Characterisation of microRNA promoters in the murine genome

<p>This poster was originally presented at the FEBS EMBO meeting held in Paris from 30th August to 4th September 2014.</p> <p><strong>Summary</strong></p> <p>The promoter of protein coding genes is conventionally considered to be located a few kilobases around their transcriptional start site (TSS). However – given the short half-life of miRNA primary transcripts (pri-miRNAs) – the TSS of miRNA is in most cases not annotated, therefore the position of the promoter is usually not know and potentially located tens or hundreds of kilobases upstream of the annotated miRNA gene (pre-miRNA). Several attempts have been made to identify miRNA TSSs and promoters in human, mouse, worm and plants. Nevertheless, to our knowledge, most of the studies in mouse only made use of a limited number of experimental techniques and/or sources of information available at the time of publication. To overcome these limitations we implemented an integrative approach based on computational predictions, sequence annotations and high-throughput chromatin modification data generated by the ENCODE project to produce an accurate and comprehensive annotation of miRNA promoters in the mouse genome. 
For each murine miRNA annotated in miRbase version 18 we scanned a genomic region of 100 kb upstream of it looking for features indicative of promoters. We found on average 15.7 candidate promoters for each miRNA with a mean size of ~1.4 kb in accordance with the typical size of known promoters. We also found that they were typically located 5 kb upstream of the miRNA, supporting the validity of our approach and in accordance with previous reports. When we inspected what chromatin modifications supported each of the candidate promoters we found that nearly all of them have a DNase Hypersensitivity Site (DHS), more than 50% have both a DHS and H3K4 trimethylation (H3K4me3), and more than 30% have a DHS mark, an H3K4me3 mark and a CpG island. We also found that the highest scoring promoters are on average more conserved than all other promoters and more conserved than random genomic locations of the same size, strongly supporting the validity of our approach. Finally, by chromatin immunoprecipitation assay (ChIP) and methyl-CpG immunoprecipitation (mCIP) we validated H3K4me3 and CpG methylation of the predicted promoters of selected miRNAs, confirming the validity of our approach. Taken together, these results provide a valuable resource that will help to shed light on the transcriptional regulation of miRNAs in the murine genome.</p> <p> </p> <p><strong>Methods</strong></p> <p>- CpG islands were downloaded from the UCSC genome browser in BED format and intersected with the miRbase annotation of murine miR to retain only those within 100kb upstream of any miR.</p> <p>- DNAse Hypersensitivity Sites (DHS): data were downloaded from the DNaseI Hypersensitivity by Digital DNaseI from ENCODE/University of Washington (UW DNaseI HS) track of the UCSC genome Browser. We used Hotspots for replicate 1 and 2 for all available samples. Data were downloaded in BED format using the Subtrack Merge function. All datasets in the track were merged (48 cell lines) and peaks common to at least 12 samples were retained.</p> <p>- H3K4me3 data was downloaded from the UCSC genome browser; track ENCODE-PSU/LICR/Caltech (all datasets, 32 different cell lines). Additionally, we also used H3K4me3 ChIP-Seq data published in (Mikkelsen et al., 2007). All datasets were merged and peaks common to at least 10 samples were retained.</p> <p>- Eponine predictions of TSSs. For each miR annotated in miRbase 18 we obtained the DNA sequence of the region 100kb upstream of the 5’ end of each miR using bedtools v2.16.2 (Quinlan and Hall, 2010) and we scanned it with Eponine (threshold 0.990) to identify candidate TSS.</p> <p>- Annotated TSS of host gene for intronic miRs. For each miR annotated as intronic according to miRbase 18 we extracted the coordinates of the 5’ end of the host transcript from ENSEMBL v64. The 5’ coordinates were expanded 50nt each side to obtain a TSS region of 100nt.</p> <p>- Genome-wide chromatin segmentation using chromHMM. The chromHMM track was downloaded from the UCSC genome browser. From the track we extracted all regions marked as promoters, merged them with bedtools and retained only peaks within 100kb of any miRbase annotated human miR. The coordinates of each human peak were then converted to the corresponding syntenic regions in the murine genome using liftover (minMatch=0.6) and intersected with bedtools to retain only those within 100kb of any murine miR.</p> <p>Of the above feature, all those bigger than the 3rd quartile of the size distribution plus 1.5 time the inter-quantile range were fragmented into n fragments of the mean feature size. After this step, all features were clustered using bedtools to produce an annotation of candidate promoters. Each resulting cluster within 100kb upstream of any miR was then intersected with the feature annotation using bedtools annotate to calculate how many features supported it. We then assigned a score to each cluster by summing the number of features that mapped to it (all features had the same weight, with the exception of DHS that weighed 20% more than the others) and weighed the resulting score according to the distance of the cluster from the corresponding miR using the following formula: Score=nFeature * (1 - ( sqrt(distance)/ sqrt(10^6) ) )</p> <p>For each miR the cluster with the highest score was the denominated the “best” cluster for that miR.</p> <p>To measure the promoter conservation the coordinates of each cluster were intersected with the PhasCons30wayPlacental (Siepel et al.) track of the UCSC genome browser using the R package rtracklayer (Lawrence et al., 2009).</p> <p>The cluster annotation was then imported in R where we calculated a z-score for each cluster. All plots were produced in R using ggplot2.</p

Accounting for Target Flexibility and Water Molecules by Docking to Ensembles of Target Structures: The HCV NS5B Palm Site I Inhibitors Case Study

The introduction of new anti-HCV drugs in therapy is an imperative need and is necessary with a view to develop an interferon-free therapy. Thus, the discovery and development of novel small molecule inhibitors of the viral NS5B polymerase represent an exciting area of research for many pharmaceutical companies and academic groups. This study represents a contribution to this field and relies on the identification of the best NS5B model(s) to be used in structure-based computational approaches aimed at identifying novel non-nucleoside inhibitors of one of the protein allosteric sites, namely, palm site I. First, the NS5B inhibitors at palm site I were classified as water-mediated or nonwater-mediated ligands depending on their ability to interact with or displace a specific water molecule. Then, we took advantage of the available X-ray structures of the NS5B/ligand complexes to build different models of protein/water combinations, which were used to investigate the influence on docking studies of solvent sites as well as of the influence of the protein conformations. As the overall trend, we observed improved performance in the docking results of the water-mediated inhibitors by inclusion of explicit water molecules, with an opposite behavior generally happening for the nonwater-mediated inhibitors. The best performing target structures for the two ligand sets were then used for virtual screening simulations of a library containing the known NS5B inhibitors along with related decoys to assess the best performing targets ensembles on the basis of their ability to discriminate active and inactive compounds as well as to generate the correct binding modes. The parallel use of different protein structures/water sets outperformed the use of a single target structure, with the two-protein 3H98/2W-2FVC/7W and 3HKY/NoW-3SKE/NoW models resulting in the best performing ensembles for water-mediated inhibitors and nonwater-mediated inhibitors, respectively. The information gathered from this work confirms the primary role of water molecules and protein flexibility in docking-based studies and can be exploited to aid NS5B-directed HCV drug discovery efforts

Design and Synthesis of DiselenoBisBenzamides (DISeBAs) as Nucleocapsid Protein 7 (NCp7) Inhibitors with anti-HIV Activity

The interest in the synthesis of Se-containing compounds is growing with the discovery of derivatives exhibiting various biological activities. In this manuscript, we have identified a series of 2,2′-diselenobisbenzamides (DISeBAs) as novel HIV retroviral nucleocapsid protein 7 (NCp7) inhibitors. Because of its pleiotropic functions in the whole viral life cycle and its mutation intolerant nature, NCp7 represents a target of great interest which is not reached by any anti-HIV agent in clinical use. Using the diselenobisbenzoic scaffold, amino acid, and benzenesulfonamide derivatives were prepared and biologically profiled against different models of HIV infection. The incorporation of amino acids such as glycine and glutamate into DISeBAs <b>7</b> and <b>8</b> resulted in selective anti-HIV activity against both acutely and chronically infected cells as well as an interesting virucidal effect. DISeBAs demonstrated broad antiretroviral activity, encompassing HIV-1 drug-resistant strains including clinical isolates, as well as simian immunodeficiency virus (SIV). Time of addition experiments, along with the observed dose dependent inhibition of the Gag precursor proper processing, confirmed that their mechanism of action is based on NCp7 inhibition

Re-evolution of the 2‑Phenylquinolines: Ligand-Based Design, Synthesis, and Biological Evaluation of a Potent New Class of Staphylococcus aureus NorA Efflux Pump Inhibitors to Combat Antimicrobial Resistance

Overexpression of efflux pumps is an important mechanism by which bacteria evade the effects of antimicrobial agents that are substrates. NorA is a Staphylococcus aureus efflux pump that confers reduced susceptibility to many structurally unrelated agents, including fluoroquinolones, biocides, and dyes, resulting in a multidrug resistant (MDR) phenotype. In this work, a series of 2-phenylquinoline derivatives was designed by means of ligand-based pharmacophore modeling in an attempt to identify improved S. aureus NorA efflux pump inhibitors (EPIs). Most of the 2-phenylquinoline derivatives displayed potent EPI activity against the <i>norA</i> overexpressing strain SA-1199B. The antibacterial activity of ciprofloxacin, when used in combination with some of the synthesized compounds, was completely restored in SA-1199B and SA-K2378, a strain overexpressing <i>norA</i> from a multicopy plasmid. Compounds <b>3m</b> and <b>3q</b> also showed potent synergistic activity with the ethidium bromide dye in a strain overexpressing the MepA MDR efflux pump

Pyrazolo[4,3-<i>c</i>][1,2]benzothiazines 5,5-Dioxide: A Promising New Class of Staphylococcus aureus NorA Efflux Pump Inhibitors

The increasing resistance to antibacterials commonly employed in the clinic and the growth of multidrug resistant strains suggest that the development of new therapeutic approaches should be of primary concern. In this context, EPIs may restore life to old drugs. In the present work, the EPI activity of the COX-2 inhibitor celecoxib was confirmed and a new class of pyrazolo­[4,3-<i>c</i>]­[1,2]­benzothiazine 5,5-dioxide analogues acting as inhibitors of the Staphylococcus aureus NorA multidrug efflux pump was identified

Pharmacophore-Based Repositioning of Approved Drugs as Novel <i>Staphylococcus aureus</i> NorA Efflux Pump Inhibitors

An intriguing opportunity to address antimicrobial resistance is represented by the inhibition of efflux pumps. Focusing on NorA, the most important efflux pump of <i>Staphylococcus aureus</i>, an efflux pump inhibitors (EPIs) library was used for ligand-based pharmacophore modeling studies. By exploitation of the obtained models, an in silico drug repositioning approach allowed for the identification of novel and potent NorA EPIs

Cell associated RNA and DNA in lymph nodes and rectum.

<p>The limit of detection for both RNA and DNA assays is 2 copies/5*10⁵ cells. Each assay was conducted in triplicate. The time points selected for the analyses are shown as days from the zero point adopted in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002774#ppat-1002774-g002" target="_blank">figures 2</a>, <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002774#ppat-1002774-g003" target="_blank">3</a> and <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002774#ppat-1002774-g004" target="_blank">4</a>.</p

MRV decreases the post-therapy viral load set point.

<p>Panel A: Pre and post therapy <i>Log₁₀</i> viral load set points of four SIVmac251 infected macaques treated with MRV-containing therapies. The <i>P</i>-value shown is the result of paired <i>t</i>-test analysis. Panel B: Correlation between the <i>Log₁₀</i> Δ viral load set point (<i>i.e.</i> the difference between pre and post therapy viral load set points) and time of exposure to MRV. Correlation was investigated using Pearson's coefficients. The treatment of macaques 4388 and 4398 prior to therapy suspension is shown in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002774#ppat.1002774.s007" target="_blank">Figure S7</a> and <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002774#ppat.1002774.s014" target="_blank">Text S2</a>.</p

H-iART decreases viral DNA in PBMCs and increases the CD4/CD8 ratio.

<p>Panel A: Viral DNA in PBMCs. Panel B: CD4/CD8 ratios. Both panels show the results from macaques enrolled in the pilot study. The sequential treatments are represented by the colored areas. In panel A, asterisks mark the significant differences from baseline values (<i>P</i><0.05), as detected by Bonferroni's test. Panel C: Three-phase decay dynamics of total viral DNA of three macaques (BD69, 4416, P255) to which all H-iART drugs were administered simultaneously and for which viral DNA values from treatment initiation were available. Each time point represents average values (± SEM).</p

T-cell subpopulation dynamics during H-iART.

<p>Panel A: CD4⁺ central and transitional memory T-cells (T_CM/T_TM) Panel B: CD4⁺ effector memory T-cells (TEM). Panel C: CD4⁺ naïve T-cells (TN). Panel D: HLA-DR⁺ T-cell subsets. In panels A–C, individual data points are presented for each animal. The significantly decreasing trends are shown by the solid regression lines. Dashed lines refer to non significant trends (<i>P</i>>0.05). In panel D, data are presented as means ± SEM from three animals and significantly decreasing trends are shown by the asterisks.</p