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Updated: Jun 14, 2026

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

Published on: April 3, 2018

Predicting host-based, synthetic lethal antiviral targets from omics data.

Jeannette P Staheli¹, Maxwell L Neal¹, Arti Navare¹

¹Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA 98101, USA.

NAR Molecular Medicine

|July 12, 2024

Summary

This summary is machine-generated.

This study identifies novel host-based antiviral targets by analyzing synthetic lethal (SL) interactions. Gene depletion data from CRISPR knockout (KO) screens reveal potential broad-spectrum drug targets for viral infections like SARS-CoV-2 and influenza.

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Last Updated: Jun 14, 2026

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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Area of Science:

Virology
Genetics
Drug Discovery

Background:

Traditional antiviral therapies face challenges with toxicity and drug resistance.
Host-based antivirals offer an alternative but can have nonspecific effects.
Targeting synthetic lethal (SL) partners of virus-disrupted proteins enables selective elimination of infected cells.

Purpose of the Study:

To hypothesize and computationally predict antiviral drug targets based on synthetic lethal (SL) interactions.
To investigate if genes depleted in CRISPR knockout (KO) screens of virus-infected cells are enriched in SL partners of infection-altered proteins.
To identify broad-spectrum, host-based antiviral SL targets.

Main Methods:

Established a computational pipeline for predicting antiviral SL drug targets.
Identified SARS-CoV-2-induced gene product alterations using omics data.
Determined SL partners for each altered gene product.
Screened CRISPR KO data for SL partners essential for infected cell viability.

Main Results:

Despite variations in omics data, common predicted SL targets were identified.
Significant enrichment of SL targets was found in CRISPR KO-depleted datasets.
Comparison of SARS-CoV-2 and influenza data revealed shared potential broad-spectrum antiviral SL targets.

Conclusions:

CRISPR KO data contain common antiviral targets due to their SL relationship with virus-altered states.
Analysis of omics data and SL predictions can reveal these broad-spectrum antiviral targets.
This approach offers a promising strategy for developing novel host-based antiviral therapies.