylogenetic position of P. cervi in Paramphistomatidae was analyzed based on the 18S rDNA sequences. These results will aid in studying the intra- and interspecific variation of the Paramphistomatidae and the systematics and phylogenetics of Digenea.Sequences of the complete nuclear ribosomal DNA (rDNA) gene from five individual  Paramphistomum cervi (Trematoda: Digenea: Paramphistomatidae), the representative species of the genus Paramphistomum, has adult flukes that customarily inhabit the rumen and immature worms that parasitize the gallbladder and reticulum of ruminants, including cattle, sheep, goat, and some wild mammals [ P. cervi is relatively less pathogenic, acute gastroenteritis can occur in young animals when several immature worms migrate through the intestine to the rumen [ P. cervi is distributed worldwide and has been reported in many countries [ mammals , 2. Althhe rumen –5. P. ceountries –6. In Chountries . P. cervi have mainly focused on morphology, life history, and epidemiology [ P. cervi. Recently, the complete mitochondrial DNA sequence and the ITS2 rDNA sequence of P. cervi were determined [Previous studies onemiology –7. Theretermined , 9. The termined . Differetermined –14. IGS termined . However P. cervi, studied the intraspecific variation, and reconstructed the phylogenetic relationship of P. cervi within the family Paramphistomatidae.To identify novel genetic markers for studying intra- and interspecific variation in the Paramphistomatidae and to further study the systematics and phylogenetics of Digenea trematodes, the present study determined and characterized the complete rDNA sequence of P. cervi flukes were collected from the rumen of naturally infected cattle in Qiqihaer, Heilongjiang Province, China. Five adult flukes were washed extensively with physiological saline and identified to the species level based on morphological features described previously [μL double-distilled water. The obtained DNA samples were stored at −20°C until use.Adulteviously . Total g Carmyerius spatiosus , Fischoederius elongatus , Gastrothylax crumenifer , Schistosoma haematobium (AY157263), S. japonicum (AY157607), and S. mansoni (AY157173) rDNA sequences available in GenBank. The primer sequences are listed in Six pairs of primers were designed based on the multiple alignments ofμL contained 1 μL DNA template, 5 μL of 5× colorless Go Taq flexi buffer (pH 8.5), 2 μL of MgCl2 (25 mM), 2 μL of dNTP Mixture (2.5 mM), 0.5 μL of each primer (10 pmol/μL), and 0.2 μL of Go Taq DNA polymerase (5 U/μL). The reactions were performed in a thermocycler under the following conditions: 95°C for 2 min , followed by 35 cycles of 95°C, 1 min (denaturation), 51.7–57.1°C for 1 min , 72°C (~1 kb region) for 1 min (extension), and a final extension of 72°C for 5 min. Each amplicon was examined in a 1.0% (w/v) agarose gel, stained with ethidium bromide (EB), and photographed upon transillumination. The DL2000 marker was used to estimate the sizes of the rDNA amplicons. Representative PCR products were sent to Life Technology Company  for sequencing using the same primers used in the primary amplifications. The five complete rDNA sequences of P. cervi were assembled using DNAStar software.PCR reactions of 25  P. cervi were initially determined by comparing them with previously published rDNA sequences of other trematodes. For example, the 5′ end of 18S and the 3′ end of 28S were established by a comparison with the S. japonicum sequence [ Paragonimus kellicotti (HQ900670) and Echinostoma revolutum (GQ463130), respectively, and the 5′ termini of ITS2 and 28S were determined by referring to a previous study on P. cervi isolated from Slovakia (HM026462) [ P. cervi were examined using the palindrome program in EMBOSS 6.3.1 [http://mobyle.pasteur.fr/cgi-bin/portal.py?#forms::palindrome) to find inverted repeats and REPFIND [http://cagt.bu.edu/page/REPFIND_submit) to identify direct repeats. These repeats were determined using the criteria of a nuclear match ≥10 bp and a mismatch ≤1.The 5′ and 3′ ends of the 18S, ITS1, 5.8S, ITS2, 28S, and IGS rDNA sequences ofsequence , the 5′ icotti HQ00670 andM026462) . The intM026462) . The basSS 6.3.1   as the outgroup. A maximum parsimony (MP) analysis was performed using the PAUP 4.0 Beta 10 program [The phylogenetic relationship of program , and 1,0 program . A Bayes program  with fou program .All five complete rDNA sequences have been deposited in GenBank (accession numbers KJ459934–KJ459938). The lengths of five complete rDNA sequences were 8,493 bp, 9,908 bp, 10,056 bp, 10,167 bp, and 10,221 bp, respectively. The six regions  of the five complete rDNA sequences are shown in  P. cervi samples in this study, which were 157 bp, 286 bp, and 4,186 bp, respectively. The intraspecific variations within P. cervi were 0% for 5.8S and ITS2 and 0–0.5% for 28S rDNA. The G+C content was 54.14% for 5.8S, 51.75% for ITS2, and between 51.74% and 51.82% for 28S rDNA. The ITS2 rDNA sequences of the five samples and another stomach fluke from red deer (Cervus elaphus) in Slovakia [There was no variation in the lengths of the 5.8S, ITS2, and 28S rDNA regions obtained from fiveSlovakia  showed 1 S. mansoni from several geographical locations in Africa and the western hemisphere [ E. revolutum, Clonorchis sinensis, and Opisthorchis viverrini [Despres et al. found no differences among the ITS2 sequences ofmisphere . Similariverrini –28. Thus P. cervi for the first time. Therefore, no interspecific variations were investigated.The present study reported the complete 5.8S and 28S rDNA sequences, which are the only representative sequences of the family Paramphistomatidae, of O. felineus and C. sinensis [ C. sinensis (54.2%) [ P. cervi were 0–0.4%; thus, the nucleotide diversity in the ITS1 rDNA among the five samples was low. The result was in accordance with that of a study of C. sinensis [The length of the ITS1 rDNA sequences obtained from the five samples was 1,293 bp, which was longer than that of other trematodes, such assinensis , 29. The (54.2%) . The intsinensis . P. cervi contains repeated sequences with the following characteristics: three copies of a 23 nt complete direct repeat, A, located at 209 nt upstream of the ITS1 sequences; six copies of a 27 nt complete direct repeat, B, separated by 1 nt, which occur after repeat A; five copies of a 27 nt nearly complete direct repeat, C, separated by 1 nt, which occur after repeat B; four copies of a 28 nt complete direct repeat, D; three copies of a 19 nt direct repeat, E; and a 26 nt direct repeat, F . After the removal of flanking 28S and 18S rDNA sequences, the lengths of the five IGS rDNA sequences were 577 bp, 1,992 bp, 2,140 bp, 2,251 bp, and 2,305 bp, respectively. The G+C content was 47.98–50.26%, and the pairwise sequence differences had a range of 0.2–46.3%. The IGS regions of the five P. cervi samples had strikingly different structures. In contrast to S. intercalatum, S. haematobium, and S. japonicum, the AT-rich regions of the five samples were absent, similar to the IGS region of S. mansoni [ P. cervi could be roughly divided into two types based on their characteristics  and the shortest one, PCB, were considered the same type; the other three samples had the second type. Only PCB contained one 13 nt complete direct repeat (J1 and J2) and one 12 nt incomplete inverted repeat (W and W reverse complement), which were missing the intervening sequences. PCA exhibited the following features: (1) 11 types , containing complete and incomplete direct repeats; (2) five types of short and incomplete inverted repeats (only W and W rep comp shown); and (3) a complete direct repeat H and an incomplete inverted repeat W (W rev comp) were shared by all five P. cervi samples. Compared with PCA and PCB, PCC-PCE had the following features: (1) nine types of complete and incomplete direct repeats ; (2) three types of inverted repeats W (W rev comp), O (O rev comp), and P (P rev comp); (3) incomplete inverted repeats O and P were only possessed by PCD; and (4) some differences were present in the intervening sequences of PCE. In contrast to the results of previous studies on S. haematobium, S. intercalatum, S. mansoni, and S. japonicum [ P. cervi was polytype. For example, it contained complete and incomplete direct repeats, as well as incomplete inverted repeats, and no identical direct or inverted repeat was found between the Schistosoma spp. and P. cervi.The IGS rDNA of mansoni , 37. Theeristics . The lonaponicum , 37, P.  P. cervi were different from one another, some characteristics were similar. For example, the 5′ and 3′ termini of the five P. cervi samples' rDNA sequences were identical, indicating there were no length variations in this region. Similarly, there were no geographical or individual length variations in this region among samples of S. japonicum from several geographical locations in China [Although the lengths and structures of the five IGS rDNA sequences ofin China .Because no other IGS rDNA sequences of the family Paramphistomatidae were available in GenBank, the interspecific differences were not examined. P. cervi was determined by a comparison with those of Paragonimus kellicotti (HQ900670) and E. revolutum (GQ463130). The five 18S rDNA sequences obtained in this study were all 1,994 bp in length, and the G+C contents were 50.30–50.35%. A pairwise comparison of the aligned sequences was performed using MegAlign, and the comparison indicated that the intraspecific variations within P. cervi were between 0 and 0.2% for 18S.The complete 18S rDNA sequence of P. cervi was determined. Using MP, BI, and ML analyses, the phylogenetic relationships among members of trematodes were constructed based on sequences of the 18S rDNA sequences available in GenBank ( Taenia solium (GQ260091) as the outgroup. Three trees all placed P. cervi within the family Paramphistomatidae, as shown in  P. cervi (PCA-PCE) isolates, Carmyerius spatiosus, Fischoederius elongatus, and Gastrothylax crumenifer formed a tight cluster, while the Fasciolidae and Echinostomatidae occupied the proximate cluster as the sister group. These results indicated that the evolutionary relationship of P. cervi was closer to other members of the Paramphistomatidae than to other families . The phylogenetic relationships of families within the Digenea were reported previously, indicating there were some discrepancies between the molecular features and some morphological characteristics [The 18S rRNA sequence is useful for studying the phylogeny of members of Digenea –40. Usin GenBank  without eristics , but thi P. cervi samples for the first time. These results showed that the 5.8S and ITS2 rDNA sequences of P. cervi were quite conserved, with no within-species variation, but the IGS rDNA displayed the fastest evolutionary rate. These data provided novel and useful genetic markers for studying intra- and interspecific variation of the Paramphistomatidae and provided new sequence data for studying the systematics and phylogenetics of Digenea.In conclusion, the present study determined and characterized complete rDNA sequences from


Our results explain the distinct binding specificities of TAM and MBD domains to RNA and methylated DNA, respectively, and reveal structural features for the interaction of NoRC with non-coding RNA.The chromatin remodeling complex NoRC, comprising the subunits SNF2h and TIP5/BAZ2A, mediates heterochromatin formation at major clusters of repetitive elements, including rRNA genes, centromeres and telomeres. Association with chromatin requires the interaction of the TAM (TIP5/ARBP/MBD) domain of TIP5 with noncoding RNA, which targets NoRC to specific genomic loci. Here, we show that the NMR structure of the TAM domain of TIP5 resembles the fold of the MBD domain, found in methyl-CpG binding proteins. However, the TAM domain exhibits an extended MBD fold with unique C-terminal extensions that constitute a novel surface for RNA binding. Mutation of critical amino acids within this surface abolishes RNA binding  The trahylation ,4. Targehylation –8. Consihylation ,9. pRNA hylation . Mutatiohylation . A recenhylation . Signifihylation . Paradoxhylation .The TAM domain in the N-terminal region of TIP5 exerts sequence homology to methyl-CpG-binding domains (MBDs), which are found in proteins that bind to methylated DNA and mediate methylation-dependent gene silencing . TIP5 alin vitro and in vivo. Our results provide structural details for interactions between TIP5 and RNA that are essential for recruitment of the chromatin remodeling complex NoRC to specific genomic loci.Here, we report the three-dimensional (3D) solution structure of the TAM domain of human TIP5 (hTIP5). We show that the TAM domain represents a variant of the MBD fold with unique C-terminal extensions that are required for the interaction of TIP5 with pRNA. Using biochemical studies and NMR-spectroscopy we show that point mutations in this surface impair RNA binding 6-tag for purification, a GB1-tag at the N-terminus for protein solubility and a tobacco etch virus cleavage site for removal of the tags. TIP5 variants were obtained by QuickChange site-directed mutagenesis (Agilent Technologies). pJC40-hTIP5(N332–723) and pJC40-mTIP5(N1–598) used for purification of human or murine TIP5 for in vitro studies were described before (in vivo experiments was described before (HEK293T cells were grown in DMEM (Invitrogen) supplied with 10% fetal calf serum , 11 mg/ml sodium pyruvate and 11 mg/ml penicillin and streptomycin (PAA Laboratories GmbH). DNA encoding human TIP5/TAM-AT (residues 496–664) and human TIP5/TAM (residues 516–623) were subcloned from pJC40-hTIP5(N332–723) using standard cloning protocols into modified pETM-11 vector (EMBL) containing a HisEscherichia coli BL21 (DE3) cells in minimal medium (M9) enriched with 15N, 13C and/or 2H, supplemented with 50μg/ml kanamycin. 15N/13C- and 15N-labeled proteins were prepared using M9 medium with [U-13C]-D-glucose (2g/l) and/or 15NH4Cl (1g/l) as sole carbon and nitrogen sources. For 50% random fractional deuteration the M9 medium was based on 90% 2H2O. Media were inoculated overnight and grown to OD600 = 0.8 at 37°C. Expression was induced with 0.2 mM IPTG and performed at 18°C for at least 16 h.Isotopically labeled proteins for NMR studies were produced by overexpression in Escherichia coli transformed with cDNA encoding wild-type or mutant TIP5 were resuspended in lysis buffer containing 50 mM HEPES [pH 8.0], 1 M NaCl, 10 mM imidazole, 2 mM β-mercaptoethanol, 1 mM Pefabloc SC (Serva), 1 μg/ml lysozyme and 7μg/ml DNase I, 0.2% w/v IgepalCA-630. After sonication and high-speed centrifugation, His-tagged proteins were bound to Ni-NTA Agarose (Quiagen). Insoluble TIP5/TAM was purified from the pellet by resuspending in low salt lysis buffer with 6 M guanidinium hydrochloride and refolding on a Ni2+ column by washing with decreasing concentrations of guanidinium hydrochloride (2–0 M). After washing with low salt and high salt buffer containing 1.8 M NaCl, bound proteins were eluted with 300 mM imidazole, digested with tobacco etch virus protease (1 μg/mg protein at 4°C overnight). The tags were removed with a second Ni2+column and recombinant proteins were further purified by size-exclusion chromatography  in low salt lysis buffer. For NMR measurements proteins were kept in 20 mM sodium phosphate [pH 7.0], 50 mM NaCl, 1 mM Dithiothreitol (DTT), 10% v/v 2H2O.mini was transcribed in vitro by T7 RNA polymerase from a double stranded synthetic DNA template  and cytidine triphosphate (CTP), 4.8 mM guanosin-5'-triphosphate (GTP) and uridine-5'-triphosphate (UTP) and 0.08mg/ml PEG8000 (Promega). For reduction of n + 1 products the bottom strand was methoxylated at two 5′-terminal nucleotides  to yield a complex of 0.15 mM TIP5/TAM-AT with molar equivalents of pRNAmini. NMR data were recorded on the complex in a Shigemi tube directly thereafter.Complex formation was performed at low concentrations on ice. The required amount of pRNA15N/13C uniformly labeled and in case of TIP5/TAM-AT 50% randomly deuterated protein samples. Secondary chemical shifts were calculated and used for secondary structure prediction as described previously (1H and 13C side chain resonances were assigned based on 3D HCCH-TOCSY experiments with 13C and 1H evolution and correlated to amide group by CC(CO)NH-TOCSY. 13Cβ and 1Hδ/ϵ of aromatic residues were correlated via scalar couplings  and 293K (TIP5/TAM) on Bruker 900-, 750- or 600 MHz spectrometers equipped with room temperature (750 MHz) or cryoprobes  and pulsed field gradients. Spectra were processed with NMRPipe  and analeviously ,23. 1H aouplings . The tau15N-HMQC . Stereosescribed ,27. Backescribed . NOE-assescribed .1HN−15N and 13CO−15N RDC were recorded in a two-dimensional doublet-separated, sensitivity enhanced HSQC experiment  was estimated from R2/R1 . Radiolabeled RNA was incubated for 30 min on ice with purified recombinant protein (10–50 nM) in 12 mM Tris–HCl, pH 8.0, 100 mM KCl, 5mM MgCl2, 0.1 mM EDTA, 0.5 mM DTT, 3% glycerol. In competition experiments , unlabeled competitor RNA was added after 5 min and reactions were incubated for additional 30 min. RNA–protein complexes were resolved in 6% native polyacrylamide gels in 0.5× Tris/Borate/EDTA buffer (TBE). Gels were vacuum-dried, exposed to a screen and visualized by PhosphorImaging. The amount of bound RNA in band shifts and unbound RNA probes was compared by quantification of images using Image Gauge 4.0 software.Electrophoretic mobility shift assays (EMSA) experiments were performed as described previously . RadiolaFilter binding assays (FBA) were performed essentially as described .32P]-labeled pRNA, pTOPO2.1-mrDNA(-204/-1) was linearized with EcoRI and transcribed with T7 RNA polymerase in the presence of 50 μCi [32P]UTP . Labeled pRNA was incubated with increasing amounts (2.5–160 nM) of recombinant proteins in RNA binding buffer  for 30 min on ice and filtered through nitrocellulose membranes . To measure input RNA probes nylon membrane  was used. For measuring the specific binding affinity of TIP5/TAM-AT to pRNA  supplemented with 100 u/ml of RNasin (Promega) and 25 μl/ml of proteinase inhibitor cocktail (Sigma). Lysates were sonicated, spun and digested with 30 u/100 μl of Dnase I (Roche) for 20 min at room temperature. After addition of 5 mM EDTA, FLAG-tagged proteins were bound to M2 beads (Sigma), washed 10× with 10 volumes of RIP lysis buffer. RNA was recovered with Trizol (Invitrogen) and subjected to RT-PCR using the forward primer 5′-CGATGGTGGCGTTTTT (hrDNA/-150/-130F) and the reverse primer 5′-CTCGGAGCGAAAGATATACC (hrDNA/-30/-50R).HEK293T cells were transfected with expression vectors encoding FLAG/HA-tagged wild-type or mutant full-length human TIP5 or a control vector (pCS2+). Forty-eight hours after transfection cells were lysed in 500 μl of RNA immunoprecipitation assays (RIP) lysis buffer  with phylogenetically related homologs ,47–48 an15N relaxation experiments  indicate that these regions are flexible and intrinsically disordered, while residues 520–621 are structured and define the globular fold of the TAM domain.To study the structure and RNA binding properties of the TAM domain, we analyzed TIP5/TAM-AT, a TIP5 fragment comprising residues 496–664, by NMR chemical shifts and s Figure . The 13C32P-labeled MCS-RNA. As shown in Figure KD = 5.5 nM , 496–664 (‘TIP5/TAM-AT’) and 516–623 (‘TIP5/TAM’) and M Figure .13C secondary chemical shifts and supported by H/D exchange experiments  follows the β5 strand at the C-terminus of the protein. The N-terminal helix α1 (Pro522-Gln530) is slightly bent, presumably due to the presence of a conserved proline residue (Pro528). The TAM domain adopts a stable and rigid fold, with only few residues in the linker following helix α2 (Asn573-Arg579), and the β1–β2 with β4–β5 loops showing increased flexibility as indicated by low {1H}-15N heteronuclear NOE values  is well-ordered and not flexible.Next, we determined the solution structure of TIP5/TAM by heteronuclear NMR spectroscopy. The secondary structure of the TAM domain is indicated by s Figure . The β-sAlthough the structural core of the TAM domain resembles the MBD domain Figure  it exhibmini. pRNAmini has been previously described and represents a fusion of pRNA nucleotides -127/-113 with nucleotides -60/-49. This preserves a specific stem-loop structure that is recognized by TIP5 with features conserved between mouse and human . This is consistent with the low nanomolar affinity observed for the pRNA-TIP5 interaction in EMSA experiments  nanomolar dissociation constant. Thus, although pRNAmini has (about 10-fold) reduced affinity to TIP5, our data suggest that it harbors important structural features required for efficient binding to TIP5 and was therefore used for NMR titration experiments.To map the RNA binding surface of the TIP5/TAM-AT domain by NMR chemical shift perturbation experiments, we designed a shorter version of pRNA, dubbed pRNAnd human ,55 . This observation is consistent with the EMSA data and previous studies showing that TIP5 does not recognize a specific RNA sequence but rather distinct structural features, e.g. double-helical regions in stem-loop structures that may also be present in other RNAs.We also monitored the interaction of TIP5/TAM with other RNAs . While negligible chemical shift changes were observed with single-stranded poly-U RNA, significant spectral changes occurred in the presence of an RNA hairpin found in the A-repeat region of Xist a noncoding RNA that is required for X-chromosome inactivation ,58. InteTo validate the NMR data and examine the functional significance of the RNA binding surface, we monitored RNA binding of a panel of mutants that comprise specific amino acid exchanges in the TAM domain. Solvent exposed amino acid residues within the pRNA binding interface as well as conserved residues within the β-sheet surface of MBD domains  were altNext, we monitored RNA binding of TIP5, TIP5/TAM-AT and several TIP5/TAM-AT mutants using filter binding assays and EMSA. As expected, mutations altering residues remote from the TAM-pRNA interface  did not significantly affect RNA binding without affecting the tertiary structure . In contrast, mutations within the RNA binding interface  significantly reduced binding to pRNA, without affecting the domain fold Figure , indicatin vitro also affect TIP5-pRNA interaction in vivo, we performed RIP assays to monitor the association of pRNA with wild-type and mutant TIP5. Lysates from cells expressing FLAG-tagged TIP5, TIP5-R545E or TIP5-W546A were precipitated with anti-FLAG antibodies, and co-precipitated pRNA was monitored by RT-PCR. Cells transfected with the empty vector were used as a control. As shown in Figure in vitro data demonstrating the functional relevance of residues R545 and W546 for TIP5 binding to RNA.To examine whether mutations that impair pRNA binding We report the solution structure of the TAM (TIP5/ARBP/MBD) domain of human TIP5 and show that it represents a novel extended MBD fold that is normally found in MBD-containing proteins such as MeCP1, MBD1, MBD2, MBD3 and MBD4. MBD-containing proteins function by recruiting various protein complexes to methyl-CpG sites, linking DNA methylation with chromatin modification and transcriptional repression. Significantly, the structurally related TAM domain does not exhibit binding preference to methylated DNA but associates with RNA. Previous studies have shown that the interaction of TIP5 with pRNA is crucial for targeting the chromatin remodeling complex NoRC to nucleoli and the establishment of heterochromatic features at a subset of rRNA genes . TIP5 biin vitro and in vivo. We found that two of the tested residues, W546 and R545, are the most important for RNA binding. Notably, these residues are not conserved in human MBDs, suggesting that the interaction of TAM-containing proteins with specific regulatory RNA may guide chromatin modifying enzymes to regulatory gene regions.Our structure reveals that the structural core of the TAM domain resembles the MBDs of MBD1 and MeCP2. However, the TAM domain comprises unique C-terminal extensions, with the C-terminal α/β motif not being present in canonical MBD structures. Significantly, the positively charged C-terminal α/β motif constitutes the binding interface of TIP5 and pRNA. The sequence of the TAM domain and residues involved in RNA binding are conserved in vertebrates, underscoring the functional importance of the TAM domain distinct from MBD proteins. Mutational analysis revealed that residues in the RNA binding surface are required for RNA binding Canonical MBDs recognize methylated CpGs in DNA by hydrophobic residues located at the β-sheet surface, with additional DNA backbone contacts mediated by the extended loop L1  Figure . Althougmini, which has two helical stem regions. Also, the observation of supershifts in the EMSA experiments demonstrates the formation of higher order complexes, which could involve binding of TIP5 to different helical stem regions in larger RNA ligands.Interestingly, our NMR titrations show that the TAM domain also binds to a hairpin found in Xist RNA, a non-coding RNA involved in dosage compensation and X-chromosome inactivation. A common structural feature between pRNA and Xist RNA are double-helical regions. This suggests that the TIP5/TAM domain preferably recognizes double-helical regions in NoRC-associated RNAs. This may explain why our NMR titrations indicate that two TAM-AT proteins can bind to pRNAA general preference for helical regions in RNAs by TIP5 is supported by recent data showing that the function of NoRC is not restricted to silencing of rRNA genes but is also required for heterochromatin formation and silencing of centromeres and telomeres . Like rRThe atomic coordinates for the NMR ensemble of the TIP5/TAM domain have been deposited in the Protein Data Bank under accession number 5AGQ. The chemical shift assignments have been deposited in the Biological Magnetic Resonance Data Bank under accession number 26517.Supplementary Data are available at NAR Online.


The dynamics of the RNA polymerase II (Pol II) backtracking process is poorly understood. We built a Markov State Model from extensive molecular dynamics simulations to identify metastable intermediate states and the dynamics of backtracking at atomistic detail. Our results reveal that Pol II backtracking occurs in a stepwise mode where two intermediate states are involved. We find that the continuous bending motion of the Bridge helix (BH) serves as a critical checkpoint, using the highly conserved BH residue T831 as a sensing probe for the 3′-terminal base paring of RNA:DNA hybrid. If the base pair is mismatched, BH bending can promote the RNA 3′-end nucleotide into a frayed state that further leads to the backtracked state. These computational observations are validated by site-directed mutagenesis and transcript cleavage assays, and provide insights into the key factors that regulate the preferences of the backward translocation. RNA Polymerase II can detect and cleave mis-incorporated nucleotides by a proofreading mechanism that requires backtracking of the enzyme. Here the authors show that Pol II backtracking occurs in a stepwise mode that involves two intermediate states where the fraying of the terminal RNA nucleotide is a prerequisite. RNA polymerase is the key enzyme in gene expression responsible for synthesizing messenger RNA (mRNA) based on the DNA template1235791011et al.et al.et al.Recently, the X-ray structures of Pol II EC arrested in backtracked states have been solved7The experimental approaches to address the above issues are limited and challenging, as most of these approaches cannot directly provide dynamical information for backtracking at the molecular level. Molecular dynamics (MD) simulations can greatly complement experimental observations on elucidating the dynamics at the atomic level. MD simulations have been widely applied to study proteins and nucleic acids, including systems as large as Pol II refs , 23, 24.The above-mentioned timescale gap can be bridged by Markov State Models (MSMs), which allow us to model long timescale dynamics from many parallel short MD simulations2728293031323334353637394041424445i+1 site, and determine the direction of translocation (whether the RNA:DNA hybrid moves forward or backward by fraying and backtracking). In particular, the conserved BH residue T831, which directly points at the i+1 site, can facilitate the separation of the mismatched base pair and drive the Pol II complex into the backtracked state. The critical role of T831 predicted by simulations was further validated by site-directed mutagenesis experiments, which show that the T831A substitution can substantially decrease the backtracking-dependent transcript cleavage rate.In this study, we built a MSM to investigate the mechanisms of Pol II backtracking upon nucleotide mis-incorporation, a critical step for proofreading. We identified a stepwise backtracking process in which the first step is the fast fraying motion (at sub-microsecond) of the RNA 3′-end nucleotide. This step is followed by the rotation of the DNA transition nucleotide (TN) to stack with BH residues, which results in the formation of an additional intermediate state. During the last step, the reverse translocation of the upstream RNA:DNA hybrid takes place at around 100 μs to reach the final backtracked register. Interestingly, we found that the bending of the BH can serve as a checkpoint that examines the stability of the base pair (bp) of RNA:DNA hybrid at We first set up the frayed and one-nucleotide backtracked states based on the crystal structures with PDB ID: 3HOZFrom the MSM, we found that backtracking follows a stepwise mechanism rather than a concerted one. In particular, we used Transition Path Theory to compute the probability of each of the two competing mechanisms of backtracking from the flux through each pathway41Our MSM for backtracking contains four metastable states  move back to the S1 state , indicatTo reveal the functional roles of the BH in the fraying motion of the RNA 3′-end nucleotide, we carried out a cross-correlation analysis for both the RNA 3′-end nucleotide and the DNA TN . We founi+1 site. The results indicate a significant difference between the WT and the T831A mutant. As shown in To further examine the specific roles of the BH residue T831 in backtracking, we first performed additional MD simulations for the rG:dG system with the T831A mutation on the BH. We started the simulations from the same set of conformations near the TS between S1 and S2 states, as used in the above MD simulations of different base pairs at the To validate the above prediction, we conducted mutagenesis studies to evaluate the role of the BH residue T831 by measuring the transcript cleavage rate, which is dependent on the extent of the backtracking. Based on our model, the T831A substitution on Pol II can diminish the ability of the BH domain to promote the fraying of the RNA 3′-end nucleotide, which, in turn, would slow down the cleavage reaction. We designed the same RNA:DNA scaffold for the rG:dG system as we used in our theoretical studies and measured the cleavage rate with both WT and T831A mutant of Pol II EC. Here, we employed both intrinsic and TFIIS-stimulated transcript cleavage assays to probe the impact of T831A on backtracking behaviour of RNA pol II. Indeed, as expected, we found reduced transcript cleavage activity with T831A pol II mutant in comparison with WT pol II see , which pAs a control, we further evaluated the effects of the T831A mutation on the rG:dC system by performing both MD simulations and experimental studies, following the same procedure as we did for the rG:dG system. The MD simulations show that, for the rG:dC system, the T831A substitution slightly favours the forward translocation over the fraying motions  can quickly move to the frayed state (S2), which is promoted by the continuous bending motions of BH . We suggi+1 site, which in turn, depending on the stabilities of the base pairs, results in either facilitating the upstream RNA:DNA hybrid to move one register forward (translocation) or the RNA 3′-end nucleotide to fray and consequently lead to backtracking.It has been well documented that the backtracking is sequence-dependent and particularly on the sequence near the RNA 3′-end nucleotide1053Quantitative comparisons of dynamics between our model's predictions and experimental observations are challenging as it is difficult for existing experimental techniques to directly monitor the dynamics of backtracking conformational changes alone. However, recent optical tweezers experiments monitored the dynamics of complete Pol II elongation cycles upon external force, and then they obtained rates of individual conformational changes such as translocation and backtracking by fitting to a kinetic model of the elongation cycle576-methyguanine (O6-mdG) and so on, and how some of these lesions can escape Pol II proofreadingIn the future, it will be interesting to extend our study to the mismatched pyrimidine base pair, for example, U:T wobble pair, as it may have different dynamic behaviour during the backtracking process compared with the purine base pair because of its smaller molecular size101555We constructed the MSM to investigate the backtracking mechanism in Pol II EC, and our algorithm consists of the following steps: (i) Model the Pol II EC in pre-, frayed and backtracked states; (ii) generate initial low-energy pathways using a modified version of the Climber8 and 3HOZ+ ions were added to neutralize the system. The final system contains 369,947 atoms. The AMBER force field was used to describe the system 67We performed additional MD simulations to investigate the backtracking propensity for one matched base pair: rG:dC. In addition, mutant MD simulations were designed to evaluate the functional roles of several critical residues in the backtracking. Finally, to test if the frayed intermediate state is also required for other mismatched base pair systems during the backtracking process, we designed three more Pol II systems with the following base pairs: rA:dG, rC:dT and rU:dT  to generate one 10-ms long trajectory, from which we calculated the stationary distributions of the 4 macrostates and the MFPT for each transition. Finally, the average values and corresponding standard deviations were calculated by averaging the results from all 100 samples.To study the correlated motions between the DNA TN/RNA 3′-end nucleotide and the Pol II residues, we calculated the cross-correlations for the pairs we are interested in (see i+1 site. Reactions were quenched at various time points and the products were separated by denaturing PAGE (see TFIIS cleavage reactions were performed by pre-incubating purified Pol II (WT or Rpb1 T831A mutant) with the RNA:DNA scaffolds as used in the theoretical studies with either rG:dC or rG:dG at 2+.Cleavage reactions were performed by pre-incubating purified Pol II (WT or Rpb1 T831A mutant) with the RNA:DNA scaffolds containing either rG:dC or rG:dG bp. The Pol II EC was assembled with the following scaffold in a 20 mM Tris-HCl (pH=7.5) without Mg2+. Final concentrations for intrinsic cleavage were 20 mM Tris-HCl (pH=9), 100 nM Pol II, 25 nM scaffold and 50 mM MgCl2. The reaction was quenched in 0.5 mM EDTA at various time points and analysed by denatured PAGE. Time points in this assay were 1 min, 5 min, 20 min, 1 h, 3 h, 8 h and 24 h. .Supplementary Figures 1-18, Supplementary Notes 1-6 and Supplementary ReferencesMovie showing dynamics of a complete Pol II backtracking event


The interaction of viral RNA with PTB was validated in infected cells by JEV RNA co-immunoprecipitation and JEV RNA-PTB colocalization experiments. Interestingly, we observed phosphorylation-coupled translocation of nuclear PTB to cytoplasmic foci that co-localized with JEV RNA early during JEV infection. Our studies employing the PTB silencing and over-expression in cultured cells established an inhibitory role of PTB in JEV replication. Using RNA-protein binding assay we show that PTB competitively inhibits association of JEV 3NCR(-) RNA with viral RNA-dependent RNA polymerase (NS5 protein), an event required for the synthesis of the plus-sense genomic RNA. cAMP is known to promote the Protein kinase A (PKA)-mediated PTB phosphorylation. We show that cells treated with a cAMP analogue had an enhanced level of phosphorylated PTB in the cytoplasm and a significantly suppressed JEV replication. Data presented here show a novel, cAMP-induced, PTB-mediated, innate host response that could effectively suppress JEV replication in mammalian cells.Japanese encephalitis virus (JEV) has a single-stranded, positive-sense RNA genome containing a single open reading frame flanked by the 5′- and 3′-non-coding regions (NCRs). The virus genome replicates via a negative-sense RNA intermediate. The NCRs and their complementary sequences in the negative-sense RNA are the sites for assembly of the RNA replicase complex thereby regulating the RNA synthesis and virus replication. In this study, we show that the 55-kDa polypyrimidine tract-binding protein (PTB) interacts  Flavivirus genus of the Flaviviridae family of animal viruses contains more than 70 viruses including medically important dengue virus (DENV), tick-borne encephalitis virus (TBEV), West Nile virus (WNV), Yellow fever virus (YFV) and Japanese encephalitis virus (JEV). JEV is responsible for frequent epidemics of encephalitis in humans in most parts of Southeast Asia, China, Korea, Japan, and India. It is a neurotropic virus accounting for ∼50,000 cases of encephalitis annually of which ∼30% result in mortality and another ∼30% in long lasting neuropsychiatric complications The JEV has a single-stranded positive-sense RNA genome encased in the nucleocapsid surrounded by membrane envelope containing structural proteins. The genomic RNA has a type I cap (m7GpppAmp) at the 5′-end and lacks polyadenylation at its 3′-end. The ∼11-kb genome has a single open reading frame (ORF) encoding a polyprotein of 3432 amino acids that is subsequently cleaved to produce three structural proteins, capsid (C), pre-membrane (prM) and envelope (E), and seven non-structural proteins, NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5 (REF). The ORF is flanked by 95- and 585-nucleotides long non-coding regions (NCRs) at the 5′- and 3′-ends, respectively Following the infection, the positive-strand JEV genomic RNA is released from endocytosed virions into the cytoplasm of the infected cell initiating the replication and synthesis of viral proteins. The positive-sense genome is transcribed into the negative-sense RNA replication intermediate (antigenome) which is then used as a template for the synthesis of a large number of copies of the positive-sense genomic RNA. The genome replication involves its circularization that is mediated by long-range RNA-RNA interactions between sequences from 5′- and 3′-NCR A number of host proteins have been identified that interact specifically with NCRs of genome and/or complementary antigenome regions of the different flaviviruses. Interestingly, most of these host proteins have been shown to regulate the viral replication process either positively or negatively. Thus, interaction of TIA-1 and TIAR proteins with WNV antigenome in vitro. We also show that nuclear PTB relocalized to cytoplasm during JEV infection where it associated with viral RNA leading to inhibition of RNA transcription and virus replication. This constitutes a novel cellular mechanism employed by the host to control JEV infection.PTB belongs to an hnRNP1 family of RNA-binding proteins 2. HEK and Vero cells were transfected with plasmid DNA using Effectene (Qiagen) and JetPrime (Polyplus Transfection) reagents, respectively, as per the manufacturers' instructions. The nuclear and cytoplasmic extracts from cultured cells were prepared using NE-PER nuclear and cytoplasmic extraction reagent kit (Thermo Scientific).The P20778 strain of JEV was grown in porcine stable kidney (PS) cells and titrated on PS cell monolayers by plaque assay in vitro transcription using plasmids containing the cDNAs corresponding to JEV NCR sequences cloned under the control of bacteriophage T7 promoter. Plasmids pJE5NCR and pJE3SL, containing the JEV 5′-NCR cDNA in ApaI-BamHI sites and 3′-SL cDNA in ApaI-XbaI sites, respectively, have been described previously in vitro at 37°C for 1 h in a 20 µl reaction containing 40 mM Tris-Cl (pH 7.5), 6 mM MgCl2, 10 mM NaCl, 2 mM spermidine, 10 mM dithiothreitol (DTT), 0.5 mM each of ribonucleotides , 12 µM UTP (Promega), 50 µCi (32P)-UTP , 1 µg linearized DNA template, 20 U RNasin (Promega) and 20 U T7 RNA polymerase (Promega). The reaction product was then treated with 10 U RNase-free DNase (Promega) for 20 min at 37°C. The transcript was then treated with phenol-chloroform, precipitated with ethanol, and resuspended in 20 µl RNase-free water. RNA yield was determined by the trichloroacetic acid (TCA) precipitation. A specific activity of ∼108 cpm/µg was routinely obtained.The RNA transcripts used to study RNA-protein interactions were generated by E. coli BL21 (DE3) cells were transformed with the expression vector pET28a-PTB (a generous gift from Dr. J.G. Patton) and pET28a-NS5 (a generous gift from Dr. R. Chang), and the expression of his-tagged recombinant PTB or JEV NS5, respectively, was induced by 1 mM IPTG. The recombinant protein was purified using Ni2+-nitrilotriacetic acid-agarose (Qiagen) under non-denaturing conditions and eluted with 250 mM imidazole. The protein was electrophoresed on a 10% Sodium dodecyl sulphate (SDS)-poly acrylamide gel (PAGE) to confirm the size and the purity. Protein concentration was determined by the BCA method and the aliquots were snap frozen and stored at −70°C. Freshly-thawed protein aliquot was used for all assays.32P-labelled RNA and incubation for 30 min at 30°C. The RNA-protein complexes were resolved by non-denaturing 6% polyacrylamide gel (PAGE) (acrylamide: bisacrylamide 50: 1) containing 2.5% glycerol in 0.5X TBE buffer  at 4°C. The gels were autoradiographed after drying.Purified recombinant PTB was incubated in binding buffer  with 500 ng yeast t-RNA and 1 U RNasin in a 20 µl reaction for 10 min at 30°C followed by the addition of 1 ng 32P-labelled RNA in binding buffer  containing 500 ng yeast tRNA. The reaction products were loaded onto nitrocellulose filters  pre-equilibrated with 2 ml binding buffer, and filtered under vacuum. The filters were then washed twice with 2 ml binding buffer and dried, and the counts retained were measured in a liquid scintillation counter. The percentage of bound RNA was calculated from the counts and a saturation binding curve was plotted. The binding affinity of the RNA–protein complex, denoted as the dissociation constant (Kd) of the binding reaction, was calculated as the protein concentration at which 50% of the RNA was bound. This was carried out by fitting the data into the binding curve (Langmuir) equation using GraphPad Prism software.Increasing amounts of PTB were incubated at 30°C for 15 min in a 20 ml reaction with 100 fmol The RNA-protein binding reaction was set up as described above. After incubation for 30 min at room temperature, the binding reaction mixture was transferred to an ice bath and irradiated with 254-nm UV lamp (4 Watts) held at a 3 cm distance from the reaction mixture for 30 min. After irradiation, RNase A (20 µg) was added and the reaction mixture was incubated for 30 min at 37°C to digest the unprotected RNA. The UV cross-linked products were boiled in Laemmli sample buffer  for 5 min at 100°C. The samples were electrophoresed on a discontinuous 6% sodium dodecyl sulphate (SDS)-PAGE. The gel was fixed in 7% acetic acid, dried and autoradiographed.2, 1 mM DTT, 2 mM phenylmethylsulfonyl fluoride, 100 µg/ml aprotinin and 0.5% Nonidet P-40). RNasin (Promega) and Protease inhibitor mixture (Roche) were added freshly. Cells were lysed by incubating on ice for 20 min with frequent pipetting up and down. The lysate was then centrifuged at 16,000 g for 15 min at 4°C and the supernatant collected. Protein G sepharose beads (10 µl) (Amersham) were washed with PBS, blocked with 2% bovine serum albumin (BSA) and incubated for 1 h with PTB (Abcam) or luciferase (Promega) antibody. Beads were treated with buffer B  and subsequently resuspended in 850 µl buffer B, 40 µl 0.5 M EDTA, 150 units RNasin (Promega) and 100 µl lysate. The reaction tubes were incubated on an end-to-end rotor overnight at 4°C. Beads were then washed thoroughly with ice-cold buffer B and resuspended in 100 µl buffer B supplemented with 0.5% SDS and incubated at 37°C for 30 min. RNA was extracted using the TRIzol reagent (Invitrogen). The RNA pellet resuspended in diethyl pyrocarbonate (DEPC)-treated water was used as the template for RT-PCR using JEV-specific oligonucleotide primers  that produce a 397-bp amplicon containing JEV genomic sequence between nucleotides 96–476 (GenBank accession no. NC_001437).Cells infected with JEV at a multiplicity of infection (moi) of 10 were harvested, washed twice with ice-cold phosphate-buffered saline (PBS), and collected in 500 µl cell lysis buffer A  and mouse dsRNA antibody (English & Scientific Consulting Bt.). Following a wash with 1% BSA the cells were incubated with rabbit anti-goat Cy5 (Invitrogen) and donkey anti-mouse Alexa 488 (Invitrogen) secondary antibodies. The cells were washed again with 1% BSA and overlaid with mounting medium containing DAPI (Invitrogen) to stain the nucleus. Cells were visualized by Olympus Fluoview FV1000 microscope and images analysed using Olympus Fluoview Ver 2.1c software.5′-TCTAGAGAATTCAAAAAATGACAAGAGCCGTGACTACTCTCTTGAAGTAGTCACGGCTCTTGTCACGGATCC-3′ (bottom strand). A shRNA directed against luciferase gene (Clontech) was used as a negative control. For PTB over-expression, cells were transfected with pcPTB that was produced by cloning the PTB cDNA in pcDNA 3.1(-) vector (Invitrogen) under the control of CMV promoter. Plasmid pcDNA 3.1(-) vector was used as a negative control. Levels of PTB in transfected cells were measured by immunoblotting the cell lysate with PTB antibody.A plasmid encoding PTB shRNA was used for down-regulating the PTB level by targeted gene silencing in cultured cells. The PTB shRNA-encoding DNA was inserted between the BamHI-EcoRI sites in the RNAi-Ready pSIREN-RetroQ-ZsGreen vector (Invitrogen). The DNA encoding the shRNA was synthetically made by annealing the following oligonucleotides: 5′GGATCCGTGACAAGAGCCGTGACTACTTCAAGAGAGTAGTCACGGCTCTTGTCATTTTTTGAATTCTCTAGA-3′ (top strand); 5′-AATAGGTTGTAGTTGGGCACTCTG-3′) and negative strand RNA (5′-AGAGCACCAAGGGAATGAAATAGT-3′). Maxima SYBR Green qPCR Master Mix (Thermo Scientific) was then used for quantitative real-time PCR using these primers 5′-CGAAAGCATTTGCCAAGAAT-3′; Reverse, 5′-AGTCGGCATCGTTTATGGTC-3′.RNA from JEV-infected cells was isolated using TRIzol reagent (Invitrogen) and used for cDNA synthesis employing iScript cDNA synthesis kit (Bio-Rad) and primers specific for JEV positive (in vitro. Distinct retarded bands of radiolabelled RNA in RNA-protein complexes were observed on a native gel indicating that PTB bound to JEV 5′-NCR of the genomic RNA - 5NCR(+), and the corresponding complementary sequence in the antigenome - 3NCR(-) (Radiolabelled 5NCR(+) and 3NCR(-) RNAs were incubated with increasing amounts of recombinant PTB in an EMSA in presence of 60 mM KCl to study possible RNA-protein interaction  3NCR(-) . These cKd of the binding reaction using filter-binding assays (Kd for 5NCR(+) RNA binding with PTB was 1362 nM, whereas it was 41 nM for the 3NCR(-) RNA. These experiments thus show that PTB binding to JEV 5NCR(+) and 3NCR(-) was strong and specific, and the protein had higher affinity to 3NCR(-) RNA compared to 5NCR(+) RNA.The binding affinity of JEV RNAs with PTB was determined as the g assays . Thus, tin vivo, it does not necessarily indicate a direct binding of the two. However, in light of direct RNA-protein binding in vitro (shown above) it may be inferred that PTB protein associated with JEV RNA during the virus infection of the cell. More evidence to this effect was provided by the experiment described below.PTB interaction with JEV RNA during the course of the virus replication in the mammalian cell was studied by the Co-IP RT-PCR. JEV-infected HEK cells were lysed at 24 h post-infection (pi) and PTB-bound RNA precipitated using PTB antibody. RT-PCR of the precipitated RNA is expected to yield a 397-bp product in case it contained JEV RNA. A distinct band of expected size in the RT-PCR product was seen when PTB antibody was used for immunoprecipitation . No amplFlavivirus replication occurs in replication complexes located on the membranous compartments of the endoplasmic-reticulum also known as vesicle packets. Several of viral non-structural proteins and the replicative form of the viral genome as the dsRNA have been shown to colocalize in these replication complexes PTB is located mainly in the nucleus in association with hnRNA. We studied the location of PTB during JEV infection and its localization with respect to JEV RNA by staining with dsRNA antibodies. In mock-infected Vero cells, PTB was located mostly in the nucleus and no dsRNA could be seen in the cell . In the To corroborate the above findings, nuclear and cytoplasmic extracts were prepared from JEV-infected Vero cells at various times pi using NE-PER Nuclear and Cytoplasmic Extraction Kit (Pierce) and the purity of the fractions established by Western blotting with specific antibodies. Before the virus infection, PTB was mainly in nuclear extract and only a small amount of it was detected in the cytoplasm . As the In order to determine the role of PTB in JEV life cycle, the expression of PTB was knocked-down in cultured mammalian cells using plasmid expressing PTB-specific shRNA (sh-PTB). An shRNA against luciferase (sh-Luc) was used as a negative control. The level of PTB in Vero cells was reduced significantly 48 h after transfection with sh-PTB . The sh-To further confirm the inhibitory role of PTB in JEV replication, PTB was over-expressed by transfecting Vero cells with PTB-expressing plasmid pcPTB and viral titers determined. Western blotting showed a significant increase in PTB levels in pcPTB-transfected cells 48 h later . Vero cePTB binding to viral RNA could affect both RNA transcription as well as translation that may result in reduced viral titers. We therefore analysed the effect of PTB silencing and over-expression on JEV RNA level by performing quantitative real time RT-PCR for JEV RNA in infected Vero cells. The knock-down of PTB label and its over-expression was established by Western blotting as before. While there was no effect seen on the levels of JEV negative-strand RNA, a ∼150% increase in JEV plus-strand RNA accumulation was observed at 24 h pi in PTB-silenced cells when compared with the control . On the Enhanced plus-strand RNA levels in PTB knocked-down cells may not necessarily result in enhanced levels of NS5/NS3 if protein synthesis capability of the cell is limiting. On the other hand, reduced levels of plus-strand RNA may result in reduced NS5 accumulation. The data presented above, thus, indicate that PTB plays a negative role in JEV replication by suppressing plus-strand RNA synthesis that in turn could affect the accumulation of viral proteins and viral titers.in vitro, the competitive binding of PTB and NS5 to 5NCR(+) and 3NCR(-) was studied by UV-induced cross-linking assay followed by RNase digestion. A constant amount of NS5 protein and increasing amount of PTB was added in a binding reaction with radiolabelled 5NCR(+) and 3NCR(-) RNA. The UV cross-linked RNA-protein complexes were treated with RNase A and the products were electrophoresed and autoradiographed. As shown in Association of NS5, the viral replicase protein, with NCRs is necessary for flavivirus replication to occur Nucleo-cytoplasmic transport of PTB is known to be regulated by the cAMP-dependent Protein kinase A (PKA) through direct phosphorylation of PTB The NCRs of the plus-strand genome of RNA viruses, and their complementary sequences in the antigenome, contain elements important for protein translation and RNA replication. Several host proteins have been described that bind these regions and are exploited by virus for its replication. It could also be envisaged that host defence proteins may bind these NCRs and suppress viral replication by interfering with the RNA translation and/or replication processes. Here we describe a novel antiviral function for PTB during JEV infection where it down modulates genomic RNA synthesis by binding to the 3′-end of the antigenome thereby suppressing JEV replication.in vitroPTB is a 531-amino acid, 58-kDa ubiquitous RNA-binding protein. It was originally identified as a protein with a role in splicing but it is now known to function in a large number of diverse cellular processes including alternative spicing trans-acting factor activating viral translation initiation In picornaviruses, such as polio and CVB3, PTB acts as an IRES PTB has been shown to negatively regulate translation in the case of Feline calicivirus PTB is primarily a nuclear protein and to play any role in JEV replication, it should translocate to the cytoplasm. In the present study, PTB was found to translocate to cytoplasm and colocalize with JEV RNA as early as 6 h pi. The translocation of PTB from the nucleus to the cytoplasm has been documented during infection with several RNA viruses, such as poliovirus, rhinovirus, feline calicivirus, and HCV The N-terminal 55-amino acids of PTB contain both nuclear localization signal (NLS) and nuclear export signal (NES) The innate immune response is the first line of defence against foreign pathogens. The recognition of virus-associated molecular patterns, including double- and single-stranded RNA, by pattern recognition receptors initiates a cascade of signalling reactions. These result in the transcriptional up regulation and secretion of pro-inflammatory cytokines that induce an antiviral state. IL-6 and other cytokines are synthesized by cells following the JEV infection


Metatranscriptomics is rapidly expanding our knowledge of gene expression patterns and pathway dynamics in natural microbial communities. However, to cope with the challenges of environmental sampling, various rRNA removal and cDNA synthesis methods have been applied in published microbial metatranscriptomic studies, making comparisons arduous. Whereas efficiency and biases introduced by rRNA removal methods have been relatively well explored, the impact of cDNA synthesis and library preparation on transcript abundance remains poorly characterized. The evaluation of potential biases introduced at this step is challenging for metatranscriptomic samples, where data analyses are complex, for example because of the lack of reference genomes.Herein, we tested four cDNA synthesis and Illumina library preparation protocols on a simplified mixture of total RNA extracted from four bacterial species. In parallel, RNA from each microbe was tested individually. cDNA synthesis was performed on rRNA depleted samples using the TruSeq Stranded Total RNA Library Preparation, the SMARTer Stranded RNA-Seq, or the Ovation RNA-Seq V2 System. A fourth experiment was made directly from total RNA using the Encore Complete Prokaryotic RNA-Seq. The obtained sequencing data were analyzed for: library complexity and reproducibility; rRNA removal efficiency and bias; the number of genes detected; coverage uniformity; and the impact of protocols on expression biases. Significant variations, especially in organism representation and gene expression patterns, were observed among the four methods. TruSeq generally performed best, but is limited by its requirement of hundreds of nanograms of total RNA. The SMARTer method appears the best solution for smaller amounts of input RNA. For very low amounts of RNA, the Ovation System provides the only option; however, the observed biases emphasized its limitations for quantitative analyses.cDNA and library preparation methods may affect the outcome and interpretation of metatranscriptomic data. The most appropriate method should be chosen based on the available quantity of input RNA and the quantitative or non-quantitative objectives of the study. When low amounts of RNA are available, as in most metatranscriptomic studies, the SMARTer method seems to be the best compromise to obtain reliable results. This study emphasized the difficulty in comparing metatranscriptomic studies performed using different methods.The online version of this article (doi:10.1186/1471-2164-15-912) contains supplementary material, which is available to authorized users. Metatranscriptomic approaches based on next generation sequencing have emerged as a powerful tool to provide insight into microbial activity in complex environmental communities, such as marine –7, soil Recently, several new cDNA preparation methods especially conceived for RNA-Seq have been released and, for some of them, the impact on data analysis and interpretation in eukaryotic transcriptomes has been reported –24. Our In this study, we tested the four RNA-Seq methods on four well-known microbial species, individually and pooled together, and assessed their performances. We evaluated rRNA depletion efficiency and bias; library complexity; alignment efficiency; gene detection sensitivity and abundance; evenness of transcript coverage; and technical reproducibility. Our results revealed the strengths and weaknesses of each method, and provided guidelines for choosing the method that is best adapted for metatranscriptomic studies.Escherichia coli MG1655 and Acinetobacter baylyi ADP1; and two Gram positive organisms, Lactococcus lactis MG1363 and Bacillus subtilis 168. The four species, for which reference genomes are available, have different GC contents, heterogeneous genome sizes and gene contents  and depleted RNA individually from the four microorganisms and from a pool of four RNAs (hereafter named MIX) in duplicate, starting from the same individual RNAs and MIX sample. Using Illumina paired-end sequencing, we generated, on average, 20.5 M reads and 0.8 M reads for the control and depleted RNA libraries, respectively , followed by OV and SMART (61 to 65%)  and OV had the lowest (13%)  and performed a linear correlation between the L. lactis samples were calculated  for each method in the L. lactis genes detected as differentially expressed (p < 0.01). The OV experiments performed slightly better (r =0.696) with 28% of DEGs. SMART was the best correlated method (r =0.805) and the number of genes detected as differentially expressed in comparison to the TS experiment was not significant. The same comparison made within the MIX samples showed similar results. The SMART MIX sample was the best correlated, even if the percentage of DEGs was slightly higher (9.2%) than in the L. lactis libraries , with 31% of We showed previously that the GC content could influence the results. Therefore, we verified if highly differentially expressed genes were correlated with their GC content. This analysis was limited to ENC and OV MIX libraries as the number of genes to be considered in the SMART libraries was not statistically significant. The results showed that the ENC profiles of overexpressed and underexpressed genes were similar Figure . No corrL. lactis genes and compared qRT-PCR quantifications with the read counts of the same genes in the sequenced transcriptomes. A strong positive correlation was observed for the TS and SMART experiments . ENC showed a slightly lower correlation (r =0.899), whereas gene expression levels in OV were significantly different (r =0.603)  or depleted ones showed similar percentages of mapped reads Table , as wellL. lactis and MIX libraries, respectively). This observation suggested that the OV RNA priming strategy has a decreased affinity for rRNA. At least two hypotheses could explain this observation: first, the presence of oligo(dT) primers could influence priming of RNA by an unknown mechanism. Second, in this system, random and oligo(dT) primers are coupled to an RNA stretch that is necessary to create the anchoring site for the primer used in subsequent linear amplification. The sequence and length of this RNA stretch, and of the primer, are not available; therefore, we cannot exclude the possibility that they recognize some specific RNA regions, introducing a bias. The same arguments could explain the strong preference of this method towards amplification of low GC content genes.Curiously, we noticed that in the OV control libraries, the percentage of rRNA reads was significantly lower than expected . ENC performed less well for the The impact of the four library preparation methods on data quality was evaluated by comparing library complexity, specificity in mRNA detection, evenness of CDS coverage in individual and mixed populations, and gene expression patterns. For most of these values, TS performed best, showing the best transcriptional coverage and parameters on CDS alignments (more than 70%) and gene expression profiles. This is not surprising, as initial experiments were conducted from 4 μg total RNA. Lowering the input to 400 ng did not seem to skew the library composition . cDNA synthesis was successful even at concentrations of only a few pg (data not shown). A cDNA aliquot could then be used, after shearing, in a standard Illumina library preparation. All the preparation steps are easily amenable to automation.de novo transcriptome assembly and gene annotation. For all these reasons, we would be very cautious in applying this method for metatranscriptomic studies and, more generally, in quantitative studies.Reads mapping from OV libraries clearly showed that the CDSs detected were the same found by the other methods and technical reproducibility was satisfactory. By contrast, in the MIX experiments, species distribution was significantly biased. Moreover, we detected evidence of a strong bias in gene expression profiles (r =0.589), particularly in favor of low GC content genes. Another surprising result was the relatively low percentage of rRNA reads in the total RNA control libraries. This led us to hypothesize that the random priming is not truly “random”, or that the concurrent presence of oligo(dT) or addition of RNA primer stretches could bias the RNA retrotranscription or cDNA amplification. This should be further explored by including more complex samples with GC and AT-rich species. Finally, this strategy does not provide information about strand orientation, which could be very useful for The SMARTer method is a recent release from Clontech that aims to provide a random and stranded version of the first SMARTer Low Input library kit (oligo(dT) priming), which has been used successfully for eukaryotic single cell transcriptomic studies , 27. IndE. coli[The same control experiment performed starting from 5 ng of total RNA produced much better results, leading us to conclude that the minimal RNA input indicated by the manufacturer is not sufficient to preserve sample diversity, even in samples with only one bacterial species. This should be taken into account when choosing the initial RNA quantity to be depleted. Fifty ng seems to be the lower threshold to obtain sufficient library yield and satisfactory transcriptional coverage. In fact, with these inputs, the MIX libraries showed good library complexity, species representation, and gene detection and abundance. However, we could not estimate the exact depleted RNA quantity used for cDNA synthesis after RNA depletion. The rRNA quantity present in a given sample is variable, and depends on growth conditions and the state of the cell at the moment of extraction. It has been demonstrated that non-cultivated environmental bacterial cells contain less mRNA molecules than laboratory strains. For example, only ~200 transcripts are present simultaneously in a marine bacterial cell versus ~1800 mRNA molecules in exponentially growing E. coli. mRNA yiOne of the most relevant results of this study is the difference in gene expression patterns among the methods. We clearly demonstrated that comparison of ENC or OV data with TS or SMART would lead to false DEGs detection. By contrast, TS and SMART libraries appear to produce more accurate results, which were corroborated by qRT-PCR experiments. This emphasizes that comparison of RNA-Seq studies performed using different library protocols could be hazardous, and should be carefully evaluated. Knowledge of biases of each method, as provided in the present study, is essential to help interpret the results.In this work, we evaluated the impact of cDNA synthesis and library preparation methods on a simplified metatranscriptome. We used four well-characterized bacterial species, individually or pooled together, to mimic a controlled metatranscriptome. First, we showed that the ribosomal depletion treatment by Ribo-Zero efficiently reduces the rRNA reads and does not introduce significant bias, even at an input as low as 50 ng of total RNA. We therefore strongly suggest applying this treatment before library preparation to reduce sequencing costs, particularly in metatranscriptomics studies where deep coverage is needed.Second, the best library preparation method was closely associated with the initial total RNA quantity used for cDNA synthesis. The TruSeq Stranded protocol produces accurate and reproducible transcriptomic patterns when RNA quantity is not limiting. However, it is not realistic to apply this method for metatranscriptomic samples, usually characterized by very low RNA quantities. Among the methods adapted to lower-input RNA, SMARTer Stranded performed adequately both in terms of library yield and data quality. It has the advantage of retaining the coding strand orientation information and it would be suitable for quantitative analysis as shown by the consistency of gene expression patterns. Importantly, we show that TS and SMART results are comparable and can be used together in DEGs analyses. Finally, with very low amounts, the Ovation RNA-Seq System V2 is the only one to guarantee the success of library preparation and sequencing. Nevertheless, this method introduces a significant bias in species abundance and gene expression patterns, probably at priming steps in reverse transcription or linear amplification. Therefore, it may not be helpful for quantitative analysis. However, it allows the production of gene catalogs with extremely low-input samples, which could not be explored otherwise. Thus, the present study provides guidelines that allow the choice of the best-fitted library preparation method for microbial metatranscriptomic studies.E. coli MG1655 and B. subtilis 168 were grown in 10 ml LB broth with shaking at 37°C to an O.D.600 of approximately 0.5. L. lactis MG1363 was grown in 10 ml M17 broth supplemented with 0.5% glucose, without shaking, at 30°C to an O.D.600 of about 0.5. A. baylyi ADP1 was grown in 10 ml MAS broth at 30°C with shaking to an O.D.600 of approximately 0.7. For each culture, 6–9-ml aliquots, corresponding to about 1 × 108 cells, were harvested by centrifugation at 6000 × g for 5 min at 4°C. The supernatant was removed. Cell lysis was performed differently depending on Gram+ or Gram– species. L. lactis and B. subtilis pellets (Gram+) were resuspended in 100 μL TE buffer  plus 10 μl lysozyme  and 20 μl proteinase K . Samples were incubated at room temperature for 10 minutes with 10 sec vortexing every 2 minutes during incubation. E. coli and A. baylyi pellets were resuspended in 200 μl of preheated (95°C) Max Bacterial Enhancement Reagent  and the tubes were incubated for 4 minutes at 95°C.RNA extractions were performed immediately after lysis by using TRIzol solution (Ambion), followed by chloroform phase separation and isopropanol/ethanol RNA precipitation. After a first quantification by Qubit 2.0 fluorometer using Qubit RNA HS Assay Kit , 10 μg total RNA aliquots were treated with Turbo DNA-free kit (Ambion), according to the manufacturer’s instructions. After 30 minutes of incubation and final addition of the inactivation reagent, the supernatant was recovered and the Qubit 2.0 fluorometer was used to quantify the RNA.Capillary electrophoresis on an Agilent Bioanalyzer  was used to analyze approximately 3 ng of each RNA, using the RNA 6000 Pico LabChip kit. All RNA samples showed RIN values between 8 and 9.7. Assessment of the efficiency of DNAse treatment by PCR on 10 ng total RNA using strain-specific primers completed the validation of the samples. Phusion High Fidelity PCR Master Mix (Life Technologies) was used to run reactions which included a positive (fresh colony lysate in water) and a negative (nuclease-free water) amplification control. Equimolar amounts of total RNA from the four species were pooled to form the MIX sample. This was the starting material for all the synthetic metatranscriptome experiments.A Ribo-Zero Magnetic Kit for Bacteria  was used to deplete ribosomal RNA before each cDNA synthesis by TS, SMART or OV methods. The Ribo-Zero depletion protocol was modified to be adapted to different RNA input amounts. Different total RNA inputs were depleted, varying from 50 ng up to 4 μg. The volume of magnetic beads and rRNA removal solution varied according to the RNA input as follows: 225 μl beads and 10 μl removal solution in a final volume of 40 μl for inputs >1 μg; 125 μl beads and 4 μl rRNA removal solution in a 20 μL final reaction volume for 100 ng to 1 μg inputs; 90 μl beads and 2 μl rRNA removal solution in a 20 μl reaction volume for <100 ng inputs. Except for these modifications, depletion was performed according to the manufacturer’s instructions.Depleted RNA was purified with the RNA Clean and Concentrator–5 kit , following the procedure described for retention of >17 nt RNA fragments. RNA was eluted in 8 μl nuclease-free water in two elution steps to maximize recovery. When the total RNA input was > or equal to 400 ng, a qualitative and quantitative assessment was performed using 1 μl of depleted RNA diluted to 1:1 with water. One μl was used for quantification by the Qubit RNA HS Assay and 1 μl was run on an Agilent Bioanalyzer, using the RNA 6000 Pico LabChip kit. The Agilent profile showed a dominating small size (<200 nt) peak presumably corresponding to 5S and tRNA species. No 16S and 23S peaks were detectable. Upon Qubit quantification, recovery from 400 ng inputs was approximately 8%.Libraries were constructed using depleted RNA obtained from 4 μg, 400 ng and 100 ng total RNA. In the case of 4 μg total RNA input in depletion, RNA was quantified by Qubit and 30 ng of depleted RNA was used for library preparation. In the other cases, RNA was not quantifiable and the entire volume was used for library preparation. The TruSeq Stranded mRNA Sample Preparation kit  was used. This kit includes a first poly(A) + RNA selection step by oligo(dT) beads, which may be skipped when working with rRNA-depleted RNAs, as in our case. Therefore, we introduced depleted RNAs in the preparation at the RNA fragmentation and priming step. Five μl of depleted RNA was directly added to 13 μl of Fragment, Prime, Finish mix and incubated for 8 min at 94°C for RNA fragmentation and priming. Thereafter, the libraries were prepared according to the manufacturer’s protocol without further modifications. Total RNA control libraries were constructed from 50 ng of total RNA following the same protocol modifications (poly(A) + selection step skipped).Depleted RNA obtained from Ribo-Zero treatment of 50 ng total RNA was used to synthetize and amplify cDNA using the Ovation RNA-Seq System Version 2 , following the manufacturer’s protocol. Control cDNA libraries were also prepared from 0.5 ng total RNA following the same protocol. cDNA yield, measured fluorometrically by Qubit DNA HS Assay, was >2 μg. An Agilent Bioanalyzer evaluated the cDNA profile using a DNA High Sensitivity LabChip kit. cDNA (500 ng) was sheared by a Covaris E210 instrument  under the following conditions: 10% duty cycle, 5% intensity, 200 cycles per burst, 300 sec in frequency sweeping mode.Sheared cDNA was used for Illumina library preparation by a semi-automatized protocol. Briefly, end repair, A-tailing and Illumina compatible adaptors  ligation were performed using the SPRIWorks Library Preparation System and SPRI TE instrument , according to the manufacturer’s protocol. No size selection was applied, such that most of the fragments were recovered. Twelve PCR cycles using a Platinum Pfx Taq Polymerase Kit (Life Technologies) and Illumina adapter-specific primers amplified the DNA fragments. AMPure XP beads  were used to purify the libraries.One hundred ng total RNA was used to prepare libraries for the Encore Complete Prokaryotic RNA-Seq System (NuGEN), following the manufacturer’s protocol without modifications. Briefly, after first-strand synthesis with selective proprietary primers, and second-strand synthesis with nucleotide analogs, cDNA was sheared by Covaris E210 using the same parameters described for the fragmentation of cDNA obtained using the OV method. Sheared cDNA was ligated to Illumina compatible adaptors containing the nucleotide analog in one strand. Next, the cDNA and adaptor strands containing the analog were selectively removed (Strand Selection), leaving only one cDNA strand with both adaptor sequences attached. This product was then converted into a ready-for-sequencing library by PCR amplification followed by 1× AMPure XP beads purification.Depleted RNA obtained from Ribo-Zero treatment of 50 ng total RNA was used to synthetize and amplify cDNA using the SMARTer Stranded RNA-Seq Kit , following the manufacturer’s protocol. After first-strand synthesis with SMARTer oligonucleotides, including partial Illumina adaptor sequences, single stranded cDNA was purified by two rounds of clean-up with 1× AMPure XP beads. The purified product was amplified by 18 cycles PCR with SeqAmp DNA polymerase and the Illumina Index Primer set, both provided in the kit. 1× AMPure XP beads were used to purify the final libraries. Control libraries were prepared from 1 ng and 5 ng total RNA.Libraries were quantified by qPCR using the KAPA Library Quantification Kit for Illumina Libraries  and library profiles were assessed using the DNA High Sensitivity LabChip kit on an Agilent Bioanalyzer. Libraries were sequenced on either an Illumina Miseq or a HiSeq2500 instrument using 150 base-length read chemistry in a paired-end mode.After sequencing, an in-house quality control process was applied to reads that passed the Illumina quality filters (raw reads). The sequences of the Illumina adapters and primers used during the library construction were removed from the whole reads. Low-quality nucleotides  were removed from both ends. The longest sequence without adapters and low-quality bases was kept. Sequences between the second unknown nucleotide (N) and the end of the read were also trimmed. Reads shorter than 30 nucleotides after trimming were discarded. These trimming steps were achieved using internal software based on the FastX package . The reaAll standard metrics  were checked on the raw, cleaned and trimmed reads to verify the effects of each treatment. Further analyses were performed on the remaining high-quality data (cleaned reads). Data have been deposited in the European Bioinformatics Institute Short Read Archive (SRA) under accession numbers listed in Additional file 10 of the FPKM. The Pearson correlation coefficient of the gene expressions between the samples were calculated using the R package version 3.0.2 [Each sample was mapped against its respective CDS sequences using BWA software version 0.6.1  with itson 3.0.2 . The staon 3.0.2  to deterL. lactis genes. Six of them were selected on the basis of read count levels in the TS experiments, from high (eno and tuf) and intermediate (nrdE and secA) to low (gltB and thiD). The other four genes, tpx, gpmA, llmg0195 (putative NADH dehydrogenase) and llmg2023 , were selected among the DEGs observed in the SMART, OV and ENC experiments. Primers designed using the Primer3Plus software are listed in Additional file L. lactis total RNA using random hexamers and Superscript II RT (Life Technologies), according to the manufacturer’s instructions. The synthesized cDNA was used as the template for qPCR reactions performed using the KAPA SYBR Fast qPCR kit (KapaBiosystems) on an Mx3005P qPCR System (Agilent Technologies). cDNA copies were quantified for each gene relative to a standard curve made from serial dilutions of purified L. lactis genomic DNA. Comparison of RNA-Seq and qRT-PCR quantifications was visualized by a scatter plot of the LOG10 of the read counts and the LOG10 of the qPCR quantification [qRT-PCR quantification was performed on 10 fication .The data sets supporting the results of this article are included within this article and its additional files.Table S2: Proportions of detected genes and of covered intergenic bases. Table S3. Taxonomic assignation of the MIX libraries reads. Table S4: Sequences statistics for SMART control libraries prepared with 5 ng total RNA. Table S5. Sequences statistics for A. baylyi, B. subtilis, and E. coli libraries. Table S6. Proportions of detected genes and of CDS covered bases for other bacteria species libraries. Table S7. Pearson correlation coefficients related to A. baylyi, B.subtilis, and E. coli libraries comparisons. Table S8. Oligonucleotides used for qRT-PCR analysis. (PDF 155 KB)Additional file 1: Table S1: Characteristics of each bacterium genome. L. lactis samples versus MIX samples. Figure S2. Gene expression profile of the two replicates from L. lactis samples. Figure S3. Gene expression profile of the two replicates from MIX samples. Figure S4. Gene expression profile of TS versus OV, SMART and ENC methods in L. lactis samples. Figure S5. mRNA differential expression profile in L. lactis samples. Figure S6. mRNA differential expression profile in MIX samples. Figure S7. Scatter plots of gene expression levels detected by RNA-Seq and qRT-PCR for 10 L. lactis genes. Figure S8. Gene expression profile of depleted RNA L. lactis samples versus total RNA L. lactis samples. Figure S9. Gene expression profile of depleted RNA MIX samples versus total RNA MIX samples. Figure S10: Gene expression profile of SMART control libraries. Figure S11: Comparison between L. lactis TS libraries prepared with two different RNA inputs. (PDF 14 MB)Additional file 2: Figure S1: Gene expression profile of SRA accession numbers.(CSV 5 KB)Additional file 3: