Antiviral and Neuroprotective Role of Octaguanidinium Dendrimer-Conjugated Morpholino Oligomers in Japanese Encephalitis

Japanese encephalitis (JE) is caused by a flavivirus that is transmitted to humans by mosquitoes belonging to the Culex sp. The threat of JE looms over a vast geographical realm, encompassing approximately 10 billion people. The disease is feared because currently there are no specific antiviral drugs available. There have been reports where other investigators have shown that agents that block viral replication can be used as effective therapeutic countermeasures. Vivo-Morpholinos (MOs) are synthetically produced analogs of DNA or RNA that can be modified to bind with specific targeted regions in a genome. In this study the authors propose that in an animal model of JE, MOs specifically designed to bind with specific region of JE virus (JEV) genome, blocks virus production in cells of living organisms. This results in reduced mortality of infected animals. As the major target of JEV is the nerve cells, analysis of brain of experimental animals, post treatment with MOs, showed neuroprotection. Studies in cultured cells were also supportive of the antiviral role of the MOs. The potent anti-sense effect in animals and lack of obvious toxicity at the effective dosage make these MOs good research reagents with future therapeutic applications in JE

Machine learning models identify molecules active against the Ebola virus in vitro [version 3; referees: 2 approved]

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro

Machine learning models identify molecules active against the Ebola virus in vitro [version 1; referees: 2 approved]

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro

Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads

We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal. This suggested that we can learn from the published data on EBOV inhibition and use it to select new compounds for testing that are active in vivo. We used these previously built Bayesian machine learning EBOV models alongside our chemical insights for the selection of 12 molecules, absent from the training set, to test for in vitro EBOV inhibition. Nine molecules were directly selected using the model, and eight of these molecules possessed a promising in vitro activity (EC50 < 15 μM). Three further compounds were selected for an in vitro evaluation because they were antimalarials, and compounds of this class like pyronaridine and quinacrine have previously been shown to inhibit EBOV. We identified the antimalarial drug arterolane (IC50 = 4.53 μM) and the anticancer clinical candidate lucanthone (IC50 = 3.27 μM) as novel compounds that have EBOV inhibitory activity in HeLa cells and generally lack cytotoxicity. This work provides further validation for using machine learning and medicinal chemistry expertize to prioritize compounds for testing in vitro prior to more costly in vivo tests. These studies provide further corroboration of this strategy and suggest that it can likely be applied to other pathogens in the future

An Intrinsically Disordered Peptide from Ebola Virus VP35 Controls Viral RNA Synthesis by Modulating Nucleoprotein-RNA Interactions

During viral RNA synthesis, Ebola virus (EBOV) nucleoprotein (NP) alternates between an RNA-template-bound form and a template-free form to provide the viral polymerase access to the RNA template. In addition, newly synthesized NP must be prevented from indiscriminately binding to noncognate RNAs. Here, we investigate the molecular bases for these critical processes. We identify an intrinsically disordered peptide derived from EBOV VP35 (NPBP, residues 20–48) that binds NP with high affinity and specificity, inhibits NP oligomerization, and releases RNA from NP-RNA complexes in vitro. The structure of the NPBP/ΔNPNTD complex, solved to 3.7 Å resolution, reveals how NPBP peptide occludes a large surface area that is important for NP-NP and NP-RNA interactions and for viral RNA synthesis. Together, our results identify a highly conserved viral interface that is important for EBOV replication and can be targeted for therapeutic development

Interferon-γ Inhibits Ebola Virus Infection

<div><p>Ebola virus outbreaks, such as the 2014 Makona epidemic in West Africa, are episodic and deadly. Filovirus antivirals are currently not clinically available. Our findings suggest interferon gamma, an FDA-approved drug, may serve as a novel and effective prophylactic or treatment option. Using mouse-adapted Ebola virus, we found that murine interferon gamma administered 24 hours before or after infection robustly protects lethally-challenged mice and reduces morbidity and serum viral titers. Furthermore, we demonstrated that interferon gamma profoundly inhibits Ebola virus infection of macrophages, an early cellular target of infection. As early as six hours following <i>in vitro</i> infection, Ebola virus RNA levels in interferon gamma-treated macrophages were lower than in infected, untreated cells. Addition of the protein synthesis inhibitor, cycloheximide, to interferon gamma-treated macrophages did not further reduce viral RNA levels, suggesting that interferon gamma blocks life cycle events that require protein synthesis such as virus replication. Microarray studies with interferon gamma-treated human macrophages identified more than 160 interferon-stimulated genes. Ectopic expression of a select group of these genes inhibited Ebola virus infection. These studies provide new potential avenues for antiviral targeting as these genes that have not previously appreciated to inhibit negative strand RNA viruses and specifically Ebola virus infection. As treatment of interferon gamma robustly protects mice from lethal Ebola virus infection, we propose that interferon gamma should be further evaluated for its efficacy as a prophylactic and/or therapeutic strategy against filoviruses. Use of this FDA-approved drug could rapidly be deployed during future outbreaks.</p></div

IFNγ reduces EBOV-GP/rVSV morbidity and mortality.

<p>(A) IFNγ enhances survival of EBOV GP/rVSV infected mice. IFNγ (10 μg) or PBS was administered by i.p. injection to BALB/c IFNAR^-/- mice 24 hours prior to or 2, 6, 12 or 48 hours following EBOV GP/rVSV infection (n≥8/treatment). (B) IFNγ treatment as a 24 hour pre-treatment or a 2 hour post-treatment reduces serum viremia and organ titers of EBOV GP/rVSV-infected mice. Sera and organs were harvested at 48 hours following infection (n≥ 4/treatment) in mice treated with 3.3μg of IFNγ. Viremia and organ virus titers were determined by endpoint dilution of serum or homogenized organ samples on Vero cells. Significance was calculated by Mann-Whitney test compared to PBS control, *<i>p</i> < 0.05, **<i>p</i> < 0.01. ns, not significant. (C) Intraperitoneal IFNγ treatment of mice significantly inhibits EBOV GP/rVSV infection of peritoneal cells. Peritoneal cells were isolated from EBOV GP/rVSV infected mice treated with 3.3μg of IFNγ at times noted prior to or following challenge. Amount of VSV-L RNA was determined by qRT-PCR. Significance was determined by ANOVA with a Tukey post-test, ***<i>p</i> < 0.001. (D) Intramuscular administration of IFNγ increases survival of IFNAR^-/- mice. PBS or IFNγ at the indicated concentration was administered by i.m. injection 24 hours prior to i.p. injection of EBOV GP/rVSV. For A & D, significance was determined by Mantel-Cox Test, **<i>p</i> < 0.01, ***<i>p</i> < 0.001.</p

Identification of IFNγ-stimulated genes that inhibit EBOV infection.

<p>(A) mRNA validation of hMDM profiling results for several of the top IFNγ-stimulated genes identified in our gene arrays. RNA obtained for the microarray analysis was assessed for mRNA levels of the selected genes by qRT-PCR. Results are represented as the log2 values. (B) Identification of IFNγ-stimulated genes that inhibit EBOV GP/rVSV infection. Highly permissive HEK 293T cells stably expressing TIM -1 (H3 cells) were infected with EBOV GP/rVSV 48 hours following transfection of 2 μg of ISG-RFP lentiviral constructs. Cells gated for RFP expression were assessed for EBOV GP/rVSV infection by detection of GFP. Shown is infection of EBOV GP/rVSV in ISG expressing cells relative to infection of cells transfected with a fluc-RFP expressing lentivirus (control). (C) Novel ISGs that inhibit EBOV GP/rVSV also block EBOV infection. HeLa cells were infected with EBOV 48 hours following electroporation of 5 μg of ISG-RFP lentiviral constructs. Infection was assessed by microscopy 24 hours later and percent of cells that were GFP positive were calculated by CellProfiler image analysis software. A lentiviral construct expressing IRF1 served as a positive control in these studies. (D) IRF1 knock down increases EBOV GP/rVSV infection following IFNγ stimulation. IRF1 or scrambled (Scr) siRNA were loaded into HEK 293T derived exosomes. SiRNA loaded exosomes (2.5 μg) were delivered and IFNγ added to BALB/c IFNAR^-/- peritoneal macrophages 24 hours prior to EBOV GP/rVSV infection (MOI = 0.1). Twenty-four hours following infection, total RNA was isolated from the macrophages. Amount of IRF1 expression and infection (by detection of VSV polymerase (L)) was quantified by qRT-PCR. Results represent the means ± s.e.m. Significance was determined by Student’s t-test analysis, *<i>p</i> < 0.05, **<i>p</i> < 0.01, ***<i>p</i> < 0.001.</p

Protein Interaction Mapping Identifies RBBP6 as a Negative Regulator of Ebola Virus Replication

Ebola virus (EBOV) infection often results in fatal illness in humans, yet little is known about how EBOV usurps host pathways during infection. To address this, we used affinity tag-purification mass spectrometry (AP-MS) to generate an EBOV-host protein-protein interaction (PPI) map. We uncovered 194 high-confidence EBOV-human PPIs, including one between the viral transcription regulator VP30 and the host ubiquitin ligase RBBP6. Domain mapping identified a 23 amino acid region within RBBP6 that binds to VP30. A crystal structure of the VP30-RBBP6 peptide complex revealed that RBBP6 mimics the viral nucleoprotein (NP) binding to the same interface of VP30. Knockdown of endogenous RBBP6 stimulated viral transcription and increased EBOV replication, whereas overexpression of either RBBP6 or the peptide strongly inhibited both. These results demonstrate the therapeutic potential of biologics that target this interface and identify additional PPIs that may be leveraged for novel therapeutic strategies