Molecular Neurobiology

, Volume 56, Issue 7, pp 4708–4717 | Cite as

Zika Virus Infection of Human Mesenchymal Stem Cells Promotes Differential Expression of Proteins Linked to Several Neurological Diseases

  • Walter O. Beys-da-SilvaEmail author
  • Rafael L. Rosa
  • Lucélia Santi
  • Markus Berger
  • Sung Kyu Park
  • Alexandre R. Campos
  • Paula Terraciano
  • Ana Paula M. Varela
  • Thais F. Teixeira
  • Paulo M. Roehe
  • André Quincozes-Santos
  • John R. YatesIII
  • Diogo O. Souza
  • Elizabeth O. Cirne-Lima
  • Jorge A. GuimarãesEmail author


The recent microcephaly outbreak in Brazil has been associated with Zika virus (ZIKV) infection. The current understanding of damage caused by ZIKV infection is still unclear, since it has been implicated in other neurodegenerative and developmental complications. Here, the differential proteome analysis of human mesenchymal stem cells (hMSC) infected with a Brazilian strain of ZIKV was identified by shotgun proteomics (MudPIT). Our results indicate that ZIKV induces a potential reprogramming of the metabolic machinery in nucleotide metabolism, changes in the energy production via glycolysis and other metabolic pathways, and potentially inhibits autophagy, neurogenesis, and immune response by downregulation of signaling pathways. In addition, proteins previously described in several brain pathologies, such as Alzheimer’s disease, autism spectrum disorder, amyotrophic lateral sclerosis, and Parkinson’s disease, were found with altered expression due to ZIKV infection in hMSC. This potential link between ZIKV and several neuropathologies beyond microcephaly is being described here for the first time and can be used to guide specific follow-up studies concerning these specific diseases and ZIKV infection.


Zika virus Brain diseases Human mesenchymal stem cells Proteome Microcephaly 



The authors would like to thank Dr. E. Durigon, ICB/USP, for supplying the ZIKV strain. PMR is a 1A CNPq research fellow. APMV and TFT acknowledges postdoctoral fellowship support by CNPq/HCPA.

Funding Information

This work was supported by the Brazilian funding agencies Coordenação de Aperfeiçoamento Pessoal de Nível Superior (CAPES), FAPERGS, Edital MCTIC/FNDCT-CNPq/ MEC-CAPES/ MS-Decit / No 14/2016, project 440763/2016-9. The study was also supported by NIH grants NIH/NIHGM P41 GM103533-22 and NIH/NIMH 5 R01 MH067880-14 (to JRY).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

The study was approved by the institutional research ethics committee of Hospital de Clínicas de Porto Alegre (Federal University of Rio Grande do Sul) under protocol # 2018-0059.

Supplementary material

12035_2018_1417_MOESM1_ESM.pdf (122 kb)
ESM 1 (PDF 121 kb)
12035_2018_1417_MOESM2_ESM.pdf (58 kb)
Supplementary Figure 1 PI3K-AKT signaling pathway affected by ZIKV infection in hMSC. Green rectangle: protein down-regulated in ZIKV infection. (PDF 58 kb)
12035_2018_1417_MOESM3_ESM.pdf (41 kb)
Supplementary Figure 2 mTOR signaling pathway affected by ZIKV infection in hMSC. Green rectangle: proteins down-regulated in ZIKV infection; red rectangle: protein up-regulated in ZIKV infection. (PDF 40 kb)
12035_2018_1417_MOESM4_ESM.pdf (178 kb)
Supplementary Figure 3 Phosphatidylinositol signaling system affected by ZIKV infection in hMSC. Green rectangle: proteins down-regulated in ZIKV infection. (PDF 177 kb)
12035_2018_1417_MOESM5_ESM.docx (13 kb)
Supplementary Table 1 (DOCX 13 kb)
12035_2018_1417_MOESM6_ESM.xlsx (160 kb)
Supplementary Table 2 (XLSX 159 kb)
12035_2018_1417_MOESM7_ESM.docx (27 kb)
Supplementary Table 3 (DOCX 26 kb)


  1. 1.
    França GV, Schuler-Faccini L, Oliveira WK, Henriques CM, Carmo EH, Pedi VD et al (2016) Congenital Zika virus syndrome in Brazil: a case series of the first 1501 livebirths with complete investigation. Lancet 388(10047):891–897. CrossRefPubMedGoogle Scholar
  2. 2.
    Schuler-Faccini L, Ribeiro EM, Feitosa IM et al (2016) Possible association between Zika virus infection and microcephaly - Brazil, 2015. MMWR Morb Mortal Wkly Rep 65:59–62. CrossRefPubMedGoogle Scholar
  3. 3.
    McDonald WH, Yates JR 3rd (2002) Shotgun proteomics and biomarker discovery. Dis Markers 18(2):99–105CrossRefGoogle Scholar
  4. 4.
    Beys-da-Silva WO, Santi L, Berger M, Calzolari D, Passos DO, Guimarães JA, Moresco JJ, Yates JR (2014) Secretome of the biocontrol agent Metarhizium anisopliae induced by the cuticle of the cotton pest Dysdercus peruvianus reveals new insights into infection. J Proteome Res 13:2282–2296. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Santi L, Beys-da-Silva WO, Berger M, Calzolari D, Guimarães JA, Moresco JJ, Yates JR (2014) Proteomic profile of Cryptococcus neoformans biofilm reveals changes in metabolic processes. J Proteome Res 13:1545–1559. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Berger M, Santi L, Beys-da-Silva WO, Oliveira FMS, Caliari MV, Yates JR, Vieira MAR, Guimarães JA (2015) Mechanisms of acute kidney injury induced by experimental Lonomia obliqua envenomation. Arch Toxicol 89:459–483. CrossRefPubMedGoogle Scholar
  7. 7.
    Garcez PP, Nascimento JM, Vasconcelos JM, Costa RM, Delvecchio R, Trindade P et al (2017) Zika virus disrupts molecular fingerprinting of human neurospheres. Sci Rep 7:40780. CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Zomer HD, Vidane AS, Gonçalves NN, Ambrosio CE (2015) Mesenchymal and induced pluripotent stem cells: general insights and clinical perspectives. Stem Cells Cloning 8:125–134. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Olmo IG, Carvalho TG, Costa VV, Alves-Silva J, Ferrari CZ, Izidoro-Toledo TC, da Silva JF, Teixeira AL et al (2017) Zika virus promotes neuronal cell death in a non-cell autonomous manner by triggering the release of neurotoxic factors. Front Immunol 8:1016. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    El Costa H, Gouilly J, Mansoy J-M, Chen Q, Levy C, Cartron G et al (2016) ZIKA virus reveals broad tissue and cell tropism during the first trimester of pregnancy. Sci Rep 6:35296. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Zuk PA, Zhu M, Ashjian P, de Ugarte DA, Huang JI, Mizuno H, Alfonso ZC, Fraser JK et al (2002) Human adipose tissue is a source of multipotent stem cells. Mol Biol Cell 13:4279–4295. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Meirelles LDS, Nardi NB (2003) Murine marrow-derived mesenchymal stem cell: isolation, in vitro expansion, and characterization. Br J Haematol 123(4):702–711CrossRefGoogle Scholar
  13. 13.
    Terraciano P, Garcez T, Ayres L, Durli I, Baggio M, Kuhl CP, Laurino C, Passos E et al (2014) Cell therapy for chemically induced ovarian failure in mice. Stem Cells Int 2014:720753. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Washburn MP, Wolters D, Yates JR 3rd (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19(3):242–247. CrossRefPubMedGoogle Scholar
  15. 15.
    Xu T, Venable JD, Park SK, Cociorva D, Lu B, Liao L et al (2006) ProLuCID, a fast and sensitive tandem mass spectra-based protein identification program. Mol Cell Proteomics 5:S174Google Scholar
  16. 16.
    Tabb DL, McDonald WH, Yates JR 3rd (2002) DTASelect and contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J Proteome Res 1:21–26CrossRefGoogle Scholar
  17. 17.
    He L, Diedrich JK, Chu YY, Yates JR (2015) Extracting accurate precursor information for tandem mass spectra by RawConverter. Anal Chem 87(22):11361–11367. CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Park SK, Venable JD, Xu T, Yates JR 3rd (2008) A quantitative analysis software tool for mass spectrometry-based proteomics. Nat Methods 5(4):319–322. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ et al (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36(10):3420–3435. CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gaudet P, Livstone MS, Lewis SE, Thomas PD (2011) Phylogenetic-based propagation of functional annotations within the gene ontology consortium. Brief Bioinform 12(5):449–462. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Rappaport N, Nativ N, Stelzer G, Twik M, Guan-Golan Y, Stein TI et al (2013) MalaCards: an integrated compendium for diseases and their annotation. Database 2013:018. CrossRefGoogle Scholar
  22. 22.
    Rolfe AJ, Bosco DB, Wang J, Nowakowski RS, Fan J, Ren Y (2016) Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells. Cell Biosci 6:42. CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT et al (2017) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D368. CrossRefPubMedGoogle Scholar
  24. 24.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Scardoni G, Tosadori G, Faizan M, Spoto F, Fabbri F, Laudanna C (2014) Biological network analysis with CentiScaPe: centralities and experimental dataset integration. F1000Research 3:139. CrossRefPubMedGoogle Scholar
  26. 26.
    Tiwari SK, Dang J, Qin Y, Lichinchi G, Bansal V, Rana TM (2017) Zika virus infection reprograms global transcription of host cells to allow sustained infection. Emerg Microbes Infect 6(4):e24. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Brahma R, Gurumayum S, Naorem LD, Muthaiyan M, Gopal J, Venkatesan A (2018) Identification of hub genes and pathways in Zika virus infection using rna-seq data: a network-based computational approach. Viral Immunol 31(4):321–332. CrossRefPubMedGoogle Scholar
  28. 28.
    Caires-Júnior LC, Goulart E, Melo US, Araujo BHS, Alvizi L, Soares-Schanoski A, de Oliveira DF, Kobayashi GS et al (2018) Discordant congenital Zika syndrome twins show differential in vitro viral susceptibility of neural progenitor cells. Nat Commun 9(1):475. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Melo CFOR, Delafiori J, Oliveira DN, Guerreiro TM, Esteves CZ et al (2017) Serum metabolic alterations upon Zika infection. Front Microbiol 8:2373. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Wang L, Yang L, Fikrig E, Wang P (2017) An essential role of PI3K in the control of West Nile virus infection. Sci Rep 7:3724. CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Nakajima S, Kitamura M (2013) Bidirectional regulation of NF-kappaB by reactive oxygen species: a role of unfolded protein response. Free Radic Biol Med 65:162–174. CrossRefPubMedGoogle Scholar
  32. 32.
    Kaur U, Banerjee P, Bir A, Sinha M, Biswas A, Chakrabarti S (2015) Reactive oxygen species, redox signaling and neuroinflammation in Alzheimer’s disease: the NF-kappaB connection. Curr Top Med Chem 15:446–457. CrossRefPubMedGoogle Scholar
  33. 33.
    Gorina R, Font-Nieves M, Marquez-Kisinousky L, Santalucia T, Planas AM (2011) Astrocyte TLR4 activation induces a proinflammatory environment through the interplay between MyD88-dependent NFkappaB signaling, MAPK, and Jak1/Stat1 pathways. Glia 59:242–255. CrossRefPubMedGoogle Scholar
  34. 34.
    Saxton RA, Sabatini DM (2017) mTOR signaling in growth, metabolism, and disease. Cell 168(6):960–976. CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Sanchez EL, Lagunoff M (2015) Viral activation of cellular metabolism. Virology 479-480:609–618. CrossRefPubMedGoogle Scholar
  36. 36.
    Qimron U, Tabor S, Richardson CC (2010) New details about bacteriophage T7-host interactions. Microbe 5(3):117–122Google Scholar
  37. 37.
    Enav H, Mandel-Gutfreund Y, Béjà O (2014) Comparative metagenomic analyses reveal viral-induced shifts of host metabolism towards nucleotide biosynthesis. Microbiome 2:9. CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Schrimpf SP, Weiss M, Reiter L, Ahrens CH, Jovanovic M, Malmström J, Brunner E, Mohanty S et al (2009) Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol 7(3):e48. CrossRefPubMedGoogle Scholar
  39. 39.
    Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J et al (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342. CrossRefPubMedGoogle Scholar
  40. 40.
    de Sousa Abreu R, Penalva LO, Marcotte EM, Vogel C (2009) Global signatures of protein and mRNA expression levels. Mol BioSyst 5:1512–1526. CrossRefPubMedGoogle Scholar
  41. 41.
    Maier T, Guell M, Serrano L (2009) Correlation of mRNA and protein in complex biological samples. FEBS Lett 583:3966–3973. CrossRefPubMedGoogle Scholar
  42. 42.
    Rappaport N, Twik M, Plaschkes I, Nudel R, Stein TI, Levitt J et al (2017) MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res 45:D877–D887. CrossRefPubMedGoogle Scholar
  43. 43.
    Sadleir KR, Vassar R (2012) Cdk5 protein inhibition and Aβ42 increase bace1 protein level in primary neurons by a posttranscriptional mechanism implications of cdk5 as a therapeutic target for Alzheimer disease. J Biol Chem 287(10):7224–7235. CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Sontag JM, Nunbhakdi-Craig V, White CL, Halpain S, Sontag E (2012) The protein phosphatase pp2a/bα binds to the microtubule-associated proteins Tau and MAP2 at a motif also recognized by the kinase Fyn: implications for tauopathies. J Biol Chem 287(18):14984–14993. CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    D’Amelio M, Cavallucci V, Middei S, Marchetti C, Pacioni S, Ferri A et al (2011) Caspase-3 triggers early synaptic dysfunction in a mouse model of Alzheimer’s disease. Nat Neurosci 14:69–76. CrossRefPubMedGoogle Scholar
  46. 46.
    Liu JS (2011) Molecular genetics of neuronal migration disorders. Curr Neurol Neurosci Rep 11(2):171–178. CrossRefPubMedGoogle Scholar
  47. 47.
    McNamara CR, Degterev A (2011) Small-molecule inhibitors of the PI3K signaling network. Future Med Chem 3(5):549–565. CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Zelenaia O, Schlag BD, Gochenauer GE, Ganel R, Song W, Beesley JS, Grinspan JB, Rothstein JD et al (2000) Epidermal growth factor receptor agonists increase expression of glutamate transporter GLT-1 in astrocytes through pathways dependent on phosphatidylinositol 3-kinase and transcription factor NF-kappaB. Mol Pharmacol 57:667–678. CrossRefPubMedGoogle Scholar
  49. 49.
    Janssens S, Schotsaert M, Karnik R, Balasubramaniam V, Dejosez M, Meissner A, García-Sastre A, Zwaka TP (2018) Zika virus alters DNA methylation of neural genes in an organoid model of the developing human brain. mSystems 3(1):e00219–e00217. CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Schuler-Faccini L, Roehe P, Zimmer ER, Quincozes-Santos A, de Assis AM, Lima EOC, Guimarães JA, Victora C et al (2018) ZIKA virus and neuroscience: the need for a translational collaboration. Mol Neurobiol 55(2):1551–1555. CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Walter O. Beys-da-Silva
    • 1
    • 2
    Email author
  • Rafael L. Rosa
    • 2
    • 3
  • Lucélia Santi
    • 1
    • 2
  • Markus Berger
    • 2
  • Sung Kyu Park
    • 4
  • Alexandre R. Campos
    • 5
  • Paula Terraciano
    • 2
  • Ana Paula M. Varela
    • 6
  • Thais F. Teixeira
    • 6
  • Paulo M. Roehe
    • 7
  • André Quincozes-Santos
    • 8
  • John R. YatesIII
    • 4
  • Diogo O. Souza
    • 8
  • Elizabeth O. Cirne-Lima
    • 2
    • 9
    • 10
  • Jorge A. Guimarães
    • 2
    • 3
    Email author
  1. 1.Faculdade de FarmáciaUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  2. 2.Centro de Pesquisa ExperimentalHospital de Clínicas de Porto AlegrePorto AlegreBrazil
  3. 3.Programa de Pós-Graduação em Biologia Celular e MolecularUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  4. 4.Department of Chemical PhysiologyThe Scripps Research InstituteLa JollaUSA
  5. 5.Proteomics CoreSanford Burnham Prebys Medical Discovery InstituteLa JollaUSA
  6. 6.Centro de Cardiologia ExperimentalInstituto de Cardiologia/Fundação Universitária de CardiologiaPorto AlegreBrazil
  7. 7.Departamento de MicrobiologiaUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  8. 8.Departamento de BioquímicaUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  9. 9.Programa de Pós-Graduação em Ginecologia e ObstetríciaUniversidade Federal do Rio Grande do SulPorto AlegreBrazil
  10. 10.Departamento de Patologia Clínica Veterinária, Faculdade de VeterináriaUniversidade Federal do Rio Grande do SulPorto AlegreBrazil

Personalised recommendations