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PLoS One. 2015 Sep 28;10(9):e0138110. doi: 10.1371/journal.pone.0138110. eCollection 2015.

Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice.

Author information

1
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, 14642, United States of America.
2
Department of Medicine, University of Rochester Medical Center, Rochester, NY, 14642, United States of America.
3
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, 14642, United States of America; Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642 United States of America.
4
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, 14642, United States of America; Department of Biomedical Engineering, Shandong University, Jinan, Shandong, China.
5
Functional Genomics Center, University of Rochester, Rochester, NY, 14642, United States of America.
6
Department of Medicine, University of Rochester Medical Center, Rochester, NY, 14642, United States of America; Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642 United States of America.
7
Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States of America.

Abstract

Current approaches to study transcriptional profiles post influenza infection typically rely on tissue sampling from one or two sites at a few time points, such as spleen and lung in murine models. In this study, we infected female C57/BL6 mice intranasally with mouse-adapted H3N2/Hong Kong/X31 avian influenza A virus, and then analyzed the gene expression profiles in four different compartments (blood, lung, mediastinal lymph nodes, and spleen) over 11 consecutive days post infection. These data were analyzed by an advanced statistical procedure based on ordinary differential equation (ODE) modeling. Vastly different lists of significant genes were identified by the same statistical procedure in each compartment. Only 11 of them are significant in all four compartments. We classified significant genes in each compartment into co-expressed modules based on temporal expression patterns. We then performed functional enrichment analysis on these co-expression modules and identified significant pathway and functional motifs. Finally, we used an ODE based model to reconstruct gene regulatory network (GRN) for each compartment and studied their network properties.

PMID:
26413862
PMCID:
PMC4586376
DOI:
10.1371/journal.pone.0138110
[Indexed for MEDLINE]
Free PMC Article

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