Next generation sequencing (NGS) technologies have revolutionized gene expression studies and functional genomics analysis. However, further improvement of RNA sequencing protocols is still desirable, in order to reduce NGS costs and to increase its accuracy. In bacteria, a major problem in RNA sequencing is the abundance of ribosomal RNA (rRNA), which accounts for 95-98% of total RNA and can therefore hinder sufficient coverage of mRNA, the main focus of transcriptomic studies. Thus, efficient removal of rRNA is necessary to achieve optimal coverage, good detection sensitivity and reliable results. An additional challenge is presented by microorganisms with GC-rich genomes, in which rRNA removal is less efficient.Burkholderia thailandensis. This bacterium, chosen as representative of the important Burkholderia genus, which includes both pathogenic and environmental bacteria, has a rather large (6.72 Mb) and GC-rich (67.7%) genome. Each enriched mRNA sample was sequenced through paired-end Illumina GAIIx run in duplicate, yielding between 10 and 40 million reads. We show that combined treatment with both kits allows an mRNA enrichment of more than 238-fold, enabling the sequencing of almost all (more than 90%) B. thailandensis transcripts from less than 10 million reads, without introducing any bias in mRNA relative abundance, thus preserving differential expression profile.In this work, we tested two commercial kits for rRNA removal, either alone or in combination, on The mRNA enrichment protocol presented in this work leads to an increase in detection sensitivity up to 770% compared to total RNA; such increased sensitivity allows for a corresponding reduction in the number of sequencing reads necessary for the complete analysis of whole transcriptome expression profiling. Thus we can conclude that the MICROBExpress/Ovation combined rRNA removal method could be suitable for RNA sequencing of whole transcriptomes of microorganisms with high GC content and complex genomes enabling at the same time an important scaling down of sequencing costs. The advent of functional genomics and the availability of next generation sequencing (NGS) technologies have dramatically changed the approach to studying gene expression. Massive RNA sequencing provides a detailed snapshot of the total RNA (the “transcriptome”) present at a given time in a cell. The transcriptome comprises coding (mRNA) and non-coding RNA . Quantitative differences in the gene expression patterns, either between cells grown in different conditions or between a mutant and its parental strain, can be identified in their entirety through this approach. However, in order to obtain reliable information, it is crucial that RNA sequencing achieves a sufficient “coverage”, enough to detect even rare RNA species. In bacterial cells, a disadvantage of the transcriptomic approach is the high amount of ribosomal RNA (rRNAs), which accounts for more than 95% of total cellular RNA, greatly reducing useful transcript coverage. Thus, efficient removal of rRNA is critical for successful transcriptome profiling. Unlike for eukaryotic mRNA, polyadenylation of bacterial mRNAs is limited and is mostly involved in targeting mRNA for degradation by PNPase; hence, Papers describing the use of high-throughput sequencing for transcriptomics in bacteria have used mRNA enrichment methods usually based on depletion of rRNA and other RNAs-4, utiliThe rRNA capture approach using the MICROBExpress Kit (Ambion) has widely been applied in RNA-seq studies-8, inclui.e., processed RNA such as rRNA and tRNA, while mRNAs, carrying a 5′-triphosphate group, are not affected[e.g., environmental metatranscriptomics)[Escherichia coli grown in four different conditions[Alternatively to the capture-based methods, rRNA removal can also be achieved through its degradation by specific enzymes. An example is the mRNA-ONLY Prokaryotic mRNA Isolation Kit (Epicentre Biotechnologies), based on selective degradation of processed RNAs by the enzyme terminator 5′-phosphate-dependent exonuclease (TEX). This enzyme exclusively degrades RNA molecules carrying a 5′-monophosphate, affected. This meptomics), but it ptomics),16. An aptomics),17,18. Tnditions.In this work we tested the Ovation Prokaryotic RNA-Seq System kit for bacteria ribosomal RNA removal developed by NuGEN . Unlike the approaches described so far, which are based on rRNA removal or degradation, the Ovation kit relies on the synthesis of first and second strand cDNA using a random primer mix selectively designed to enrich the mRNA portion of bacterial total RNA. The selective random primers are designed against a sequence database composed of 50 bacterial and archaeal strains representing all of the major phylogenetic subgroups. The predicted binding site density of these primers on target (mRNA) and non-target (rRNA) transcripts is nearly identical across these species. The resulting cDNA is compatible with NuGEN’s Encore™ NGS Library Systems as well as other library workflows using double-stranded cDNA as input for the creation of sequencing libraries.i.e., the MICROBExpress Kit from Ambion. A comparison of the results obtained from rRNA removal procedures based on different chemistries (capture with probes versus retro-transcription with selective random primers) and from libraries prepared with different protocols (Illumina TruSeq RNA libraries versus NuGEN’s Encore™ NGS libraries) was performed in order to evaluate the efficiency of the two methods, either alone or in combination, and to test the robustness of the protocols. The rRNA removal efficiency of the two kits, either separately or in combination was evaluated on RNA extracted from Burkholderia thailandensis in two different growth conditions. This bacterium was chosen because of its importance as a model organism for pathogenic species of Burkholderia such as B. pseudomallei, the etiological agent of melioidosis[This new method was tested either in the absence of further treatments or in combination with an rRNA capture-based approach, ioidosis, and sinOne of the main goals of this work was the reduction of sequencing costs: to this aim, we evaluated whether the two rRNA removal treatments, tested either separately or in combination, could result in a scaling down of the sequencing size  while preserving the whole transcriptome coverage. Our results show that the combination of the two kits leads to optimal results in terms of rRNA removal, without introducing a significant bias on relative mRNA abundances, and allow the sequencing of a GC rich bacteria transcriptome with less than 10 millions reads.Burkholderia thailandensis strain E264 (BtE264), a bacterium with a highly complex genome, both in terms of length/organization and of GC content, was used to test the mRNA enrichment efficiency by two different ribosomal RNA removal methods. RNA was extracted from B. thailandensis E264 cultures in stationary phase, grown ca. 20 hours in LB medium either in full aeration or in oxygen-limiting conditions.In this comparative study, the B. thailandensis E264 was subjected to ribosomal RNA removal treatment using either the MICROBExpress Bacterial mRNA Enrichment Kit (Mex) or the Ovation Prokaryotic RNA-seq System (Ov) separately, or a combination of the two kits (Mex-Ov). Total RNA, not subjected to any rRNA removal procedure, was used as a control in transcriptome analysis. The RNA quality, measured using RNA electropherograms by Agilent 2100 Bioanalyzer, showed that the total RNA extracted was of good quality, with an RNA Integrity Number (RIN) higher than 9.0. The disappearance of the rRNA peaks, after the rRNA removal process, was also clearly detectable from the electropherograms.Total RNA from For total RNA and for samples processed with the MICROBExpress kit only, the sequencing libraries were obtained using the Illumina TruSeq RNA library preparation kit, while the sequencing libraries for the RNA samples treated either with the Ovation kit or with the Mex/Ov combination were constructed using NuGEN’s Encore™ NGS Library System. Due to utilization of different methods, the sequencing libraries differ in the length of generated fragments: Illumina TruSeq RNA libraries are characterized by an average fragment length of 260bp, NuGEN’s Encore™ NGS libraries show an average fragment length of 160bp. Due to their shorter length we performed 48-cycle sequencing runs on NuGEN’s Encore™ NGS libraries, opposed to 86-cycle runs on Illumina TruSeq RNA libraries.The profiles of the sequencing libraries prepared with the NuGEN’s Encore™ NGS system are shown in Additional fileIn order to test the reproducibility of the rRNA removal methods, each sample was prepared as a replicate and sequenced Table; the seqMore than 40 million reads were generated for total RNA samples. For samples processed for rRNA removal we progressively reduced the number of reads generated to evaluate if the Mex and Ov treatments, separately and in combination, allowed a scaling down of the sequencing size while preserving the whole transcriptome coverage. This scaling down was obtained by increasing the multiplexing of different samples in the same lane. An average of 28 million reads for the samples treated with MICROBExpress kit, of 14 million reads for the samples processed with Ovation and an average of 9 million reads for the samples subjected to double treatment were generated , while it was reduced to an average of 61% using the Ovation kit, and to an average of 54% using the combined treatment , which represents the minimum number of reads mapping on a given transcript that is necessary to consider that gene as significantly expressed.A Detection Threshold (DTh), based on the geometric distribution of the number of sequencing reads, was calculated as described in Materials and Methods . The DTh was determined independently on each of the samples sequenced: the DTh obtained for the total RNA sample was fixed at 20 reads ; for the Mex-Ov double treatment this resulted in more than 770% increase compared to the total RNA sample. Such increased sensitivity allows for a corresponding reduction in the number of sequencing reads necessary for the complete analysis of whole transcriptome expression profiling.Indeed, it is possible to calculate the increase of efficiency in transcriptome coverage of each treatment alone or in combination by comparing the number of total reads necessary to target all the BtE264 CDSs. The Ov and Mex-Ov treatments allow the detection of almost all the CDSs with only about 10 million reads, with the Mex treatment only 50% of transcripts were covered with 28 millions reads, and in the control sample only 10% of CDSs were targeted with more than 40 millions reads. As a consequence, to cover all the CDSs in samples treated with only Mex more than 56 millions reads should be necessary and more than 400 millions reads should be produced to sequence the transcriptome in totRNA samples. The double treatments allows a scaling down of sequencing, in terms of total reads, by 40-fold, with respect to the totRNA sequencing, and by 5.6-fold with respect to the Mex treated samples sequencing, thus dramatically reducing the costs of sequencing.i.e., total RNA) and the samples subjected to Mex, Ov, and Mex-Ov treatments. Figure10 of RPKM value for the same transcripts in the samples treated is reported.An important criterion in validating mRNA enrichment methodologies is the evaluation of the potential bias introduced by the rRNA removal treatment on absolute and relative gene expression levels; this bias could derive for example from undesired removal of mRNA subsets. To assess the possible bias introduced in mRNA relative abundance, we evaluated the correlation of mRNA expression patterns between the control sample , treatment with the Ovation kit reduced the correlation with the control (r = 0.714), suggesting that the selective amplification methodology results in the introduction of a quite significant bias in mRNA relative abundance. However, the combination of the Ovation kit with MICROBExpress appears to overcome, albeit partially, the bias introduced by the Ovation alone, resulting in an improved correlation coefficient with the total RNA sample (r = 0.817) Figure.As the correlation of mRNA expression patterns of the treated samples, in comparison to the control, had only taken into account 573 CDS, and considering that the Pearson’s correlation between the Mex treated samples and the control samples was very high (0.979), we decided to evaluate also the correlation of the Ov and Mex-Ov treatments against the mRNA expression patters obtained only after Mex treatment, thus correlating a wider number of transcripts (2368 CDS). As shown in FigureWe can thus infer that the combination of the two treatments results in a significant improvement of mRNA detection sensitivity, allowing the expression analysis of almost all the BtE264 CDS with a low number of total reads, and at the same time does not introduce a consistent bias on their relative level of expression.B. thailandensis by comparing bacterial cultures grown at 37°C with vigorous shaking (fully aerated conditions) versus no shaking (oxygen-limiting condition).In order to estimate possible bias introduced by the rRNA removal treatments on mRNA differential expression profiles, the fold-change in gene expression levels of BtE264 samples grown in two different conditions was evaluated. We chose to analyze the effect of the anoxic stress on Total RNA was prepared in duplicate and each replicate from both conditions was subjected to MICROBExpress alone or to the combined method Mex-Ov because these are the treatments that introduced the lowest bias on mRNA relative abundance. The genes found to be above DTh in both samples  were analysed with the DESeq pipeline to determine the statistically significant differentially expressed genes and derive their Fold Change (FC) values.2FC of all the 2368 genes above DTh in the two growth conditions is less wide in samples treated with the Mex-Ov combined treatment than in samples treated only with MICROBExpress, evidencing reduced variability in fold change values, and reflecting part of the bias introduced by the Ov treatment. However, further correlation analysis of log2FC values of statistically significant differentially expressed genes (DEGs) showed very high correlation between the samples subjected to the combined rRNA removal treatment with respect to the simple MICROBExpress one. In particular, correlating the 408 DEGs with p-val<0.01, we obtained a Pearson’s correlation coefficient of 0.951 , a Pearson’s correlation coefficient of 0.972 was obtained  and of the high GC content (67.7%) of its genome. Indeed, genomes with high GC contents constitute a problem in transcriptomic studies, since they reduce the efficiency of most steps in the preparation of sequencing libraries.In this work, we have developed a method for rRNA removal deriving from the combination of two commercially available kits: the MICROBexpress kit from Ambion and the Ovation kit from NuGEN, based respectively on rRNA capture by specific oligonucleotides and on selective amplification using random primers that do not recognize bacterial rRNA sequences. To validate this method, we chose the bacterium B. thailandensis transcripts with less than 10 million sequencing reads. Therefore using this combined removal method it could be possible to multiplex up to 25 different bacterial transcriptomes, of the same size and complexity as BtE264 transcriptome, in a single GAIIx run (1 Illumina flow cell), thus resulting in a significant reduction in sequencing costs.We found that the Ovation kit was much more effective then MICROBexpress in rRNA removal; however, combined MICROBexpress/Ovation treatment was crucial to reduce the bias in mRNA relative abundance, thus preserving differential expression profile. The combination of the two methods results in an mRNA enrichment of more than 238-fold enabling the coverage of almost all (more than 90%) Several recently published papers describe various rRNA removal methods for Prokaryotic RNA sequencing improving the performance in rRNA removal, in comparison to standard kits, that have been the most widely used procedure for rRNA removal so far,20. The 2= 0.979 in our study and R2= 0.90 in Giannoukos paper, respectively).A recent work by Giannoukos et al., compariIt should be noted that good rRNA removal in GC-rich bacteria with the Ovation kit obtained in our experimental conditions (rRNA amount decreased to an average of 61%), is in contrast with the results of Giannoukos et al. , although the correlation between Ov-treated samples and control RNA is similar in both studies. However, in our study, we prepared the sequencing libraries using the ENCORE kit from NuGEN, since it is the most compatible with the size of cDNAs synthesized by the random primer mix selectively designed to enrich the mRNA portion of bacterial total RNA.i.e., the ENCORE library preparation from NuGEN, without modifying the Illumina protocol.The Ovation prokaryotic RNA–seq system kit is not compatible with the standard Illumina RNA library preparation kit, because this protocol makes a size selection of fragments above 200bp length, but it can be made compatible with a modified version of this Illumina protocol by changing the size selection step. However, the same protocol modification should have been applied also to the samples treated solely with MICROBexpress. The selection of fragments of 200bp size, in the Illumina library preparation protocol, might lead to a loss of the mRNA enriched during the retro-transcription with the not-so random primer mix of the Ovation kit, which generates cDNAs fragments of about 150bp. Thus, the residual rRNA portion, which is made of repeated sequences, could be preferentially amplified during the PCR step of library preparation. Probably the low performance of the Ovation kit observed by Giannoukos does not derive from the rRNA removal step, but it could be a bias introduced by the Illumina library preparation method and, in particular, by the size selection step followed by preferential amplification of residual ribosomal RNA. Therefore, we preferred to use the most compatible kit with the Ovation treatment, To reduce the bias introduced by this selective priming step we coupled the Ov treatment with a previous removal step by Mex treatment.We compared results obtained from rRNA removal protocols based on different chemistries (capture with probes against retro-transcription with selective random primers) and from libraries prepared with different protocols (Illumina TruSeq RNA libraries against NuGEN’s Encore™ NGS libraries) in order to really evaluate the efficiency of the combined method here proposed and to actually test the robustness of the whole protocol respect to the MICROBExpress that, till now, has been the most widely used procedure for rRNA removal.Streptosporangium roseum and Amycolatopsis mediterranei, both having large genomes with more than 70% GC content. We decreased the rRNA content, after the combined treatment, to 66% and to 70%, thus obtaining an mRNA enrichment of 68-fold and 60-fold respectively (unpublished data), and mRNA relative abundance correlation to totRNA similar to that obtained with B. thailandensis.This combined rRNA removal method was applied to enrich mRNAs in two highly GC rich microorganisms: We believe that the MICROBExpress/Ovation combined method described here could be worthy of consideration for RNAseq projects that must be carried out in GC-rich bacteria, and that it could be a possible alternative to other valid methods. Indeed, the combination of MICROBExpress and Ovation highly increases the mRNA detection sensitivity and does not introduce any significant bias on relative mRNA abundance, allowing a reliable analysis of differentially expressed genes.In contrast, our results suggest that the Ovation kit alone might introduce significant bias in mRNA relative abundance. Thus, we can conclude that the combined method can be valid for RNA sequencing of whole transcriptomes of microorganisms with high GC content and complex genomes enabling at the same time an important scaling down of sequencing costs.Burkholderia thailandensis strain E264[B. thailandensis E264 was grown in LB medium[The bacterium utilized in this study is ain E264. B. thaiB medium at 37°C Total RNA samples were treated with Recombinant DNase I (Ambion) to remove genomic DNA contamination. Two units of DNase I were added for up to 10 μg RNA in a 50 μl reaction. The reaction was incubated at 37°C for 10 minutes; afterwards RNA was purified by precipitation at −80°C for 2 hours by adding 0.1 volume 3M sodium acetate, 5 μg glycogen and 3 volume 100% ethanol.After extraction and DNase treatment, total RNA samples were quantified with a NanoDrop spectrophotometer (NanoDrop Technologies) and analyzed by capillary electrophoresis on an Agilent Bioanalyzer (Agilent), using the RNA 6000 Nano LabChip Kit. The RNA samples showing an RIN  value higher than 8 were processed.B. thailandensis E264 total RNA through subtractive hybridization using the MICROBExpress. Bacterial mRNA Enrichment Kit  was performed according to the manufacturer’s protocol. Briefly, total RNA (5 μg) was purified and incubated with the capture oligonucleotide mix in binding buffer for 30 minutes. Magnetic beads loaded with an oligonucleotide sequence able to hybridize to the unpaired region of the capture oligonucleotide were added to the mixture and allowed to hybridize for 15 minutes in order to form a ternary complex with the capture oligonucleotide and the rRNAs molecules. The beads were pulled to the side of the tube with a magnet and the unbound RNA in the solution was moved to a fresh tube. The magnetic beads were briefly washed to ensure full recovery of the unbound RNA; the unbound RNA was pooled and precipitated with ethanol. rRNA removal after each treatment was visually assessed by Agilent 2100 Bioanalyzer with the RNA 6000 Nano LabChip Kit. mRNA enriched from MICROBExpress Bacterial mRNA Enrichment Kit and total RNA (500 ng) were used for the preparation of Illumina TruSeq RNA libraries, following manufacturer’s instructions.rRNA removal from For removal of rRNA by selective amplification, total RNA (500 ng) was retro-transcribed using the Ovation Prokaryotic RNA-seq System, which uses random primers selectively designed to avoid rRNA amplification. After first strand synthesis double stranded cDNA is obtained and about 200 ng were used to prepare NGS library with NuGEN’s Encore™ NGS Library System, according to the manufacturer’s protocol. Due to small size of cDNA obtained no fragmentation treatment was performed prior to library preparation, as suggested by NuGEN’s instructions. For double treatment (subtractive hybridization and selective amplification of mRNA), 500 ng of mRNA-enriched from MICROBExpress kit were used for the cDNA synthesis by Ovation Prokaryotic RNA-seq System. Each sample was prepared as a replicate and sequenced.Sequencing was performed using the Illumina Genome Analyzer IIx platform to generate paired-end 48bp reads in the case of NuGEN’s Encore™ NGS Libraries and paired-end 86bp reads in the case of Illumina TruSeq RNA library.Raw sequences reported in this article have been deposited in the NCBI Short Read Archive (SRA). Accession number: SRA062706.Raw reads generated by the sequencing run were extracted and divided into separated datasets according to Illumina index (de-multiplexed) using the GERALD script developed by Illumina.For the samples prepared with NuGEN Encore™ NGS kit, two mate reads (read 1 and read 2) were linked and de-multiplexed with fastx-toolkit package (fastx_barcode_splitter.pl). In ordeBurkholderia thailandensis strain E264 genome  using BWA software[B. thailandensis E264 genome, then all the reads with less than 90% of identity and less than 25 nucleotide matching were filtered out. The reads mapped with BWA and those mapped with BLAST were merged in a unique BAM alignment file.All the samples were mapped against the software allowingB. thailandensis E264 genome.The statistical analysis for rRNA depletion and mRNA enrichment were performed with BEDTools using asIn RNA-seq experiments, to define background noise and thus discriminate between random and real expression values, it is fundamental to find out those transcripts that are covered by a number of reads too low to be considered proportional to their level of expression.The definition of the background can be achieved by determining the Detection Threshold (DTh). The DTh was calculated by plotting the read depth (x-axis) and the log10 number of transcript covered by a given read depth (y-axis).It can be assumed that reads sampled randomly from the genome follow a geometric distribution, but in a RNA-seq experiment the distribution of the reads per transcript should be proportional to that transcript level of expression.Assuming that the number of reads per transcript follows a null distribution represented by:P(X=k) is linear in k. Using this hypothesis we can estimate the read depth threshold by plotting the linear fitting line for the reads per transcript distribution for each control sample (totRNA). Since our samples are not randomly distributed, the transcripts covered by a number of reads below the Detection Threshold were excluded from the analysis.from this distribution we can derive that 10 of RPKM[http://www.R-project.org/). DESeq was used because it is the simplest available tool for gene expression analysis; it doesn’t need a gene annotation table (.gtf file) for transcripts recostruction, and we believe that this feature can make DESeq particularly suitable for bacterial RNA-Seq data analysis respect to other tools such as for example Cufflinks.The improvement in mRNA detection sensitivity was calculated as described previously by He et al. by compa of RPKM. The R p of RPKM; statistThe authors declare that they have no competing interests.CP conceived the design of the study, carried out the samples processing and sequencing experiments, wrote the manuscript; AP participated in the design of the study, performed the bioinformatic and statistical Data Analysis, helped to draft the manuscript; CC participated in the design of the study, carried out the samples processing and sequencing experiments, helped to draft the manuscript; ER carried out the cell growths, participated to the Data Analysis, helped to draft the manuscript; LP participated in the design of the study, participated to the Data Analysis; LT carried out the cell growths, performed the RNA extractions; GDB participated in the design of the study, coordinated the efforts; PL participated in the design of the study, wrote the manuscript, coordinated the efforts. All authors read and approved the final manuscript.Figure S1. The Agilent Bioanalyzer profiles of: ▪ totRNAs and Mex-RNAs ▪ the Illumina sequencing libraries of Mex samples (DNA High Sensitivity assay) ▪ the sequencing libraries of the samples treated with the Mex-Ov combined treatment and with the Ov treatment alone, prepared with the NuGEN’s Encore™ NGS system (DNA High Sensitivity assxay).Click here for fileFigure S2. Pearson’s correlation plots showing the reproducibility of Illumina sequencing on samples treated with the rRNA removal combined method; each sample was prepared as a replicate and sequenced in two separate Illumina Runs.Click here for fileFigure S3. Graphical representation of the reads count frequencies in control sample (tot-RNA) for the Detection Threshold determination; on the X-axis the number of read mapping on each CDS, on the Y-axis the log10 of the read count.Click here for file


Coronaviruses are single stranded, positive sense RNA viruses, which induce the rearrangement of cellular membranes upon infection of a host cell. This provides the virus with a platform for the assembly of viral replication complexes, improving efficiency of RNA synthesis. The membranes observed in coronavirus infected cells include double membrane vesicles. By nature of their double membrane, these vesicles resemble cellular autophagosomes, generated during the cellular autophagy pathway. In addition, coronavirus infection has been demonstrated to induce autophagy. Here we review current knowledge of coronavirus induced membrane rearrangements and the involvement of autophagy or autophagy protein microtubule associated protein 1B light chain 3 (LC3) in coronavirus replication. Nidovirales, and are known to infect a variety of hosts. Several human coronaviruses have been identified, causing mainly mild respiratory infections, with the exception of severe acute respiratory syndrome coronavirus (SARS-CoV). In addition, coronavirus infections have an economic impact on livestock industries worldwide. The avian coronavirus, infectious bronchitis virus (IBV), causes infectious bronchitis (IB), a mild respiratory infection, but as a consequence is responsible for serious effects on the global poultry industries due to poor weight gain in broiler chickens as well as reduced egg production and egg quality in layers. In addition, some strains of IBV are nephropathogenic whilst others result in severe pathology in the reproductive organs. Bovine coronavirus (BCoV) causes respiratory infection and diarrhoea in cattle, transmissible gastroenteritis virus (TGEV) and porcine epidemic diarrhoea virus (PEDV) cause diarrhoea in pigs and porcine haemagglutinating encephalomyelitis virus (PHEV) causes vomiting and wasting disease in pigs.Coronaviruses are single stranded positive sense RNA viruses belonging to the order Following attachment of coronavirus particles to virus specific receptors on the host cell and uptake of the virus into the cytoplasm, viral genomic RNA is released. This genomic RNA is recognised directly by the host cell translation machinery and two large polyproteins, pp1a and pp1ab, of approximately 400 and 800 kDa are translated. The two polyproteins encode the 15 (IBV) or 16  non-structural proteins (nsps), which are generated by co- or post-translational cleavage by virally encoded proteases. These proteins assemble into viral replication-transcription complexes (RTCs), providing the virus with the enzymes required for viral RNA transcription and replication, as well as proof-reading and capping of new viral transcripts ,3. Non-sThe nature of membrane rearrangements has been well studied in both SARS-CoV and MHV infected cells by conventional transmission electron microscopy or by electron tomography. Both viruses have been shown to induce double membrane vesicles (DMVs) as well as regions of convoluted membranes ,8,9. EleThe site of assembly of coronavirus RTCs is currently unclear. During the process of transcription and replication of the viral genome, both positive and negative sense RNAs are synthesised. As a result, dsRNA can form, possibly as a replicative intermediate. This dsRNA has been used as a marker for sites of viral RNA synthesis. However, in SARS-CoV infected cells, dsRNA was found to predominantly locate on the interior of DMVs while the majority of the nsps were found to locate on the convoluted membranes. High resolution immunofluorescence microscopy also demonstrated a separation in the signals for nsps and dsRNA. In addition, due to the lack of any connecting channels between the interior of DMVs and the cytoplasm, questions were raised about transport of RNA to sites of virus assembly at the ERGIC . SubsequAutophagy is a cellular pathway for self-degradation. The pathway allows a cell to degrade long-lived proteins, aggregated proteins and organelles during periods of starvation to provide nutrients for continued cellular processes, as well as playing an important role in cellular homeostasis, ageing and development ,13,14,15Elongation of the autophagosome membrane and formation of the complete autophagosome requires the recruitment of 2 ubiquitin-like (Ubl) conjugation systems. In the first, ATG12 becomes conjugated to ATG5 in a process requiring the E1-like enzyme ATG7 and the E2-like enzyme ATG10. ATG12-ATG5 then binds to ATG16L and forms a large complex known as the ATG16L complex. This complex localises to the phagophore and can determine the site of conjugation of the second Ubl system. In this second system, microtubule associated protein 1B light chain 3 (LC3) is initially cleaved by ATG4 near the C-terminus at position G120 to generate cytoplasmic LC3-I. This subsequently becomes lipidated with phosphatidylethanolamine (PE) in a process requiring ATG7 and another E2-like enzyme ATG3 to generate membrane tethered LC3-II . LC3-II In addition to its role in cellular homeostasis, autophagy has been shown to have a function in innate immunity by degrading intracellular pathogens, including viruses. Furthermore, autophagy plays a role in presenting pathogen components to the immune system ,23,24,25−/− embryonic stem cell lines but virus titre was rescued in the presence of an ATG5 expressing plasmid [−/− primary murine embryonic fibroblasts (MEFs), showed that MHV replication does not require either an intact autophagy pathway or conversion of LC3I to LC3II. They did however confirm that SARS-CoV nsps co-localised with LC3 [−/− MEFs, indicating no requirement for a complete autophagy pathway [et al. were unable to show co-localisation between MHV nsp8 and GFP-LC3 [et al. were unable to show co-localisation between SARS-CoV nsp3 and either endogenous LC3 or GFP-LC3A, GFP-LC3B or GFP-LC3C [The presence of DMVs in coronavirus infected cells suggested that this group of viruses might, like other positive sense RNA viruses, utilise the autophagy pathway to generate the membrane structures required for replication. Initial work showed that MHV infection induced autophagy . Nsp8 co plasmid . Further plasmid . Howeverwith LC3 . Recentl pathway . Interes pathway . Finally GFP-LC3 , and SniGFP-LC3C . DespiteGFP-LC3C ,51,52,54Consistent with previous observations, recent experiments performed in mammalian cells using IBV showed that this avian coronavirus is also capable of inducing autophagy during infection . Here, f−/− cells, where LC3I to LC3II conversion cannot occur and also in cells expressing LC3 that cannot be processed to LC3II (LC3 G120A). In addition, although the absence of the complete autophagy pathway due to a lack of ATG7 did not alter MHV replication, reduced expression of LC3 by RNAi did significantly reduce virus replication. Furthermore, viral protein expression could be rescued in the presence of LC3 G120A [In other work, it has been suggested that whilst endogenous LC3 could co-localise with MHV RTCs, GFP-LC3 showed significantly reduced co-localisation . This maC3 G120A . This deC3 G120A . InteresC3 G120A ,57, vesiC3 G120A . In the C3 G120A . During C3 G120A ,60. LC3IAlthough mounting evidence suggests that autophagy is unlikely to play a role in the replication of coronaviruses and the generation of coronavirus replicative structures, several questions remain unanswered. Work performed so far focusses mainly of members of the betacoronaviruses, MHV and SARS-CoV. Detailed characterisation of the membrane structures induced in cells infected with alphacoronaviruses, like transmissible gastroenteritis virus (TGEV), or gammacoronaviruses, like IBV, needs to be performed. Although these viruses are related, it is possible that there are differences in the mechanisms of membrane rearrangement and the types of structures induced. In addition, the location of RTC assembly and the site of viral RNA transcription and replication need to be identified. The membranes induced in MHV and SARS-CoV infected cells are complex and how the different structures play a role in virus replication is currently unknown. Furthermore, the mechanism by which these rearrangements are generated is not understood. Whether MHV induced membranes are altered upon inhibition of ERAD tuning and which proteins might be involved in hijacking the pathway remains unknown. Moreover, whether this pathway is important for the replication of other coronaviruses is also unknown.Nidovirales order, has been shown to induce autophagy [Recently the replication of IBV has been shown to induce autophagy in mammalian cells . Furtherutophagy . Howeverutophagy ,52,54,55utophagy ,55,56. T


Marine organism diversity typically attenuates latitudinally from tropical to colder climate regimes. Since the distribution of many marine species relates to certain habitats and depth regimes, mapping data provide valuable information in the absence of detailed ecological data that can be used to identify and spatially quantify smaller scale (10 s km) coral reef ecosystem regions and potential physical biogeographic barriers. This study focused on the southeast Florida coast due to a recognized, but understudied, tropical to subtropical biogeographic gradient. GIS spatial analyses were conducted on recent, accurate, shallow-water (0–30 m) benthic habitat maps to identify and quantify specific regions along the coast that were statistically distinct in the number and amount of major benthic habitat types. Habitat type and width were measured for 209 evenly-spaced cross-shelf transects. Evaluation of groupings from a cluster analysis at 75% similarity yielded five distinct regions. The number of benthic habitats and their area, width, distance from shore, distance from each other, and LIDAR depths were calculated in GIS and examined to determine regional statistical differences. The number of benthic habitats decreased with increasing latitude from 9 in the south to 4 in the north and many of the habitat metrics statistically differed between regions. Three potential biogeographic barriers were found at the Boca, Hillsboro, and Biscayne boundaries, where specific shallow-water habitats were absent further north; Middle Reef, Inner Reef, and oceanic seagrass beds respectively. The Bahamas Fault Zone boundary was also noted where changes in coastal morphologies occurred that could relate to subtle ecological changes. The analyses defined regions on a smaller scale more appropriate to regional management decisions, hence strengthening marine conservation planning with an objective, scientific foundation for decision making. They provide a framework for similar regional analyses elsewhere. Latitudinal gradients have been identified as a biogeographic indicator for the large-scale distribution and diversity of marine organisms in situ data to indentify previously unattainable, larger-scale relationships in situ data, spatial analyses of benthic habitat maps may fill an important role in identifying statistically distinct coral reef ecosystem regions based on habitat morphology.Remote sensing and management priorities have facilitated increased marine coastal ecosystem mapping in the last several years Since the distribution of many marine species relates to certain habitats and depth regimes, mapping data provide valuable information that can be used to identify and spatially quantify smaller scale management regions and potential physical biogeographic barriers. High resolution Light Detection and Ranging (LIDAR) bathymetry and benthic habitat maps are useful tools for spatially analyzing coastal morphology extents and the spatial relationship of seafloor features The southeast Florida shallow-water (0–30 m) coastline is an ideal locale to apply such spatial analyses. Southeast Florida consists of several linear shore-parallel high-latitude coral reef communities extending north from the tropical Florida Keys. It has a linear shoreline with a recognized large-scale terrestrial biogeographic gradient transitioning from a tropical to a temperate Holdridge Life Zone Diadema antillarum as well.Large-scale latitudinal biogeographic gradients on coral reefs have been reported worldwide Latitudinal and cross-shelf community differences have been reported in Broward County Montastraea annularis had higher growth rates in south Broward at 9 m depth than similar M. annularis colonies further north (north Broward), attributing this result to slightly warmer water and enhanced light availability in the south.Latitudinal differences in scleractinian coral growth rates have been reported Anisotremus surinamensis, Haemulon parra, Diplodus spp., and Labrisomus nuchipinnis were found in significantly greater abundances in the North Palm Beach region Latitudinal changes in the ichthyofaunal assemblages have been reported Although marine faunal latitudinal differences have been recognized in SE FL, there are currently no synoptic regional survey data available to define separate regions within the larger area. Previous work along the southeast Florida coast has identified several distinct areas based on geomorphology Southeast Florida is comprised of four counties (listed from south to north): Miami-Dade, Broward, Palm Beach, and Martin . ExistinIn situ data, video camera groundtruthing, and acoustic ground discrimination were used to help substantiate the classification of the habitats. Accuracy assessment of the maps showed high levels of accuracy (>89%) which were comparable to that of using aerial photography in clear water Benthic habitat map creation for all counties involved a combined-technique approach incorporating LIDAR bathymetry, aerial photography, acoustic ground discrimination (AGD), video groundtruthing, limited subbottom profiling, and expert knowledge This study analyzed all mapped habitat types for coral reef and colonized hardbottom and seagrass. The following is a list of the habitat types and their definitions. The criteria for habitat classification were defined by their biologic communities, location, geomorphologic characteristics, and acoustic characteristics Substrates formed by the deposition of calcium carbonate by reef building corals and other organisms. Habitats within this category have some colonization by live coral.Nearshore Ridge Complex (NRC): A combination of shallow colonized pavement and ridges found near shore in 3–5 m depth that are relatively flat, low-relief, solid carbonate rock. This habitat is dominated by a combination of scleractinian and octocorals, Palythoa, macroalgae, and sponges. Benthic coverage is highly variable but scleractinians are over 40% in some parts and several extensive monospecific aggregations Acropora cervicornis occur that are unique to the Florida Reef Tract Inner Reef (IR): A distinct, relatively continuous, shore-parallel reef that consists of a rich coral reef community which crests in approximately 8 m depth. The inner reef has an immature reef formation growing atop antecedent shallow colonized pavement. Acoustic and biological data indicates a distinct benthic community Middle Reef (MR): A distinct, relatively continuous, shore-parallel reef that consists of a rich coral reef community which crests in approximately 15 m depth. Acoustic and biological data indicate that it harbors a distinct benthic community from the NRC and IR Outer Reef (OR): A distinct, relatively continuous, shore-parallel reef that crests in approximately 16 m depth. It consists of a rich coral reef community living on relic reef morphology including a back reef, reef crest, and spur and groove. Acoustic and biological data indicate that it harbors a distinct benthic community Deep Ridge (DR): Linear, often shore-parallel, low-relief features that mostly occur deeper than 25 m. It consists of hardbottom with sparse benthic communities in most parts likely due to variable and shifting rubble and sand cover. Acoustic data indicate a distinct benthic community Patch Reef: Coral or hardbottom formations that are isolated from other coral reef formations by sand, seagrass, or other habitats and that have no organized structural axis relative to the contours of the shore or shelf edge.Thalassia testudinum and/or Syringodium filiforme.Habitat with 10 percent or more cover of Continuous Seagrass: Seagrass community covering 90 percent or greater of the substrate. May include blowouts of less than 10 percent of the total area that are too small to be mapped independently (less than 4000 m2).Discontinuous Seagrass: Seagrass community with breaks in coverage that are too diffuse, irregular, or result in isolated patches that are too small (less than 4000 m2) to be mapped as continuous seagrass.Unconsolidated sediment with less than 10 percent cover of submerged vegetation.2 h−1, and a depth range of 70 m, depending on water clarity. Vertical accuracy is depth dependant 2 of marine seafloor. They were gridded in ArcGIS by the nearest neighbor algorithm and sun-shaded at a 45° angle and azimuth.Bathymetric data were used to determine average depth among the reef habitats and as the foundation for benthic habitat mapping. Three prior bathymetric surveys were conducted between 2002 and 2008 by Tenix LADS Corporation of Australia, using a LIDAR system with a sounding rate of 900 Hz (3.24 million soundings per hour), a position accuracy of 95% at 5-m circular error probable, a horizontal sounding density of 4 m×4 m, a swath width of 240 m, area coverage of 64 kmBenthic habitat polygons were statistically tested for any spatial autocorrelation in ArcGIS using Moran's Index to determine any significant patterns in the underlying data significantly different from a random distribution.Benthic habitat data were then statistically examined to determine where the number and size of seagrass, coral reef, and colonized hardbottom habitats significantly differ. Two-hundred and nine parallel, cross-shelf vector-line transects spaced approximately 750 m apart were created in GIS throughout the entire mapped region . An inte10(x+1) to normalize the data. Transformed data met both normality and homogeneity assumptions for analysis of variance (ANOVA). One way ANOVA was used to separately test for significant differences in habitat width, habitat depth, distance from shore, and distance from Inner Reef between regions. Then a Student-Newman-Keuls (SNK) post-hoc test between means was performed. A p value<0.05 in both ANOVA and SNK were accepted as a significant difference. In all comparisons, p values were <0.001 unless otherwise noted.Within each identified region, the planar areal extent of the Seagrass and Coral Reef and Colonized Hardbottom habitats polygons was calculated in ArcGIS 9.3. The mean distance among and between reef habitats and distance from shore was measured. Thirty parallel, evenly spaced, east-west cross-shelf vector line transects throughout each region were intersected with the benthic habitat polygons. The distances of the resulting line fragments were used to measure habitat width, distance from shore, and distance from Inner Reef. Mean benthic habitat depth was calculated by statistically summarizing all LIDAR depths within each habitat polygon for each region. Shapiro-Wilk W tests were performed to determine data normality. There was at least one case for each test (i.e. one habitat in one of the regions) where the W statistic was significant indicating a non-normal distribution, therefore all data were log transformed using the formula logSpatial autocorrelation tests on the benthic habitat polygon areas using Moran's Index did not show a pattern significantly different from random .Cluster analysis of the cross-shelf transects yielded nine clusters at the 75% similarity level and the two dimensional MDS plot showed relatively low stress (0.11) . BecauseThe analysis of similarity (ANOSIM) performed to statistically determine the similarity of the five final regions based on the cross-shelf transect data showed strong differences (R statistic>0.92) between categories in eight of the ten pairwise tests . The regThe number of major habitat types in the identified regions progressively increased from north to south from 9 in the Biscayne Region to 4 in the North Palm Beach Region . The areThe North Palm Beach region spans approximately 32 km of coastline from the northern extent of the mapped area (27°N) south to the Bahamas Fault Zone (26°43′4.62″N) . This co2. It contained four major habitat types, Nearshore Ridge Complex (NRC), Patch Reef, Deep Ridge, and Sand, yet it was dominated by the latter two. North Palm Beach contained the most Sand (74.23 km2) and Deep Ridge (100.51 km2) habitats of the 6 regions and these two habitats comprised 99.58% of the area. NRC in this region was small (0.62 km2) and was limited to one place along the coast near the southern transition point. The Deep Ridge was significantly widest (3076 m±1521)   and shal m±1521)  in this The South Palm Beach region  spans ap2 and contained five major habitat types. The Outer Reef was a conspicuous feature in the region ranking second among the regions in planar area (4.52 km2) behind Broward-Miami. South Palm Beach had one of the thinnest shelf widths evinced by the Outer Reef (1336 m±171) and Deep Ridge (1600 m±205) being significantly closer to shore than all regions except Deerfield (2) of Deep Ridge but very little NRC  and Patch Reef . Most notable in this region were the small amount of NRC and the absence of Middle Reef, Inner Reef, and Seagrass habitats.South Palm Beach ranked fourth in size with a mapped area of 60.05 kmeerfield . The mea2. Deerfield contained six major habitat types. The southern boundary marked the northern terminus of the Inner Reef which has been previously identified in a geologic context The Deerfield Region spans approximately 15 km of coastline bounded by the Boca Raton boundary and Hillsboro Inlet (26°15′32.73″N) . This ra2), the Middle Reef and Outer Reef were both conspicuous habitats comprising the highest percentage of all the regions; 6.88% (1.74 km2) and 9.14% (2.31 km2) respectively. It contained 3.07% Deep Ridge (0.78 km2) and 1.46% NRC (0.37 km2). The Middle Reef was significantly closest to shore than in any other region (692 m±140)  , but did2 and contained seven major habitat types. The shelf here was wider than in South Palm Beach and Deerfield as evidenced by the significantly greater distance from shore of the Middle Reef (2111 m±765), Outer Reef (2594 m±605), and the Deep Ridge (2949 m±440). Although Sand was dominant , Broward-Miami contained high percentages of NRC , Inner Reef , Middle Reef , Outer Reef , and Deep Ridge . Broward-Miami NRC was significantly wider than NRC in Deerfield, South Palm Beach, and North Palm Beach  in Broward-Miami to 386 m (±304) in Biscayne. Further inspection of the habitat maps showed that the two features began converging north of Government Cut at the same latitude (25°49′55.31″N) where examination of pre-developed shoreline maps showed a previous natural river inlet named Boca Ratones mapped by DeBrahm in 1770 that closed pre-1887 2 and contained nine major habitat types. The boundary at Government Cut marked the northern extent of known seagrass beds on the ocean side of the coast. Although Sand was dominant , Continuous  and Discontinuous Seagrass  combined comprised 36.6% of the mapped area.The Biscayne Region  spans ap2) and Inner Reef  but their proportions were lower than Broward-Miami. Moderate amounts of Outer Reef  and Deep Ridge  were present and although small, the Patch Reef habitat area  ranked first between all regions. The Middle Reef was barely evident .Biscayne contained a large amount of NRC  of NRC and Middle Reef end; 6.5 km north of Boca Inlet. Northward of this transition, there is a 36 km stretch of coast without significant hardbottoms outside of the intertidal zone that are shallower than 15 m depth. Thus the ecosystem functions associated with the NRC  are absent in South Palm Beach, making it a biogeographic barrier for fauna specific to these habitats.The Bahamas Fault Zone transition was distinct by the obvious change in habitat morphology between the South Palm Beach and North Palm Beach regions. Although not an obvious spatial barrier because coral reef habitat occurs in similar depths in both regions, feature morphology (and perhaps the composition) is quite different. Support for a spatial barrier here may be found in the ichthyofauna. An analysis of 2440 surveys showed, of the 400 total species seen, 43 species were seen exclusively in the North Palm Beach region and 56 exclusively in the South Palm Beach region Coincident with coastal morphology and potential spatial barriers, there is an obvious change in water temperatures and currents from Biscayne to North Palm Beach Spatial barriers coincident with significant changes temperature and currents could have an influence on short-term range expansion of benthic species. Warmer temperatures have shifted the ranges of many species worldwide and are expected to continue over the next century Historically in southeast Florida, reefs initiated on beach ridges inundated by coastal flooding during the Holocene transgression Acropora cervicornis has likely increased density and cover in southeast Florida over the past few decades, making it a dominant part of localized NRC coral communities A. cervicornis communities until climate change has significantly altered the shallow water conditions in the North Palm Beach region to support this species.For example, the threatened staghorn coral Acropora palmata-dominated Caribbean reef, now resides in much deeper water.Previous geologic analyses have shown that Outer Reef growth in southeast Florida terminated approximately 8 ka . Hypotheses of the inability for historical reef accretion to keep up with rising sea-level include rate of sea-level-rise , the introduction of inimical waters from the flooding of coastlines , and a Caribbean-wide reduction in reef building corals . Whatever the cause, most of the Outer Reef, which once resembled a classic cresting Analyses of present-day reef depths between regions revealed that the Outer Reef is significantly shallower in Biscayne than more northern regions . PresentIncreased erosion in the north is possible due to the location of the Florida Current. Historical Florida Current location and rates are unknown, but the distance between the Outer Reef and present Florida Current lessens northward along the coast as the shelf thins. Assuming the historical current was similar in location and strength, it is plausible that the northern Outer Reef had more interaction with the current over the past 8,000 years and thus eroded more.Variable historical reef growth could also explain latitudinal differences in depth along the Outer Reef. This elicits two scenarios: 1) reef accretion terminated earlier in the north or 2) accretion occurred at a slower pace in the north. These growth scenarios suggest that climatic variability along the southeast Florida latitudinal gradient affected reef growth during the Holocene. Previous Outer Reef ages came from one site near the Hillsboro transition , thus latitudinal variability of reef growth and termination ages along the northern Florida reef tract remain unknown.The first historical growth scenario may be supported by present-day reef morphology. In the Biscayne region , the Inner and Outer Reefs appear to converge offshore of Key Biscayne . Here th2 of shallow-water coral reef benthic habitats within the US, its territories, and commonwealths spanning the Caribbean and Pacific oceans. The areas mapped thus far include Hawaii and the northwest Hawaiian Islands, American Samoa, Guam, Commonwealth of the Northern Mariana Islands, Palau, Palmyra, Puerto Rico, and the US Virgin Islands. The spatial analyses used herein could be applied to any large-scale mapping effort to statistically determine distinct coral reef ecosystem regions and potential biogeographic boundaries. The outcomes of which would strengthen scientific research by informing the appropriate spatial information during research sample site planning and allow for samples to be randomly stratified across regions and habitats based on local habitat morphology.Globally, the area of mapped coastal marine ecosystems is increasing. For example, in the last 10–15 years the United States (US) National Oceanic and Atmospheric Administration has mapped over 9,000 kmFurthermore, the analyses herein provide a scientific basis for local marine conservation spatial planning. According to Lourie and Vincent (2004) “…biogeography should be at the forefront of determining spatial priorities for proactive marine conservation planning. The spatial distribution and scale of biodiversity, the processes maintaining it, and the threats to it need to be understood so that appropriate conservation measures may be initiated.” The analyses herein defined regions at a scale appropriate to regional management decisions that relate to benthic habitat morphology and potentially to regional biogeography. This information will strengthen systematic marine conservation planning by furnishing necessary, relevant spatial distribution information that provides an objective, scientific foundation for decision making. As more regional biological data become available, the regions defined herein can be tested to better understand how the benthic fauna and ichthyofauna composition differ and how they are affected by differences in the major spatial relationships and sea floor morphologies.


Using this system, we present genetic and biochemical evidence of a general mechanism of antitermination used by ANTARs, including details of the antiterminator structure. The novel antiterminator structure consists of two small hairpins with highly conserved terminal loop residues, both features being essential for successful antitermination. The ANTAR protein dimerizes and associates with its substrate RNA in response to signal-induced phosphorylation. Furthermore, bioinformatic searches using this conserved antiterminator motif identified many new ANTAR target RNAs in phylogenetically diverse bacterial species, some comprising complex regulons. Despite the unrelatedness of the species in which they are found, the majority of the ANTAR–associated genes are thematically related to nitrogen management. These data suggest that the central tenets for gene regulation by ANTAR antitermination occur widely in nature to specifically control nitrogen metabolism.ANTAR proteins are widespread bacterial regulatory proteins that have RNA–binding output domains and utilize antitermination to control gene expression at the post-initiation level. An ANTAR protein, EutV, regulates the ethanolamine-utilization genes ( In bacteria, two-component regulatory systems comprise the primary mechanisms for how microorganisms respond to changes in their environment. These signal transduction systems rely upon phosphotransfer between two conserved proteins, a histidine kinase and a response regulator, to propagate the signal and affect cellular biology. Phosphorylation of the response regulator has been shown in many systems to control DNA–binding activity, protein–protein interactions, or enzymatic activity. However, in this study, we discover a general RNA substrate for a large family of putative RNA–binding response regulator proteins called ANTAR proteins. By identifying the general architecture of this RNA recognition element, our bioinformatic searches were then able to discover many more examples of these RNA motifs in bacteria. Indeed, our data together revealed that the regulatory relationship between ANTAR proteins and the RNA motif identified in this study is widespread among phylogenetically diverse bacteria for control of numerous nitrogen metabolism genes. The events surrounding control of transcription initiation are arguably the best studied; however, many genes are also controlled by post-initiation regulatory mechanisms. For example, transcription elongation is oftentimes subjected to post-initiation control by the presence of intrinsic terminator hairpins in the nascent transcript. Typically, intrinsic terminator sites occur at ends of operons in order to promote site-specific dissociation of RNA polymerase. However, they are also frequently arranged upstream of open reading frames (ORFs), typically within 5′ leader regions, where they participate in signal-responsive regulatory mechanisms. In certain instances, proteins with RNA-binding domains interact with these 5′ leader regions to influence terminator formation and thereby control downstream gene expression AmiR and NasR Transcriptional Antiterminator Regulator domain (ANTAR) http://pfam.sanger.ac.uk/; Pfam: PF03861). ANTAR-containing proteins typically occur as multi-domain proteins. A significant class of ANTAR proteins appear to possess an N-terminal domain that resembles a pseudo-receiver domain capable of protein-protein interactions Pseudomonas aeruginosa dimerizes upon binding two molecules of its negative regulator AmiC. Under inducing conditions AmiC binds a small amide compound, allowing association of AmiR with the 5′ leader of the appropriate target mRNA. This has been hypothesized to prevent formation of an intrinsic terminator. However, the molecular mechanism of antitermination, including the AmiR RNA recognition determinants, has yet to be revealed Another important family of proteins with putative RNA-binding activity contains the nasFEDCBA operon in Klebsiella species, which is required for nitrogen assimilation nasF transcript. Association of NasR inhibits formation of a transcription terminator within the 5′ leader region, thereby allowing synthesis of the downstream nas operon. Like the AmiR system, the molecular mechanism of antitermination, including the NasR RNA determinants, have not been identified. In fact, it has been speculated that the mechanism might not even involve formation of a specific antiterminator structure, in contrast to the BglG/SacY family of antiterminators The ANTAR domain also occurs in combination with a diverse set of signal-sensing domains . For exaPeriod clock protein, Aryl hydrocarbon receptor and Single minded protein) and GAF  domains  of ANTAR-containing proteins is comprised of response regulators that are part of bacterial two-component regulatory systems (TCS). TCS typically consist of a sensor histidine kinase that undergoes autophosphorylation upon sensing its signal and in turn transfers the phosphoryl group to the receiver domain of a cognate response regulator ethanolamine utilization operon (eut) in Enterococcus faecalis and this mode of regulation appears to be conserved in many Firmicutes that contain eut operons E. faecalis, the corresponding sensor kinase, EutW, undergoes autophosphorylation in response to ethanolamine whereupon the phosphoryl group is transferred to EutV eutP, eutG, eutS and eutA (eut operon were found to share a common 13-nucleotide sequence (AGCAANGRRGCUY) overlapping the 5′-proximal portion of their corresponding intrinsic terminator elements. We previously proposed that these sites could serve as part of the recognition sequence for ANTAR-based regulators in order to promote antitermination and allow production of the downstream transcript eutG in E. faecalis supports the model that antitermination occurs at this consensus sequence eut system or any other system that utilizes ANTAR-based regulatory proteins.EutV, a representative of ANTAR-containing response regulators, was discovered to regulate the and eutA ; its asseut operon from E. faecalis as a model system, we present evidence that a novel RNA motif comprises a specific antiterminator structure containing the full determinants for recognition by the EutV ANTAR domain. Importantly, the same RNA motif could be identified for the other ANTAR-based regulatory systems that have been studied (AmiR and NasR), suggesting that it is likely to constitute the general recognition element of ANTAR-based regulatory proteins. This structure consists of a pair of small stem-loops, one of which contains the previously identified 13-nucleotide sequence described above. Recognition of RNA by EutV relies on a combination of structure and primary sequence determinants. Specifically, certain residues within the hexanucleotide terminal loops share primary sequence conservation, particularly at the first and fourth positions, and are important for binding. We also discovered that the minimum RNA-binding module of EutV is composed of a dimer of the ANTAR domain, and that dimerization is stimulated in a signal-responsive manner. Moreover, conditions that mimic phosphorylation improved RNA-binding activity of EutV, suggesting that signal-induced dimerization is likely to stimulate RNA-binding activity. Therefore, in aggregate, these data suggest that RNA-binding response regulator proteins are likely to generally rely upon protein dimerization and recognition of tandem nucleic acid substrates, which are mechanistic features that conceptually resemble regulation by many DNA-binding factors. Finally, to assess whether the dual hairpin RNA structure might be present in other bacteria, we employed a bioinformatics-based search for this element across many bacterial genomes. These searches led to the discovery of many new regulons that are likely to be coordinated by ANTAR recognition elements. These searches also revealed that the ANTAR recognition elements described herein are generally involved in coordinating expression of nitrogen metabolism genes. In aggregate, these data reveal that ANTAR-based genetic circuits are widespread in bacteria and broadly share certain conserved molecular features.Using the lacZ reporter translationally fused to the 5′ leader region of the first gene of the eut operon (eutP), we observed that rich medium containing serum modestly induced expression and this induction did not occur in a eutVW in-frame deletion mutant E. faecalis could grow anaerobically in minimal medium with ethanolamine as the sole source of carbon, as long as AdoCbl was also provided; however, a eutVW mutant was unable to grow under these conditions eutVW mutant. As shown in eutP-lacZ that was dependent on ethanolamine, AdoCbl, EutV and EutW. Importantly, all strains, including the eutVW deletion, grew equally well (data not shown). We constructed and tested individual mutants of eutV and eutW (in-frame deletions) and observed the same lack of induction in medium containing ethanolamine and AdoCbl (data not shown). We additionally constructed a eutS-lacZ reporter and also observed induced expression dependent on ethanolamine, AdoCbl and EutVW  and RNAfold (http://rna.tbi.univie.ac.at/cgi-bin/RNAfold.cgi). A mini-hairpin with a short paired stem and a hexanucleotide loop was predicted upstream of the terminator in each of the four candidates  and NasR RNA substrate (the leader region of nasF) ; however at the onset of our studies, the RNA determinants for protein recognition had not been identified. To identify features that dictate recognition by EutV, we analyzed the sequences directly upstream of ndidates . Interesndidates . Sequencositions . Howeverequences  and alsoof nasF) . These oof nasF) .eutP-lacZ construct  within the base-paired region of the second hairpin (P2), which is predicted to prevent its formation, also reduced the efficacy of in vivo activation by EutV. Furthermore, we tested constructs containing mutations in the two residues that appeared to be most conserved - positions 1 and 4 within the terminal loops; alteration of any one of these four residues (A29U or G32A in L1 and A53U or G56A in L2) also resulted in reduced induction of the eutP-lacZ constructs  or elongated to 14 bases (long linker). Both of these changes caused drastic reductions in induction, suggesting that an optimal proximity of the two stem-loops is required for successful interaction with EutV.To test the predicted importance of these structural features, site-directed mutations were introduced in the onstruct . Deletionstructs . To testse pairs . Based oeutP 5′ leader region was then tested directly in vitro via an electrophoretic mobility-shift assay (EMSA) using 5′ radiolabeled RNA and purified protein . Therefore, together, these data demonstrate that the tandem hairpins are important recognition elements for regulation by EutV.The capability of EutV to bind to the wild-type and mutant versions of the  protein . These dE. faecalis EutV is predicted to possess two domains - an N-terminal phospho-accepting receiver domain and a C-terminal ANTAR domain . As fractions elute from a gel filtration column, which separates proteins based on size and shape, they are passed through a light scattering device. The latter conducts measurements of the differential refractive index of the various macromolecules as they elute from the column. MALLS is independent of the shape of the molecule, thereby allowing precise calculation of the molar mass for all the fractionated species. Analysis using MALLS after fractionation on a Superdex-200 column is shown in After determining the oligomeric state of the isolated ANTAR domains, we investigated the oligomeric state of full length EutV. Since we were unable to quantitatively resolve EutV oligomeric states by SEC alone, we employed a sensitive method where SEC is coupled in tandem with differential radial capillary action of ligand assay (DRaCALA) was instead employed for these purposes Many response regulators are capable of autophosphorylation in the presence of small molecule phospho-donors such as acetyl phosphate, carbamoyl phosphate, or phosphoramidate. We tested the two most common small-molecule phosphodonors (acetyl phosphate and carbamoyl phosphate) for their ability to induce dimer formation. Although EutV did not form dimers in response to addition of these small molecules, they appeared to provoke a moderate conformational change in EutV, visualized as a delay in the elution volume. However, further tests revealed that magnesium alone was responsible for promoting the moderate conformational change in EutV as it had also been included with the small molecule phosphodonor solutions . Since tFrom these combined results we propose a general model for EutV regulation . Proteineut loci of E. faecalis, Clostridium and Listeria species, as well as the corresponding RNA sequences for Klebsiella oxytoca nasF and Pseudomonas aeruginosa amiE, which are the target substrates for NasR and AmiR, respectively  whereas others, especially the Firmicutes, are regulated by a RNA-binding, ANTAR-containing homolog of EutV, as in E. faecaliseut pathway homologues but lack any ANTAR-encoding genes, while other genomes contain both. As predicted, we recovered less RNA hits in genomes that lack ANTAR-encoding genes , we hypothesized that the RNA motif as identified herein might be generally representative of ANTAR substrates in other organisms. Also, the three previously characterized ANTAR regulatory systems each affected a single locus in their respective host organisms, and we reasoned that a subset of bacteria might instead incorporate multiple ANTAR-responsive RNA elements at disparate genomic locations for coordination of ANTAR-based regulons. To this end, we searched for additional occurrences of the putative ANTAR RNA substrate using a bioinformatics-based approach. Specifically, we used a covariance model-based approach ectively . This RNng genes . Anothere.g., Mycobacterium, Streptococcus, Fusobacterium, Alkaliphilus, etc.) and Gram-negative bacteria , and resulted in a consensus pattern that resembled the input consensus pattern . In Pelobacter carbinolicus new ANTAR substrates were located within three separate transcriptional units, which are each predicted to be important for nitrogenase function, suggesting a comprehensive ANTAR-based regulon for nitrogenase regulation in this microorganism  can alternately base pair with the downstream transcript to form an intrinsic terminator. In this way, the ANTAR target RNA appears to act as a transcriptional antiterminator that may be stabilized upon ANTAR binding. The dual hairpin motif is unique compared to the antiterminators employed by other bacterial regulators such as the Sac/Bgl and TRAP proteins. For the trp operon, multiple molecules of the RNA-binding TRAP protein associate with tandem UAG trinucleotide repeats in the transcript. Some of these trinucleotide repeats are located within the antiterminator sequence The ANTAR protein domain appears to be a global regulatory domain that is used in multiple combinations with putative sensory domains. These combinations yield signal-responsive regulatory proteins that are likely to respond to a wide variety of signals, but rely on common interactions between the ANTAR domain and its RNA substrate for affecting regulation. This has been the motivation for this study, wherein we have identified a common ANTAR substrate RNA and determined some basic features that govern the ANTAR-RNA recognition. Specifically, our data in aggregate suggest that ANTAR proteins recognize a common RNA motif consisting of two tandem stem-loops. Certain primary sequence determinants within the terminal loops and at the apex of the helices appear to be conserved for these RNA elements. These features appear in the putative ANTAR substrates of both Gram-positive bacteria, such as the eutG was noted under phosphorylating conditions of EutV We find that the minimum ANTAR domain alone can dimerize, although the adjacent coiled-coil region is likely to alter the dimer to improve its RNA-binding ability. The functional importance of protein dimerization is further strengthened by the observation that other classes of ANTAR proteins are also likely to form dimers or higher ordered oligomers. For example, many other ANTAR proteins possess an N-terminal coiled-coil region (Pfam PF03861), suggesting a broad role for dimerization. Similarly, most other ANTAR architectures include protein subdomains that are known to dimerize, such as PAS and GAF domains. Our data also suggest that dimer formation is likely to affect the mechanism of activation by ANTAR response regulators. SEC-MALLS analysis of full length EutV indicated that it exists as a monomer in the unphosphorylated state. However, phosphoryl transfer from the cognate sensor kinase, EutW, induced EutV dimerization, suggesting that dimerization is integrated with the appropriate TCS signal. Moreover, phosporylation-induced dimerization also improves RNA-binding affinity overall. This is in agreement with previous observations by Baker and Perego where a 3-fold increase in transcriptional readthrough of an intrinsic terminator upstream of The ANTAR domain itself is widespread in bacteria. Therefore we reasoned that the RNA substrate that is recognized by this domain should also exhibit a high degree of conservation in the critical recognition elements. Hence we employed bioinformatic searches to identify ANTAR target RNAs in bacterial genomes. This revealed a correlation between the presence of the ANTAR domain and the dual hairpin substrate RNA. Moreover, the search for ANTAR RNA substrates led to the discovery of many new ANTAR-based regulons, which mostly appear to be part of functionally-related operons involved in various aspects of nitrogen metabolism. Why one mechanism of regulatory control would be so tightly associated with nitrogen management in unrelated bacteria is intriguing but will warrant further investigation.in vitro, in vivo and in silico techniques using the E. faecalis EutV/EutW TCS as a model. These data together elucidate the general properties of the ANTAR RNA substrate consisting of a dual hairpin element, a generally novel antiterminator structural arrangement. Now that we understand many of the critical features of this RNA element, it can be used to predict ANTAR-based operons across all sequenced bacteria, and for the future design of orthogonal ANTAR-based synthetic gene regulatory circuits.Our findings represent a significant advancement in the understanding of antitermination mechanisms in general and that of the ANTAR family of regulatory proteins in particular. It provides, a common model of antitermination employed by ANTAR proteins that was validated by E. coli strains were routinely cultured in Luria Bertani broth at 37°C. Antibiotics were used at the following concentrations (µg/ml): ampicillin, 100; spectinomycin, 100; erythromycin 300. E. faecalis strains were cultured in Brain Heart Infusion (BHI) medium at 37°C. Antibiotics were added at the following concentrations (µg/ml): erythromycin, 50; and rifampicin, 100.All bacterial strains used in this study are listed in Details on plasmid construction are described in the supplementary materials .2HPO4/40 mM NaH2PO4/10 mM KCl/1 mM MgSO4/5 mM β-mercaptoethanol, pH 7.0) and lysed using 0.1 mm glass disruption beads  for 2 minutes in a mini bead beater. Samples were cleared by centrifugation and incubated with o-nitrophenyl β-D-galactoside (ONPG). Color development was measured at 414 nm using a plate reader. Total protein in samples was estimated using a Pierce BCA kit (Thermo Scientific) as per manufacturer's instructions. Reporter activity was reported in arbitrary units that indicate absorbance per µg/µl total protein. Samples were assayed in triplicate. Mean and standard deviations were calculated from experimental replicates. Each mutant was analysed on at least three independent occasions. Data shown reflects the average and standard deviations of all occasions of which a mutant was analysed.Overnight cultures were grown in BHI broth with 50 µg/mL erythromycin. Cultures were then diluted 1∶20 into modified M9HY (MM9HY) medium or MM9HY supplemented with 33 mM ethanolamine, or 40 µg/mL AdoCbl or both. The MM9HY medium was prepared as described in Del Papa and Perego, but included 0.2% ribose. Strains were then grown anaerobically, without agitation for 3.5 hrs in the dark, and samples were collected by centrifugation. Cells were resuspended in 1∶10 Z buffer  with template DNA oligos  purchased from Integrated DNA technologies. The forward primers added the promoter sequence for T7 RNA transcription. PCR products were processed using the Qiagen PCR clean-up kit. RNAs were synthesized by T7 RNA polymerase at 37°C for 2.5 hours and the reactions were terminated with 2× volume 8 M urea. Transcript products were resolved by denaturing 6% PAGE and visualized by UV shadowing. Passive elution RNA for eutP were transcribed and radiolabeled with γ-32P ATP. RNA (∼2 fmol) was incubated with increasing concentrations of protein in a 10 µl reaction containing 50 mM Hepes pH 7.5, 150 mM sodium chloride,10 mM MgCl2 and 2.5 ng/µl yeast tRNA for 30 minutes at 25°C. Beryllium fluoride was used as a phosphor-Asp mimic, to generate phosphorylated EutV. For this, EutV at 30 µM concentration was mixed with 5 mM beryllium chloride and 15 mM sodium fluoride and allowed to equilibrate at room temperature for 30 minutes. The samples were then subjected to either EMSA or differential radial capillary action of ligand assay (DRaCALA) Different RNAs corresponding to the 5′ region of 2 (5 mM), ethanolamine (2 mM). In runs containing small-molecule phosphodonors, carbamoyl phosphate (5 mM) or acetyl phosphate (10 mM) were used. Elution profiles of differential refractive index versus volume were recorded and analyzed using the Optilab instruments and the Astra software (Wyatt Technologies Inc.). Data were plotted using the Astra software (Wyatt Technologies Inc.).Size-exclusion chromatography was performed for EutV on an HR 10/300 Superdex-200 column  and for ANTAR or ANTARcc on an HR 10/300 Superdex-75 column at a flow rate of 0.5 ml/min in running buffer . Runs were performed with EutV at 10 µM and EutW at 0.5 µM. Phosphorylation conditions included ATP (5 mM), MgClhttp://www.ncbi.nlm.nih.gov/RefSeq/) and were downloaded and combined in a searchable database. An input RNA alignment consisting of 17 ANTAR substrate sequences was manually created. These sequences represented eut intergenic regions from E. faecalis, Clostridium and Listeria sp. where we manually identified the ANTAR-binding dual hairpin motif. This motif consisted of 3 bp stem regions with hexa-nucleotide terminal loops. The distance between the two stems was variable. The alignment was used as input for Infernal Enterococcus, Clostridium, and Listeria included in the seed alignment. This cut-off was retained for all further searches. Also, all hits located within coding regions were eliminated from any further analysis. To create a template alignment for the catalog of ANTAR substrate hits, the portions of the hits that included the stem 1 and stem 2 regions were separately subjected to the comparative sequence alignment software LocaRNA ftp://ftp.ncbi.nih.gov/genomes/Bacteria/ and filtered to remove all but one genomic sequence from each available strain. Again, an arbitrary bit score cut-off of 10 was used to catalog putative ANTAR substrate hits. To further filter these hits, they were screened for the presence of putative intrinsic transcription terminator hairpins using TransTermHP. For this step, we utilized the default model with an adjustment to allow for a larger (up to 26 nucleotide) terminal terminator loop. The only hits that passed both filters were those that were arranged such that the second stem-loop (P2) overlapped with a putative intrinsic terminator that was also oriented in the correct direction. We reasoned that this subset of hits represented the most likely examples to participate in transcription attenuation mechanisms.Previous work by Tsoy et al. listed 84 bacterial genomes that contain putative ethanolamine utilization genes Figure S1eut pathways from Enterococcus, Listeria, and Clostridium species. Our subsequent inspection of these regions (described in this manuscript) revealed the presence of two tandem stem-loops . Therefore, instead of one putative conserved region (asterisks), it appeared that two conserved hairpins were located upstream of each of the eut terminators from these organisms. We also noted that the first and fourth positions of both terminal loops were an A an G, respectively, for all of these putative regulatory elements. These features are also present in the K. oxytoca nasF and P. aeruginosa amiE leader sequences. Therefore, these general criteria were used to generate a seed alignment for a broader search against bacterial genomes that contained eut pathways Click here for additional data file.Figure S2eut-containing bacteria. The seed alignment of the ANTAR RNA substrates from the eut pathways of E faecalis and Listeria monocytogenes was used to perform an Infernal-based search eut-containing bacterial genomes. The results of this search are summarized in Alignment of ANTAR substrate hits from (DOCX)Click here for additional data file.Figure S3eut-containing organisms overlapped with an intrinsic transcription terminator, despite the fact that inclusion of an intrinsic terminator was not part of the original search criteria (highlighted in yellow in Example of an ANTAR target RNA predicted to influence translation rather than transcription attenuation. A subset of the putative ANTAR target RNAs identified in (DOCX)Click here for additional data file.Figure S4Purification of EutV. Purification of EutV on a DEAE column following purification on TALON affinity resin. EutV elutes as two peaks (black), which differ in the ratio of absorbance 260 nm/280 nm (blue). The first peak is enriched in free EutV whereas the second peak contains EutV contaminated with nucleic acids.(DOCX)Click here for additional data file.Table S1eut-Containing Bacteria.Covariance Search Results for Discovery of ANTAR Substrates in (DOCX)Click here for additional data file.Table S2Covariance Search Results for Discovery of ANTAR Substrates that Overlap Terminator Hairpins in Diverse Bacteria.(DOCX)Click here for additional data file.Table S3Strains, plasmids and primers used in this study.(DOCX)Click here for additional data file.Text S1Supplementary methods.(DOC)Click here for additional data file.


Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition. Because of the importance of protein–RNA interactions on fundamental biological processes such as protein synthesis, DNA replication and repair, regulation of gene expression and defence against pathogens , determiAlthough molecular docking for protein–protein recognition has been developed for more than one decade , the proTo perform molecular docking, there are two equally important components, sampling and scoring. During the sampling process, all the possible binding modes of one molecule are sampled relative to the other molecule. Conformational changes may be considered during the sampling process. Following or during the sampling process, a scoring function is used to evaluate the energy associated with each generated binding pose and to rank the poses accordingly. The scoring function will be the focus of the present study. Although several attempts have been made recently toward the development of scoring functions for protein–RNA interactions ,44–46, tRecently, we have developed a statistical mechanics-based method for scoring function construction by extracting atomic, distance-dependent interaction potentials from experimentally determined structures . The metTo construct a diverse training set of protein–RNA complex structures, we searched the PDB for all the radiographic crystal structures that have a resolution better than 3.5 Å and contain at least one protein and one RNA chain but no DNA chains. As of 7 December 2012, the query yielded 962 entries, which were further manually examined. Only the appropriate protein–RNA complexes that met the following criteria were considered. First, both the protein and RNA chains should belong to the same biological unit according to the description in the PDB file. Second, the individual protein and RNA should be large enough to have a stable structure, but not too large for docking calculations. In this study, the number of the residues in the protein was set to be >20 and <1000, and the number of the residues in the RNA was between 10 and 200. Third, to ensure that the binding interface forms a whole patch rather than multiple separate patches for the sake of simplicity, only the PDB entries that contain a moderate number of chains in the protein oligomers or multiple RNA segments were considered. Specifically, we empirically allowed for no more than six chains in the protein or RNA. Finally, the complexes with only backbone atoms in the protein or in the RNA were excluded. The complexes that met the above criteria were then clustered according to their sequence similarities to remove the redundancy. Namely, two protein–RNA complexes were grouped into the same cluster if they met the following two criteria: First, any protein chain of the first complex has a sequence identity >30% with a protein chain from the second complex. Second, any RNA chain of the first complex has a sequence identity >70% with an RNA chain from the second complex. The higher sequence identity cutoff for RNA during clustering was because RNA molecules have a much lower sequence-structure homology than proteins . A totalSupplementary Table S1.These 175 protein–RNA complex structures were found to have some overlap with the protein–RNA docking benchmark 1.0 that we developed recently . Considen stands for the iterative step, i and j represent the types of a pair of atoms in the protein and the RNA, respectively. ij calculated from the experimentally observed protein–RNA complex structures in the training set:K is the total number of the protein–RNA complexes in the training data set and k-th native complex structure  crystal structures in the training set. The method can be represented mathematically by the following iterative formula:tructure ,56,58,59ilities   that are complex ,56.  areTheoretically, DOCK 2.1  was usedNext, a simplex optimization  was perfThen, for a given set of initial potentials an force . The firi-th bin was set to the weighted average of 1:2:4:2:1 of the potentials from bins The final set of pair potentials were treated with the following smoothing algorithm to account for statistical fluctuations in the experimentally determined structures in the training set: The potential at the In this study, only heavy atoms were considered and the effects of hydrogens were implicitly incorporated in the potentials. The categorization for the protein atom types is the same as in our previous study for ITScorePP , giving distance . The maxIn this study, we have used four test sets that were constructed by different groups using different algorithms to test the performance of ITScore-PR on identification of near native binding modes for both bound and unbound docking.This test set consists of five protein–RNA complexes . For each complex, 2000 binding decoys were generated by Chen et al. in the Varani Group  using thhttp://zoulab.dalton.missouri.edu/RNAbenchmark/). Briefly, the benchmark contains 72 diverse targets of protein–RNA complex structures. Each target includes both the bound structures and unbound structures of the protein and the RNA. The unbound structure is defined as a structure in free form or being a binding partner in a different complex. In addition, following the sequence alignment, a residue mapping between the bound and unbound structures was obtained for both the protein and the RNA, respectively. Based on the residue mapping, a second set of mapped bound and unbound structures was created by removing the mismatched residues in the alignments from the original structure files. The mapped bound and unbound structures are important for binding mode quality assessment. The benchmark also contains other useful information, such as the interface RMSD between the bound complex and the unbound complex after optimal superimposition and the percentage of native contacts of the unbound complex compared with the bound complex. The information provides the benchmark users a clear picture about the degree of conformation changes on RNA binding. Detailed description about the benchmark can be found in our protein–RNA benchmark paper (The second test set for this study is the protein–RNA docking benchmark 1.0  that we rk paper . This behttp://life.bsc.es/pid/protein-rna-benchmark/)  . The autet al. (Supplementary Table S2). Specifically, first, the authors used the RPDOCK program to generate the binding decoys based on the unbound/modeled structures in the benchmarks. Only those test cases with at least one near-native poses in the top 1000 decoys were kept, where the near-native pose was defined as the pose with an RMSD of the RNA molecule <10 Å compared with the corresponding native complex after the superimposition of the proteins. This protocol resulted in 43 test cases for the decoy set based on the benchmark by Perez-Cano et al.  potential for the N+–O− pair is due to the attractive electrostatic interaction between these two oppositely charged atom types in addition to the hydrogen-bonding interactions.Using the 1001 binding modes (1000 decoys plus one native structure) for each of the 110 protein–RNA complexes in the training set, we have extracted the effective pairwise potentials for protein–RNA interactions with our statistical mechanics-based iterative method. The 20 protein atom types and 12 RNA atom types, which are listed in et al. , which may serve as a useful decoy set for scoring function assessment on protein–RNA docking. Lastly, for each complex, all the orientations were ranked according to their ITScore-PR scores and clustered based on their RMSD distances. If two orientations have an RMSD of <5 Å for the heavy atoms in the RNA molecule, only the orientation with a lower ITScore-PR score was kept. For consistency, the same clustering strategy was also applied to the ranked orientations during the calculations of the success rates for other scoring functions being tested in this study.The validation process is described as follows. First, for each protein–RNA complex in the test set, 2000 binding orientations of the RNA molecule were generated globally relative to the protein for the bound conformations and unbound conformations, respectively, using the third-party docking software ZDOCK 2.1 . ZDOCK 2Supplementary Figure S4). If the top 10 ranked orientations were considered, ITScore-PR achieved a success rate of 94.4%, compared with 79.2% for both dRNA and DARS-RNP, 68.1% for QUASI-RNP, 65.3% for ZDOCK 2.1 and 46.2% for PMF .Supplementary Table S6, most of the successful binding modes predicted by ITScore-PR were high-accuracy structures according to the CAPRI criteria and were ranked as No. 1, whereas the other scoring functions yielded relatively less accurate modes (i.e. more models with medium or acceptable accuracy) and lower rankings for the best successful predictions. PMF was even unable to predict any successful binding modes for two bound cases (Supplementary Table S6).To analyze the accuracy of ITScore-PR in more details, we also examined the accuracy qualities and the rankings of the first successful predictions for the benchmark of 72 bound cases. As shown in Next, ITScore-PR was assessed using the ‘unbound’ cases of these 72 complexes in which at least one partner of each complex has a nonnative structure (i.e. either an apo structure or a structure taken from a different protein–RNA complex) , followiSupplementary Figure S5a). When the top 10 predictions were considered, the success rate of ITScore-PR increased to 55.6%, compared with 51.4% for dRNA, 47.2% for DARS-RNP, 41.7% for QUASI-RNP, 38.9% for ZDOCK 2.1 and 26.4% for PMF .tyr . Similar to the bound cases, ITScore-PR yielded relatively higher accuracy predictions and lower rankings for the first predicted successful modes than the other scoring methods like ZDOCK2.1 and PMF, even though the improvement is relatively less than bound docking because of the effect of molecular flexibility.We also analyzed the accuracy qualities and rankings of the first successful predictions for the 72 unbound cases of the benchmark (Supplementary Table S8).To further test ITScore-PR, we created a subset of ‘truly’ unbound cases from the 72 protein–RNA complexes in our protein–RNA docking benchmark by removing those homologous unbound cases. Here, ‘truly’ unbound cases are defined as the cases in which at least one of the binding partners is a ‘truly’ unbound structure, either in the free form or bound to an RNA (or a protein) that has a sequence identity of <70% (or <30%) compared with the RNA (or the protein) in the corresponding native complex. We ended with 50 truly unbound test cases out of the original 72 cases . If the top 10 predictions were considered, the success rate of ITScore-PR increased to 46%, compared with 44% for dRNA, 38% for DARS-RNP, 32% for both QUASI-RNP and ZDOCK 2.1 and 16% for PMF .The success rates are lower for the subset of truly unbound cases than for the original set of 72 complexes  are expeThe similar performance of ITScore-PR and dRNA on the unbound test cases as shown in et al. . If the top 10 predictions were considered, both ITScore-PR and dRNA obtain a good success rate of ∼40% that is significantly higher than DARS-RNP (26.4%), QUASI-RNP (22.2%), ZDOCK 2.1 (20.8%) and PMF (15.3%) .ITScore-PR was further tested on the 72 complexes from the protein–RNA docking benchmark constructed by Perez-Cano et al. , which cet al. . For the docking decoys based on the benchmark by Huang and Zou .ng Group  and DECKng Group ,69. The ng Group a and b.   for bound docking and 24% (46%) for (truly) unbound docking if the top one (http://zoulab.dalton.missouri.edu/RNAdecoys.The bound and unbound docking decoy sets that were generated and optimized in this study using ITScore-PR for the 72 complexes in our protein–RNA docking benchmark are free to download at Supplementary Data are available at NAR Online.National Institute of Health [R21GM088517]; National Science Foundation CAREER Award [DBI0953839]. Funding for open access charge: National Science Foundation CAREER Award [DBI0953839].Conflict of interest statement. None declared.