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Author

Virginia Chu

Other affiliations: National Institutes of Health
Bio: Virginia Chu is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Induced pluripotent stem cell & Embryonic stem cell. The author has an hindex of 7, co-authored 7 publications receiving 391 citations. Previous affiliations of Virginia Chu include National Institutes of Health.

Papers
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Journal ArticleDOI
TL;DR: It is determined that, despite an immature phenotype, differentiated HLCs are permissive to hepatitis C virus (HCV) infection and mount an interferon response to HCV infection in vitro.
Abstract: The demonstrated ability to differentiate both human embryonic stem cells (hESCs) and patient-derived induced pluripotent stem cells (hiPSCs) into hepatocyte-like cells (HLCs) holds great promise for both regenerative medicine and liver disease research. Here, we determined that, despite an immature phenotype, differentiated HLCs are permissive to hepatitis C virus (HCV) infection and mount an interferon response to HCV infection in vitro. HLCs differentiated from hESCs and hiPSCs could be engrafted in the liver parenchyma of immune-deficient transgenic mice carrying the urokinase-type plasminogen activator gene driven by the major urinary protein promoter. The HLCs were maintained for more than 3 months in the livers of chimeric mice, in which they underwent further maturation and proliferation. These engrafted and expanded human HLCs were permissive to in vivo infection with HCV-positive sera and supported long-term infection of multiple HCV genotypes. Our study demonstrates efficient engraftment and in vivo HCV infection of human stem cell-derived hepatocytes and provides a model to study chronic HCV infection in patient-derived hepatocytes, action of antiviral therapies, and the biology of HCV infection.

132 citations

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TL;DR: Chlorcyclizine HCl (CCZ), an over-the-counter drug for allergy symptoms, is identified and characterized as an anti-HCV drug in vitro and in vivo, and represents a promising candidate for drug repurposing and further development as an effective and accessible agent for treatment of HCV infection.
Abstract: Hepatitis C virus (HCV) infection affects an estimated 185 million people worldwide, with chronic infection often leading to liver cirrhosis and hepatocellular carcinoma. Although HCV is curable, there is an unmet need for the development of effective and affordable treatment options. Through a cell-based high-throughput screen, we identified chlorcyclizine HCl (CCZ), an over-the-counter drug for allergy symptoms, as a potent inhibitor of HCV infection. CCZ inhibited HCV infection in human hepatoma cells and primary human hepatocytes. The mode of action of CCZ is mediated by inhibiting an early stage of HCV infection, probably targeting viral entry into host cells. The in vitro antiviral effect of CCZ was synergistic with other anti-HCV drugs, including ribavirin, interferon-α, telaprevir, boceprevir, sofosbuvir, daclatasvir, and cyclosporin A, without significant cytotoxicity, suggesting its potential in combination therapy of hepatitis C. In the mouse pharmacokinetic model, CCZ showed preferential liver distribution. In chimeric mice engrafted with primary human hepatocytes, CCZ significantly inhibited infection of HCV genotypes 1b and 2a, without evidence of emergence of drug resistance, during 4 and 6 weeks of treatment, respectively. With its established clinical safety profile as an allergy medication, affordability, and a simple chemical structure for optimization, CCZ represents a promising candidate for drug repurposing and further development as an effective and accessible agent for treatment of HCV infection.

127 citations

Journal ArticleDOI
TL;DR: This high-resolution profiling methodology will be useful for next-generation drug development to select drugs with higher fitness costs to resistance, and also for informing the rational use of drugs based on viral variant spectra from patients.
Abstract: Widely used chemical genetic screens have greatly facilitated the identification of many antiviral agents. However, the regions of interaction and inhibitory mechanisms of many therapeutic candidates have yet to be elucidated. Previous chemical screens identified Daclatasvir (BMS-790052) as a potent nonstructural protein 5A (NS5A) inhibitor for Hepatitis C virus (HCV) infection with an unclear inhibitory mechanism. Here we have developed a quantitative high-resolution genetic (qHRG) approach to systematically map the drug-protein interactions between Daclatasvir and NS5A and profile genetic barriers to Daclatasvir resistance. We implemented saturation mutagenesis in combination with next-generation sequencing technology to systematically quantify the effect of every possible amino acid substitution in the drug-targeted region (domain IA of NS5A) on replication fitness and sensitivity to Daclatasvir. This enabled determination of the residues governing drug-protein interactions. The relative fitness and drug sensitivity profiles also provide a comprehensive reference of the genetic barriers for all possible single amino acid changes during viral evolution, which we utilized to predict clinical outcomes using mathematical models. We envision that this high-resolution profiling methodology will be useful for next-generation drug development to select drugs with higher fitness costs to resistance, and also for informing the rational use of drugs based on viral variant spectra from patients.

71 citations

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TL;DR: This protocol affords the unique opportunity to miniaturize the hPSC-based differentiation technology and facilitates screening for molecules in modulating liver differentiation, metabolism, genetic network, and response to infection or other external stimuli.

67 citations

Journal ArticleDOI
TL;DR: In this paper, a high-throughput profiling platform was developed to enable mapping of hepatitis C virus (HCV) sequences critical for anti-IFN function at high resolution, and it was found that p7 is a critical immune evasion protein that suppresses the antiviral IFN function by counteracting the function of IFI6-16.
Abstract: Hepatitis C virus (HCV) encodes mechanisms to evade the multilayered antiviral actions of the host immune system. Great progress has been made in elucidating the strategies HCV employs to down-regulate interferon (IFN) production, impede IFN signaling transduction, and impair IFN-stimulated gene (ISG) expression. However, there is a limited understanding of the mechanisms governing how viral proteins counteract the antiviral functions of downstream IFN effectors due to the lack of an efficient approach to identify such interactions systematically. To study the mechanisms by which HCV antagonizes the IFN responses, we have developed a high-throughput profiling platform that enables mapping of HCV sequences critical for anti-IFN function at high resolution. Genome-wide profiling performed with a 15-nt insertion mutant library of HCV showed that mutations in the p7 region conferred high levels of IFN sensitivity, which could be alleviated by the expression of WT p7 protein. This finding suggests that p7 protein of HCV has an immune evasion function. By screening a liver-specific ISG library, we identified that IFI6-16 significantly inhibits the replication of p7 mutant viruses without affecting WT virus replication. In contrast, knockout of IFI6-16 reversed the IFN hypersensitivity of p7 mutant virus. In addition, p7 was found to be coimmunoprecipitated with IFI6-16 and to counteract the function of IFI6-16 by depolarizing the mitochondria potential. Our data suggest that p7 is a critical immune evasion protein that suppresses the antiviral IFN function by counteracting the function of IFI6-16.

27 citations


Cited by
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TL;DR: This study presents an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network.
Abstract: Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV–host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the “Complementary Exposure” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

1,226 citations

Journal ArticleDOI
TL;DR: A pan-caspase inhibitor, emricasan, inhibited ZIKV-induced increases in caspase-3 activity and protected human cortical neural progenitors in both monolayer and three-dimensional organoid cultures, and combination treatments using one compound from each category (neuroprotective and antiviral) further increased protection of human neural progensitors and astrocytes from ZIKv-induced cell death.
Abstract: In response to the current global health emergency posed by the Zika virus (ZIKV) outbreak and its link to microcephaly and other neurological conditions, we performed a drug repurposing screen of ∼6,000 compounds that included approved drugs, clinical trial drug candidates and pharmacologically active compounds; we identified compounds that either inhibit ZIKV infection or suppress infection-induced caspase-3 activity in different neural cells. A pan-caspase inhibitor, emricasan, inhibited ZIKV-induced increases in caspase-3 activity and protected human cortical neural progenitors in both monolayer and three-dimensional organoid cultures. Ten structurally unrelated inhibitors of cyclin-dependent kinases inhibited ZIKV replication. Niclosamide, a category B anthelmintic drug approved by the US Food and Drug Administration, also inhibited ZIKV replication. Finally, combination treatments using one compound from each category (neuroprotective and antiviral) further increased protection of human neural progenitors and astrocytes from ZIKV-induced cell death. Our results demonstrate the efficacy of this screening strategy and identify lead compounds for anti-ZIKV drug development.

566 citations

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TL;DR: This work presents EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions and shows that it outperforms methods that do not account for epistasis.
Abstract: Many high-throughput experimental technologies have been developed to assess the effects of large numbers of mutations (variation) on phenotypes. However, designing functional assays for these methods is challenging, and systematic testing of all combinations is impossible, so robust methods to predict the effects of genetic variation are needed. Most prediction methods exploit evolutionary sequence conservation but do not consider the interdependencies of residues or bases. We present EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions. We validate EVmutation by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations and show that it outperforms methods that do not account for epistasis. EVmutation can be used to assess the quantitative effects of mutations in genes of any organism. We provide pre-computed predictions for ∼7,000 human proteins at http://evmutation.org/.

515 citations

Journal ArticleDOI
TL;DR: This paper presents Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism, and provides laboratory scientists with an easy and systematic way to analyze their data.
Abstract: Motivation: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. Results: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. Availability and Implementation: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). Contact: Giovanni.DiVeroli@cruk.cam.ac.uk. Supplementary information:Supplementary data are available at Bioinformatics online.

444 citations

Journal ArticleDOI
TL;DR: DeepSequence is an unsupervised deep latent-variable model that predicts the effects of mutations on the basis of evolutionary sequence information that is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space.
Abstract: The functions of proteins and RNAs are defined by the collective interactions of many residues, and yet most statistical models of biological sequences consider sites nearly independently Recent approaches have demonstrated benefits of including interactions to capture pairwise covariation, but leave higher-order dependencies out of reach Here we show how it is possible to capture higher-order, context-dependent constraints in biological sequences via latent variable models with nonlinear dependencies We found that DeepSequence ( https://githubcom/debbiemarkslab/DeepSequence ), a probabilistic model for sequence families, predicted the effects of mutations across a variety of deep mutational scanning experiments substantially better than existing methods based on the same evolutionary data The model, learned in an unsupervised manner solely on the basis of sequence information, is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space

385 citations