ArticlePloS one2015
Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Decellularization of various tissues and organs through chemical methods.Tissue & cell · 2024Review
- Discrete-state models identify pathway specific B cell states across diseases and infections at single-cell resolution.Journal of theoretical biology · 2024Article
- Inhibition of KIF20A suppresses the replication of influenza A virus by inhibiting viral entry.Journal of microbiology (Seoul, Korea) · 2022Article
- Inflammatory pathways are upregulated in the nasal epithelium in patients with idiopathic pulmonary fibrosis.Respiratory research · 2018Article
- FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis.Bioinformatics (Oxford, England) · 2017Article
- A Four-Biomarker Blood Signature Discriminates Systemic Inflammation Due to Viral Infection Versus Other Etiologies.Scientific reports · 2017Article
- Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes.BMC bioinformatics · 2017Article
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Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.