Induction of Atlantic salmon type I interferon and antagonism by infectious pancreatic necrosis virus

Viral infections are a leading cause of mortality in the farming of Atlantic salmon in Norway. Viral outbreaks cause heavy losses each year, and one of the most predominant viral diseases is infectious pancreatic necrosis (IPN). The virus causing this disease, the infectious pancreatic necrosis virus (IPNV), belongs to the Birnaviridae family of viruses with a double-stranded RNA (dsRNA) genome. Although viral disease is prevalent in the Atlantic salmon aquaculture industry, fish possess an immune system fully equipped to handle viral disease. In this work we have studied induction of the type I interferon (IFN) response in Atlantic salmon in response to intracellular foreign RNA. IFNs are pivotal alarm proteins alerting other cells of the infection, contributing to the establishment of an antiviral state. In order for IFNs to be produced, the cell must recognize the presence of the viral intruder. Viruses are recognized by a wide range of different receptors. The receptors recognize characteristic pathogen associated molecular patterns (PAMPs), for viruses usually the nucleic acids. The receptors proceed to signal through a conserved signaling pathway that culminates in the induction of IFN production. We have identified a central member of the signaling pathway, IPS-1, a signal mediator conserved through evolution. We have also investigated the promoter elements of two type I IFNa promoters, characterizing the regulatory regions. The induction of IFN production is a common target for viral proteins trying to subvert the IFN response and subsequent antiviral state. Using the IFN promoters and IPS-1 protein, we identified IFN antagonistic effects of separate IPNV proteins

Infectious pancreatic necrosis virus proteins VP2, VP3, VP4 and VP5 antagonize IFNa1 promoter activation while VP1 induces IFNa1

Published version. Source at http://doi.org/10.1016/j.virusres.2014.11.018.Infectious pancreatic necrosis virus (IPNV) is one of the major viral pathogens causing disease in farmed Atlantic salmon worldwide. In the present work we show that several of the IPN proteins have powerful antagonistic properties against type I IFN induction in Atlantic salmon. Each of the five IPNV genes cloned into an expression vector were tested for the ability to influence activation of the Atlantic salmon IFNa1 promoter by the interferon promoter inducing protein one (IPS-1) or interferon regulatory factors (IRF). This showed that preVP2,VP3 andVP5 inhibited activation of both promoters, whileVP4 only antagonized activation of the IFNa1 promoter. The viral protease VP4 was the most potent inhibitor of IFN induction, apparently targeting the IRF1 and IRF3 branch of the signaling cascade. VP4 antagonism is independent of its protease activity since the catalytically dead mutant VP4K674A inhibited activation of the IFNa1 promoter to a similar extent as wild type VP4. In contrast to the other IPNV proteins, the RNA-dependent RNA polymerase VP1 activated the IFNa1 promoter. The ability to activate the IFN response was disrupted in the mutant VP1S163A, which has lost the ability to produce dsRNA. VP1 also exhibited synergistic effects with IRF1 and IRF3 in inducing an IFNa1-dependent antiviral state in cells. Taken together these results suggest that IPNV has developed multiple IFN antagonistic properties to prevent IFN-induction by VP1 and its dsRNA genome

Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones

Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92–94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants

RESTAMP – Rate estimates by sequence-tag analysis of microbial populations

Microbial division rates determine the speed of mutation accumulation and thus the emergence of antimicrobial resistance. Microbial death rates are affected by antibiotic action and the immune system. Therefore, measuring these rates has advanced our understanding of host-pathogen interactions and antibiotic action. Several methods based on marker-loss or few inheritable neutral markers exist that allow estimating microbial division and death rates, each of which has advantages and limitations. Technical bottlenecks, i.e., experimental sampling events, during the experiment can distort the rate estimates and are typically unaccounted for or require additional calibration experiments. In this work, we introduce RESTAMP (Rate Estimates by Sequence Tag Analysis of Microbial Populations) as a method for determining bacterial division and death rates. This method uses hundreds of fitness neutral sequence barcodes to measure the rates and account for experimental bottlenecks at the same time. We experimentally validate RESTAMP and compare it to established plasmid loss methods. We find that RESTAMP has a number of advantages over plasmid loss or previous marker based techniques. (i) It enables to correct the distortion of rate estimates by technical bottlenecks. (ii) Rate estimates are independent of the sequence tag distribution in the starting culture allowing the use of an arbitrary number of tags. (iii) It introduces a bottleneck sensitivity measure that can be used to maximize the accuracy of the experiment. RESTAMP allows studying microbial population dynamics with great resolution over a wide dynamic range and can thus advance our understanding of host-pathogen interactions or the mechanisms of antibiotic action

Synthesis and biological evaluation of zinc chelating compounds as metallo-β-lactamase inhibitors

International audienceThe syntheses of metallo-β-lactamase inhibitors comprising chelating moieties, with varying zinc affinities,and peptides partly inspired from bacterial peptide sequences, have been undertaken. The zinc chelatorstrength was varied using the following chelators, arranged in order of ascending binding affinity:dipicolylamine (DPA, tridentate), dipicolyl-1,2,3-triazolylmethylamine (DPTA, tetradentate) dipicolyl ethylenediamine(DPED, tetradentate) and trispicolyl ethylenediamine (TPED, pentadentate). The chosen peptideswere mainly based on the known sequence of the C-terminus of the bacterial peptidoglycan precursors.Biological evaluation on clinical bacterial isolates, harbouring either the NDM-1 or VIM-2 metallo-β-lactamase, showed a clear relationship between the zinc chelator strength and restoration of meropenemactivity. However, evaluation of toxicity on different cancer cell lines demonstrated a similar trend, and thusinclusion of the bacterial peptides did possess rather high toxicity towards eukaryotic cells

Proteins encoded by the PRV genome and functional properties as predicted from comparative studies with selected reovirus prototype strains.

a<p>L1-M3 PRV gene segments are annotated according to mammalian reoviruses (MRV). PRV L1 has been changed to L3, and vice versa, compared to that suggested by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070075#pone.0070075-Palacios1" target="_blank">[1]</a>. PRV S-class gene segments are annotated according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070075#pone.0070075-Palacios1" target="_blank">[1]</a>. For mammalian reovirus (MRV), avian orthoreovirus (ARV) and grass carp reovirus (GCRV) several proteins are produced from alternative reading frames or by post-translational proteolytic cleavage. In the latter case, if the exact cleavage site is known, the lengths of both proteolytic fragments are included in the table.</p>b<p>T3D = Type 3 Dearing strain.</p>c<p>GCRV contains an eleventh genomic segment which encodes a non-structural protein, NS26. VP7 is homologues to σ3/σB.</p>d<p>Cytotoxic, nonfusogenic integral membrane protein <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070075#pone.0070075-Key1" target="_blank">[96]</a>.</p

Percentage amino acid identity among all ungapped positions between pairs; predicted PRV proteins and the homologues proteins from three reovirus prototype strains.

a,b,c<p>Ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070075#pone-0070075-t001" target="_blank">Table 1</a> for gene segment annotations and names of homologues proteins in MRV, ARV and GCRV. Identity values are from separate pairwise alignments of the protein sequences.</p>d<p>Value from a manually adjusted pairwise alignment of the two proteins.</p>f<p>GCRV does not appear to have a cell attachment protein homologue to σ1/σC.</p

Expression and subcellular localization of the S1-encoded σ3 and p13 proteins in mammalian VERO and salmonid CHSE cells.

<p>Immunofluorescent staining of σ3 or p13 (green colour), and staining with the trans-Golgi marker WGA (red colour). Transfected VERO cells (A) and CHSE cells (B) expressing both σ3 and p13 from the large S1 ORF (upper panels), and p13 expression from the S1 internal ORF (lower panels). Nuclei are stained with DAPI (blue colour). Yellow colour indicates colocalization of p13 and WGA. Non-transfected cells stained with WGA and anti-p13 serum was used as controls (CTRL).</p

Multiple sequence alignment of PRV σ1 with MRV T3D σ1.

<p>Black lines represent putative nuclear export signals (NEP) in MRV and PRV, respectively, as predicted by NetNes 1.1. ▪ = L₁₄₉ in the MRV protein involved in a second predicted NES. ▴ = residues in the MRV protein involved in binding to sialic acid residues. The alignment has been manually adjusted.</p