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. 2011 Dec 21;481(7381):365-70.
doi: 10.1038/nature10719.

Global landscape of HIV-human protein complexes

Stefanie Jäger  1 , Peter Cimermancic, Natali Gulbahce, Jeffrey R Johnson, Kathryn E McGovern, Starlynn C Clarke, Michael Shales, Gaelle Mercenne, Lars Pache, Kathy Li, Hilda Hernandez, Gwendolyn M Jang, Shoshannah L Roth, Eyal Akiva, John Marlett, Melanie Stephens, Iván D'Orso, Jason Fernandes, Marie Fahey, Cathal Mahon, Anthony J O'Donoghue, Aleksandar Todorovic, John H Morris, David A Maltby, Tom Alber, Gerard Cagney, Frederic D Bushman, John A Young, Sumit K Chanda, Wesley I Sundquist, Tanja Kortemme, Ryan D Hernandez, Charles S Craik, Alma Burlingame, Andrej Sali, Alan D Frankel, Nevan J Krogan
Affiliations

Global landscape of HIV-human protein complexes

Stefanie Jäger et al. Nature. .

Abstract

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host's cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV-human protein-protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Affinity purification of HIV-1 proteins, analysis and scoring of mass spectrometry data
a, Flowchart of the proteomic AP–MS used to define the HIV–host interactome. PAGE, polyacrylamide gel electrophoresis. SF, 2×Strep–3×Flag affinity tag. b, Data from AP–MS experiments are organized in an interaction table with cells representing amount of prey protein purified (for example spectral counts or peptide intensities). Three features are used to describe bait–prey relationships: abundance (blue), reproducibility (the invariability of bait–prey pair quantities; red) and specificity (green). c, All bait–prey pairs are mapped into the three-feature space (abundance, reproducibility and specificity). The MiST score is defined as a projection on the first principal component (red line). All interactions, represented as nodes, above the defined threshold (0.75) are shown in red. This procedure separates the interactions more likely to be biologically relevant (for example Vif–ELOC (ELOC also known as TCEB1), Vpr–VPRBP and Tat–CCNT1) from the interactions that are likely to be less relevant owing to low reproducibility (Vpu–ATP4A) or specificity (RT–HSP71 (HSP71 also known as HSPA1A) and NC–RL23A (RL23A also known as RPL23A)). d, The histogram of MiST scores (real data) is compared with a randomized set of scores obtained from randomly shuffling the bait–prey table (simulated data). The MiST score threshold (0.75) was defined using a benchmark (Supplementary Table 3) whereby the predictions are enriched for these interactions by a factor of at least ten relative to random predictions (as well as through ROC (receiver operating characteristic) and recall plots (Supplementary Fig. 6)). e, Bar graph of the number of host proteins we found interacting with each HIV factor (MiST score, >0.75). The cell type in which the interaction was found is represented in blue (HEK293 only), yellow (Jurkat only) or red (both). f, g, Heat maps representing enriched biological functions (f) and domains (g) from the human proteins identified as interacting with HIV proteins (Supplementary Methods). ER, endoplasmic reticulum; mRNA, messenger RNA; tRNA, transfer RNA. TPR, tetratricopeptide repeat; HTH, helix-turn-helix; SPFH, stomatin–prohibitin–flotillin–HflK/C.
Figure 2
Figure 2. Comparison of PPI data with other HIV data sets
a, Overlap of the 497 HIV–human PPIs with the 587 PPIs reported in VirusMint (Supplementary Table 5). b, Overlap of the 435 human proteins with the genes identified in four HIV-dependency RNAi screens- (Supplementary Table 6). c, Number of interactions overlapping with VirusMint (solid red line) and proteins with RNAi screens (solid blue line) as functions of the MiST cut-off. The P values of the overlap are represented as dashed lines using the same colours (Supplementary Data 6 and 8). d, Comparative genomics analysis of divergence patterns between human and rhesus macaque reveals strong evolutionary constraints on human proteins binding to HIV proteins. The x and y axes represent P values for the synonymous (dS) and non-synonymous (dN) rates of evolution (Supplementary Methods). Horizontal and vertical dotted lines are drawn at 0.5% to indicate the Bonferroni significance threshold for each axis. For the VirusMint data, the significance of ω (dN/dS) is primarily driven by higher rates of synonymous evolution. ∪, union; ∩, intersection; Pω, bootstrap-based P value for ω.
Figure 3
Figure 3. Network representation of the HIV–human PPIs
In total, 497 HIV–human interactions (blue) are represented between 16 HIV proteins and 435 human factors. Each node representing a human protein is split into two colours and the intensity of each colour corresponds to the MiST score from interactions derived from HEK293 (blue) or Jurkat (red) cells. Black edges correspond to interactions between host factors (289) that were obtained from publicly available databases; dashed edges correspond to interactions also found in VirusMint.
Figure 4
Figure 4. eIF3d is cleaved by HIV-1 PR and inhibits infection
a, MiST scores for eIF3 subunits associated with PR (right) and Pol (left) in HEK293 and Jurkat cells. Sizes of the proteins and numbers of significant interactions (MiST score, >0.75) detected for Pol and its subunits are shown below, as is a modular representation of the eIF3 complex. The cleaved subunit, eIF3d, is in red. b, Western blot of HEK293 cell lysate expressing FLAG-tagged eIF3 subunits in the absence (−) or presence (+) of active PR probed with an anti-FLAG antibody. c, HEK293 cells were co-transfected with Gag, or FLAG-tagged eIF3d, PABPC1, BCL2 and increasing amounts of PR. Cell lysates were probed against Gag (upper panel), FLAG-tagged eIF3d (middle panel) or tubulin as control (lower panel). d, Silver stain of purified eIF3 complex incubated with recombinant HIV-1 PR. The residues corresponding to the eIF3d cleavage site (red) is located within the RNA-binding domain. e, f, HeLa-derived P4/R5 MAGI cells were transfected with two different short interfering RNAs (siRNAs) targeting individual subunits of the eIF3 complex (Supplementary Tables 7 and 9) and subsequently infected with either a pNL4-3-derived, VSV-G-pseudotyped, single-cycle virus (HIV–VSV-G) (e) or wild-type pNL4-3 (f). NC, negative control. g, Early (left) and late (right) HIV-1 DNA levels measured by quantitative PCR amplification in cells transfected with two independent eIF3d siRNAs or with control siRNAs. Samples were normalized by input DNA amount or by cellular gene (HMBS) copy number. The RT and replication assays were done three to five times and the standard deviations are shown (Supplementary Tables 7, 10 and 11). *P < 0.05 (Kruskal–Wallis test with Dunn’s correction for multiple comparisons).
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Comment in

  • HIV: Tagged for destruction.
    Molloy S. Molloy S. Nat Rev Microbiol. 2012 Jan 16;10(2):81. doi: 10.1038/nrmicro2739. Nat Rev Microbiol. 2012. PMID: 22245926 No abstract available.

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