Detecting host factors involved in virus infection by observing the clustering of infected cells in siRNA screening images

Abstract

Motivation: Detecting human proteins that are involved in virus entry and replication is facilitated by modern high-throughput RNAi screening technology. However, hit lists from different laboratories have shown only little consistency. This may be caused by not only experimental discrepancies, but also not fully explored possibilities of the data analysis. We wanted to improve reliability of such screens by combining a population analysis of infected cells with an established dye intensity readout.Results: Viral infection is mainly spread by cell–cell contacts and clustering of infected cells can be observed during spreading of the infection in situ and in vivo. We employed this clustering feature to define knockdowns which harm viral infection efficiency of human Hepatitis C Virus. Images of knocked down cells for 719 human kinase genes were analyzed with an established point pattern analysis method (Ripley's K-function) to detect knockdowns in which virally infected cells did not show any clustering and therefore were hindered to spread their infection to their neighboring cells. The results were compared with a statistical analysis using a common intensity readout of the GFP-expressing viruses and a luciferase-based secondary screen yielding five promising host factors which may suit as potential targets for drug therapy.Conclusion: We report of an alternative method for high-throughput imaging methods to detect host factors being relevant for the infection efficiency of viruses. The method is generic and has the potential to be used for a large variety of different viruses and treatments being screened by imaging techniques.Contact: r.eils@dkfz.de; r.koenig@dkfz.deSupplementary information: Supplementary data are available at Bioinformatics online.

RNA-RNA and RNA-protein interactions in coronavirus replication and transcription

“…hnRNP A1 specifically binds to MHV RNA consensus sequences present in viral genome negative RNAs cTRS-L and cTRS-B, complementary to TRS-L and TRS-B RNA motifs, respectively. 168 Another assays using host cell mutants or small interfering RNAs (siR-NAs) affecting viral RNA amplification and expression applied to HCV, 113,114 and human immunodeficiency virus (HIV). 115,116 One of the largest limitations in the identification of factors affecting replication or transcription is to distinguish between direct and indirect effects.…”

A Combined Proteomics/Genomics Approach Links Hepatitis C Virus Infection with Nonsense-Mediated mRNA Decay

“…Systematic approaches have identified host proteins key to HCV infection, including RNAi screens (Li et al, 2009; Suratanee et al, 2010; Tai et al, 2009) and a two-hybrid method (de Chassey et al, 2008; Dolan et al, 2013). A systematic affinity tag purification/mass spectrometry (AP-MS) approach was first used to construct a comprehensive host-pathogen protein-protein interaction (PPI) map for HIV-1 (Jager et al, 2011).…”

SANTA: Quantifying the Functional Content of Molecular Networks

“…If the points are densely clustered, most of them will be found for small values of , while for uniformly spread points the achieves larger values only for large values of . Applications of the in computational biology include detecting host factors involved in virus infection by observing the clustering of infected cells in siRNA screening images [27] .…”

“…where n is the number of points, λ is the density (number of points per unit area), d(i, j) is the distance between two points in R 2 , and I(d(i, j) < s) is an indicator function with value 1 if the distance d(i, j) between points i and j is smaller than s, and 0 otherwise. If the points are densely clustered, most of them will be found for small values of s, while for uniformly spread points the K(s) function achieves larger values only for large values of s. Applications of the K-function in computational biology include detecting host factors involved in virus infection by observing the clustering of infected cells in siRNA screening images [27].…”