Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1–day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1–day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1–day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8–day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.
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References
1.
AijöT, and LähdesmäkiH. (2009). Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics, 25, 2937–2944.
2.
AltayG, and Emmert-StreibF. (2010). Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics, 26, 1738–1744.
3.
AuffrayC, ChenZ, and HoodL. (2009). Systems medicine: The future of medical genomics and healthcare. Genome Med, 1, 2.
4.
BansalM, BelcastroV, Ambesi-ImpiombatoA, and di BernardoD. (2007). How to infer gene networks from expression profiles. Mol Syst Biol, 3, 78.
5.
Bassaganya-RieraJ, SongR, RobertsPC, and HontecillasR. (2010). PPAR-gamma activation as an anti-inflammatory therapy for respiratory virus infections. Viral Immunol, 23, 343–352.
6.
BernsteinA, PulendranB, and RappuoliR. (2011). Systems vaccinomics: The road ahead for vaccinology. OMICS, 15, 529–531.
7.
BindeaG, MlecnikB, HacklH, et al. (2009). ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 25, 1091–1093.
8.
BonneauR, ReissDJ, ShannonP, 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-NetoUM, and MarquesETJr. (2006). From functional genomics to functional immunomics: New challenges, old problems, big rewards. PLoS Comput Biol, 2, e81.
10.
CantoneI, MarucciL, IorioF, et al. (2009). A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell, 137, 172–181.
11.
De GrootAS. (2006). Immunomics: Discovering new targets for vaccines and therapeutics. Drug Discov Today, 11, 203–209.
DimitrakopoulouK, TsimpourisC, PapadopoulosG, et al. (2011). Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection. J Clin Bioinforma, 1, 27.
14.
GardnerTS, di BernardoD, LorenzD, and CollinsJJ. (2003). Inferring genetic networks and identifying compound mode of action via expression profiling. Science, 301, 102–105.
15.
GarskeT, LegrandJ, DonnellyCA, et al. (2009). Assessing the severity of the novel influenza A/H1N1 pandemic. BMJ, 339, b2840.
16.
GentlemanRC, CareyVJ, BatesDM, et al. (2004). Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol, 5, R80.
17.
GuthkeR, MöllerU, HoffmannM, ThiesF, and TöpferS. (2005). Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics, 21, 1626–1634.
18.
HandelA, LonginiIMJr, and AntiaR. (2010). Towards a quantitative understanding of the within-host dynamics of influenza A infections. J R Soc Interface, 7, 35–47.
19.
HeY, XuK, KeinerB, et al. (2010). Influenza A virus replication induces cell cycle arrest in G0/G1 phase. J Virol, 84, 12832–12840.
20.
HorvathS, and DongJ. (2008). Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol, 4, e1000117.
21.
HuangDW, ShermanBT, and LempickiRA. (2009). Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc, 4, 44–57.
22.
IlyushinaNA, KhalenkovAM, SeilerJP, et al. (2010). Adaptation of pandemic H1N1 influenza viruses in mice. J Virol, 84, 8607–8616.
23.
InoueLY, NeiraM, NelsonC, GleaveM, and EtzioniR. (2007).Cluster-based network model for time-course gene expression data. Biostatistics, 8, 507–525.
24.
KellerM, MazuchJ, AbrahamU, et al. (2009). A circadian clock in macrophages controls inflammatory immune responses. Proc Natl Acad Sci USA, 106, 21407–21412.
25.
LangfelderP, and HorvathS. (2007). Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol, 1, 54.
26.
LeekJT, MonsenE, DabneyAR, and StoreyJD. (2006). EDGE: Extraction and analysis of differential gene expression. Bioinformatics, 22, 507–508.
27.
LiZ, ShawSM, YedwabnickMJ, and ChanC. (2006). Using a state-space model with hidden variables to infer transcription factor activities. Bioinformatics, 22, 747–754.
28.
LiaoTW. (2005). Clustering of time series data—A survey. Pattern Recog, 38, 1857–1874.
29.
LoukasA, GazeS, MulvennaJP, et al. (2011). Vaccinomics for the major blood feeding helminths of humans. OMICS, 15, 567–577.
30.
MaraziotisIA, DragomirA, and ThanosD. (2010). Gene regulatory networks modelling using a dynamic evolutionary hybrid. BMC Bioinformatics, 11, 140.
31.
MarbachD, CostelloJC, KüffnerR, et al. (2012). Wisdom of crowds for robust gene network inference. Nat Methods, 9, 796–804.
32.
MatsuokaY, LamirandeEW, and SubbaraoK. (2009). The mouse model for influenza. Curr Protoc Microbiol Chapter, 15, Unit 15G.3
33.
McDermottJE, ShankaranH, EisfeldAJ, 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.
McWilliamHE, DriguezP, PiedrafitaD, McManusDP, and MeeusenEN. (2012). Novel immunomic technologies for schistosome vaccine development. Parasite Immunol, 34, 276–284.
35.
Möller-LevetCS, KlawonnbF, ChocKH, YinaH, and WolkenhauereO. (2005). Clustering of unevenly sampled gene expression time-series data. Fuzzy Sets Bioinformatics, 152, 49–66.
36.
Muñoz-FontelaC, PazosM, DelgadoI, 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.
NagarajanR, and UpretiM. (2010). Granger causality analysis of human cell-cycle gene expression profiles. Stat Appl Genet Mol Biol, 9, 31.
38.
PaquetteSG, BannerD, ZhaoZ, et al. (2012). Interleukin-6 is a potential biomarker for severe pandemic H1N1 influenza A infection. PLoS One, 7, e38214.
39.
ParnellG, McLeanA, BoothD, HuangS, NalosM, and TangB. (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.
PawelekKA, HuynhGT, QuinlivanM, CullinaneA, RongL, and PerelsonAS. (2012). Modeling within-host dynamics of influenza virus infection including immune responses. PLoS Comput Biol, 8, e1002588.
41.
PolandGA, KennedyRB, and OvsyannikovaIG. (2011). Vaccinomics and personalized vaccinology: Is science leading us toward a new path of directed vaccine development and discovery?. PLoS Pathog, 7, e1002344.
42.
PommerenkeC, WilkE, SrivastavaB, 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.
SaenzRA, QuinlivanM, EltonD, et al. (2010). Dynamics of influenza virus infection and pathology. J Virol, 84, 3974–3983.
44.
SchäferJ, and StrimmerK. (2005). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics, 21, 754–764.
45.
SchughartK, Libert CSY, SGENET consortium, and KasMJ. (2013). Controlling complexity: The clinical relevance of mouse complex genetics. Eur J Hum Genet, 21, 1191–1196.
46.
SetteA, FleriW, PetersB, SathiamurthyM, BuiHH, and WilsonS. (2005). A roadmap for the immunomics of category A–C pathogens. Immunity, 22, 155–161.
47.
SilverAC, ArjonaA, HughesME, NitabachMN, and FikrigE. (2012). Circadian expression of clock genes in mouse macrophages, dendritic cells, and B cells. Brain Behav Immun, 26, 407–413.
48.
ShannonP, MarkielA, OzierO, et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res, 13, 2498–2504.
49.
ShayT, and KangJ. (2013). Immunological Genome Project and systems immunology. Trends Immunol, 43, 602–609.
50.
SongL, KolarM, and XingE. (2009a). KELLER: Estimating time-varying interactions between genes. Bioinformatics, 25, i128–i136.
51.
SongL, KolarM, and XingE. (2009b). Time-varying dynamic Bayesian networks. Advances in Neural Information Processing Systems 22 (NIPS 2009).
52.
StrausDS, and GlassCK. (2007). Anti-inflammatory actions of PPAR ligands: New insights on cellular and molecular mechanisms. Trends Immunol, 28, 551–558.
53.
SweetC, and SmithH. (1980). Pathogenicity of influenza virus. Microbiol Rev, 44, 303–330.
54.
TerrierO, JossetL, TextorisJ, et al. (2011). Cellular transcriptional 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 NoortV, SnelB, and HuynenMA. (2003). Predicting gene function by conserved co-expression. Trends Genet, 19, 238–242.
56.
VillersF, SchaefferB, BertinC, and HuetS. (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.
WarsowG, GreberB, FalkSS, 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.
WeiH, PerssonS, MehtaT, et al. (2006). Transcriptional coordination of the metabolic network in Arabidopsis. Plant Physiol, 142, 762–774.
59.
YeungMKS, TegnérJ, and CollinsJJ. (2002). Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci USA, 99, 6163–6168.
60.
YeungT, OzdamarB, ParoutisP, and GrinsteinS. (2006). Lipid metabolism and dynamics during phagocytosis. Curr Opin Cell Biol, 18, 429–437.
61.
YoonH, KimTS, and BracialeTJ. (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.
ZoppoliP, MorganellaS, and CeccarelliM. (2010). TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics, 11, 154.
63.
ZouM, and ConzenSD. (2005). A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics, 21, 71–79.
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