Letter | Published:

An orthogonal proteomic survey uncovers novel Zika virus host factors

Naturevolume 561pages253257 (2018) | Download Citation


Zika virus (ZIKV) has recently emerged as a global health concern owing to its widespread diffusion and its association with severe neurological symptoms and microcephaly in newborns1. However, the molecular mechanisms that are responsible for the pathogenicity of ZIKV remain largely unknown. Here we use human neural progenitor cells and the neuronal cell line SK-N-BE2 in an integrated proteomics approach to characterize the cellular responses to viral infection at the proteome and phosphoproteome level, and use affinity proteomics to identify cellular targets of ZIKV proteins. Using this approach, we identify 386 ZIKV-interacting proteins, ZIKV-specific and pan-flaviviral activities as well as host factors with known functions in neuronal development, retinal defects and infertility. Moreover, our analysis identified 1,216 phosphorylation sites that are specifically up- or downregulated after ZIKV infection, indicating profound modulation of fundamental signalling pathways such as AKT, MAPK–ERK and ATM–ATR and thereby providing mechanistic insights into the proliferation arrest elicited by ZIKV infection. Functionally, our integrative study identifies ZIKV host-dependency factors and provides a comprehensive framework for a system-level understanding of ZIKV-induced perturbations at the levels of proteins and cellular pathways.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

The mass-spectrometry-based proteomics data were deposited at the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the following dataset identifiers: PXD009551, PXD009557, PXD009560 and PXD009561. The protein interactions from this publication have been submitted to the IMEx (http://www.imexconsortium.org) consortium through IntAct38 with the identifier IM-26452.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Miner, J. J. & Diamond, M. S. Zika virus pathogenesis and tissue tropism. Cell Host Microbe 21, 134–142 (2017).

  2. 2.

    Neufeldt, C. J., Cortese, M., Acosta, E. G. & Bartenschlager, R. Rewiring cellular networks by members of the Flaviviridae family. Nat. Rev. Microbiol. 16, 125–142 (2018).

  3. 3.

    Chatel-Chaix, L. et al. Dengue virus perturbs mitochondrial morphodynamics to dampen innate immune responses. Cell Host Microbe 20, 342–356 (2016).

  4. 4.

    Slomnicki, L. P. et al. Nucleolar enrichment of brain proteins with critical roles in human neurodevelopment. Mol. Cell Proteomics 15, 2055–2075 (2016).

  5. 5.

    Li, H. et al. Ly-1 antibody reactive clone is an important nucleolar protein for control of self-renewal and differentiation in embryonic stem cells. Stem Cells 27, 1244–1254 (2009).

  6. 6.

    Jung, M. Y., Lorenz, L. & Richter, J. D. Translational control by neuroguidin, a eukaryotic initiation factor 4E and CPEB binding protein. Mol. Cell. Biol. 26, 4277–4287 (2006).

  7. 7.

    Liang, Q. et al. Zika virus NS4A and NS4B proteins deregulate Akt–mTOR signaling in human fetal neural stem cells to inhibit neurogenesis and induce autophagy. Cell Stem Cell 19, 663–671 (2016).

  8. 8.

    Radke, J., Stenzel, W. & Goebel, H. H. Human NCL neuropathology. Biochim. Biophys. Acta 1852, 2262–2266 (2015).

  9. 9.

    Ochrietor, J. D. & Linser, P. J. 5A11/Basigin gene products are necessary for proper maturation and function of the retina. Dev. Neurosci. 26, 380–387 (2004).

  10. 10.

    Politis, P. K. et al. BM88/CEND1 coordinates cell cycle exit and differentiation of neuronal precursors. Proc. Natl Acad. Sci. USA 104, 17861–17866 (2007).

  11. 11.

    Gehman, L. T. et al. The splicing regulator Rbfox2 is required for both cerebellar development and mature motor function. Genes Dev. 26, 445–460 (2012).

  12. 12.

    Tarlungeanu, D. C. et al. Impaired amino acid transport at the blood brain barrier is a cause of autism spectrum disorder. Cell 167, 1481–1494 (2016).

  13. 13.

    Souza, B. S. et al. Zika virus infection induces mitosis abnormalities and apoptotic cell death of human neural progenitor cells. Sci. Rep. 6, 39775 (2016).

  14. 14.

    Tang, H. et al. Zika virus infects human cortical neural progenitors and attenuates their growth. Cell Stem Cell 18, 587–590 (2016).

  15. 15.

    Watanabe, K. et al. A ROCK inhibitor permits survival of dissociated human embryonic stem cells. Nat. Biotechnol. 25, 681–686 (2007).

  16. 16.

    Wojcechowskyj, J. A. et al. Quantitative phosphoproteomics reveals extensive cellular reprogramming during HIV-1 entry. Cell Host Microbe 13, 613–623 (2013).

  17. 17.

    Söderholm, S. et al. Phosphoproteomics to characterize host response during influenza A virus infection of human macrophages. Mol. Cell Proteomics 15, 3203–3219 (2016).

  18. 18.

    Humphrey, S. J., Azimifar, S. B. & Mann, M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat. Biotechnol. 33, 990–995 (2015).

  19. 19.

    Cao, B., Parnell, L. A., Diamond, M. S. & Mysorekar, I. U. Inhibition of autophagy limits vertical transmission of Zika virus in pregnant mice. J. Exp. Med. 214, 2303–2313 (2017).

  20. 20.

    Ghouzzi, V. E. et al. ZIKA virus elicits P53 activation and genotoxic stress in human neural progenitors similar to mutations involved in severe forms of genetic microcephaly. Cell Death Dis. 7, e2440 (2016).

  21. 21.

    Morooka, T. & Nishida, E. Requirement of p38 mitogen-activated protein kinase for neuronal differentiation in PC12 cells. J. Biol. Chem. 273, 24285–24288 (1998).

  22. 22.

    Li, H., Chen, G., Zhou, B. & Duan, S. Actin filament assembly by myristoylated alanine-rich C kinase substrate-phosphatidylinositol-4,5-diphosphate signaling is critical for dendrite branching. Mol. Biol. Cell 19, 4804–4813 (2008).

  23. 23.

    Yamashita, N. et al. Phosphorylation of CRMP2 (collapsin response mediator protein 2) is involved in proper dendritic field organization. J. Neurosci. 32, 1360–1365 (2012).

  24. 24.

    Marceau, C. D. et al. Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens. Nature 535, 159–163 (2016).

  25. 25.

    Savidis, G. et al. Identification of Zika virus and dengue virus dependency factors using functional genomics. Cell Rep. 16, 232–246 (2016).

  26. 26.

    Chavali, P. L. et al. Neurodevelopmental protein Musashi-1 interacts with the Zika genome and promotes viral replication. Science 357, 83–88 (2017).

  27. 27.

    Barrows, N. J. et al. A screen of FDA-approved drugs for inhibitors of Zika virus infection. Cell Host Microbe 20, 259–270 (2016).

  28. 28.

    Shao, Q. et al. The African Zika virus MR-766 is more virulent and causes more severe brain damage than current Asian lineage and dengue virus. Development 144, 4114–4124 (2017).

  29. 29.

    Havlicek, S. et al. Gene dosage-dependent rescue of HSP neurite defects in SPG4 patients' neurons. Human Molecular Genetics 23, 2527–2541 (2014).

  30. 30.

    Paul, D., Bartenschlager, R. & McCormick, C. The predominant species of nonstructural protein 4B in hepatitis C virus-replicating cells is not palmitoylated. J. Gen. Virol. 96, 1696–1701 (2015).

  31. 31.

    Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).

  32. 32.

    Steger, M. et al. Phosphoproteomics reveals that Parkinson’s disease kinase LRRK2 regulates a subset of Rab GTPases. eLife 5, e12813 (2016).

  33. 33.

    Scaturro, P., Cortese, M., Chatel-Chaix, L., Fischl, W. & Bartenschlager, R. Dengue virus non-structural protein 1 modulates infectious particle production via interaction with the structural proteins. PLoS Pathog. 11, e1005277 (2015).

  34. 34.

    Gebhardt, A. et al. mRNA export through an additional cap-binding complex consisting of NCBP1 and NCBP3. Nat. Commun. 6, 8192 (2015).

  35. 35.

    Hubner, N. C. et al. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189, 739–754 (2010).

  36. 36.

    Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).

  37. 37.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

  38. 38.

    Orchard, S. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358–D363 (2014).

Download references


We thank D. Mauceri, F. Sacco and L. Chatel-Chaix for discussions and R. Hornberger, I. Paron, K. Mayr and G. Sowa for technical assistance. The work in the authors’ laboratory was funded by an ERC starting grant (StG 311339, iVIP), the Max-Planck free-floater program, the German Research Foundation (PI1084/2, PI1084/3, PI1084/4, TRR 237 and TRR179) and the Federal Ministry for Education and Research (ERA-Net grant ERASe) all to A.Pi. R.B. was supported by the Deutsche Forschungsgemeinschaft (BA1505/8-1). A. Pł. was supported by the Horizon 2020: Marie Skłodowska-Curie ETN “ANTIVIRALS” (grant agreement 642434 to R.B.). M.G. was supported by the DFG (SFB871) and the advanced ERC grant ChroNeuroRepair.

Reviewer information

Nature thanks B. Berninger, I. M. Cristea, M. Evans and J. MacKenzie for their contribution to the peer review of this work.

Author information


  1. Max-Planck Institute of Biochemistry, Innate Immunity Laboratory, Martinsried, Germany

    • Pietro Scaturro
    • , Alexey Stukalov
    • , Darya A. Haas
    •  & Andreas Pichlmair
  2. Technical University of Munich, School of Medicine, Institute of Virology, Munich, Germany

    • Pietro Scaturro
    • , Alexey Stukalov
    •  & Andreas Pichlmair
  3. Department of Infectious Diseases, Molecular Virology, University of Heidelberg, Heidelberg, Germany

    • Mirko Cortese
    • , Anna Płaszczyca
    •  & Ralf Bartenschlager
  4. Institute of Stem Cell Research, Helmholtz Center Munich, Neuherberg, Germany

    • Kalina Draganova
    •  & Magdalena Götz
  5. Physiological Genomics, Biomedical Center, Ludwig-Maximilians-Universitaet, Munich, Germany

    • Kalina Draganova
    •  & Magdalena Götz
  6. German Center for Infection Research (DZIF), Heidelberg partner site, Heidelberg, Germany

    • Ralf Bartenschlager
  7. Synergy, Excellence Cluster for Systems Neurology, Biomedical Center, Ludwig-Maximilians-Universitaet, Munich, Germany

    • Magdalena Götz
  8. German Center for Infection Research (DZIF), Munich partner site, Munich, Germany

    • Andreas Pichlmair


  1. Search for Pietro Scaturro in:

  2. Search for Alexey Stukalov in:

  3. Search for Darya A. Haas in:

  4. Search for Mirko Cortese in:

  5. Search for Kalina Draganova in:

  6. Search for Anna Płaszczyca in:

  7. Search for Ralf Bartenschlager in:

  8. Search for Magdalena Götz in:

  9. Search for Andreas Pichlmair in:


P.S. and A.Pi. conceived the study. P.S. performed most of the experiments. D.A.H. performed hNPC proteomics. K.D. performed hNPC cell culture. M.G. and R.B. intellectually contributed toward data interpretation. A.S. implemented the bioinformatic pipeline, statistical analysis and data integration. A. Pł. generated phosphoproteomic samples. M.C. generated and analysed immunofluorescence staining experiments. P.S. and A.Pi. wrote the manuscript with input from all authors.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Pietro Scaturro or Andreas Pichlmair.

Extended data figures and tables

  1. Extended Data Fig. 1 Design rationale, expression and subcellular localization of HA-tagged ZIKV proteins.

    a, Schematic representation of ZIKV HA-tagged viral proteins and controls used in this study and experimental set-up used to generate the ZIKV cellular interactome. The full-length ZIKV genome is shown at the top, with the 5′ and 3′ untranslated regions depicted with their putative secondary structures. Polyprotein cleavage products are separated by vertical lines and labelled as specified. The individual open reading frames (ORFs) of each of the ZIKV proteins were fused with an HA epitope either at their N terminus or C terminus as depicted. Numbers above each sequence refer to the nucleotide sequence of the ZIKV isolate FSS13025 used as reference (GeneBank: KU955593.1). To ensure the correct subcellular localization and protein topology, the sequence of the capsid anchor (Canch), the transmembrane domain of prM (prMTM) and the transmembrane domain of Envelope (ETM) were fused at the N termini of prM, envelope and NS1, respectively. Additionally, given the complex topology of NS2A, the transmembrane domain of envelope, and a fusion protein encoding the first and the last 50 amino acids of NS1 was fused at its N terminus. As additional controls, an empty pWPI-lentiviral vector (NT) or HA-tagged non-structural protein 4B of hepatitis C virus (NS4B-HCV) and Gaussia luciferase (G. luciferase) were included to monitor for non-specific binding to the anti-HA beads as well as organelle-dependent enrichment artefacts in AP–LC–MS/MS analysis. b, Intracellular levels of ZIKV proteins in lentivirus-transduced SK-N-BE2 cells. SK-N-BE2 cells were transduced with lentiviruses encoding each of the ZIKV proteins at an MOI of 3, and 72 h later cell lysates that had been clarified by centrifugation were used for western blotting against HA and β-actin. Numbers on the left refer to molecular weight standards given in kDa. Asterisks mark proteins expressed at a lower level, which can be observed upon longer exposure of the membrane. Note that C-terminally tagged NS2A and N-terminally tagged NS2B-3 could not be detected, whereas the C-terminally tagged NS2B-3 fusion protein was expressed at the expected molecular weight, and was therefore used for further experiments in this study. The bottom lane indicates the predicted molecular weight of each viral protein in kDa. n = 2 independent experiments. A representative blot is shown. c, Subcellular localization of ZIKV proteins in lentivirus-transduced SK-N-BE2 cells. SK-N-BE2 cells were seeded in 24-well plates and transduced as described above. After 72 h of transduction, viral proteins were detected using rabbit anti-HA antibody and Alexa-Fluor488-conjugated secondary antibody and imaged by confocal microscopy. Scale bar, 10 μm. n = 1 experiment.

  2. Extended Data Fig. 2 Volcano plots of individual ZIKV-interacting proteins.

    Volcano plots of label-free AP–LC–MS/MS of individual ZIKV proteins. Each volcano plot displays all identified proteins (median log2(fold change) of bait-specific protein enrichment in comparison to the background plotted against the corresponding −log10(P value). Dotted grey lines represent the log2(fold change) and P value cut-offs used. High-confidence interacting proteins (log2(fold change) ≥ 2.5, unadjusted one-sided P ≤ 0.01) are coloured according to their subcellular localization. In the case of capsid, due to its highly hydrophobic character and multiple subcellular localizations, a more stringent P value cut-off was used (unadjusted one-sided P ≤ 5 × 10−5) and ribosomal proteins were not individually labelled. The respective viral bait of each AP is shown in black, black circles represent known DENV target proteins while proteins with roles in neurogenesis, neurodegenerative diseases or infertility are labelled in red. n = 4 independent experiments, significance testing (P values) results from Bayesian statistical modelling (see Methods, ‘Statistical analysis of MS data’).

  3. Extended Data Fig. 3 Validation of NS4B-interacting proteins upon ZIKV infection.

    a, Reciprocal co-immunoprecipitation of NS4B with Flag-tagged host factors. SK-N-BE2 cells transiently transduced with empty lentiviruses (NT) or lentiviruses expressing Flag-tagged CEND1, CLN6, TMEM41b, RBFOX2 and CHP1 were mock-infected or infected with ZIKV H/PF/2013 (MOI = 1) and three days later Flag-immunoprecipitated proteins were probed with a ZIKV-NS4B-specific antibody. The bottom lane indicates the predicted molecular weights of each viral protein in kDa. n = 3 independent experiments. Representative blots are shown. b, Validation of anti-DENV-NS4B-specific antibody for immunodetection of ZIKV-NS4B. SK-N-BE2 cells were transiently transduced with empty lentiviruses (NT) or lentiviruses expressing HCV-NS4B–HA, DENV-NS4B or ZIKV-NS4B–HA, and 72 h later were probed consecutively with mouse anti-HA (left) or rabbit anti-DENV-NS4B (right) specific antibodies. ZIKV-infected SK-N-BE2 cell lysates, shown on the right side, were included as control. Asterisks indicate unspecific bands. n = 1 experiment. c, Reciprocal co-immunoprecipitation of capsid with Flag-tagged host factors. SK-N-BE2 cells transiently transduced with empty lentiviruses (NT) or lentiviruses expressing Flag-tagged SMN1, LYAR, LARP7 and NGDN were infected as described above and Flag-immunoprecipitated proteins probed with a ZIKV capsid-specific antibody. n = 2 independent experiments. Representative blots are shown. d, hNPCs were transduced with lentiviruses expressing Flag-tagged CLN6 alone (mock) or co-transduced with lentiviruses expressing HCV-NS4B–HA or ZIKV-NS4B–HA (MOI = 3), stained with Flag- and HA-specific antibodies and imaged by confocal microscopy. Boxed areas are enlarged on the right. Data are mean ± s.d. of Pearson’s correlation coefficients (each dot represent a single cell). Scale bars, 10 μm. n = 2 independent experiments. e, Network representation of CLN6-interacting cellular proteins and comparison with the ZIKV-NS4B interactors. AP–LC–MS/MS analysis of Flag-tagged CLN6 in SK-N-BE2 cells revealed novel CLN6-specific interacting proteins (displayed in magenta; including TELO2 and mTOR) as well as shared cellular interactors with ZIKV-NS4B (displayed in yellow, including ICMT and STT3A). Solid lines represent specific interactors identified by AP–MS/MS analysis (log2(fold change) ≥ 1.5, unadjusted one-sided P ≤ 0.02), grey dotted lines represent previously published protein–protein interactions. n = 4 independent experiments, significance testing results from Bayesian statistical modelling.

  4. Extended Data Fig. 4 Volcano plots of ZIKV-NS4B and HCV-NS4B-interacting proteins.

    a, Volcano plot comparing the specificity of protein enrichment in ZIKV-NS4B versus HCV-NS4B AP (median log2(fold change) plotted against −log10(unadjusted P) for ZIKV-NS4B versus HCV-NS4B). Specific NS4B-interacting proteins are labelled as described in Extended Data Fig. 2. Previously reported interactors of HCV-NS4B, or the ZIKV closely related DENV-NS4B are shown in blue and green, respectively. n = 4 independent experiments, significant testing results from Bayesian statistical modelling. b, Co-immunoprecipitation of ZIKV-NS4B–HA with endogenous host proteins. Cell lysates of SK-N-BE2 cells transduced with empty lentiviruses (NT) or lentivirus-expressing HA-tagged HCV-, DENV- or ZIKV-NS4B proteins were used for HA-immunoaffinity purification and probed with the indicated antibodies. n = 3 independent experiments. Representative blots are shown. c, Reciprocal co-immunoprecipitation of NS4B with Flag-tagged CLN6. SK-N-BE2 cells transiently transduced with empty lentiviruses (NT) or lentivirus-expressing Flag-tagged CLN6 were infected with ZIKV (H/PF/2013) and Flag-immunoprecipitated proteins probed with NS4B-specific antibodies. n = 3 independent experiments. Representative blots are shown.

  5. Extended Data Fig. 5 Effects of ZIKV infection or NS4B transduction on undifferentiated or differentiated hNPCs.

    a, Experimental approach used for hNPC differentiation. hNPCs were transduced with empty (NT), HCV-NS4B–HA- or ZIKV-NS4B–HA-expressing lentiviruses (MOI = 3), mock-infected or infected with ZIKV (H/PF/2013) (MOI = 0.01). After 48 h, differentiation was induced for five days by growth factor withdrawal and addition of 10 μM ROCK inhibitor, while undifferentiated cells were kept in NPC medium in the presence of bFGF. Cell pellets were then used for quantitative label-free LC–MS/MS-based profiling of the global proteome. b, Characterization of hNPCs used for proteomic analysis. Undifferentiated hNPCs kept in the presence of bFGF were stained with (i) SOX2- and GFAP-, (ii) Ki-67- and MAP2-, and (iii) nestin-specific antibodies. Alternatively, hNPCs were differentiated for five days in the presence of a ROCK inhibitor and were stained with (iv) Ki-67- and MAP2-specific antibodies. Under proliferating conditions, hNPCs expressed Ki-67, SOX2 and nestin, confirming the maintenance of their proliferative state in the presence of bFGF. No neuron-specific protein expression (MAP2) or astrocyte marker expression (GFAP) was detected under these conditions. After differentiation for five days in vitro in the presence of ROCK inhibitor, the neuronal marker MAP2 was upregulated whereas the proliferation marker Ki-67 was downregulated, suggesting commitment towards the neuronal lineage. Scale bars, 20 μm. n = 3 independent experiments. Representative images are shown. c, Cellular processes enriched in NS4B-ZIKV changes in proliferating or differentiating hNPCs. Right-tailed Fisher’s exact test, Benjamini–Hochberg-corrected P values are shown (see Supplementary Table 3). d, Total number of proteins identified across biological replicates and conditions. e, Heat map of all significant changes occurring upon proliferation and differentiation conditions in NS4B-ZIKV-transduced cells in comparison to NS4B-HCV-tranduced cells and mock transduction. Unadjusted P ≤ 0.01, |log2(fold change)| ≥ 1.0; significance testing results from Bayesian statistical modelling. ce, n = 4 independent experiments.

  6. Extended Data Fig. 6 Global proteomic analysis of ZIKV-specific effects in differentiating or proliferating hNPCs.

    a, hNPCs were mock-infected or infected with ZIKV (H/PF/2013) (MOI = 0.01) and cultured under proliferating or differentiating conditions as described in Extended Data Fig. 5a. Volcano plots display proteins that were significantly up- or downregulated by ZIKV-infection in differentiated (left) or undifferentiated (right) hNPCs. Viral proteins are labelled in red. Proteins with functions in antiviral immunity are labelled in green. Significance cut-offs are indicated by the dashed grey lines. n = 4 independent experiments. Bayesian statistical modelling, |log2(fold change)| ≥ 1; unadjusted two-sided P ≤ 0.01. b, Venn diagram displaying the total number of significantly up- or downregulated proteins in every experimental condition (|log2(fold change)| ≥ 1; P < 0.01) and the total number and gene names of significantly modulated proteins similarly regulated by ZIKV infection or ZIKV-NS4B transduction upon differentiation (diff) or proliferation (undiff). n = 4 independent experiments.

  7. Extended Data Fig. 7 Time-resolved phosphoproteomic analysis of ZIKV-infected SK-N-BE2 cells.

    a, Total number of phosphopeptides, phosphosites and phosphoproteins identified in this study. b, Total number of phosphosites identified across biological replicates and conditions (n = 4). c, Relative percentage of class I, II and III phosphosites (localization probability >0.75) and phosphorylation of specific Ser, Thr and Tyr residues. d, Venn diagram of significantly changing phosphosites at 24, 48 and 72 h post-infection (h.p.i.). e, Network analysis of cellular processes significantly modulated by ZIKV infection at phosphorylation level (IPA). Nodes are canonical pathways identified with Fisher’s exact test; edges are shared proteins between the pathways (n = 4 independent experiments, right-tailed Fisher’s exact test, Benjamini–Hochberg-adjusted P ≤ 0.05). f, Profile plots of significantly changing phosphosites, mapped to pathways in Fig. 3d, and their corresponding total protein levels at 24, 48 and 72 h after ZIKV infection (orange for phosphorylation levels, yellow for protein levels) or mock treatment (grey). Numbers next to protein names refer to MaxQuant protein group IDs (Supplementary Table 5). Points are normalized intensities of individual replicates, solid line is median, filled area corresponds to 25–75 percentiles, dashed lines mark 2.5–97.5 percentiles of the posterior distribution. n = 4 independent experiments; Bayesian statistical modelling.

  8. Extended Data Fig. 8 Biological functions and pathways modulated by ZIKV at the phosphorylation level and validation by phospho-specific antibodies.

    a, b, Biological functions (a) and canonical pathways (b) modulated by ZIKV infection (IPA, n = 4, right-tailed Fisher’s exact test, unadjusted P ≤ 0.05. Corresponding Benjamini–Hochberg-adjusted P values are provided in Supplementary Tables 6 and 7). P, pathway; R, regulation; S, signalling. ce, Immunoblot analysis of mock- or ZIKV-infected SK-N-BE2 with phosphorylation- or virus-specific antibodies. Where indicated, samples were treated with 12-O-tetradecanoylphorbol-13-acetate (TPA; 32 nM) 15 min before lysis. n = 2. Representative blots are shown.

  9. Extended Data Fig. 9 Effect of shRNA-mediated silencing of ZIKV-modulated host proteins on viral replication.

    a, Schematic representation of the experimental set-up used for the shRNA screen. Selected target genes were chosen among the cellular host factors specifically binding ZIKV-capsid or -NS4B (interactome; n = 26), significantly regulated at the phosphoproteomic level by ZIKV infection (phosphoproteome; n = 6) or significantly regulated at the proteomic level both by ZIKV infection and ZIKV-NS4B-expression in hNPCs (proteome; n = 23). Controls included non-targeting shRNA of two different pLKO-based lentivirus generations (NT1 and NT2) used as reference; and shRNAs targeting ATP6V0C and Musashi-1, which impair flavivirus pH-dependent viral entry and ZIKV replication, respectively. Two individual shRNAs per gene were selected from the MISSION TRC library (Sigma-Aldrich), and used for lentivirus production in HEK293T cells as described in the Methods. SK-N-BE2 cells were transduced with individual lentiviruses (three wells per shRNA) in three independent experiments. Three days post-transduction, cells were infected with ZIKV H/PF/2013 (MOI = 0.1) and 48 h later virus-containing supernatants were used for titration by PFU assay. The cellular viability was assessed with a resazurin-based assay that was performed in parallel. b, c, ZIKV titres and cell viability upon knockdown of selected host factors. Viral titres, as determined by PFU assay, and cell viability, as determined by resazurin assay, are expressed as a percentage of the non-targeting controls. n = 3 independent experiments. The box plot middle line corresponds to the median, the hinges represent the first and third quartiles, the lower (upper) whisker extends from the lower (upper) hinge to the minimum (maximum) value no further than 1.5× the interquartile range. d, Cell viability of SK-N-BE2 cells upon knockdown of selected host factors (n = 4 independent experiments, normalized mean ± s.d.; related to Fig. 3b). e, Validation of the silencing efficiency of selected shRNA targeting ZIKV host factors. Gene silencing was evaluated for each cellular protein via western blot detection using the antibodies specified on the right. Asterisk indicates non-specific bands, numbers on the left indicate molecular weight markers expressed in kDa. n = 2 independent experiments. Representative blots are shown.

  10. Extended Data Fig. 10 Integration of data from orthogonal proteomic screens and protein–protein interaction databases.

    Integrated network of ZIKV-interacting proteins, CLN6-interacting cellular proteins, proteins changing at proteome and/or phosphoproteome levels plus measured or published interactions between them. Baits are shown as large red squares. Solid lines represent specific interactions identified by AP–MS/MS analysis, grey lines represent published protein–protein interactions from IntAct and CORUM databases. Up- or downregulation at the proteome level is marked with filled circles (hNPCs: up, red; down, dark blue; SK-N-BE2: up, orange; down, light blue). Phosphorylation changes in SK-N-BE2 are presented as red (up) or blue (down) circle borders.

Supplementary information

  1. Supplementary Figures

    This file contains raw source data for immunoblots (Extended Data Fig. 1b, 3a, 3b, 3c, 4b, 4c, 8c, 8d, 8e, 9e).

  2. Reporting Summary

  3. Supplementary Tables

    This file contains Supplementary Tables 1-10 and a Supplementary Table Guide.

About this article

Publication history







By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.