title: PathCellNet: Cell-type specific pathogen-response network explorer.
abstract: Pathogen specific immune response is a complex interplay between several innate and adaptive immune cell-types. Innate immune cells play a critical role in pathogen recognition and initiating the antigen specific adaptive immune response. Despite specific functional roles of the innate immune cells, they share several anti-viral pathways. The question then becomes, what is the overlap in the transcriptional changes induced upon viral infections across different cell-types? Here we investigate the extent to which gene signatures are conserved across innate immune cell-types by performing a comparative analysis of transcriptomic data. Particularly, we integrate transcriptomic datasets measuring response of two innate immune cells (epithelial and dendritic cells) to influenza virus. The study reveals cell-type specific regulatory genes and a conserved network between the two cell-types. Additionally, novel functionally associated gene clusters are identified which are robustly defined across multiple independent studies. These gene clusters can be used in future investigation, and to facilitate their use we release PathCellNet (version 0), a cloud based tool to explore cell-type specific connectivity of user-defined genes. In the future, expansion of PathCellNet will allow exploration of cell-type specific responses across a variety of pathogens and cell-types.
authors.affiliation: Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, United States.
authors.forename: Dejan
authors.initials: D
authors.lastname: Katanic
authors.affiliation: Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, United States.
authors.forename: Atif
authors.initials: A
authors.lastname: Khan
authors.affiliation: Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, United States; Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, United States. Electronic address: juilee_thakar@urmc.rochester.edu.
authors.forename: Juilee
authors.initials: J
authors.lastname: Thakar
mesh_terms: D056704:Adaptive Immunity / Q000235:genetics; D000818:Animals; D000067917:Cloud Computing; D016000:Cluster Analysis; D030541:Databases, Genetic; D003713:Dendritic Cells / Q000276:immunology* / Q000821:virology; D004847:Epithelial Cells / Q000276:immunology* / Q000821:virology; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D054884:Host-Pathogen Interactions; D006801:Humans; D007113:Immunity, Innate / Q000235:genetics; D009980:Influenza A virus / Q000276:immunology / Q000472:pathogenicity; D007251:Influenza, Human / Q000235:genetics / Q000276:immunology / Q000821:virology; D009975:Orthomyxoviridae / Q000276:immunology* / Q000472:pathogenicity; D009976:Orthomyxoviridae Infections / Q000235:genetics* / Q000276:immunology / Q000821:virology; D049490:Systems Biology / Q000379:methods*; D059467:Transcriptome
keywords: Conserved gene signature; Epithelial cells; Gene-gene association networks; Gene-sets; Monocyte derived dendritic cells