Automated genome-wide visual profiling of cellular proteins involved in HIV infection

Auguste Genovesio*, Yong Jun Kwon, Marc P. Windisch, Nam Youl Kim, Seo Yeon Choi, Hi Chul Kim, Sungyong Jung, Fabrizio Mammano, Virginie Perrin, Annette S. Boese, Nicoletta Casartelli, Olivier Schwartz, Ulf Nehrbass, Neil Emans

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

46 Citations (Scopus)


Recent genome-wide RNAi screens have identified >842 human genes that affect the human immunodeficiency virus (HIV) cycle. The list of genes implicated in infection differs between screens, and there is minimal overlap. A reason for this variance is the interdependence of HIV infection and host cell function, producing a multitude of indirect or pleiotropic cellular effects affecting the viral infection during RNAi screening. To overcome this, the authors devised a 15-dimensional phenotypic profile to define the viral infection block induced by CD4 silencing in HeLa cells. They demonstrate that this phenotypic profile excludes nonspecific, RNAi-based side effects and viral replication defects mediated by silencing of housekeeping genes. To achieve statistical robustness, the authors used automatically annotated RNAi arrays for seven independent genome-wide RNAi screens. This identified 56 host genes, which reliably reproduced CD4-like phenotypes upon HIV infection. The factors include 11 known HIV interactors and 45 factors previously not associated with HIV infection. As proof of concept, the authors confirmed that silencing of PAK1, Ku70, and RNAseH2A impaired HIV replication in Jurkat cells. In summary, multidimensional, visual profiling can identify genes required for HIV infection.

Original languageEnglish
Pages (from-to)945-958
Number of pages14
JournalJournal of Biomolecular Screening
Issue number9
Publication statusPublished - Oct 2011
Externally publishedYes


  • RNA interference
  • RNAi
  • cell-based assays
  • chip technology and methods
  • high-content screening
  • image analysis
  • shRNA


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