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			PubMed Journals: PLoS Comput Biol

  Source:		PMID: 32040506


    		PLoS Comput Biol. 2020 Feb
     		10;16(2):e1007587. doi:
			10.1371/journal.pcbi.1007587. eCollection
			2020 Feb.

			Host factor prioritization for pan-viral genetic
			perturbation screens using random intercept
			models and network propagation.

			Dirmeier S(1)(2), D├Ąchert C(3)(4), van Hemert
			M(5), Tas A(5), Ogando NS(5), van Kuppeveld
			F(6), Bartenschlager R(7)(8), Kaderali L(9),
			Binder M(3), Beerenwinkel N(1)(2).

			Author Information
			(1) Department of Biosystems Science and
			Engineering, ETH Zurich, Basel, Switzerland.
			(2) SIB Swiss Institute of Bioinformatics, Basel,
			Switzerland.
			(3) Research Group "Dynamics of Early Viral
			Infection and the Innate Antiviral Response"
			(division F170),
			German Cancer Research Center, Heidelberg,
			Germany.
			(4) Faculty of Biosciences,
			Heidelberg University, Heidelberg, Germany.
			(5) Department of Medical Microbiology,
			Leiden University Medical Center, Leiden, The
			Netherlands.
			(6) Virology Division, Department of Infectious
			Diseases and Immunology, Faculty of
			Veterinary Medicine, Utrecht University, Utrecht,
			The Netherlands.
			(7) Department for Infectious Diseases,
			Molecular Virology, Heidelberg University,
			Heidelberg, Germany.
			(8) Division Virus-Associated Carcinogenesis,
			German Cancer Research Center, Heidelberg,
			Germany.
			(9) University Medicine Greifswald, Institute of
			Bioinformatics, Greifswald, Germany.

			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.

			DOI: 10.1371/journal.pcbi.1007587
			PMID: 32040506

			Conflict of interest statement: Enter: The
			authors have declared that no competing
			interests exist.

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