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Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation.
Dirmeier, Simon; Dächert, Christopher; van Hemert, Martijn; Tas, Ali; Ogando, Natacha S; van Kuppeveld, Frank; Bartenschlager, Ralf; Kaderali, Lars; Binder, Marco; Beerenwinkel, Niko.
  • Dirmeier S; Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
  • Dächert C; SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
  • van Hemert M; Research Group "Dynamics of Early Viral Infection and the Innate Antiviral Response" (division F170), German Cancer Research Center, Heidelberg, Germany.
  • Tas A; Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
  • Ogando NS; Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands.
  • van Kuppeveld F; Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Bartenschlager R; Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Kaderali L; Virology Division, Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
  • Binder M; Department for Infectious Diseases, Molecular Virology, Heidelberg University, Heidelberg, Germany.
  • Beerenwinkel N; Division Virus-Associated Carcinogenesis, German Cancer Research Center, Heidelberg, Germany.
PLoS Comput Biol ; 16(2): e1007587, 2020 02.
Article in English | MEDLINE | ID: covidwho-7370
ABSTRACT
Genetic perturbation screens using RNA interference (RNAi) have been conducted successfully to identify host factors that are essential for the life cycle of bacteria or viruses. So far, most published studies identified host factors primarily for single pathogens. Furthermore, often only a small subset of genes, e.g., genes encoding kinases, have been targeted. Identification of host factors on a pan-pathogen level, i.e., genes that are crucial for the replication of a diverse group of pathogens has received relatively little attention, despite the fact that such common host factors would be highly relevant, for instance, for devising broad-spectrum anti-pathogenic drugs. Here, we present a novel two-stage procedure for the identification of host factors involved in the replication of different viruses using a combination of random effects models and Markov random walks on a functional interaction network. We first infer candidate genes by jointly analyzing multiple perturbations screens while at the same time adjusting for high variance inherent in these screens. Subsequently the inferred estimates are spread across a network of functional interactions thereby allowing for the analysis of missing genes in the biological studies, smoothing the effect sizes of previously found host factors, and considering a priori pathway information defined over edges of the network. We applied the procedure to RNAi screening data of four different positive-sense single-stranded RNA viruses, Hepatitis C virus, Chikungunya virus, Dengue virus and Severe acute respiratory syndrome coronavirus, and detected novel host factors, including UBC, PLCG1, and DYRK1B, which are predicted to significantly impact the replication cycles of these viruses. We validated the detected host factors experimentally using pharmacological inhibition and an additional siRNA screen and found that some of the predicted host factors indeed influence the replication of these pathogens.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Viruses / Gene Regulatory Networks / Host Microbial Interactions / Models, Biological Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2020 Document Type: Article Affiliation country: Journal.pcbi.1007587

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Viruses / Gene Regulatory Networks / Host Microbial Interactions / Models, Biological Type of study: Diagnostic study / Prognostic study / Randomized controlled trials Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2020 Document Type: Article Affiliation country: Journal.pcbi.1007587