The throughput and single-base resolution of RNA-Sequencing (RNA-Seq) have contributed to a dramatic change in transcriptomic-based inquiries and resulted in many new insights into the complexities of bacterial transcriptomes. RNA-Seq could contribute to similar advances in our understanding of plant pathogenic bacteria but it is still a technology under development with limitations and unknowns that need to be considered. Here, we review some new developments for RNA-Seq and highlight recent findings for host-associated bacteria. We also discuss the technical and statistical challenges in the practical application of RNA-Seq for studying bacterial transcriptomes and describe some of the currently available solutions. Their availability has had important contributions towards deciphering the highly complex and fascinating biological interactions between symbionts and their hosts. Since the 2000s, when the first genome sequences of plant pathogens were determined, we have gained a greater appreciation into the mechanisms of virulence, such as secretion systems and repertoires of effectors, metabolic and biosynthetic capacities to adapt to different environments, biosynthesis of secondary metabolites and toxins to modulate host plants, and evolution as well as taxonomical relationships of plant pathogenic bacteria –9.Escherichia coli, the most heavily studied bacterium, had only 54% of its genes experimentally supported with another 32% computationally predicted [Pseudomonas, Xanthomonas, Ralstonia, and Agrobacterium genera have between 27%∼37% of their genes annotated as “hypothetical”. Adding to the challenges of studying plant pathogens is the amount of redundancy coded in their genomes and the subsequent difficulties that experimental biologists face in their efforts to map and characterize genes necessary for virulence [Genome sequences are by no means the end of the road. A genome sequence is a map with the challenge of exploration to improve and make sense of it. As of six years ago, even redicted . No planirulence .etc. Investigators have mostly focused on protein coding mRNAs and, more recently, on the regulatory small RNAs, while excluding the “housekeeping” functional RNAs, such as rRNA, and tRNAs. As such, from hereafter, we use “transcriptome” to imply only mRNAs and sRNAs. The transcriptome is dynamic and is constantly changing in response to endogenous and exogenous cues. Thus, transcriptomic-based approaches typically rely on the characterization of snapshots captured from cells subjected to conditions and times of interest.Transcriptomic-based approaches have the potential to help rapidly address this knowledge gap. A transcriptome represents all RNA molecules, including the coding mRNAs as well as the noncoding rRNA, tRNA, sRNAs, Microarrays were one of the earliest tools that offered researchers the once unique opportunity to investigate the reprogramming of a phytopathogenic bacterium's entire transcriptome. Microarrays were used to identify virulence regulons and study the physiological changes that occur in response to plant signaling molecules or in conditions that mimic the host environment ,11–16. MNext generation (next gen) sequencing has pushed data generation into the logarithmic growth phase. Several next gen platforms are available that use different chemistries but offer the same advantages over traditional Sanger sequencing-dramatic increases in throughput with decreases in cost, time, and labor (reviewed in ). The apRNA-Seq has been used for expression profiling as well as many other explorations into transcriptomes. Analysis of RNA-Seq has shown that, despite the perceived relative simplicity of bacterial genomes in comparison to their eukaryotic hosts, bacterial transcriptomes and their regulation are nonetheless similar in complexity. Genes that escaped annotation have been uncovered using RNA-Seq, the most prominent being those of noncoding or small RNAs –26. Subsde novo assembled to study organisms with no available reference genome sequence [Pectobacterium carotovora [A distinct advantage of RNA-Seq is that cDNA fragments are directly sequenced and the reads can be sequence ,31. For sequence . Furthersequence ,33. Thusrotovora .Pseudomonas syringae [On the surface, with these advantages, it almost seems absurd to not use RNA-Seq. Millions to billions of RNA-Seq reads, terabytes of data, will be available quickly and cheaply. However, to date, there has been only a single report describing the use of RNA-Seq to study the transcriptome of a plant pathogen, syringae . For manRNA-Seq, its uses and its analytical tools, are still in their developmental stages. In the following, we briefly review options for preparing RNA from bacteria as well as some of the computational challenges associated with RNA-Seq. Many of these topics have been comprehensively reviewed ,35–38. W2.One of the first tasks of RNA-Seq is to produce a transcriptome depleted of rRNAs and tRNAs. These functional RNAs typically exceed 90% of the total RNA preparation and will likely represent >99% of the RNA-Seq reads if not sufficiently addressed . In eukaFor many experiments, the tRNAs and 5 s rRNA are of little concern because they can be excluded simply based on their small sizes. However, a fraction of the sRNAs may also be lost with these approaches as some sRNAs are as small as 50 nucleotides in length . Thus, iP. syringae  and Ribominus . This approach has important implications in downstream data analyses and can be used to characterize bacterial transcriptomes with greater precision (see below). A third and relatively new method uses enrichment by relying on “not so random” oligonucleotides during cDNA preparation to bias towards non-rRNA transcripts  . Finallrlos, CA ,33. ThisIn addition to rRNAs, we have also found that a tmRNA can sometimes be very abundant . tmRNA iet al., for example, developed a method they called differential RNA-Seq in which two different preparation methods were used to process fractions of RNA derived from the same sample to distinguish strand-specific 5′ preprocessed transcripts [There are several methods to consider for preparing RNA for sequencing. The most straightforward method relies on sequencing randomly primed cDNAs and is sufficient for discovering genes, improving genome annotations, and assessing the transcriptome for gene expression changes. Strand-specific sequencing, in which the 3′ ends of transcripts are defined using a modification to the 3′ end prior to cDNA conversion, allows for a more precise interrogation of the transcriptome by distinguishing genes that are overlapping and expressed from different strands. Finally, treatment of RNA with a 5'-Phosphate-Dependent Exonuclease can be used to enrich preprocessed transcripts, which can help resolve alternative transcriptional start positions as well as overlapping and/or nested genes. Sharma nscripts . Transcret al., did not fragment or size-select the RNA molecules prior to conversion to cDNA [Sharma  to cDNA . These si.e., ability to identify truly differentially expressed genes. This is of greater concern with lowly expressed genes. The relation between sequencing depth and percentage of identified expressed genes for an RNA-Seq experiment of P. syringae is presented  and processors. RAM acts as a very fast temporary storage space for programs that track large quantities of information. RAM is therefore critical because it directly affects the amount of data that can be analyzed per unit of time before access to the hard drive is required. Processes that rely on the latter are slower by many orders of magnitude.Researchers may need access to large computing resources. In the absence of institutional infrastructures, cloud computing centers are cost-effective alternatives, e.g., iPlant Collaborative's Atmosphere . A cloud4.RNA-Seq has been used to profile gene expression changes of host-associated bacteria ,50–53. CFor microarrays, one of the first steps in data analysis is normalization to correct for differences in intensities across microarrays . RNA-SeqAnother source of variability is the different transcript lengths present within a transcriptome. Assuming comparable expression levels, genes that encode longer transcripts are expected to produce more fragments and consequently have more assigned RNA-Seq reads than those with shorter transcripts. The longer genes will therefore appear to be more abundantly expressed than comparably expressed shorter genes. Hence, one solution is to normalize per arbitrary number of bases ,53,57. Tt-test are met after intensity values are log transformed. This is not the case for RNA-Seq data because the comparison is based on groups of read counts and their probability distribution cannot be approximated by a normal distribution, even after transformation. Our studies using simulated data have shown that t-tests are greatly underpowered and will give an unacceptably high false negative rate [After normalization of the data, the task for identifying differentially expressed genes appears simple; it is merely to apply a statistical test for comparing two treatment groups of biologically replicated samples. For analysis of microarrays, this is straightforward because the assumptions of the two-sample ive rate . In otheThe Poisson probability distribution is a natural alternative to the normal for read count data. However, the inappropriateness of the Poisson distribution for RNA-Seq data has been repeatedly demonstrated ,56,59. Thttp://cufflinks.cbcb.umd.edu/, [Vibrio cholerae and identified all known key virulence factors as differentially expressed [When choosing a statistics package for data analysis, the appropriateness of the method in addressing small sample size and overdispersion should therefore be considered. Several packages are available, including the updated version of Cuffdiff from the Cufflinks suite of tools, edgeR, DESeq, NBPSeq, Myrna, and LOX  terms. Since tests for differential expression are usually more powerful for genes encoding longer transcripts, tests for sets of enriched and differentially expressed genes may be biased towards those that are on average longer in length . To addr5.etc. The currently available exact test based on the NB distribution, while more powerful than large sample tests, apply only to two-group comparisons and does not easily extend to the regression setting necessary for characterizing RNA-Seq experiments beyond the simple two-group comparison.The use of RNA-Seq to investigate transcriptomes of host-associated bacteria has yielded great insights into their complexity and will do the same to help address our knowledge gap in understanding the lifestyles of plant pathogenic bacteria. Collaborative teams with plant pathologists, computer scientists, and statisticians are essential. There is a need to develop systematic and unbiased approaches for RNA-Seq to help discover genes, refine transcriptional start sites, clarify operon structures, resolve nested genes, and identify differentially expressed genes. Also necessary are new tools for integrating and visualizing large -omic datasets to help biologists formulate hypothesis. There is an urgent demand for statistical methods applicable to more complex experimental designs for RNA-Seq that involve multiple variables such as genotypes of both host and pathogen, communities of bacteria, time after infection, E. coli, for example, total mRNA is estimated to have a half-life of only 6.8 minutes [For the plant pathologists, RNA-Seq can be used in combination with ChIP-seq (Chromatin immunoprecipitation coupled with next gen sequencing) and genetic mutants to help define regulons of transcriptional regulators . There w minutes . Thus, tAnother challenge is that, during certain life stages, the low densities of plant pathogenic bacteria may yield insufficient quantities of RNA for sequencing. Even at high densities in culture, there may be transcriptional heterogeneity within a clonal, synchronized population . A transP. syringae has seeded a change to RNA-Seq-based inquiries of plant pathogens [P. syringae is hypothesized to seed clouds, an interesting but challenging niche for an RNA-Seq experiment [athogens . This isperiment . Find a


Regulation of gene expression, protein synthesis, replication and assembly of many viruses involve RNA–protein interactions. Although some successful computational tools have been reported to recognize RNA binding sites in proteins, the problem of specificity remains poorly investigated. After the nucleotide base composition, the dinucleotide is the smallest unit of RNA sequence information and many RNA-binding proteins simply bind to regions enriched in one dinucleotide. Interaction preferences of protein subsequences and dinucleotides can be inferred from protein-RNA complex structures, enabling a training-based prediction approach.We analyzed basic statistics of amino acid-dinucleotide contacts in protein-RNA complexes and found their pairing preferences could be identified. Using a standard approach to represent protein subsequences by their evolutionary profile, we trained neural networks to predict multiclass target vectors corresponding to 16 possible contacting dinucleotide subsequences. In the cross-validation experiments, the accuracies of the optimum network, measured as areas under the curve (AUC) of the receiver operating characteristic (ROC) graphs, were in the range of 65-80%.http://tardis.nibio.go.jp/netasa/srcpred.Dinucleotide-specific contact predictions have also been extended to the prediction of interacting protein and RNA fragment pairs, which shows the applicability of this method to predict targets of RNA-binding proteins. A web server predicting the 16-dimensional contact probability matrix directly from a user-defined protein sequence was implemented and made available at:  Protein-RNA interactions are involved in various regulatory and constitutional cellular functions. Role for RNA in cellular defense and developmental regulation, which involves its interaction with proteins, has also been reported ,2. Most Several computational approaches have been developed to identify RNA-binding proteins and RNA-binding sites. Statistical potentials and docking scoring functions derived from databases of complex crystal structures effectively identified RNA-binding sites in proteins -7. PrediWe recently addressed the issue of specific recognition of protein and nucleotide subsequences for protein-DNA interactions by looking at the dinucleotide specific contact preferences of amino acids in proteins, using sequence and evolutionary contexts, and applied this information to predict specific contacts from sequence . As a naIn summary, the proposed method can be used in two ways: (1) to improve the performance of RNA-binding sites on proteins, as prediction scores will be modulated by the dinucleotide composition of target RNA sequences and one can look for prediction scores that correspond to the dinucleotide enriched in a known target and (2) predicted binding sites in proteins can be converted to a 16-dimensional dinucleotide score, which works like a pseudo dinucleotide composition itself and candidate target RNA sequences can thereby be scored by comparing observed dinucleotide compositions in candidate target sequences and thereby use them to predict targets of RNA-binding proteins.The dataset and annotations have been adopted from our previous work on predicting RNA-binding proteins from their electric moments . The datAll protein-RNA complexes in the Protein Data Bank (PDB) , which hThe contact profiles for the protein sequences were computed from protein-RNA complex structures in the compiled data set. We defined a contact when the distance between any atom in the protein and any atom of the RNA dinucleotide is less than 3.5Å. The contact class of all 16 dinucleotide sequence elements is written in alphabetically order viz. AA, AC, AG, AU, CA, CC, CG, CU, GA, GC, GG, GU, UA, UC, UG and UU.The statistical significance of contact preferences was evaluated using a Chi-squared test with one degree of freedom. This requires a background contact frequency, which is computed using a random docking procedure, as has been previously used for DNA-binding proteins ,22. In tWhere i, and j refer to 20 unique residues and dinucleotides and ASA refers to the absolute accessible surface area, computed by the NACCESS program . CoordinBoth observed and expected numbers of contacts were computed for each complex and data were pooled to get the overall chi-squared values for each dinucleotide-amino acid pair as follows:ij is computed as in (1) and Oij is the corresponding number of contacts in all complexes (note that (1) is for one complex but (2) refers to all complexes taken together). Chi-squared values from observed and expected number of contacts are converted to p-values using standard look-up tables. Chi-squared values computed above denote statistical significance of both positive and negative deviations from expected numbers of counts. To indicate the exclusion or enrichment of a residue-dinucleotide pair in the interface, signed chi-squared values were obtained by multiplying these values by -1 if the observed number of contacts was less than the expected counts.Where i and j have the same meaning as in equation (1) i.e. they take values from 1 to 20 and 1 to 16 respectively. Eblastpgp. Prior to neural network training, a vector representing global amino acid composition (GAC) of each protein sequence was concatenated to the feature vector of each residue. Sliding windows of sizes ranging from 1 to 8 sequence neighbours were centred on each residue. Different combinations of feature matrices were tested and the best performing combination was retained.Two types of encoding schemes of single protein sequences are widely prevalent and have been adopted for this work. A first scheme represented an amino acid residue and its neighbours by 21-bit-sparse-encoded binary vectors i.e. by taking all the 21 components to be zero except for the one identifying a given residue, which is set as 1. The first 20 components resemble the amino acid type and the last one is labelling terminal positions, without neighbours. A second scheme represented each residue by its evolutionary profile. As previously described by us and others ,25, the Three layered (one hidden layer) fully connected neural networks trained by back propagation were simulated using the SNNS software package . The numFrom the protein-RNA complexes in the PRNA160 dataset, pairs of protein and RNA sequence fragments of fixed lengths were generated by using a sliding window on both. Subsequently, all combinations of protein-RNA fragment pairs in a given complex were generated by matching each protein fragment to all RNA fragments. Fragment pairs were assigned a binary class according to the contacting information from each complex structure (0 or 1). A fragment pair was labelled as 1 if any of the atoms from the protein fragment was in contact with any of the atoms of the RNA fragment at a threshold of 3.5Å atom-atom distance; otherwise it was labeled as 0. A second neural network model was built to identify the positive class (label=1) from the negative class (label=0). The protein fragments were encoded in fragment feature vectors built by concatenation of the predicted 16 dinucleotide scores of each residue in a given fragment, while the RNA sequence fragments were encoded using dinucleotide compositions. The protein and RNA feature vectors for each fragment pair in a given complex were concatenated forming a fragment-pair feature matrix per complex that was used to train a new neural network. We used the same training algorithm described above but in this case the neural networks predict a one-dimensional binary vector encoding the “paired” or “non-paired” state of the protein-RNA fragment pairs.Neural networks are trained to return a vector of 16 dinucleotide-binding prediction scores for a protein residue between 0 and 1, which are transformed to binary states of binding or non-binding by choosing a cut-off . For each dinucletide type, the occurrence of a contact is considered positive (P) and negative (N) otherwise. True (T) indicates that the states of predicted and observed contact are identical and false (F) otherwise. The notation TP, TN, FP, FN combines these labels to return the number of data points (residues) in that category. These values correspond to a cut-off at which the neural network outputs are transformed into binary predictions. The predicted binding scores are transformed into binary predictions by using different cut-offs yielding sensitivity and specificity over the entire score range. ROC graphs for "1-specificity"  values versus corresponding sensitivity (true positive rate) values are plotted. This graph depicts the variations of the increment in false positive rate with an increase in the true positive rate. Throughout this work, the performance of the models was measured by the total AUC for the ROC.A simple look at the number of contacts with RNA revealed that on an average 1.7% of the residues in the RNA-binding proteins are in contact with a given dinucleotide in the complexes (data not shown). This result differs from our previous report on DNA-binding proteins, which bind to DNA with an average of 1% of the residues, reflecting a larger contact surface in protein-RNA complexes. In contrast to the protein-DNA complexes, in which residues have remarkable preferences for particular dinucleotides such as AG/CT, AC/GT and AT/AT, the contacts in protein-RNA complexes are more homogeneously distributed among all dinucleotides (detailed data not shown). However, contacts with CG (2.2%), GA (2.2%), GG (2.2%), CC (2.1%) and AG (2.0%) were slightly more abundant than dinucleotides UA (1.5%), AU (1.2%) and UU (1.0%). This result illustrates the greater complexity of RNA recognition in comparison to DNA. This is expected, because Protein-RNA interactions involve the matching of a variety of conformational arrangements rather than recognition of canonical helix configurations, as in the case of protein-DNA complexes .The binding preferences of the 20 amino acids towards the 16 dinucleotides and their comparison with DNA-binding proteins are shown in Figure Sequence and evolutionary information was used to model the dinucleotide contacting states of all protein residues in the PRNA160 data. Neural networks were trained with different feature matrices combining 21-bit sparse-encoded binary vectors, PSSM scores at differently sized neighbour windows and GAC scores of the whole protein sequence. Neural networks yielded an optimum model with 10 hidden nodes trained with a feature matrix of 320 input vectors. The combination of evolutionary information of the predicted residue and two neighbours with GAC values of the whole sequence yielded the optimum feature matrix. Figure PSSMs, as used in this work enumerate the residue substitution profiles during evolution at each alignment position for a given protein sequence. The evolutionary patterns of amino acid substitutions at given positions of RBPs represent constrains imposed by the requirement of specific interactions. In our previous study on dinucleotide-specific DNA binding-site prediction , we alsoBesides playing a broad range of roles in the cell, RNA molecules might also mediate unknown biological functions ,29. Two In order to improve the model performance for the different RNA functional classes, we implemented functional class-wise predictors of dinucleotide-specific RNA binding sites in proteins. In this case, neural networks were trained independently with complexes from a single functional class. After trying all combinations of sequence and evolutionary features for each RNA class, the prediction performance only partially improved for proteins binding to viral RNA. Interestingly in this case, the optimum neural network was trained with evolutionary information of the interacting residue and GAC of the whole protein sequence without considering any contribution from neighbouring residues. Despite the low accuracy (AUC<55%) yielded for AU dinucleotide .Click here for fileExpected and observed contacts and chi- squared statistics. Detailed values of expected and observed of contacts and chi- squared statistics (Table S2).Click here for fileROC plots of prediction performance per each dinucleotide class. ROC plots of prediction performance per each dinucleotide class .Click here for file


Chelonia mydas) and hawksbill turtles (Eretmochelys imbricata) showing responses that were remarkably similar to those of herbivorous fishes. Reducing the sediment load of the epilithic algal matrix on a coral reef resulted in a forty-fold increase in grazing by green turtles. Hawksbill turtles were also observed to browse transplanted thalli of the macroalga Sargassum swartzii in a coral reef environment. These responses not only show strong parallels to herbivorous reef fishes, but also highlight that marine turtles actively, and intentionally, remove algae from coral reefs. When considering the size and potential historical abundance of marine turtles we suggest that these potentially valuable herbivores may have been lost from many coral reefs before their true importance was understood.Herbivory is widely accepted as a vital function on coral reefs. To date, the majority of studies examining herbivory in coral reef environments have focused on the roles of fishes and/or urchins, with relatively few studies considering the potential role of macroherbivores in reef processes. Here, we introduce evidence that highlights the potential role of marine turtles as herbivores on coral reefs. While conducting experimental habitat manipulations to assess the roles of herbivorous reef fishes we observed green turtles ( SargassumHerbivory is widely recognised as a vital process for the health and resilience of coral reefs Eretmochelys imbricata, have been shown to structure sessile invertebrate communities Chelonia mydas, appear to have been the primary herbivores of seagrass beds in the Caribbean Worldwide, almost all populations of marine macroherbivores have suffered drastic declines. For example, many marine turtle populations have been estimated to be below 10% of their historical baselines Both green and hawksbill turtles have circumtropical distributions and occupy a range of habitats, including seagrass beds, coral and rocky reefs, and oceanic waters. Green turtles, while often considered to be consumers of seagrass Within coral reef systems herbivores may be broadly categorised into two functional groups  based on the algal material they remove and consequently the roles they perform in reef processes All procedures in this study were conducted according to the animal ethics guidelines of James Cook University, Townsville, , and permitting requirements of the Great Barrier Reef Marine Parks Authority (permit number: G10/33755.1).This study was conducted on the reefs surrounding Lizard Island (14°40′S 145°28′E), in the northern Great Barrier Reef GBR; . Two expThe exposed reef flat was selected as it has a high cover of EAM The lengths of algal turfs within the plots were measured before and after the 3-hour experimental period. The heights of 20 randomly selected algal filaments were measured from both the cleared and control plots using the depth probe of vernier callipers. Data were analysed using a two-way fixed factor analysis of variance (ANOVA) followed by residual analysis to ensure assumptions of the test were met.Sargassum swartzii (Ochrophyta: Phaeophyceae) was collected from an inshore reef in the Turtle Island Group (28 km west of Lizard Island). Similarly sized thalli (mean weight [± S.E.]  = 363.6±4.7 g) were transplanted to each of two haphazardly selected sites within each of the six habitats for 8-hours (approx. 07∶30–15∶30). Adjacent sites within each habitat were separated by a minimum of 50 m. Underwater video cameras (Sony DCR-SR100 HDD camera in Ikelite housing) mounted on concrete blocks were used to record feeding activity on the transplanted Sargassum for the 8- h experimental period within each habitat  was estimated. Throughout this study, results are presented as means ± standard error.The video footage from both experimental manipulations was examined and all bites by turtles on the EAM within the cleared and control plots, and the transplanted Chelonia mydas . Grazing by both herbivorous fishes and green turtles will have contributed to this reduction and it is impossible to attribute the cause of the reduction to either group, in fact, the reduction observed did not differ from that seen in plots without turtles (21.4%). Herbivorous reef fish, however, appeared to be deterred by the presence of turtles, and as such, the presence of a single turtle appeared to have comparable effects to multiple reef fishes. The large size and large number of bites made by turtles might indicate a possibly important role.The higher herbivory resulted in a 21.2% reduction in algal turf height in the three cleared plots grazed by turtles in just three hours , with one being recorded to feed on Sargassum in the back reef habitat, and the other in the sheltered reef base  has a home range of less than 1 ha Naso unicornis (maximum length 70 cm) maintain home ranges well under 10 ha Ecological resilience may derive from overlapping function within scales, and reinforcement across scales Green turtles made over forty times more bites on plots of algal turfs (EAMs) that had been subject to artificial sediment reduction than those with natural sediment loads. The observed behaviour of green turtles mirrors that seen by fishes in the only other study using sediment reductions on coral reefs The large size of green turtles compared to herbivorous reef fishes suggests they could play a greater functional role per-individual than herbivorous fishes. However, as turtles were not observed to respond differently to fishes, well-grazed reefs may not gain any potential benefits from increased turtle grazing. Larger turtle populations might not act as an alternative to current management techniques, only to supplement them.Green turtles are well known as herbivores , howeverE. imbricata, with macroalgae only representing a very minor component , and in combination with previous reports of the diets of marine turtles  and S2, While recent conservation efforts have yielded encouraging results Video S1Green turtle (Chelonia mydas) feeding on algal turfs. The darker area to the left of the frame is cleared of sediment.(MOV)Click here for additional data file.Video S2Hawksbill turtle (Eretmochelys imbricata), feeding on Sargassum swartzii assay at the back reef (see ).(MOV)Click here for additional data file.Video S3Hawksbill turtle (Eretmochelys imbricata), feeding on Sargassum swartzii assay at the sheltered reef base (see ).(MOV)Click here for additional data file.Table S1Summary of previous dietary studies of the green turtle, Chelonia mydas. Values represent percent volume of each dietary category. Where quantitative estimates were not available †† indicates the dominant component, and * indicates presence as a minor component. Literature cited referenced in (PDF)Click here for additional data file.Table S2Summary of previous dietary studies of the hawksbill turtle, Eretmochelys imbricata. Values represent percent volume of each dietary category. Where quantitative estimates were not available †† indicates the dominant component, and * indicates presence as a minor component. Literature cited referenced in (PDF)Click here for additional data file.Appendix S1References cited in tables S1 and S2.(DOC)Click here for additional data file.


Members of the evolutionary conserved Ser/Thr kinase Unc-51 family are key regulatory proteins that control neural development in both vertebrates and invertebrates. Previous studies have suggested diverse functions for the Unc-51 protein, including axonal elongation, growth cone guidance, and synaptic vesicle transport.Drosophila. We show that Unc-51 preferentially accumulates in newly elongating axons of the mushroom body, a center of olfactory learning in flies. Mutations in unc-51 cause disintegration of the core of the developing mushroom body, with mislocalization of Fasciclin II (Fas II), an IgG-family cell adhesion molecule important for axonal guidance and fasciculation. In unc-51 mutants, Fas II accumulates in the cell bodies, calyx, and the proximal peduncle. Furthermore, we show that mutations in unc-51 cause aberrant overshooting of dendrites in the mushroom body and the antennal lobe. Loss of unc-51 function leads to marked accumulation of Rab5 and Golgi components, whereas the localization of dendrite-specific proteins, such as Down syndrome cell adhesion molecule (DSCAM) and No distributive disjunction (Nod), remains unaltered. Genetic analyses of kinesin light chain (Klc) and unc-51 double heterozygotes suggest the importance of kinesin-mediated membrane transport for axonal and dendritic development. Moreover, our data demonstrate that loss of Klc activity causes similar axonal and dendritic defects in mushroom body neurons, recapitulating the salient feature of the developmental abnormalities caused by unc-51 mutations.In this work, we have investigated the functional significance of Unc-51-mediated vesicle transport in the development of complex brain structures in Drosophila brain. Unc-51-mediated membrane vesicle transport is important in targeted localization of guidance molecules and organelles that regulate elongation and compartmentalization of developing neurons.Unc-51 plays pivotal roles in the axonal and dendritic development of the  Neurons are highly polarized and compartmentalized cells with an extended axon and highly branched dendrites. Apart from their role in propagating electrical signals, axons serve as a track for long-distance transport of synaptic components that are synthesized in the soma and delivered to the nerve terminals C. elegans and mice via directly binding the synaptic GTPase activation protein (SynGAP) and Syntenin, which is a PDZ domain-containing scaffolding protein that binds Rab5 GTPase and is involved in endocytic vesicular turnover C. elegans, unc-51 mutants exhibit premature axonal termination, abnormal trajectories and extra-axon branches with abnormal vesicles and the accumulation of cisternae, suggesting underlying defects in membrane vesicle trafficking C. elegans have also shown that Unc-51 interacts with Unc-14, a RUN domain protein, which regulates kinesin 1-dependent vesicle transport by binding to Unc-16/JIP3/JSAP1, a cargo adaptor for the kinesin motor proteins Drosophila have shown that Unc-51 regulates the association of synaptic vesicles and motor proteins via interacting with Unc-76, a kinesin heavy chain (Khc) adaptor protein Members of the conserved Ser/Thr kinase Unc-51 family (Unc-51/Unc51.1/Unc51.2) are key regulatory proteins that control axonal elongation during nervous system development in Drosophila brain. In the larval mushroom bodies (MBs), which are centers of olfactory associative learning unc-51 cause disintegration of axonal bundles with aberrant accumulation and mislocalization of Fasciclin II (Fas II), an IgG type cell adhesion/guidance molecule. By genetic interaction experiments, we show that unc-51 interacts with the kinesin light chain (Klc) gene in MB development, and that Klc mutation causes dendritic and axonal defects that are reminiscent of unc-51 mutants. These results demonstrate the pivotal role of Unc-51 in axonal and dendritic development in the brain, and highlight the conserved functions of Unc-51 in the trafficking of diverse membrane vesicles and cargo molecules that are essential to the growth and guidance of developing neurons.In this work, we investigated the functional significance of Unc-51 in neural development, focusing on its regulatory functions in kinesin-dependent vesicle transport in the developing Drosophila brain, we first focused on the developing MBs at the larval stage  despite occasional accumulations in the cell bodies  transgene  but not a kinase-deficient unc-51 (unc-51K38A) transgene   method UAS-mCD8::GFP. Whereas the majority of the wild-type clones (7/8) maintained a single peduncular fascicle  exhibited overextension of calyx fibers. Among them, more than half (54%) of the MBs exhibited overextensions longer than the calyx diameter. On the other hand, the wild-type MBs rarely exhibited calyx overextensions, with only short extensions  (unc-51 was required for the suppression of dendritic overshooting (unc-51 mutant clones exhibited either long (48%) or short (39%) overextensions, while wild-type clones exhibiting short extensions were very rare (6%)  . Analyseshooting ; most ofare (6%) .unc-51WT transgene markedly suppressed the calyx overextension defects while expression of the kinase-deficient unc-51K38A transgene failed to rescue the phenotype    [33]. Us−/−unc-51 anterodorsal neuroblast clones (0/11) failed to show clear defects in axonal projections  exhibited fasciculation defects  while the axonal projection patterns were mostly unaffected, with a normal number of terminal branches in the LH  exhibited overshooting of their dendrite terminals beyond the AL compartment, while none of the wild-type clones (0/14) exhibited the corresponding phenotype  exhibited aberrant localization of Fas II in the calyx and the proximal peduncle  or Klc+/−  single heterozygotes  , which encodes a transmembrane domain, are targeted to dendrites by dynein/dynactin-mediated transport unc-51 mutant MBs, Dscam[TM1] partly accumulated in cell bodies but nonetheless was normally targeted to the dendrites, with no detectable signals in the lobes in all the samples examined (n = 12)  . These results as a whole highlight the importance of kinesin-mediated vesicle transport in the development and wiring of complex networks in the brain.Although previous studies have demonstrated that unc-51 plays an essential role in axonal transport by mediating the assembly of the cargos and the kinesin motor proteins. In unc-51 mutants, membrane vesicle transport is severely affected, resulting in accumulation of synaptic vesicles in the larval segmental nerves. Loss of unc-51 activity also causes aggregation of Rab5-positive membranes in the segmental nerves unc-51 transgene rescues the transport defect in synaptic vesicles in the mutant segmental nerves unc-51 transgene suggests that a similar phosphorylation-dependent regulatory mechanism is involved in the intracellular transport in developing MB neurons. In line with this, it is noteworthy that Unc-76 is preferentially expressed in developing MB neurons, in which it colocalizes with Unc-51 and kinesin motor proteins in the core fibers . Intriguingly, both Unc-51 and Unc-76 are downregulated in the mature neurons that surround the core fibers as they mature and shift to the peripheral layers. Concomitantly, both Khc and Klc are downregulated in the mature MB neurons, suggesting dynamic control of the expression of the molecular components that mediate active vesicle transport in developing MB neurons.Previously, Toda et al. Drosophila larval sensory neurons showed that the microtubule motor protein dynein controls dendritic branching by directing polarized intracellular vesicle trafficking via its role in controlling endosomal trafficking unc-51 leads to aberrant accumulations of Rab5-containing endosomes and Golgi components in developing larval MBs. On the other hand, polarized dendritic transport of Dscam is not altered in the unc-51 mutant MBs, implying that retrograde dynein/dynactin-mediated transport remains intact in the mutant MB neurons. Moreover, another dendritic molecule, Nod, is correctly targeted to the calyx, clearly indicating that unc-51 is not required for polarized retrograde transport mediated by dynein/dynactin in developing MB neurons. These results are consistent with our previous observations that unc-51 fails to interact with retrograde motor genes such as dynein heavy chain and Lissencephaly-1 in segmental nerves Recent genetic studies on the dendritic development of khc mutants showed dendritic branch abnormalities that were almost identical to those of dynein light intermediate chain (Dlic) mutants, with reduced arbors and a marked shift in branching activity in the proximal area within the arbors. In the khc mutants, the dynein complexes become aggregated distally, suggesting that kinesin plays a role in recycling dynein proximally after it has carried its organelle cargo distally. These phenotypes seem to contrast with those observed in unc-51 mutant MB neurons, which show dendritic overextensions with normal dynein/dynactin-mediated transport. The exact mechanisms underlying these discrepancies are unknown, but this might suggest different regulatory processes for kinesin-mediated transport that operate in the distinct cellular contexts of the peripheral sensory neurons and MB neurons. Toda et al. unc-51 mutation resulted in varying degrees of axonal membrane defects that were dependent on the cargo. It is possible that Unc-51 differentially regulates the transport of specific cargo subsets by phosphorylation of distinct groups of adaptor proteins in different cell types.On the other hand, Satoh et al. Drosophila identified unc-51 as a homolog of the yeast atg1, and suggested that Unc-51 kinase activity is required for the induction of autophagy Drosophila neuromuscular junction. Mutations in autophagy genes including atg1/unc-51, caused significant reduction in terminal branching of the segmental motoneurons, with reduced numbers of boutons. In contrast, our single-cell analysis of AL-PNs , GH146-GAL4UAS-Lamp1::GFPOK107-GAL4201Y-GAL4elav-GAL4elavc155tub-Gal80 FRT2Aelavc155UAS-mCD8::GFP hs-flpUAS-tau::GFPUAS-Nod::β-GalUAS-Syt1::eGFPUAS-n-Syb::eGFPUAS-Rab5::YFPUAS-mito::GFPUAS-hLC3::GFPUAS-Khc::eGFP (Bloomington Stock Center). The UAS-Klc::mRFP stock was newly constructed for this work.Fly strains were maintained on standard medium at 25°C. Construction of the Clones were generated using the MARCM method elavc155hs-FLP UAS-mCD8::GFP/+; Gal80 FRT2A/FRT2A; (2) −/−unc-51 MBs: elavc155hs-FLP UAS-mCD8::GFP/+; Gal80 FRT2A/unc-513FRT2A; (3) wild-type PNs: hs-FLP UAS-mCD8::GFP/+; GH146-GAL4/+; Gal80 FRT2A/FRT2A; (4) −/−unc-51 PNs: hs-FLP UAS-mCD8::GFP/+; GH146-GAL4/+; Gal80 FRT2A/unc-513FRT2A; (5) −/−Klc MBs: elavc155hs-FLP UAS-mCD8::GFP/+; Gal80 FRT2A/Klc8×94FRT2A.The following genotypes were examined: (1) wild-type MBs: β-Gal , mouse anti-nc82 ), mouse anti-Fas II , rat anti-N-Cadherin , mouse anti-Dac , rat anti-mCD8a , chicken anti-GFP , fluorescein isothiocyanate-conjugated horseradish peroxidase , Alexa 633-conjugated phalloidin . The secondary antibodies used were: Alexa 543-conjugated goat anti-rabbit, Alexa 488-conjugated goat anti-rabbit, Alexa 543-conjugated goat anti-guinea pig and Alexa 633-conjugated goat anti-mouse . Confocal images were captured using a Zeiss LSM510 confocal microscope and processed with Adobe Photoshop.Immunological staining of larval and abult brains was performed as described in Kurusu et al. Figure S1Localization of transport machinery proteins in unc-51 mutant MBs.  Localization of Khc as revealed with UAS-Khc::eGFP. Arrows indicate accumulations. Arrowhead in B2 indicates mislocalization at the proximal peduncle.  Localization of Klc as revealed with UAS-Klc::mRFP.  Localization of Tau as revealed with UAS-tau::GFP.  Localization of Futsch as revealed by anti-Futsch staining. The outline of the lobe is demarcated with yellow dashed lines in G3 and H3. Note that Futsch was detected in the cell bodies and the calyx but absent in lobes in both wild type and mutant MBs.  wild type.  unc-5125/25 mutant. Late third instar larval stage. The calyx is demarcated with a yellow dashed circle. In A–F, marker expression was driven with 201Y-GAL4. Scale bars, 10 µm. (I) Number of Khc::eGFP clogs in the lobes. Because of the background accumulation in the wild type lobes, only large clogs (>2 µm2) were counted. (J) Number of Khc::eGFP clogs in the cell bodies. (K) Number of Klc::mRFP clogs in the lobes. (L) Number of Klc::mRFP clogs in the cell bodies.(TIF)Click here for additional data file.Figure S2Loss of unc-51 causes axonal targeting defects in adult olfactory PNs. (A–F) Axonal targeting phenotypes of wild-type  and unc-513/3  clones in the adult brain.  anterodorsal neuroblast (adNb) clones.  lateral neuroblast (lNb) clones.  DL1 single cell clones. Clones were induced by an early first-instar heat shock and labeled with UAS-mCD8::GFP driven by GH146-GAL4 (green). Neuropil was visualized with anti-nc82 (magenta). Yellow dashed lines demarcate the MB calyx (CX) and the lateral horn (LH). The yellow arrow in (D) indicates defasciculation and misrouting of the unc-513/3 axons.  Quantification of the axonal phenotypes of the DL1 single-cell clones. (G) The number of branches in the mushroom body calyx. Note the slightly increased branch number in unc-513/3 mutant , compared with the wild type . (H) The number of branches in the LH. No significant change was caused in branch numbers between the wild-type  and the unc-513/3 mutant clones . *P<0.05 by the Student's t-test. Scale bars, 30 µm in (A–D); 10 µm in (E–F).(TIF)Click here for additional data file.Figure S3Loss of unc-51 causes aberrant dendritic overextension of the olfactory PNs. (A–F) Dendritic projection patterns of olfactory PNs in the adult brain.  anterodorsal neuroblast (adNb) clones.  lateral neuroblast (lNb) clones.  ventral neuroblast (vNb) clones. Wild-type  and unc-513/3  clones. Clones were induced by an early first instar heat shock and labeled with UAS-mCD8::GFP driven by GH146-GAL4. Many of the unc-51 mutant clones exhibited overextending dendrites. Arrow, long overextension; arrowhead, short overextension. (G) Quantification of the dendrite overextension phenotypes. Number of samples: wild type (n = 14), unc-51 mutant (n = 22). Scale bar, 30 µm.(TIF)Click here for additional data file.Figure S4Loss of unc-51 causes moderate dendritic targeting defects in adult PNs. (A–F) Dendritic targeting phenotypes of wild-type  and unc-513/3  clones. Clones were induced by an early first-instar heat shock and labeled with UAS-mCD8::GFP driven by GH146-GAL4 (green). Neuropil was visualized with anti-nc82 (magenta). Dendritic patterns of anterodorsal neuroblast (adNb) clones in the anterior , middle  and posterior  AL regions. VA1d, VA1lm, VA3, VM2, DM6, D, DL1 and VM7 are the landmark glomeruli normally innervated by GH146-positive ad-PNs (yellow letters). The VA2 glomerulus (white letters in A) is normally innervated by ad-PNs born in the embryonic stage, and is thus uninnervated in the wild-type clone. The DA1, DM5 and DM2 glomeruli (white letters) are normally innervated by the lateral PNs but not the ad-PNs. While dendritic targeting to the anterodorsal-type on-target glomeruli was normal, the lateral-type off-target glomeruli  were occasionally innervated by the unc-513/3 adNb clones (yellow arrows in B and F). Scale bar, 10 µm. (K–N) Quantification of dendritic phenotypes of adNb clones.  Dendritic innervation of on-target glomeruli. Both wild-type and unc-513/3 clones exhibited complete innervation of all the on-target glomeruli.  Dendritic innervation of off-target glomeruli. The extent of innervation of the non-target glomeruli was classified into one of three groups: irregular, weak and no input. Sample sizes: wild type (n = 9) and unc-513/3 mutant (n = 10).(TIF)Click here for additional data file.Figure S5Accumulation of endosomes and Golgi outposts in unc-51 mutant MBs.  Rab5::YFP.  Lamp1::GFP.  α-Man-II::eGFP. Late third instar larval stage.  wild type.  unc-5125/25 mutant. MB expression was driven by 201Y-GAL4. Arrows, aberrant accumulations of the marker proteins. Arrowhead, ectopic accumulation in the proximal peduncle. The calyx is demarcated with a yellow dashed circle. Scale bar, 10 µm. (G–I) Number of axonal clogs. (G) Rab5::YFP. (H) Lamp::GFP. (I) ManII::eGFP. The clog accumulations in the lobes were counted.(TIF)Click here for additional data file.Figure S6Distribution of mitochondria and autophagosomes in wild-type and unc-51 mutant MBs.  mito::GFP.  hLC3::GFP. Late third instar larval stage.  wild type.  unc-5125/25 mutant. MB expression was driven by 201Y-GAL4. Arrow, aberrant accumulations. Arrowhead, ectopic accumulation in the proximal peduncle. The calyx is demarcated with a yellow dashed circle. Scale bar, 10 µm.(TIF)Click here for additional data file.Figure S7Loss of Klc activity causes dendritic and axonal defects in MB neurons. (A–D) Larval MB neuroblast clones generated by the MARCM technique.  Wild type.  Klc8e×94/8e×94 mutant.  Peduncle sections. Note severe dendritic overextensions (arrows) and peduncle defasciculation (arrowhead) in the mutant. Clones were induced by a heat shock at the early first-instar stage and labeled with mCD8::GFP (green) driven by elavc155-GAL4. Mature axons were stained with anti-Fas II (magenta). (E–G) Quantification of the Klc mutant phenotype. (E) Quantification of the calyx (Cx) dendrite overextension phenotype. Sample sizes: wild type (n = 17), Klc8e×94/8e×94 wild type (n = 7). (F) Quantification of peduncle defasciculation. Sample sizes: wild type (n = 8), Klc8e×94/8e×94 mutant (n = 7). (G) Quantification of Fas II mislocalization in the proximal peduncle. Sample sizes: wild type (n = 11), Klc8e×94/8e×94 mutant (n = 7), and unc-51−/− wild type (n = 10). Scale bars, 10 µm.(TIF)Click here for additional data file.Figure S8unc-51 does not interact with RPTPs in axonal fasciculation. Larval MB lobes and peduncle sections (inset) from the late third instar stage. MBs were labeled with anti-Fas II (green) and anti N-Cadherin (magenta) , or phalloidin (magenta) . None of the double heterozygotes with the tested RPTP genes  showed abnormality in MBs, retaining wild-type like peduncle and lobes with a single core. Scale bars, 10 µm.(TIF)Click here for additional data file.Figure S9Localization of Dscam and Nod in unc-51 mutant MBs. (A–F) Localization of Dscam as revealed with UAS-Dscam[TM1]::GFP. (G–L) Localization of Nod as revealed with UAS-Nod::β-Gal. Late third instar stage. MB neurons were labeled with UAS-mCD8::DsRed (A–F) or UAS-mCD8::GFP (G–L) in conjunction with 201Y-GAL4. The arrowheads in (K) indicate Nod::β-Gal localization in calyx overextensions. Scale bar, 10 µm.  Quantification of the number of the Dscam::GFP clogs.  Quantification of the number of the Nod::β-Gal clogs. Clog accumulations in the lobes  or the cell bodies  were counted.(TIF)Click here for additional data file.


Gene expression studies in equine research involve the use of whole blood samples as a vital source of RNA. To determine the optimal method for RNA isolation from equine whole blood, we compared three RNA isolation strategies using different commercially available kits to evaluate the yield and quality of equine RNA. All 3 methods produced RNA with high quality. Though it did not produce the highest yield, combining the quality, yield and the need for the downstream application in our project, LeukoLOCK™ total RNA isolation system was the best RNA extraction method.The online version of this article (doi:10.1186/2193-1801-2-478) contains supplementary material, which is available to authorized users. RNA is a particularly labile macromolecule prone to degradation, and RNA degradation can result in falsely altered gene expression patterns , which comprise only a small fraction of whole blood cellular fraction.Currently, there are commercially available kits that are used to isolate and purify RNA. However, there are few studies available to date that have compared RNA extraction methods from samples of animal origin. Extraction methods from swine tissue  following the manufacturer’s instructions  following the manufacturer’s instructions  was added. The tube was vortexed for 15 sec, and incubated at room temperature for 15 min prior to centrifugation at 2000 × g for 10 min (4°C). Supernatant was discarded, the isolation of RNA from WBC pellet was performed using TRIzol® reagent (Invitrogen) following the manufacturer’s instructions  was mixed with 1 μL 6x gel-loading buffer and electrophoresed on 1.2% agarose gel in 1x MOPS running buffer (pH 7.0). Electrophoresis was performed at 100 V for 1.0 hour using Bio-Rad Power Pac 200 power source (Bio-Rad). Gel was stained with ethidium bromide. Images of the developed gels were acquired by Foto/Analyst luminary 12-bit cooled Camera Electronic Documentation System (Fotodyne Incorporated).. Each reaction mixture was incubated at 37°C for 2 hr, then at 85°C for 5 seconds. The PCR step was carried out in 25 μL, including 25 μL 2x TaqMan® Universal PCR Master Mix, 50 nM forward primer, reverse primer and TaqMan® probe, and 5 μL cDNA sample. The reaction mixtures were heated at 95°C for 10 min and then subjected to 40 cycles at 95°C for 15 sec, and 60°C for 1 min. The reaction mixture was loaded onto 7500 Real-Time PCR System (ABSciex Inc.). Method of relative quantitation of gene expression was adopted, and RNA isolate extracted with LeukoLOCK™ total RNA isolation system was used as reference.Aliquots of total RNA were amplified using specific primers corresponding to regions of equine IL-6 and internal control GAPDH. The sequence of the forward primer for IL-6 was: AACAACTCACCTCATCCTCGTAA, whereas that of the reverse primer for IL-6 was CGAACAGCTCTCAGGCTGAAC. The sequence of the forward primer for GAPDH was CAAGGCTGTGGGCAAGGT, whereas that of the reverse primer for GAPDH was GGAAGGCCATGCCAGTGA. The reverse transcription was carried out in 20 μL reaction mixtures, including 10 μL RNA, 2.0 μL reverse transcription buffer, 0.8 μL 25x dNTPs, 2.0 μL 10x random primers, and 50 U MultiScribe Reverse Transcriptase260nm/A280nm of RNA are shown in Table ™ total RNA isolating system and Tempus™ blood RNA tube produced lower but equivalent, 0.99 and 1.09 ug per ml of whole blood, respectively. The ratio of A260nm/A280nm for all 3 methods was > 1.8, indicating that there was no contamination of RNA due to protein and other organic molecules.Mean yield and ratio of absorbance (A) Electropherograms of RNA isolates obtained from the 3 different methods are shown in Figure Contamination by DNA was not detected during evaluation but, considering the sensitivity and limitation of these methods, real-time PCR was performed in order to determine if trace DNA contamination was present or not. For this purpose, Interleukin (IL)-6 was used as the target gene, and housekeeping gene, GAPDH, was used as endogenous control. The pair of primers for IL-6 was designed based on Exon 5. Expression of IL-6 in the RNA extract using TRIzol® reagent was nearly 3-times higher than the RNA isolates obtained from the other 2 methods Figure .Figure 3™ blood RNA tube with RNA preparation station, whole blood was directly lysed in Tempus™ blood RNA tube containing lysis buffer, and RNA was extracted from lysed whole blood solution. For the other two methods, WBC were separated from plasma and red RBC before extracting RNA.Various methods and kits are available for the extraction of RNA from cells, blood, tissue, and plasma. In the present study, 3 different commercially available kits for the isolation of RNA from equine whole blood based on different strategies were compared. For TempusHigh quality RNA is critical for downstream application, and the quality depends upon the technique that was employed for RNA isolation Authors’ original file for figure 1Authors’ original file for figure 2Authors’ original file for figure 3Authors’ original file for figure 4Below are the links to the authors’ original submitted files for images.


Hepatitis C is one of the most dangerous and widespread viral diseases. Currently, the World Health Organization estimates that about 170 million people are infected with the hepatitis C virus (HCV), the causative agent of infection, in almost every country in the world. The RNA-dependent RNA-polymerase  is a key fragment that carries out HCV genome replication. R-RNAP is about 65 kDA in molecular weight and localized on the endoplasmic reticulum membrane of infected hepatic cells by the C-tail α-spiral transmembrane domain 21 a.r.). One characteristic feature of R-RNAP is its ability to catalyze RNA synthesis by both primer-dependent and primer-independent (de novo) mechanisms -ATP according to the manufacturer's instructions. Phosphorylated RNA was purified on Micro-Spin G-50 microcolumns . 2, and 10 mM β-mercaptoethanol. The enzyme was diluted twice with an interval from the highest concentration value at 500 mM NaCl. The RNA assembly was carried out at 4°C for 30 minutes in the membrane moistening buffer; then it was applied to the membranes. The RNA concentration was determined experimentally and reduced to 2 nM. Dried membranes were subject to exposure for 40-60 minutes and visualized on a Storage Phosphor Screen and Phosphor Imager . Mathematical treatment of the results obtained was carried out with the Total Lab and Origin 7.0 programs. The kinetic parameters of enzyme and RNA interaction were determined with the help of dot-blot hybridization. A 48-well dotter with nylon and nitrocellulose membranes were used . A nitrocellulose membrane was put on the nylon membrane in the dotter. The membranes were preliminarily moistened in the buffer containing 50 mM HEPES-KOH pH 7.5, 5 mM MgClThe necessity of modifying the R-RNAP purification method was related to the possible presence of short impurity RNA, which could be the bacterial ribosome or transfer RNA, in the purified recombinant enzyme sample. Those impurity RNAs could competitively block the binding sites of nucleic acids in the enzyme molecule and thus impede the in vitro interaction with the RNA-matrices added in the course of the reaction. As is known, the ultrasonic treatment of bacterial cells causes the mechanical destruction of the cell's nucleic acids, which are fractionized into fine fragments of different lengths. In the case under consideration, some of them were likely bound to the recombinant R-RNAP. Moreover, this process was characterized by a high affinity that inhibited their separation at the following purification stages. Indeed, at least three discreet low-molecular RNAs 12-60 nt in length were established in the course of treating the [γ32P]-ATP enzyme samples in the presence of polynucleotide kinase, which was responsible for the specific phosphorylation of the free polynucleotide 5'-tail Finally, the previously offered method of R-RNAP recovery was modified in such a way that the active enzyme sample did not contain impurity RNA. The modification consisted in the replacement of the bacterial cell destruction method, while all the following purification stages remained unchanged. The considered method excluded cell ultrasonic treatment, which caused the formation of short RNA, while the high NaCl concentration resulted in the dissociation of the protein-nucleic complexes and inhibited the coprecipitation of the intentional protein with nucleic acids. This method has been successfully used before to purify recombinant T7 RNA-polymerase [Because the problem of enzyme oligomerization upon RNA binding remained unsolved, we used, along with the wild-type enzyme, the created double mutant (DM) of R-RNAP H502L, E18A), which, according to the literature data, was capable of oligomerization [L, E18A, However, it should be noted that the use of the new method led to a decrease in the intentional protein yield. If the R-RNAP yield attained 3-5 mg/l of the cell culture when using the standard recovery method, then the recovery by a new method reduced the R-RNAP yield to 1 mg/l. Moreover, as it turned out, the R-RNAP samples depleted in impurity polynucleotides are less stable. In contrast to the standard samples, which are stable at a storage temperature of -20°C, new samples remained active for no longer than one month at -85°C. This phenomenon is explained by the possible participation of impurity polynucleotides in the stabilization of enzyme and protection from inactivation. The R-RNAP samples devoid of impurity polynucleotides had higher in vitro affinity to different RNAs. Three types of RNAs were used in the experiments: short (rA20) and long (poly-rA) homopolymers, as well as the long heteropolymer HCV RNA (-IRES). The parameters of R-RNAP and RNA interaction were calculated in accordance with the Hill equation , because


The T6SSs encoded in SPI-6 and SPI-19 contribute to pathogenesis of serotypes Typhimurium and Gallinarum in mice and chickens, respectively. Salmonella Dublin is a pathogen restricted to cattle where it causes a systemic disease. Also, it can colonize other hosts such as chickens and mice, which can act as reservoirs of this serotype. Salmonella Dublin harbors the genes for both T6SSSPI-6 and T6SSSPI-19. This study has determined the contribution of T6SSSPI-6 and T6SSSPI-19 to host-colonization by Salmonella Dublin using avian and murine models of infection. Competitive index experiments showed that, a mutant strain lacking both T6SSs (∆T6SSSPI-6/∆T6SSSPI-19) presents a strong colonization defect in cecum of chickens, similar to the defect observed for the ∆T6SSSPI-6 mutant, suggesting that this serotype requires a functional T6SSSPI-6 for efficient colonization of the avian gastrointestinal tract. Colonization of mice was also defective, although to a lesser extent than in chickens. In contrast, the T6SSSPI-19 was not necessary for colonization of either chickens or mice. Transfer of T6SSSPI-6, but not T6SSSPI-19, restored the ability of the double mutant to colonize both animal hosts. Our data indicate that Salmonella Dublin requires only the T6SSSPI-6 for efficient colonization of mice and chickens, and that the T6SSSPI-6 and T6SSSPI-19 are not functionally redundant.The type VI secretion system (T6SS) is a virulence factor for many Gram-negative bacteria. S Salmonella contains over 2500 serotypes distributed in two species, S. enterica and S. bongori[Salmonella can produce a wide spectrum of clinical signs ranging from self-limiting diarrhea to systemic illness. Some serotypes are able to infect a wide range of hosts, while others can infect only one animal species [Salmonella enterica serotype Dublin  infection is restricted to cattle, where it causes a serious systemic disease characterized by pneumonia, arthritis, osteomielytis, meningoencephalitis, enteritis and, in some cases, abortion, leading to economic losses in animal industry [Salmonella Dublin can also infect humans via consumption of raw milk from infected cattle, constituting an important threat to public health [Salmonella Dublin from chickens and wild mice, suggesting that these animal hosts can act as environmental reservoirs of Salmonella Dublin. In one report, Salmonella Dublin was isolated from feces of wild mice in a farm with high prevalence of this pathogen in cattle, suggesting that rodents may act as reservoirs and eventually contribute to Salmonella Dublin transmission to susceptible cows [Salmonella Dublin is able to colonize chickens without clinical symptoms, contributing to transmission of salmonellosis to cattle and humans [The genus . bongori. Dependiindustry -7. Salmoc health -10. In able cows . Chickend humans .almonella contains five phylogenetically distinct T6SS loci encoded in differentially distributed Salmonella Pathogenicity Islands (SPIs)  [Salmonella Dublin includes two T6SSs encoded in SPI-6 and SPI-19, respectively.The type VI secretion system (T6SS) represents a new paradigm of protein secretion and is a key virulence factor for many important pathogenic bacteria contributing to different processes ranging from inter-bacterial killing to pathogenesis -17. The  SPI-22) ,19. SomeSalmonella virulence and colonization. The T6SS encoded in SPI-6 (T6SSSPI-6) is required by Salmonella Typhimurium for intracellular survival in avian and murine macrophages and for gastrointestinal colonization and systemic spread in orally-infected White Leghorn chicks and BALB/c mice [SPI-6 in Salmonella Typhi produced an attenuated phenotype in a novel humanized mice model of infection [SPI-19) contributes to the intracellular survival of Salmonella Gallinarum in avian macrophages [Salmonella Dublin constitutes a suitable model to study the contribution of two differentially-encoded T6SSs to host-adaptation and pathogenesis of Salmonella.Recent reports have linked these T6SSs to B/c mice -22. In anfection . On the rophages , and to rophages . These frophages ,25. In tSPI-6 and T6SSSPI-19 to the colonization of the gastrointestinal tract and deeper tissues by Salmonella Dublin using avian and murine models of infection. A strain of Salmonella Dublin lacking both T6SSSPI-6 and T6SSSPI-19 displayed a strong colonization defect of the cecum, liver and spleen in competitive infections in both animal models. Furthermore, we observed a similar phenotype in a strain that lacks only the T6SSSPI-6. Interestingly, this colonization defect could be reversed by transfer of a complete T6SSSPI-6, but not by transfer of the T6SSSPI-19. These results suggest that T6SSSPI-6 and T6SSSPI-19 are not functionally redundant in Salmonella Dublin, and that only T6SSSPI-6 is required for host colonization by this serotype.We evaluated the contribution of T6SSThe bacterial strains used in this study are listed in Table DNA manipulations were performed using standard protocols. Plasmid DNA was isolated using the “QIAprep Spin Miniprep Kit” . Genomic DNA was isolated using the “GenElute Bacterial Genomic DNA” kit . PCR products were purified using the “QIAquick PCR Purification Kit” . Ligations were performed using T4 DNA ligase  as recommended by the manufacturer. DNA samples were analyzed by electrophoresis in 1% agarose gels and were visualized under UV light after RedGel  staining.2, 100 nM dNTPs, 100 nM of each primer, 100 ng of template DNA and 0.5 to 1 U of HiFi DNA pol . Standard conditions for amplification were: 2 min at 95 °C, followed by 30–35 cycles of 94 °C for 45 s, 55 °C for 30 s and 72 °C for a suitable time (1 min/kb) according to DNA polymerase processivity, and a final extension step at 72 °C for 5 min.Primers were designed using the “Vector NTI Advance 10.0” software  and are listed in Table Salmonella Dublin with deletions in the T6SS clusters encoded in SPI-6 (SeD_A0289 to SeD_A0326) and SPI-19 (SeD_A1212 to SeD_A1243) or in the phoN gene (SeD_A4714) were constructed using the Lambda Red recombination method with modifications [Salmonella Dublin strain CT_02021853 containing the plasmid pKD46, which encodes the Lambda Red recombination system, was grown to an OD600 of 0.6 at 30 °C in LB broth supplemented with Amp and L-arabinose (10 mM). Then, bacteria were made electrocompetent and transformed by electroporation with 300 to 600 ng of each PCR product. Transformants were selected on LB agar plates supplemented with the corresponding antibiotic at 37 °C. The presence of each mutation was confirmed by PCR amplification and transferred to the wild-type background by generalized transduction using the high-frequency transducing phage P22 HT105/1 int-201.Mutant strains of ications . The oliSPI-6 gene cluster from Salmonella Dublin CT_02021853 onto plasmid R995 was done by the VEX-Capture system for the targeted excision and cloning of large DNA fragments [loxP sites were introduced at each side of the targeted genomic region by homologous recombination of PCR products by the Lambda-Red system, using as templates the plasmids pVEX1212 and pVEX2212 that encode Sp and Cam resistance cassettes, respectively. The correct insertion of loxP sites was confirmed by PCR using primers SPI-6_OUT5 and STM0266_VEX_H2_U2 for loxP insertion located in the upstream region of the T6SS cluster, and primers SPI-6_OUT_DOWN and STM0298_VEX_H2_D2 for the downstream loxP insertion. T6SSSPI-6 cluster was excised from the chromosome as a non-replicating circular DNA molecule by specific recombination of loxP sites mediated by the action of Cre recombinase encoded in plasmid pEKA30. A 1,209 bp internal region of SPI-6 was amplified using primers SD_VC_OUT5 and SD_VC_OUT3, both of which include an XbaI restriction site at the 5′ end. The PCR product was cloned into the unique XbaI site in R995 to generate R995-VC6  indicating that R995 and its derivatives are highly stable in vivo.All animal experiments conducted in this study were approved by the Texas A&M University Institutional Animal Care and Use Committee (TAMU AUP# 2010–38) and were carried out in accordance with the Guide to the Care and Use of Laboratory Animals, the Public Health Service Policy on the Human Care and Use of Laboratory Animals.9 CFU of an equal mixture of the strains to be tested in a volume of 100 μL of sterile PBS. The exact titer and ratio of strains in the inoculum were determined by serial dilution and plating on LB agar supplemented with the corresponding antibiotics. Five birds from the infected group were sacrificed by asphyxiation with CO2 on days 1, 3 and 9 post-infection. Cecum (with contents), liver and spleen were collected and homogenized in sterile PBS. Then, serial ten-fold dilutions were spread on LB agar plates containing the appropriate antibiotics for determination of CFU.For competitive infections in the avian model, fifteen 4-day-old unsexed White Leghorn chicks were orally inoculated with 106 CFU of an equal mixture of the strains to be tested in a volume of 100 μL sterile PBS. The exact titer and ratio of strains in the inoculum were determined as described above. Four days post-infection, mice were sacrificed and cecum, liver and spleen were collected and homogenized. The number of Salmonellae present in each organ were enumerated as described above. In both animal models, the Salmonella Dublin ∆phoN mutant was used as wild-type strain. Inactivation of phoN, encoding alkaline phosphatase, abolishes the ability to cleave 5-bromo-4-chloro-3-indolyl phosphate (XP), but does not reduce the ability of Salmonella to colonize chicken and mice [For competitive infections in the murine model, groups of five six- to eight-week-old female BALB/c mice were inoculated with 10and mice ,29. Growt-test) was used to determine whether differences between treatment groups were statistically significant (P < 0.05).CFU obtained from competitive experiments were used for data analysis as a mean ratio of logarithmically converted CFU of mutant to wild type, normalized to the input ratio. Error bars indicate standard error. A parametric test , that have been individually linked to virulence in other Salmonella serotypes [Salmonella Dublin, we first performed competitive infections between a mutant carrying deletions of both T6SSSPI-6 and T6SSSPI-19 gene clusters (∆T6SSSPI-6/∆T6SSSPI-19) and the wild-type strain of Salmonella Dublin.erotypes ,22-25. TSPI-6/∆T6SSSPI-19 double mutant showed a statistically significant colonization defect in each organ tested. To determine the individual contribution of each T6SS to this phenotype, competitive infections were performed between the wild-type strain and the corresponding single ∆T6SSSPI-6 and ∆T6SSSPI-19 mutant. As observed in Figure SPI-6 mutant strain was attenuated, displaying a colonization defect very similar to the ∆T6SSSPI-6/∆T6SSSPI-19 double mutant. In contrast, the ∆T6SSSPI-19 mutant reached the same levels of colonization as the wild type strain in all organs analyzed, suggesting that only T6SSSPI-6 is involved in mice colonization.As shown in Figure SPI-6 is responsible for the colonization defect observed during mice infection, the double mutant was complemented in trans with either T6SSSPI-6 (R995 + SPI-6) or T6SSSPI-19 (R995 + SPI-19) and competition experiments were performed. As shown in Figure SPI-6 restored the ability of the ∆T6SSSPI-6/∆T6SSSPI-19 double mutant to colonize the cecum. However, complementation was not achieved in the liver and spleen  mutant and the wild-type strain. As shown in Figure SPI-6/∆T6SSSPI-19 double mutant was defective for both intestinal and systemic colonization early after infection (days 1 and 3 post-infection). The colonization defect was most severe by day 9 post-infection in all organs, especially in the cecum. In contrast to the results obtained in the murine model, this mutant showed a very strong colonization defect in cecum, while only a mild phenotype was observed in the liver and spleen, indicating a critical role for the T6SSs to gastrointestinal colonization of the chicken by Salmonella Dublin.To determine the contribution of T6SSSPI-6 mutant strain was defective for chicken colonization to the same extent as the ∆T6SSSPI-6/∆T6SSSPI-19 double mutant strain, suggesting that the T6SS encoded in SPI-6 is crucial for an efficient colonization of the avian host  or T6SSSPI-19 (R995 + SPI-19) and competition experiments were performed at day 9 post-infection. As shown in Figure SPI-6, but not T6SSSPI-19, complemented the colonization defect of the ∆T6SSSPI-6/∆T6SSSPI-19 double mutant in each organ tested, indicating that T6SSSPI-6 was responsible for the colonization defect of the double mutant strain. Altogether our data shows that only the T6SSSPI-6 contributes to colonization of the murine and avian host.To verify that T6SSSPI-6 R99 + SPI-6 Burkholderia thailandesis, T6SS-5 is important for virulence while T6SS-1 participates in killing of susceptible target bacteria [Pseudomonas aeruginosa, which encodes three T6SS loci (HSI-I to HSI-III).HSI-I mediates inter-bacterial relationships [The presence of multiple T6SSs has been reported in several bacterial species suggesting functional adaptation of each T6SS to a specific niche and/or host. For example, of the five T6SS (T6SS-1 to T6SS-5) of bacteria . This isionships , while Hionships .Salmonella Dublin encodes two T6SSs (T6SSSPI-6 and T6SSSPI-19) that have been individually linked to virulence and pathogenesis in other Salmonella serotypes. Notably, while most Salmonella serotypes seem to have lost the T6SSSPI-6 locus after acquisition of SPI-19, Salmonella Dublin has retained both T6SSs. Whether the presence of these two T6SS corresponds to differential functional specialization of each system or whether they are redundant in their function, is currently unknown.Salmonella Dublin, and to gain insights into the potential functional adaptation of T6SSSPI-6 and T6SSSPI-19 to either animal host. We chose the murine and avian models of infection because it has been reported that Salmonella Dublin asymptomatically colonizes mice and chickens, suggesting that these animals can act as reservoirs and vectors for Salmonella Dublin infection in cattle and humans [SPI-6 and T6SSSPI-19 of other serotypes to the ability of Salmonella to colonize the murine and avian host.In this study we performed competitive infection experiments to determine the contribution of both T6SS to colonization of chickens and mice by d humans ,12 and bSPI-6 and T6SSSPI-19 was not able to colonize the cecum, liver and spleen as efficiently as the wild-type strain in both animal models. Interestingly, even though the double mutant was attenuated in chickens and mice, the degree of attenuation was different depending on the infected animal host. Thus, while the double mutant strain was only slightly attenuated in all organs in mice (log competitive index of -0.5), it was severely attenuated in the chicken, displaying a log competitive index of -3.72 in the cecum and of ~ -1.5 in the liver and spleen. Interestingly, a similar phenotype was observed for a single T6SSSPI-6 mutant strain in both models of infection.Competitive index data from oral infection of White Leghorn chicks and BALB/c mice showed that a mutant strain lacking both T6SSSalmonella Typhimurium have shown that T6SSSPI-6 is required for efficient colonization of the cecum of infected chicks [SPI-6 was responsible for these phenotypes, as transfer of the captured T6SSSPI-6 gene cluster, but not of the T6SSSPI-19 gene cluster, restored the ability of the double mutant to colonize the gastrointestinal tract and internal organs of infected chickens and mice.The strong colonization defect observed in the cecum of infected chicks was not unexpected, as previous studies performed in d chicks . In agreSalmonella Dublin requires a functional T6SSSPI-6 for efficient colonization and persistence in the avian gastrointestinal tract and that T6SSSPI-19 is not involved in this process. These differences support the notion that T6SSSPI-6 and T6SSSPI-19 are not functionally redundant. The fact that T6SSSPI-19 is important for colonization of the avian host by Salmonella Gallinarum [Salmonella Dublin suggests that the contribution and impact of the T6SSs to Salmonella pathogenesis depend on the serotype and the infected host.Our data strongly suggests that llinarum , but notSPI-6 among Salmonella enterica serotypes [Salmonella enterica. This would not be the case for the T6SSSPI-19 which has a much limited distribution and, as shown by this study, is not important for host-colonization in all serotypes.The wide distribution of the T6SSerotypes  and the erotypes -23 suggeSPI-6 to Salmonella Dublin virulence remain obscure. Previous reports have shown that, in Salmonella Typhimurium and Salmonella Typhi, the T6SSSPI-6 contributes to Salmonella survival within murine and avian macrophages [SPI-6 of Salmonella Dublin to these processes .The mechanisms behind the contribution of the T6SSrophages ,22, neveSalmonella Dublin has retained both T6SSs is still unanswered. Nevertheless, we cannot rule out that T6SSSPI-19 may contribute to Salmonella fitness in other natural settings, such as the environment, or to colonization of other animals such as cattle, which is the natural host for Salmonella Dublin. It is possible that T6SSSPI-19 might play a role in the course of systemic diseases such as typhoid caused by Salmonella Dublin and Salmonella Gallinarum in cattle and chickens, respectively. Further studies will have to be conducted to evaluate the contribution of both T6SSSPI-6 and T6SSSPI-19 to the ability of Salmonella Dublin to colonize cattle.The question of why SPI-6 contributes to chicken and mice colonization by Salmonella Dublin and that T6SSSPI-19 is not involved in these processes. The ancestral acquisition of the T6SSSPI-6 locus, its wide distribution among S. enterica serotypes and its contribution to virulence in Salmonella Dublin, Salmonella Typhimurium and Salmonella Typhi suggest that this T6SS belongs to the common tool-box used by S. enterica to infect and colonize a wide variety of animal hosts.Altogether, our data shows that T6SSThe authors declare that they have no competing interests.Conceived and designed the experiments: DP, CJB, HAP, CAS, IC. Performed the experiments: DP, HJY, CJB. Analyzed the data: DP, CJB, CAS, HAP, IC. Contributed reagents/materials/analysis tools: HAP, CAS, IC. Wrote the paper: DP, CJB, HAP, CAS, IC. All authors read and approved the final manuscript.


Moniliophthora perniciosa (Stahel) Aime & Phillips-Mora is the causal agent of witches’ broom disease (WBD) in cacao (Theobroma cacao). When the mitochondrial genome of this fungus had been completely sequenced, an integrated linear-type plasmid that encodes viral-like RNA polymerases was found. The structure of this polymerase was previously constructed using a homology modeling approach.M. perniciosa RNA polymerase (RPO). AutoDock Vina was used to perform docking calculations for each molecule. This software returned affinity energy values for several ligand conformations. Subsequently, we used PyMOL 1.4 and Ligand Scout 3.1 to check the stereochemistry of chiral carbons, substructure, superstructure, number of rotatable bonds, number of rings, number of donor groups, and hydrogen bond receptors.Using a virtual screening process, accessing the Kegg, PubChem and ZINC databases, we selected the eight most probable macrocyclic polymerase inhibitors to test against 2 Kcal/Mol.On the basis of this evidence we selected Rifampicin, a bacterial RNA polymerase inhibitor, and then AMBER 12 was used to simulate the behavior of the RPO-Rifampicin complex after a set of 5000 ps and up to 300 K in water. This calculation returned a graph of potential energy against simulation time and showed that the ligand remained inside the active site after the simulation was complete, with an average energy of -15 x 10in vitro and in vivo against M. perniciosa.The results indicate that Rifampicin could be a good inhibitor for testing  Moniliophthora perniciosa (Stahel) Aime & Phillips-Mora is the causal agent of witches’ broom disease (WBD) in cacao (Theobroma cacao). When the mitochondrial genome (http://www.ncbi.nlm.nih.gov/genomes/GenomesGroup.cgi?taxid=4751&opt=organelle/) of this fungus had been completely sequenced, an integrated linear-type plasmid that encodes viral-like DNA and RNA polymerases was found [M. perniciosa mitochondrial plasmid (RPO) is a 766 amino acid DNA-dependent RNA polymerase belonging to the single chain family of polymerases, which occur in viruses and cellular organelles [RNA polymerase (RNAP) is responsible for transcribing DNA and is the direct or indirect target of most regulators of transcription . The enzganelles . Its actganelles -5. The mganelles ,6, so itMycobacterium tuberculosis but is considered a broad-spectrum antibiotic [Rifampicin is a macrocyclic molecule the ansamycin family. It contains a methyl-piperazinyliminomethyl side chain at position 3, a cyclopentyl-piperazinyliminomethyl side chain at position 3, and a cyclic spiro-piperidyl side chain at positions 3 and 4 . This drtibiotic ,9. It hatibiotic . The essVirtual structure-based screening has become prominent in drug discovery, using protein targets ,12. SeveOne of the most important techniques for receptor-based drug design is molecular docking . Using cMolecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) combines molecular mechanics energy and implicit solvation models and is more rigorous than most empirical or knowledge-based scoring functions ,12. It aM. perniciosa, available at the Kegg, PubChem and Zinc databases, and to select a potential inhibitor using a coupled Molecular Docking and Molecular Dynamics - MM / PBSA - approach.The aim of this study was to search a series of molecules likely to form complexes with the RPO from http://www.genome.jp/kegg/) and PubChem (http://pubchem.ncbi.nlm.nih.gov/) databases. Only molecules described as inhibitors of RNA polymerases were selected from the outputs. All 2D structures were copied in Similes format for comparison with other Zinc database (http://zinc.docking.org/) molecules; this increases the number of molecules that can be used for docking, starting from a known ligand. The 3D structures of these molecules were downloaded in mol2 and pdb formats for use in Virtual Screening, which was carried out by Molecular Docking and Molecular Dynamics. Following a protocol described by Irwin and Schoichet (2005) [We searched initially by keywords and by nucleoside molecules in the Kegg (t (2005) , moleculThe structures downloaded from the Kegg, Pubchem and Zinc databases were first checked in PyMOL 1.4  to evaluThe ligand and receptor (RPO) molecules were prepared in AutoDockTools 1.5.6 . All polIn this study we used the MM/PBSA protocol to calculate affinity and stability in the ligand-receptor RPO complex interaction, using the package Amber 12 . InitialtLEaP was used to neutralize charges (ff99 force field) and the RPO–ligand complex was immersed in a rectangular box of TIP3P water molecules. Following the protocol we used Sander to carry out a Molecular Dynamics (MD) equilibrium, restricted to a region of the protein that contains the active site (amino acids 457-695), according to the following parameters: 1000 cycles of steepest descent and 1000 cycles of conjugate gradient minimization, heating MD for 200 picoseconds (ps), density equilibrium for 200 ps, followed by Equilibrium Dynamics for 600 ps at constant pressure and 300 K. After the system equilibrated we followed this with the MM/PBSA protocol ,19,20. WEscherichia coli Rifampicin binds in a pocket of the RNAP β subunit deep within the DNA/RNA channel and blocks the RNA exit pathway [Thermus aquaticus RNA polymerase complexed with Rifampicin, and the results of their biochemical experiments indicated that its predominant effect is to block the path of the elongating RNA transcript directly at the 5’ end when the transcript reaches either two or three nucleotides in length [After searching the KEGG, Pubchem and Zinc databases, we selected eight structures Figure  that cou pathway . In anot pathway  describen length .vitro and in vivo, inhibiting mitochondrial transcription by RPO and thus blocking the mitochondrial metabolism of M. perniciosa.The use of RNA polymerases as molecular targets for virtual screening is not restricted to prokaryotes. An RNA-dependent RNA polymerase (RdRp) is an attractive target for anti-HCV agents . However2 Kcal/Mol. We also considered the RMSD generated during all processes (Figure Using a Molecular Dynamics approach we analyzed the performance and stability of the RPO-Rifampicin complex. We then evaluated the potential energy of the complex during the simulation process and its final energy. The graph in Figure s Figure , and we M. perniciosa RPO activity.The pdb of the complex, generated after molecular dynamics, showed that Rifampicin remains within the active site of RPO after 5000 ps of simulation. Furthermore, we can infer that this simulation time was sufficient to show that Rifampicin could block M. perniciosa mitochondrial plasmid RNA polymerase from among eight molecules found in public databases, using a virtual screening approach. Rifampicin is a bacterial RNAPs inhibitor; nevertheless it forms a very stable complex with RPO, perhaps because this type of enzyme is highly conserved among organisms. Rifampicin forms complexes with exactly those amino acids in the active site pocket that are involved in transcription by RPO. This integration remained stable throughout the 5000 ps of Molecular Dynamics.In this article we describe the selection of one potential inhibitor of the In a further step, we could analyze different mechanisms of Biomolecular Simulation to describe the mechanism by which Rifampicin inhibits the RPO, and whether Rifampicin will acquire different conformations within this process that can effectively interact within the active site of this enzyme.in vitro and in vivo to verify whether our selected inhibitor can act effectively against M. perniciosa by making RPO-dependent transcription unfeasible.We cannot discard use of the other ligands described in Table The authors declare that they have no competing interests.BA carried out the ligand searching and Docking studies, performed the Molecular Dynamics of complexes, and drafted the manuscript. CS participated in the implementation of Molecular Dynamics and participated in its design and coordination. AGN participated in the design and coordination of this work. All authors read and approved the final manuscript.