Global landscape of HIV–human protein complexes

Journal name:
Nature
Volume:
481,
Pages:
365–370
Date published:
DOI:
doi:10.1038/nature10719
Received
Accepted
Published online

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 spectrometry1, 2, 3 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.

At a glance

Figures

  1. Affinity purification of HIV-1 proteins, analysis and scoring of mass spectrometry data.
    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.

  2. Comparison of PPI data with other HIV data sets.
    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 screens15, 16, 17, 18 (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.

  3. Network representation of the HIV-human PPIs.
    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 VirusMint14.

  4. eIF3d is cleaved by HIV-1 PR and inhibits infection.
    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 complex29. 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 domain26. 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).

References

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Author information

Affiliations

  1. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, USA

    • Stefanie Jäger,
    • Natali Gulbahce,
    • Jeffrey R. Johnson,
    • Kathryn E. McGovern,
    • Michael Shales,
    • Kathy Li,
    • Hilda Hernandez,
    • Gwendolyn M. Jang,
    • Marie Fahey,
    • Cathal Mahon &
    • Nevan J. Krogan
  2. California Institute for Quantitative Biosciences, QB3, San Francisco, California 94158, USA

    • Stefanie Jäger,
    • Peter Cimermancic,
    • Natali Gulbahce,
    • Jeffrey R. Johnson,
    • Kathryn E. McGovern,
    • Michael Shales,
    • Kathy Li,
    • Hilda Hernandez,
    • Gwendolyn M. Jang,
    • Eyal Akiva,
    • Marie Fahey,
    • Cathal Mahon,
    • Tanja Kortemme,
    • Ryan D. Hernandez,
    • Charles S. Craik,
    • Alma Burlingame,
    • Andrej Sali,
    • Alan D. Frankel &
    • Nevan J. Krogan
  3. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158, USA

    • Peter Cimermancic,
    • Eyal Akiva,
    • Tanja Kortemme,
    • Ryan D. Hernandez &
    • Andrej Sali
  4. J. David Gladstone Institutes, San Francisco, California 94158, USA

    • Jeffrey R. Johnson &
    • Nevan J. Krogan
  5. Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94158, USA

    • Starlynn C. Clarke,
    • Kathy Li,
    • Hilda Hernandez,
    • Cathal Mahon,
    • Anthony J. O’Donoghue,
    • John H. Morris,
    • David A. Maltby,
    • Charles S. Craik,
    • Alma Burlingame &
    • Andrej Sali
  6. Department of Biochemistry, University of Utah, Salt Lake City, Utah 84112, USA

    • Gaelle Mercenne &
    • Wesley I. Sundquist
  7. Sanford-Burnham Medical Research Institute, La Jolla, California 92037, USA

    • Lars Pache &
    • Sumit K. Chanda
  8. Department of Biochemistry and Biophysics, University of California, San Francisco, California 94158, USA

    • Gwendolyn M. Jang,
    • Iván D’Orso,
    • Jason Fernandes &
    • Alan D. Frankel
  9. Department of Microbiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Shoshannah L. Roth,
    • Melanie Stephens &
    • Frederic D. Bushman
  10. The Salk Institute for Biological Studies, La Jolla, California 92037, USA

    • John Marlett &
    • John A. Young
  11. Department of Chemistry, University of California, Berkeley, California 94720, USA

    • Aleksandar Todorovic
  12. Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA

    • Tom Alber
  13. Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland

    • Gerard Cagney
  14. Host Pathogen Circuitry Group, University of California, San Francisco, California 94158, USA

    • Tanja Kortemme,
    • Ryan D. Hernandez,
    • Andrej Sali,
    • Alan D. Frankel &
    • Nevan J. Krogan
  15. Present address: Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA, 75390

    • Iván D’Orso

Contributions

S.J. generated the protein–protein interaction map; P.C. developed the MiST scoring system; N.G., M. Shales, E.A., M.F., J.H.M., J.R.J. and R.D.H. provided computational support; K.E.M., K.L., J.R.J., H.H., G.M.J., I.D., J.F. and D.A.M. provided experimental support; S.J., S.C.C., A.J.O. and A.T. characterized the PR–eIF3d interaction; S.J., G.M.J., C.M. and G.M. confirmed the interactions by immunoprecipitation/western blot; L.P., S.L.R., J.M. and M. Stephens used RNAi for functional verification; T.A., G.C., F.D.B., J.A.Y., S.K.C., W.I.S., T.K., R.D.H., C.S.C., A.B., A.S., A.D.F. and N.J.K. supervised the research; and S.J., P.C., A.S. and N.J.K. wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Information (5.7M)

    The file contains Supplementary Figures 1-20 with legends, Supplementary Tables 1-14, Supplementary Methods, a Supplementary Discussion and Supplementary References.

Excel files

  1. Supplementary Data 1 (10.6M)

    The data shows raw MS data.

  2. Supplementary Data 2 (1.8M)

    The data shows three components used for MiST scoring.

  3. Supplementary Data 3 (1.5M)

    The data shows MiST scored MS data (>0.75 and full list).

  4. Supplementary Data 4 (223K)

    The data shows functional enrichment of host factors.

  5. Supplementary Data 5 (115K)

    The data shows VirusMint PPIs.

  6. Supplementary Data 6 (28K)

    The data shows overlap of VirusMint with all MiST scores.

  7. Supplementary Data 7 (109K)

    The data shows published RNAi screens.

  8. Supplementary Data 8 (210K)

    The data shows overlap of RNAi screens with all MiST scores.

  9. Supplementary Data 9 (44K)

    The data shows human-human PPIs used in HIV-human network representation.

Additional data