Host Transcriptional Response to Influenza and Other Acute Respiratory Viral Infections – A Prospective Cohort Study
Gene expression profiling of human blood cells might uncover the complex dynamics of host response to ARIs such as pandemic H1N1. However, only limited data are available on the system level response to naturally acquired infections. To understand the molecular bases and network orchestration of host responses, we prospectively enrolled 1610 healthy adults in the fall of 2009 and 2010, followed the subjects with influenza-like illness (N = 133) for 3 weeks, and examined changes in their peripheral blood gene expression. We discovered distinct phases of the host response spanning 6 days after infection, and identified genes that differentiate influenza from non-influenza virus infection. We then moved the focus from gene expression patterns to gene co-expression patterns. We detected gene modules that are related to core features of regulatory networks and found a substantial increase in the connectivity of the influenza responsive genes. Finally, we identified a molecular signature that correlated significantly with antibody response to pH1N1 virus. Taken together, our findings offer insights into the molecular mechanisms underlying host response to influenza virus infection, and provide a valuable foundation for investigation of the global coordinated responses to ARIs. Molecular correlates of the immune response suggest targets for intervention and improved vaccines.
Vyšlo v časopise:
Host Transcriptional Response to Influenza and Other Acute Respiratory Viral Infections – A Prospective Cohort Study. PLoS Pathog 11(6): e32767. doi:10.1371/journal.ppat.1004869
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.ppat.1004869
Souhrn
Gene expression profiling of human blood cells might uncover the complex dynamics of host response to ARIs such as pandemic H1N1. However, only limited data are available on the system level response to naturally acquired infections. To understand the molecular bases and network orchestration of host responses, we prospectively enrolled 1610 healthy adults in the fall of 2009 and 2010, followed the subjects with influenza-like illness (N = 133) for 3 weeks, and examined changes in their peripheral blood gene expression. We discovered distinct phases of the host response spanning 6 days after infection, and identified genes that differentiate influenza from non-influenza virus infection. We then moved the focus from gene expression patterns to gene co-expression patterns. We detected gene modules that are related to core features of regulatory networks and found a substantial increase in the connectivity of the influenza responsive genes. Finally, we identified a molecular signature that correlated significantly with antibody response to pH1N1 virus. Taken together, our findings offer insights into the molecular mechanisms underlying host response to influenza virus infection, and provide a valuable foundation for investigation of the global coordinated responses to ARIs. Molecular correlates of the immune response suggest targets for intervention and improved vaccines.
Zdroje
1. World Health Organization. (2009) Seasonal Influenza: World Health Organization.
2. Centers for Disease Control and Prevention: Seasonal Influenza. (2013) http://www.cdc.gov/flu/about/qa/disease.htm
3. Molinari NM, Ortega-Sanchez IR, Messonnier ML, Thompson WW, Wortley PM, et al. (2007) The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine 25(27): 5086–96. 17544181
4. Garten RJ, Davis CT, Russell CA, Shu B, Lindstrom S, et al. (2009) Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans. Science 325(5937): 197–201. doi: 10.1126/science.1176225 19465683
5. Couch RB, Atmar RL, Franco LM, Quarles JM, Niño D, et al. (2012) Prior infections with seasonal influenza A/H1N1 virus reduced the illness severity and epidemic intensity of pandemic H1N1 influenza in healthy adults. Clin Infect Dis 54(3): 311–7. doi: 10.1093/cid/cir809 22075792
6. Couch RB, Atmar RL, Franco LM, Quarles JM, Wells J, et al. (2013) Antibody correlates and predictors of immunity to naturally occurring influenza in humans and the importance of antibody to the neuraminidase. J Infect Dis 207(6): 974–81. doi: 10.1093/infdis/jis935 23307936
7. Takeuchi O, Akira S. (2009) Innate immunity to virus infection. Immunol Rev 227: 75–86. doi: 10.1111/j.1600-065X.2008.00737.x 19120477
8. Pichlmair A, Reise SC. (2007) Innate recognition of viruses. Immunity 27: 370–383.
9. Haller O, Weber F. (2009) The interferon response circuit in antiviral host defense. Verh K Acad Geneeskd Belg 71: 73–86. 19739399
10. Kohlmeier JE, Woodland DL. (2009) Immunity to respiratory viruses. Annu Rev Immunol 27: 61–82. doi: 10.1146/annurev.immunol.021908.132625 18954284
11. McGill J, Heusel JW, Legge KL. (2009) Innate immune control and regulation of influenza virus infections. J Leukoc Biol 86: 803–812.
12. Gazit R, Gruda R, Elboim M, Arnon TI, Katz G, et al. (2006) Lethal influenza infection in the absence of the natural killer cell receptor gene Ncr1. Nat Immunol 7(5): 517–23. 16565719
13. Chaussabel D, Pascual V, Banchereau J. (2010) Assessing the human immune system through blood transcriptomics. BMC Biology 8: 84.
14. Zaas AK, Chen M, Varkey J, Veldman T, Hero AO 3rd, et al. (2009) Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. Cell Host Microbe 6: 207–17. doi: 10.1016/j.chom.2009.07.006 19664979
15. Thach DC, Agan BK, Olsen C, Diao J, Lin B, et al. (2005) Surveillance of transcriptomes in basic military trainees with normal, febrile respiratory illness, and convalescent phenotypes. Genes Immun 6: 588–95. 16034474
16. Ramilo O, Allman W, Chung W, Mejias A, Ardura M, et al. (2007) Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109(5): 2066–77. 17105821
17. Ioannidis I, McNally B, Willette M, Peeples ME, Chaussabel D, et al. (2012) Plasticity and virus specificity of the airway epithelial cell immune response during respiratory virus infection. J Virol 86(10): 5422–36. doi: 10.1128/JVI.06757-11 22398282
18. Parnell GP, McLean AS, Booth DR, Armstrong NJ, Nalos M, et al. (2012) A distinct influenza infection signature in the blood transcriptome of patients who presented with severe community acquired pneumonia. Crit Care 16(4): R157. doi: 10.1186/cc11477 22898401
19. Bacasas KL, Franco LM, Shaw CA, Bray MS, Wells JM, et al. (2011) Early patterns of gene expression correlate with the humoral immune response to influenza vaccination in humans. J Infect Dis 203(7): 921–9. doi: 10.1093/infdis/jiq156 21357945
20. Pommerenke C, Wilk E, Srivastava B, Schulze A. (2012) Global transcriptome analysis in influenza-infected mouse lungs reveals the kinetics of innate and adaptive host immune responses. PLoS One 7(7):e41169. doi: 10.1371/journal.pone.0041169 22815957
21. Huang Y, Zaas AK, Rao A, Dobigeon N, Woolf PJ, et al. (2011) Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection. PLoS Genet 7(8):e1002234. doi: 10.1371/journal.pgen.1002234 21901105
22. Menachery VD, Eisfeld AJ, Schäfer A, Josset L, Sims AC, et al. (2014) Pathogenic influenza viruses and coronaviruses utilize similar and contrasting approaches to control interferon-stimulated gene responses. MBio 5(3):e01174–14. doi: 10.1128/mBio.01174-14 24846384
23. Tan Y, Tamayo P, Nakaya H, Pulendran B, Mesirov JP, et al. (2014) Gene signatures related to B-cell proliferation predict influenza vaccine-induced antibody response. Eur J Immunol 44(1):285–95. doi: 10.1002/eji.201343657 24136404
24. Franco LM, Bucasas KL, Wells JM, Niño D, Wang X, et al. (2013) Integrative genomic analysis of the human immune response to influenza vaccination. Elife 2:e00299 doi: 10.7554/eLife.00299 23878721
25. Poland GA, Ovsyannikova IG, Jacobson RM. (2008) Immunogenetics of Seasonal Influenza Vaccine Response. Vaccine 26(Suppl 4): D35–D40. 19230157
26. Zhang B, Horvath S. (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4: Article17.
27. Ferris MT, Aylor DL, Bottomly D, Whitmore AC, Aicher LD, et al. (2013) Modeling host genetic regulation of influenza pathogenesis in the collaborative cross. PLoS Pathog 9(2): e1003196. doi: 10.1371/journal.ppat.1003196 23468633
28. Li C, Bankhead A 3rd, Eisfeld AJ, Hatta Y, Jeng S, et al. (2011) Host regulatory network response to infection with highly pathogenic H5N1 avian influenza virus. J Virol 85(21): 10955–67. doi: 10.1128/JVI.05792-11 21865398
29. Langfelder P, Horvath S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9: 559. doi: 10.1186/1471-2105-9-559 19114008
30. Wang E, Marincola FM. (2008) Bottom Up: A Modular View of Immunology. Immunity 29(1): 9–11.
31. Tesson B, Breitling R, Jansen R. (2010) DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics 11:497.
32. Amar D, Safer H, Shamir R. (2013) Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression. PLoS Comput Biol 9(3): e1002955. doi: 10.1371/journal.pcbi.1002955 23505361
33. Fuente A. (2010) From ‘differential expression’ to ‘differential networking’–identification of dysfunctional regulatory networks in diseases. Trends in Genetics 26: 326–33. doi: 10.1016/j.tig.2010.05.001 20570387
34. Douglas RG Jr, Alford RH, Cate TR, Couch RB. (1966) The leukocyte response during viral respiratory illness in man. Ann Intern Med 64(3): 521–30. 4286006
35. McClain MT, Park LP, Nicholson B, Veldman T, Zaas AK, et al. (2013) Longitudinal analysis of leukocyte differentials in peripheral blood of patients with acute respiratory viral infections. J Clin Virol 58(4):689–95. doi: 10.1016/j.jcv.2013.09.015 24140015
36. Bucasas KL, Mian AI, Demmler-Harrison GJ, Caviness AC, Piedra PA, et al. (2013) Global gene expression profiling in infants with acute respiratory syncytial virus broncholitis demonstrates systemic activation of interferon signaling networks. Pediatr Infect Dis J 32(2): e68–76. doi: 10.1097/INF.0b013e318278b4b3 23190772
37. Roghanian A, Williams SE, Sheldrake TA, Brown TI, Oberheim K, et al. (2006) The antimicrobial/elastase inhibitor elafin regulates lung dendritic cells and adaptive immunity. Am J Respir Cell Mol Biol 34(5): 634–42. 16424380
38. Verrier T, Solhonne B, Sallenave JM, Garcia-Verdugo I. (2012) The WAP protein Trappin-2/Elafin: a handyman in the regulation of inflammatory and immune responses. Int J Biochem Cell Biol 44(8): 1377–80. doi: 10.1016/j.biocel.2012.05.007 22634606
39. Wang Z, Beach D, Su L, Zhai R, Christiani DC. (2008) A genome-wide expression analysis in blood identifies pre-elafin as a biomarker in ARDS. Am J Respir Cell Mol Biol 38(6): 724–32. doi: 10.1165/rcmb.2007-0354OC 18203972
40. He Y, Xu K, Keiner B, Zhou J, Czudai V, et al. (2010) Influenza A Virus Replication Induces Cell Cycle Arrest in G0/G1 Phase. J Virol 84(24):12832–40. doi: 10.1128/JVI.01216-10 20861262
41. Parnell G, McLean A, Booth D, Huang S, Nalos M, et al. (2011) Aberrant cell cycle and apoptotic changes characterise severe influenza A infection—a meta-analysis of genomic signatures in circulating leukocytes. PLoS One 6(3):e17186. doi: 10.1371/journal.pone.0017186 21408152
42. Wagner EK, Hewlett MJ, Bloom DC, Camerini D. The Beginning and End of the Virus Replication Cycle. In: Basic Virology. 3rd ed. New York: Wiley-Blackwell; 2007.
43. Du P, Kibbe WA, Lin SM. (2008) lumi: a pipeline for processing Illumina microarray. Bioinformatics 24(13):1547–8. doi: 10.1093/bioinformatics/btn224 18467348
44. R Development Core Team. (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria ISBN 3-900051-07-0, URL http://www.R-project.org.
45. Wettenhall JM, Smyth GK. (2004) limmaGUI: a graphical user interface for linear modeling of microarray data. Bioinformatics 20(18):3705–6. 15297296
46. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004; 3:Article3.
47. Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z, Clark HF. (2009) Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One 4(7):e6098. doi: 10.1371/journal.pone.0006098 19568420
Štítky
Hygiena a epidemiológia Infekčné lekárstvo LaboratóriumČlánok vyšiel v časopise
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