Influenza a immunomics and public health omics: The dynamic pathway interplay in host response to H1N1 infection

OMICS A Journal of Integrative Biology

Volumn 18, Issue 3, 2014, Pages 167-183

  Dimitrakopoulou, Konstantina ^a   Dimitrakopoulos, Georgios N ^a,b   Wilk, Esther ^c   Tsimpouris, Charalampos ^b   Sgarbas, Kyriakos N ^b   Schughart, Klaus ^c,d   Bezerianos, Anastasios ^a,e  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

^c UNIVERSITY OF VETERINARY MEDICINE HANNOVER   (Germany)

^d UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^e NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

B LYMPHOCYTE RECEPTOR; CHEMOKINE; CYTOKINE;

ADAPTIVE IMMUNITY; ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CONTROLLED STUDY; GENE REGULATORY NETWORK; IMMUNE RESPONSE; INFLAMMATION; INFLUENZA A (H1N1); INFLUENZA A (H5N1); INNATE IMMUNITY; NONHUMAN; PHAGOCYTOSIS; PRIORITY JOURNAL; PUBLIC HEALTH SERVICE; VIRAL CLEARANCE;

ANIMALS; CLUSTER ANALYSIS; DISEASE MODELS, ANIMAL; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; GENOMICS; HOST-PATHOGEN INTERACTIONS; INFLUENZA A VIRUS, H1N1 SUBTYPE; INFLUENZA A VIRUS, H5N1 SUBTYPE; MICE; ORTHOMYXOVIRIDAE INFECTIONS; SIGNAL TRANSDUCTION; TIME FACTORS; TRANSCRIPTOME;

EID: 84896880604     PISSN: 15362310     EISSN: 15578100     Source Type: Journal    
DOI: 10.1089/omi.2013.0062     Document Type: Article

References (63)

1. Aijö T, and Lähdesmäki H. (2009). Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics 25, 2937-2944.
2. Altay G, and Emmert-Streib F. (2010). Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics 26, 1738-1744.
3. Auffray C, Chen Z, and Hood L. (2009). Systems medicine: The future of medical genomics and healthcare. Genome Med 1, 2.
4. Bansal M, Belcastro V, Ambesi-Impiombato A, and di Bernardo D. (2007). How to infer gene networks from expression profiles. Mol Syst Biol 3, 78.
5. Bassaganya-Riera J, Song R, Roberts PC, and Hontecillas R. (2010). PPAR-gamma activation as an anti-inflammatory therapy for respiratory virus infections. Viral Immunol 23, 343-352.
6. Bernstein A, Pulendran B, and Rappuoli R. (2011). Systems vaccinomics: The road ahead for vaccinology. OMICS 15, 529-531.
7. Bindea G, Mlecnik B, Hackl H, et al. (2009). ClueGO: A Cy-toscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091-1093.
8. Bonneau R, Reiss DJ, Shannon P, et al. (2006). The Inferelator: An algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 7, R36.
9. Braga-Neto UM, and Marques ET Jr. (2006). From functional genomics to functional immunomics: New challenges, old problems, big rewards. PLoS Comput Biol 2, e81.
10. Cantone I, Marucci L, Iorio F, et al. (2009). A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137, 172-181.
11. De Groot AS. (2006). Immunomics: Discovering new targets for vaccines and therapeutics. Drug Discov Today 11, 203-209.
12. Deng Y, Jiang YH, Yang Y, He Z, Luo F, and Zhou J. (2012). Molecular ecological network analyses. BMC Bioinformatics 13, 113.
13. Dimitrakopoulou K, Tsimpouris C, Papadopoulos G, et al. (2011). Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection. J Clin Bioinforma 1, 27.
14. Gardner TS, di Bernardo D, Lorenz D, and Collins JJ. (2003). Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102-105. (Pubitemid 36801571)
15. Garske T, Legrand J, Donnelly CA, et al. (2009). Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ 339, b2840.
16. Gentleman RC, Carey VJ, Bates DM, et al. (2004). Bio-conductor: Open software development for computational biology and bioinformatics. Genome Biol 5, R80.
17. Guthke R, Möller U, Hoffmann M, Thies F, and Töpfer S. (2005). Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics 21, 1626-1634. (Pubitemid 40542721)
18. Handel A, Longini IM Jr, and Antia R. (2010). Towards a quantitative understanding of the within-host dynamics of influenza A infections. J R Soc Interface 7, 35-47.
19. He Y, Xu K, Keiner B, et al. (2010). Influenza A virus replication induces cell cycle arrest in G0/G1 phase. J Virol 84, 12832-12840.
20. Horvath S, and Dong J. (2008). Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 4, e1000117.
21. Huang DW, Sherman BT, and Lempicki RA. (2009). Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc 4, 44-57.
22. Ilyushina NA, Khalenkov AM, Seiler JP, et al. (2010). Adaptation of pandemic H1N1 influenza viruses in mice. J Virol 84, 8607-8616.
23. Inoue LY, Neira M, Nelson C, Gleave M, and Etzioni R. (2007).Cluster-based network model for time-course gene expression data. Biostatistics 8, 507-525.
24. Keller M, Mazuch J, Abraham U, et al. (2009). A circadian clock in macrophages controls inflammatory immune responses. Proc Natl Acad Sci USA 106, 21407-21412.
25. Langfelder P, and Horvath S. (2007). Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol 1, 54.
26. Leek JT, Monsen E, Dabney AR, and Storey JD. (2006). EDGE: Extraction and analysis of differential gene expression. Bioinformatics 22, 507-508. (Pubitemid 43231431)
27. Li Z, Shaw SM, Yedwabnick MJ, and Chan C. (2006). Using a state-space model with hidden variables to infer transcription factor activities. Bioinformatics 22, 747-754.
28. Liao TW. (2005). Clustering of time series data-A survey. Pattern Recog 38, 1857-1874.
29. Loukas A, Gaze S, Mulvenna JP, et al. (2011). Vaccinomics for the major blood feeding helminths of humans. OMICS 15, 567-577.
30. Maraziotis IA, Dragomir A, and Thanos D. (2010). Gene regulatory networks modelling using a dynamic evolutionary hybrid. BMC Bioinformatics 11, 140.
31. Marbach D, Costello JC, Küffner R, et al. (2012). Wisdom of crowds for robust gene network inference. Nat Methods 9, 796-804.
32. Matsuoka Y, Lamirande EW, and Subbarao K. (2009). The mouse model for influenza. Curr Protoc Microbiol Chapter 15, Unit 15G.3.
33. McDermott JE, Shankaran H, Eisfeld AJ, et al. (2011). Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems. BMC Syst Biol 5, 190.
34. McWilliam HE, Driguez P, Piedrafita D, McManus DP, and Meeusen EN. (2012). Novel immunomic technologies for schistosome vaccine development. Parasite Immunol 34, 276-284.
35. Möller-Levet CS, Klawonnb F, Choc KH, Yina H, and Wolk-enhauere O. (2005). Clustering of unevenly sampled gene expression time-series data. Fuzzy Sets Bioinformatics 152, 49-66. (Pubitemid 40391251)
36. Munoz-Fontela C, Pazos M, Delgado I, et al. (2011). p53 serves as a host antiviral factor that enhances innate and adaptive immune responses to influenza A virus. J Immunol 187, 6428-6436.
37. Nagarajan R, and Upreti M. (2010). Granger causality analysis of human cell-cycle gene expression profiles. Stat Appl Genet Mol Biol 9, 31.
38. Paquette SG, Banner D, Zhao Z, et al. (2012). Interleukin-6 is a potential biomarker for severe pandemic H1N1 influenza A infection. PLoS One 7, e38214.
39. Parnell G, McLean A, Booth D, Huang S, Nalos M, and Tang B. (2011). Aberrant cell cycle and apoptotic changes characterise severe influenza A infection-A meta-analysis of genomic signatures in circulating leukocytes. PLoS One 6, e17186.
40. Pawelek KA, Huynh GT, Quinlivan M, Cullinane A, Rong L, and Perelson AS. (2012). Modeling within-host dynamics of influenza virus infection including immune responses. PLoS Comput Biol 8, e1002588.
41. Poland GA, Kennedy RB, and Ovsyannikova IG. (2011). Vaccinomics and personalized vaccinology: Is science leading us toward a new path of directed vaccine development and discovery? PLoS Pathog 7, e1002344.
42. Pommerenke C, Wilk E, Srivastava B, et al. (2012). Global transcriptome analysis in influenza-infected mouse lungs reveals the kinetics of innate and adaptive host immune responses. PLoS One 7, e41169.
43. Saenz RA, Quinlivan M, Elton D, et al. (2010). Dynamics of influenza virus infection and pathology. J Virol 84, 3974-3983.
44. Schäfer J, and Strimmer K. (2005). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21, 754-764. (Pubitemid 40439411)
45. Schughart K, Libert C, SYSGENET consortium, and Kas MJ. (2013). Controlling complexity: The clinical relevance of mouse complex genetics. Eur J Hum Genet 21, 1191-1196.
46. Sette A, Fleri W, Peters B, Sathiamurthy M, Bui HH, and Wilson S. (2005). A roadmap for the immunomics of category A-C pathogens. Immunity 22, 155-161. (Pubitemid 40255707)
47. Silver AC, Arjona A, Hughes ME, Nitabach MN, and Fikrig E. (2012). Circadian expression of clock genes in mouse mac-rophages, dendritic cells, and B cells. Brain Behav Immun 26, 407-413.
48. Shannon P, Markiel A, Ozier O, et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498-2504. (Pubitemid 37428274)
49. Shay T, and Kang J. (2013). Immunological Genome Project and systems immunology. Trends Immunol 43, 602-609.
50. Song L, Kolar M, and Xing E. (2009a). KELLER: Estimating time-varying interactions between genes. Bioinformatics 25, i128-i136.
51. Song L, Kolar M, and Xing E. (2009b). Time-varying dynamic Bayesian networks. Advances in Neural Information Processing Systems 22 (NIPS 2009).
52. Straus DS, and Glass CK. (2007). Anti-inflammatory actions of PPAR ligands: New insights on cellular and molecular mechanisms. Trends Immunol 28, 551-558. (Pubitemid 350101420)
53. Sweet C, and Smith H. (1980). Pathogenicity of influenza virus. Microbiol Rev 44, 303-330. (Pubitemid 11128311)
54. Terrier O, Josset L, Textoris J, et al. (2011). Cellular tran-scriptional profiling in human lung epithelial cells infected by different subtypes of influenza A viruses reveals an overall down-regulation of the host p53 pathway. Virol J 8, 285.
55. van Noort V, Snel B, and Huynen MA. (2003). Predicting gene function by conserved co-expression. Trends Genet 19, 238-242. (Pubitemid 36507003)
56. Villers F, Schaeffer B, Bertin C, and Huet S. (2008). Assessing the validity domains of graphical Gaussian models in order to infer relationships among components of complex biological systems. Stat Appl Genet Mol Biol 7, 14.
57. Warsow G, Greber B, Falk SS, et al. (2010). ExprEssence-Revealing the essence of differential experimental data in the context of an interaction/regulation net-work. BMC Syst Biol 4, 164.
58. Wei H, Persson S, Mehta T, et al. (2006). Transcriptional coordination of the metabolic network in Arabidopsis. Plant Physiol 142, 762-774. (Pubitemid 44583145)
59. Yeung MKS, Tegnér J, and Collins JJ. (2002). Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci USA 99, 6163-6168. (Pubitemid 34493283)
60. Yeung T, Ozdamar B, Paroutis P, and Grinstein S. (2006). Lipid metabolism and dynamics during phagocytosis. Curr Opin Cell Biol 18, 429-437. (Pubitemid 44016055)
61. Yoon H, Kim TS, and Braciale TJ. (2010). The cell cycle time of CD8 + T cells responding in vivo is controlled by the type of antigenic stimulus. PLoS One 5, e15423.
62. Zoppoli P, Morganella S, and Ceccarelli M. (2010). TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics 11, 154.
63. Zou M, and Conzen SD. (2005). A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21, 71-79.