Quantitative Temporal Viromics

Literature Cited

1. 1. 

   Britt W 2018. Maternal immunity and the natural history of congenital human cytomegalovirus infection. Viruses 10:8405
   [Google Scholar]
2. 2. 

   Lussignol M, Esclatine A 2017. Herpesvirus and autophagy: “All right, everybody be cool, this is a robbery!”. Viruses 9:12372
   [Google Scholar]
3. 3. 

   Altman AM, Miller MJ, Mahmud J, Smith NA, Chan GC 2020. A Human cytomegalovirus-induced autophagy prevents necroptosis of infected monocytes. J. Virol. 94:22e01022-20
   [Google Scholar]
4. 4. 

   Weekes MP, Tomasec P, Huttlin EL, Fielding CA, Nusinow D et al. 2014. Quantitative temporal viromics: an approach to investigate host-pathogen interaction. Cell 157:1460–72
   [Google Scholar]
5. 5. 

   Wang LW, Shen H, Nobre L, Ersing I, Paulo JA et al. 2019. A Epstein-Barr-virus-induced one-carbon metabolism drives B cell transformation. Cell Metab 30:3539–55
   [Google Scholar]
6. 6. 

   Ersing I, Nobre L, Wang LW, Soday L, Ma Y et al. 2017. A temporal proteomic map of Epstein-Barr virus lytic replication in B cells. Cell Rep 19:71479–93
   [Google Scholar]
7. 7. 

   Caller LG, Davies CTR, Antrobus R, Lehner PJ, Weekes MP, Crump CM. 2019. Temporal proteomic analysis of BK polyomavirus infection reveals virus-induced G2 arrest and highly effective evasion of innate immune sensing. J. Virol. 93:16e00595-19
   [Google Scholar]
8. 8. 

   Soday L, Lu Y, Albarnaz JD, Davies CTR, Antrobus R et al. 2019. Quantitative temporal proteomic analysis of vaccinia virus infection reveals regulation of histone deacetylases by an interferon antagonist. Cell Rep 27:1920–33
   [Google Scholar]
9. 9. 

   Soh TK, Davies CTR, Muenzner J, Hunter LM, Barrow HG et al. 2020. Temporal proteomic analysis of herpes simplex virus 1 infection reveals cell-surface remodeling via pUL56-mediated GOPC degradation. Cell Rep 33:1108235
   [Google Scholar]
10. 10. 

    Turnbull ML, Wise HM, Nicol MQ, Smith N, Dunfee RL et al. 2016. Role of the B allele of influenza A virus segment 8 in setting mammalian host range and pathogenicity. J. Virol. 90:209263–84
    [Google Scholar]
11. 11. 

    Nightingale K, Lin K-M, Ravenhill BJ, Davies C, Nobre L et al. 2018. High-definition analysis of host protein stability during human cytomegalovirus infection reveals antiviral factors and viral evasion mechanisms. Cell Host Microbe 24:447–60.e11
    [Google Scholar]
12. 12. 

    Fletcher-Etherington A, Nobre L, Nightingale K, Antrobus R, Nichols J et al. 2020. Human cytomegalovirus protein pUL36: a dual cell death pathway inhibitor. PNAS 117:3118771–79
    [Google Scholar]
13. 13. 

    Bojkova D, Klann K, Koch B, Widera M, Krause D et al. 2020. Proteomics of SARS-CoV-2-infected host cells reveals therapy targets. Nature 583:7816469–72
    [Google Scholar]
14. 14. 

    Appelberg S, Gupta S, Akusjärvi SS, Ambikan AT, Mikaeloff F et al. 2020. Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells. Emerg. Microbes Infect. 9:11748–60
    [Google Scholar]
15. 15. 

    Stukalov A, Girault V, Grass V, Bergant V, Karayel O et al. 2020. Multi-level proteomics reveals host-perturbation strategies of SARS-CoV-2 and SARS-CoV. bioRxiv 2020.06.17.156455. https://doi.org/10.1101/2020.06.17.156455
    [Crossref]
16. 16. 

    Bouhaddou M, Memon D, Meyer B, White KM, Rezelj VV et al. 2020. The global phosphorylation landscape of SARS-CoV-2 infection. Cell 182:3685–712
    [Google Scholar]
17. 17. 

    Hekman RM, Hume AJ, Goel RK, Abo KM, Huang J et al. 2020. Actionable cytopathogenic host responses of human alveolar type 2 cells to SARS-CoV-2. Mol. Cell 80:1104–22.e9
    [Google Scholar]
18. 18. 

    Jean Beltran PM, Mathias RA, Cristea IM 2016. A portrait of the human organelle proteome in space and time during cytomegalovirus infection. Cell Syst 3:4361–73
    [Google Scholar]
19. 19. 

    O'Connor CM, DiMaggio PA, Shenk T, Garcia BA. 2014. Quantitative proteomic discovery of dynamic epigenome changes that control human cytomegalovirus (HCMV) infection. Mol. Cell. Proteom. 13:92399–410
    [Google Scholar]
20. 20. 

    Milbradt J, Kraut A, Hutterer C, Sonntag E, Schmeiser C et al. 2014. Proteomic analysis of the multimeric nuclear egress complex of human cytomegalovirus. Mol. Cell. Proteom. 13:82132–46
    [Google Scholar]
21. 21. 

    Karniely S, Weekes MP, Antrobus R, Rorbach J, Van Haute L et al. 2016. Human cytomegalovirus infection upregulates the mitochondrial transcription and translation machineries. mBio 7:2e00029-16
    [Google Scholar]
22. 22. 

    Ouwendijk WJD, Dekker LJM, van den Ham H-J, Lenac Rovis T, Haefner ES et al. 2020. Analysis of virus and host proteomes during productive HSV-1 and VZV infection in human epithelial cells. Front. Microbiol. 11:1179
    [Google Scholar]
23. 23. 

    Lum KK, Song B, Federspiel JD, Diner BA, Howard T, Cristea IM. 2018. Interactome and proteome dynamics uncover immune modulatory associations of the pathogen sensing factor cGAS. Cell Syst 7:6627–42
    [Google Scholar]
24. 24. 

    Dembowski JA, Deluca NA. 2018. Temporal viral genome-protein interactions define distinct stages of productive herpesviral infection. mBio 9:4e01182-18
    [Google Scholar]
25. 25. 

    Kulej K, Avgousti DC, Sidoli S, Herrmann C, Della Fera AN et al. 2017. Time-resolved global and chromatin proteomics during herpes simplex virus type 1 (HSV-1) infection. Mol. Cell. Proteom. 16:4S92–107
    [Google Scholar]
26. 26. 

    Berard AR, Coombs KM, Severini A. 2015. Quantification of the host response proteome after herpes simplex virus type 1 infection. J. Proteome Res. 14:2121–42
    [Google Scholar]
27. 27. 

    Gabaev I, Williamson JC, Crozier TWM, Schulz TF, Lehner PJ. 2020. Quantitative proteomics analysis of lytic KSHV infection in human endothelial cells reveals targets of viral immune modulation. Cell Rep 33:2108249
    [Google Scholar]
28. 28. 

    Valdés A, Zhao H, Pettersson U, Lind SB. 2020. Phosphorylation time-course study of the response during adenovirus type 2 infection. Proteomics 20:71900327
    [Google Scholar]
29. 29. 

    Valdés A, Zhao H, Pettersson U, Lind SB. 2018. Time-resolved proteomics of adenovirus infected cells. PLOS ONE 13:9e0204522
    [Google Scholar]
30. 30. 

    Källsten M, Gromova A, Zhao H, Valdés A, Konzer A et al. 2017. Temporal characterization of the non-structural adenovirus type 2 proteome and phosphoproteome using high-resolving mass spectrometry. Virology 511:240–48
    [Google Scholar]
31. 31. 

    Fu YR, Turnell AS, Davis S, Heesom KJ, Evans VC, Matthews DA. 2017. Comparison of protein expression during wild-type, and E1B-55k-deletion, adenovirus infection using quantitative time-course proteomics. J. Gen. Virol. 98:1377–88
    [Google Scholar]
32. 32. 

    Zhao H, Konzer A, Mi J, Chen M, Pettersson U, Lind SB. 2017. Posttranscriptional regulation in adenovirus infected cells. J. Proteome Res. 16:2872–88
    [Google Scholar]
33. 33. 

    Glover KKM, Zahedi-Amiri A, Lao Y, Spicer V, Klonisch T, Coombs KM. 2020. Zika infection disrupts proteins involved in the neurosensory system. Front. Cell Dev. Biol. 8:571
    [Google Scholar]
34. 34. 

    Beys-da-Silva WO, Rosa RL, Santi L, Berger M, Park SK et al. 2019. Zika virus infection of human mesenchymal stem cells promotes differential expression of proteins linked to several neurological diseases. Mol. Neurobiol. 56:74708–17
    [Google Scholar]
35. 35. 

    Scaturro P, Stukalov A, Haas DA, Cortese M, Draganova K et al. 2018. An orthogonal proteomic survey uncovers novel Zika virus host factors. Nature 561:7722253–57
    [Google Scholar]
36. 36. 

    Vasconcellos AF, Mandacaru SC, De Oliveira AS, Fontes W, Melo RM et al. 2020. Dynamic proteomic analysis of Aedes aegypti Aag-2 cells infected with Mayaro virus. Parasites Vectors 13:297
    [Google Scholar]
37. 37. 

    Cui Y, Liu P, Mooney BP, Franz AWE. 2020. Quantitative proteomic analysis of chikungunya virus-infected Aedes aegypti reveals proteome modulations indicative of persistent infection. J. Proteome Res. 19:62443–56
    [Google Scholar]
38. 38. 

    Treffers EE, Tas A, Scholte FEM, Van MN, Heemskerk MT et al. 2015. Temporal SILAC-based quantitative proteomics identifies host factors involved in chikungunya virus replication. Proteomics 15:132267–80
    [Google Scholar]
39. 39. 

    Nemeth J, Vongrad V, Metzner KJ, Strouvelle VP, Weber R et al. 2017. In vivo and in vitro proteome analysis of human immunodeficiency virus (HIV)-1-infected, human CD4⁺ T cells. Mol. Cell. Proteom. 16:4S108–23
    [Google Scholar]
40. 40. 

    Csosz É, Tóth F, Mahdi M, Tsaprailis G, Emri M, Tozsér J. 2019. Analysis of networks of host proteins in the early time points following HIV transduction. BMC Bioinform. 20:398
    [Google Scholar]
41. 41. 

    Golumbeanu M, Desfarges S, Hernandez C, Quadroni M, Rato S et al. 2019. Proteo-transcriptomic dynamics of cellular response to HIV-1 infection. Sci. Rep. 9:213
    [Google Scholar]
42. 42. 

    Lapek JD, Lewinski MK, Wozniak JM, Guatelli J, Gonzalez DJ. 2017. Quantitative temporal viromics of an inducible HIV-1 model yields insight to global host targets and phospho-dynamics associated with protein Vpr. Mol. Cell. Proteom. 16:81447–61
    [Google Scholar]
43. 43. 

    Greenwood EJD, Matheson NJ, Wals K, van den Boomen DJH, Antrobus R et al. 2016. Temporal proteomic analysis of HIV infection reveals remodelling of the host phosphoproteome by lentiviral Vif variants. eLife 5:e18296
    [Google Scholar]
44. 44. 

    Naamati A, Williamson JC, Greenwood EJD, Marelli S, Lehner PJ, Matheson NJ 2019. Functional proteomic atlas of HIV infection in primary human CD4⁺ T cells. eLife 8:e41431
    [Google Scholar]
45. 45. 

    Matheson NJ, Sumner J, Wals K, Rapiteanu R, Weekes MP et al. 2015. Cell surface proteomic map of HIV infection reveals antagonism of amino acid metabolism by Vpu and Nef. Cell Host Microbe 18:4409–23
    [Google Scholar]
46. 46. 

    Lupberger J, Croonenborghs T, Roca Suarez AA, Van Renne N, Jühling F et al. 2019. Combined analysis of metabolomes, proteomes, and transcriptomes of hepatitis C virus-infected cells and liver to identify pathways associated with disease development. Gastroenterology 157:2537–51
    [Google Scholar]
47. 47. 

    Ong JW, Tan KS, Ler SG, Gunaratne J, Choi H et al. 2019. Insights into early recovery from influenza pneumonia by spatial and temporal quantification of putative lung regenerating cells and by lung proteomics. Cells 8:9975
    [Google Scholar]
48. 48. 

    Shen S, Li J, Hilchey S, Shen X, Tu C et al. 2016. Ion-current-based temporal proteomic profiling of influenza-A-virus-infected mouse lungs revealed underlying mechanisms of altered integrity of the lung microvascular barrier. J. Proteome Res. 15:2540–53
    [Google Scholar]
49. 49. 

    Simon PF, McCorrister S, Hu P, Chong P, Silaghi A et al. 2015. Highly pathogenic H5N1 and novel H7N9 influenza A viruses induce more profound proteomic host responses than seasonal and pandemic H1N1 strains. J. Proteome Res. 14:114511–23
    [Google Scholar]
50. 50. 

    Galassie AC, Goll JB, Samir P, Jensen TL, Kristen L et al. 2017. Proteomics show antigen presentation processes in human immune cells after AS03-H5N1 vaccination. Proteomics 17:121600453
    [Google Scholar]
51. 51. 

    Ward MD, Brueggemann EE, Kenny T, Reitstetter RE, Mahone CR et al. 2019. Characterization of the plasma proteome of nonhuman primates during Ebola virus disease or melioidosis: a host response comparison. Clin. Proteom. 16:7
    [Google Scholar]
52. 52. 

    Kandasamy RK, Vladimer GI, Snijder B, Müller AC, Rebsamen M et al. 2016. A time-resolved molecular map of the macrophage response to VSV infection. Syst. Biol. Appl. 2:16027
    [Google Scholar]
53. 53. 

    Munday DC, Howell G, Barr JN, Hiscox JA. 2015. Proteomic analysis of mitochondria in respiratory epithelial cells infected with human respiratory syncytial virus and functional implications for virus and cell biology. J. Pharm. Pharmacol. 67:3300–18
    [Google Scholar]
54. 54. 

    Giacomantonio MA, Sterea AM, Kim Y, Paulo JA, Clements DR et al. 2020. Quantitative proteome responses to oncolytic reovirus in GM-CSF- and M-CSF-differentiated bone marrow-derived cells. J. Proteome Res. 19:2708–18
    [Google Scholar]
55. 55. 

    Clements DR, Murphy JP, Sterea A, Kennedy BE, Kim Y et al. 2017. Quantitative temporal in vivo proteomics deciphers the transition of virus-driven myeloid cells into M2 macrophages. J. Proteome Res. 16:93391–406
    [Google Scholar]
56. 56. 

    Zhu W, Smith JW, Huang C-M. 2010. Mass spectrometry-based label-free quantitative proteomics. J. Biomed. Biotechnol. 2010:840518
    [Google Scholar]
57. 57. 

    O'Brien JJ, Gunawardena HP, Paulo JA, Chen X, Ibrahim JG et al. 2018. The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments. Ann. Appl. Stat. 12:42075–95
    [Google Scholar]
58. 58. 

    Röst HL, Rosenberger G, Navarro P, Gillet L, Miladinoviä SM et al. 2014. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32:3219–23
    [Google Scholar]
59. 59. 

    Chen X, Wei S, Ji Y, Guo X, Yang F 2015. Quantitative proteomics using SILAC: principles, applications, and developments. Proteomics 15:183175–92
    [Google Scholar]
60. 60. 

    Merrill AE, Hebert AS, MacGilvray ME, Rose CM, Bailey DJ et al. 2014. NeuCode labels for relative protein quantification. Mol. Cell. Proteom. 13:92503–12
    [Google Scholar]
61. 61. 

    Overmyer KA, Tyanova S, Hebert AS, Westphall MS, Cox J, Coon JJ. 2018. Multiplexed proteome analysis with neutron-encoded stable isotope labeling in cells and mice. Nat. Protoc. 13:1293–306
    [Google Scholar]
62. 62. 

    Rauniyar N, Yates JR. 2014. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13:5293–309
    [Google Scholar]
63. 63. 

    Dayon L, Affolter M. 2020. Progress and pitfalls of using isobaric mass tags for proteome profiling. Expert Rev. Proteom. 17:2149–61
    [Google Scholar]
64. 64. 

    McAlister GC, Nusinow DP, Jedrychowski MP, Wühr M, Huttlin EL et al. 2014. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86:147150–58
    [Google Scholar]
65. 65. 

    Thompson A, Wö N, Koncarevic S, Selzer S, Bö G et al. 2019. TMTpro: design, synthesis, and initial evaluation of a proline-based isobaric 16-plex tandem mass tag reagent set. Anal. Chem. 91:15941–50
    [Google Scholar]
66. 66. 

    Choe L, D'Ascenzo M, Relkin NR, Pappin D, Ross P et al. 2007. 8-Plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease. Proteomics 7:203651–60
    [Google Scholar]
67. 67. 

    O'Connell JD, Paulo JA, O'Brien JJ, Gygi SP 2018. Proteome-wide evaluation of two common protein quantification methods. J. Proteome Res. 17:51934–42
    [Google Scholar]
68. 68. 

    Itzhak DN, Davies C, Tyanova S, Mishra A, Williamson J et al. 2017. A mass spectrometry-based approach for mapping protein subcellular localization reveals the spatial proteome of mouse primary neurons. Cell Rep 20:112706–18
    [Google Scholar]
69. 69. 

    Stepath M, Zülch B, Maghnouj A, Schork K, Turewicz M et al. 2020. Systematic comparison of label-free, SILAC, and TMT techniques to study early adaption toward inhibition of EGFR signaling in the colorectal cancer cell line DiFi. J. Proteome Res. 19:2926–37
    [Google Scholar]
70. 70. 

    Karimpour-Fard A, Epperson LE, Hunter LE. 2015. A survey of computational tools for downstream analysis of proteomic and other omic datasets. Hum. Genom. 9:28
    [Google Scholar]
71. 71. 

    Wu X, Al Hasan M, Chen JY 2014. Pathway and network analysis in proteomics. J. Theor. Biol. 362:44–52
    [Google Scholar]
72. 72. 

    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H et al. 2000. Gene ontology: tool for the unification of biology. Nat. Genet. 25:25–29
    [Google Scholar]
73. 73. 

    Kanehisa M, Goto S. 2000. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28:127–30
    [Google Scholar]
74. 74. 

    Huang DW, Sherman BT, Lempicki RA. 2008. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4:144–57
    [Google Scholar]
75. 75. 

    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL et al. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:4315545–50
    [Google Scholar]
76. 76. 

    Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S et al. 2019. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–13
    [Google Scholar]
77. 77. 

    Jassal B, Matthews L, Viteri G, Gong C, Lorente P et al. 2020. The Reactome pathway knowledgebase. Nucleic Acids Res 48:D1D498–503
    [Google Scholar]
78. 78. 

    Krämer A, Green J, Pollard J, Tugendreich S. 2014. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30:4523–30
    [Google Scholar]
79. 79. 

    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:112498–504
    [Google Scholar]
80. 80. 

    Duggal NK, Emerman M. 2012. Evolutionary conflicts between viruses and restriction factors shape immunity. Nat. Rev. Immunol. 12:10687–95
    [Google Scholar]
81. 81. 

    Carlyle BC, Kitchen RR, Zhang J, Wilson RS, Lam TT et al. 2018. Isoform-level interpretation of high-throughput proteomics data enabled by deep integration with RNA-seq. J. Proteome Res. 17:103431–44
    [Google Scholar]
82. 82. 

    Stastna M, Van Eyk JE. 2012. Analysis of protein isoforms: Can we do it better?. Proteomics 12:19–202937–48
    [Google Scholar]
83. 83. 

    Cook KC, Cristea IM. 2019. Location is everything: protein translocations as a viral infection strategy. Curr. Opin. Chem. Biol. 48:34–43
    [Google Scholar]
84. 84. 

    Jean Beltran PM, Federspiel JD, Sheng X, Cristea IM 2017. Proteomics and integrative omic approaches for understanding host-pathogen interactions and infectious diseases. Mol. Syst. Biol. 13:922
    [Google Scholar]
85. 85. 

    Weekes MP, Tan SYL, Poole E, Talbot S, Antrobus R et al. 2013. Latency-associated degradation of the MRP1 drug transporter during latent human cytomegalovirus infection. Science 340:6129199–202
    [Google Scholar]
86. 86. 

    Li Y, Qin H, Ye M. 2020. An overview on enrichment methods for cell surface proteome profiling. J. Sep. Sci. 43:1292–312
    [Google Scholar]
87. 87. 

    Weekes MP, Antrobus R, Talbot S, Hör S, Simecek N et al. 2012. Proteomic plasma membrane profiling reveals an essential role for gp96 in the cell surface expression of LDLR family members, including the LDL receptor and LRP6. J. Proteome Res. 11:31475–84
    [Google Scholar]
88. 88. 

    Weekes MP, Antrobus R, Lill JR, Duncan LM, Hör S, Lehner PJ. 2010. Comparative analysis of techniques to purify plasma membrane proteins. J. Biomol. Tech. 21:3108–15
    [Google Scholar]
89. 89. 

    Carter DM, Westdorp K, Noon K, Terhune SS. 2015. Proteomic identification of nuclear processes manipulated by cytomegalovirus early during infection. Proteomics 15:121995–2005
    [Google Scholar]
90. 90. 

    Wu X, Wang H, Bai L, Yu Y, Sun Z et al. 2013. Mitochondrial proteomic analysis of human host cells infected with H3N2 swine influenza virus. J. Proteom. 91:136–50
    [Google Scholar]
91. 91. 

    Salomon D, Orth K. 2013. What pathogens have taught us about posttranslational modifications. Cell Host Microbe 14:3269–79
    [Google Scholar]
92. 92. 

    Chen L, Keppler OT, Schölz C. 2018. Post-translational modification-based regulation of HIV replication. Front. Microbiol. 9:2131
    [Google Scholar]
93. 93. 

    Larsen MR, Trelle MB, Thingholm TE, Jensen ON. 2006. Analysis of posttranslational modifications of proteins by tandem mass spectrometry. Biotechniques 40:6790–98
    [Google Scholar]
94. 94. 

    Perwitasari O, Yan X, O'Donnell J, Johnson S, Tripp RA 2015. Repurposing kinase inhibitors as antiviral agents to control influenza A virus replication. Assay Drug Dev. Technol. 13:10638–49
    [Google Scholar]
95. 95. 

    Keating JA, Striker R. 2012. Phosphorylation events during viral infections provide potential therapeutic targets. Rev. Med. Virol. 22:166–81
    [Google Scholar]
96. 96. 

    Ding X, Lu J, Yu R, Wang X, Wang T et al. 2016. Preliminary proteomic analysis of A549 cells infected with avian influenza virus H7N9 and influenza A virus H1N1. PLOS ONE 11:5e0156017
    [Google Scholar]
97. 97. 

    Sloan E, Orr A, Everett RD. 2016. MORC3, a component of PML nuclear bodies, has a role in restricting herpes simplex virus 1 and human cytomegalovirus. J. Virol. 90:198621–33
    [Google Scholar]
98. 98. 

    Lomonte P, Morency E. 2007. Centromeric protein CENP-B proteasomal degradation induced by the viral protein ICP0. FEBS Lett 581:4658–62
    [Google Scholar]
99. 99. 

    Conway JR, Lex A, Gehlenborg N. 2017. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33:182938–40
    [Google Scholar]
100. 100. 

     Stern-Ginossar N, Weisburd B, Michalski A, Thuy V, Le K et al. 2012. Decoding human cytomegalovirus. Science 338:61101088–93
     [Google Scholar]
101. 101. 

     Xiang Y, Zou Q, Zhao L. 2020. VPTMdb: a viral posttranslational modification database. Brief. Bioinform. 2020:bbaa251
     [Google Scholar]
102. 102. 

     Jäger S, Cimermancic P, Gulbahce N, Johnson JR, McGovern KE et al. 2012. Global landscape of HIV-human protein complexes. Nature 481:7381365–70
     [Google Scholar]
103. 103. 

     Germain MA, Chatel-Chaix L, Gagné B, Bonneil É, Thibault P et al. 2014. Elucidating novel hepatitis C virus-host interactions using combined mass spectrometry and functional genomics approaches. Mol. Cell. Proteom. 13:1184–203
     [Google Scholar]
104. 104. 

     Davis ZH, Verschueren E, Jang GM, Kleffman K, Johnson JR et al. 2015. Global mapping of herpesvirus-host protein complexes reveals a transcription strategy for late genes. Mol. Cell 57:2349–60
     [Google Scholar]
105. 105. 

     Batra J, Hultquist JF, Liu D, Shtanko O, Von Dollen J et al. 2018. Protein interaction mapping identifies RBBP6 as a negative regulator of Ebola virus replication. Cell 175:71917–30.e13
     [Google Scholar]
106. 106. 

     Nobre LV, Nightingale K, Ravenhill BJ, Antrobus R, Soday L et al. 2019. Human cytomegalovirus interactome analysis identifies degradation hubs, domain associations and viral protein functions. eLife 8:e49894
     [Google Scholar]
107. 107. 

     Srivastava M, Zhang Y, Chen J, Sirohi D, Miller A et al. 2020. Chemical proteomics tracks virus entry and uncovers NCAM1 as Zika virus receptor. Nat. Commun. 11:3896
     [Google Scholar]
108. 108. 

     Hashimoto Y, Sheng X, Murray-Nerger LA, Cristea IM. 2020. Temporal dynamics of protein complex formation and dissociation during human cytomegalovirus infection. Nat. Commun. 11:806
     [Google Scholar]
109. 109. 

     Wani SA, Sahu AR, Saxena S, Hussain S, Pandey A et al. 2016. Systems biology approach: panacea for unravelling host-virus interactions and dynamics of vaccine induced immune response. Gene Rep 5:23–29
     [Google Scholar]
110. 110. 

     Chasman D, Walters KB, Lopes TJS, Eisfeld AJ, Kawaoka Y, Roy S 2016. Integrating transcriptomic and proteomic data using predictive regulatory network models of host response to pathogens. PLOS Comput. Biol. 12:7e1005013
     [Google Scholar]
111. 111. 

     Krasny L, Huang PH. 2021. Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. Mol. Omics 17:29–42
     [Google Scholar]
112. 112. 

     Pappireddi N, Martin L, Wühr M. 2019. A review on quantitative multiplexed proteomics. ChemBioChem 20:101210–24
     [Google Scholar]
113. 113. 

     Braun CR, Bird GH, Wu M, Erickson BK, Rad R et al. 2015. Generation of multiple reporter ions from a single isobaric reagent increases multiplexing capacity for quantitative proteomics. Anal. Chem. 87:9855–63
     [Google Scholar]
114. 114. 

     Schweppe DK, Eng JK, Yu Q, Bailey D, Rad R et al. 2020. Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics. J. Proteome Res. 19:2026–34
     [Google Scholar]
115. 115. 

     Barglow KT, Cravatt BF. 2007. Activity-based protein profiling for the functional annotation of enzymes. Nat. Methods 4:10822–27
     [Google Scholar]
116. 116. 

     Desrochers GF, Pezacki JP 2019. ABPP and host-virus interactions. Activity-Based Protein Profiling: Current Topics in Microbiology and Immunology B Cravatt, KL Hsu, E Weerapana 131–54 Cham, Switz: Springer
     [Google Scholar]
117. 117. 

     Strmiskova M, Desrochers GF, Shaw TA, Powdrill MH, Lafreniere MA, Pezacki JP. 2016. Chemical methods for probing virus-host proteomic interactions. ACS Infect. Dis. 2:11773–86
     [Google Scholar]