Identification and Validation of Ifit1 as an Important Innate Immune Bottleneck

The innate immune system plays important roles in a number of disparate processes. Foremost, innate immunity is a first responder to invasion by pathogens and triggers early defensive responses and recruits the adaptive immune system. The innate immune system also responds to endogenous damage signals that arise from tissue injury. Recently it has been found that innate immunity plays an important role in neuroprotection against ischemic stroke through the activation of the primary innate immune receptors, Toll-like receptors (TLRs). Using several large-scale transcriptomic data sets from mouse and mouse macrophage studies we identified targets predicted to be important in controlling innate immune processes initiated by TLR activation. Targets were identified as genes with high betweenness centrality, so-called bottlenecks, in networks inferred from statistical associations between gene expression patterns. A small set of putative bottlenecks were identified in each of the data sets investigated including interferon-stimulated genes (Ifit1, Ifi47, Tgtp and Oasl2) as well as genes uncharacterized in immune responses (Axud1 and Ppp1r15a). We further validated one of these targets, Ifit1, in mouse macrophages by showing that silencing it suppresses induction of predicted downstream genes by lipopolysaccharide (LPS)-mediated TLR4 activation through an unknown direct or indirect mechanism. Our study demonstrates the utility of network analysis for identification of interesting targets related to innate immune function, and highlights that Ifit1 can exert a positive regulatory effect on downstream genes.

[1]  Zachary D. Smith,et al.  Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses , 2009 .

[2]  Matthew W. Hahn,et al.  Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. , 2005, Molecular biology and evolution.

[3]  W. Merrick,et al.  Mouse p56 Blocks a Distinct Function of Eukaryotic Initiation Factor 3 in Translation Initiation* , 2005, Journal of Biological Chemistry.

[4]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[5]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[6]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[7]  Antonio Sanfilippo,et al.  Defining the Players in Higher-Order Networks: Predictive Modeling for Reverse Engineering Functional Influence Networks , 2011, Pacific Symposium on Biocomputing.

[8]  Sonia Sharma,et al.  Convergence of the NF‐κB and Interferon Signaling Pathways in the Regulation of Antiviral Defense and Apoptosis , 2003, Annals of the New York Academy of Sciences.

[9]  Shizuo Akira,et al.  Toll-like receptor signalling , 2004, Nature Reviews Immunology.

[10]  C. Molnar,et al.  Drosophila Axud1 is involved in the control of proliferation and displays pro-apoptotic activity , 2009, Mechanisms of Development.

[11]  G. Superti-Furga,et al.  IFIT1 is an antiviral protein that recognizes 5′-triphosphate RNA , 2011, Nature Immunology.

[12]  J. Mcdermott,et al.  Separating the Drivers from the Driven: Integrative Network and Pathway Approaches Aid Identification of Disease Biomarkers from High-Throughput Data , 2010, Disease markers.

[13]  G. A. West,et al.  Proof of Concept: Pharmacological Preconditioning with a Toll-like Receptor Agonist Protects against Cerebrovascular Injury in a Primate Model of Stroke , 2011, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[14]  Mark Gerstein,et al.  The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics , 2007, PLoS Comput. Biol..

[15]  R. Simon,et al.  Toll-Like Receptor 9: A New Target of Ischemic Preconditioning in the Brain , 2008, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[16]  Qibin Zhang,et al.  Temporal Proteome and Lipidome Profiles Reveal Hepatitis C Virus-Associated Reprogramming of Hepatocellular Metabolism and Bioenergetics , 2010, PLoS pathogens.

[17]  Richard D. Smith,et al.  Systems Virology Identifies a Mitochondrial Fatty Acid Oxidation Enzyme, Dodecenoyl Coenzyme A Delta Isomerase, Required for Hepatitis C Virus Replication and Likely Pathogenesis , 2011, Journal of Virology.

[18]  K. Isobe,et al.  Suppression of Viral Replication by Stress-Inducible GADD34 Protein via the Mammalian Serine/Threonine Protein Kinase mTOR Pathway , 2007, Journal of Virology.

[19]  Joshua N. Adkins,et al.  Controlling the Response: Predictive Modeling of a Highly Central, Pathogen-Targeted Core Response Module in Macrophage Activation , 2011, PloS one.

[20]  Hyunjin Yoon,et al.  Coordinated Regulation of Virulence during Systemic Infection of Salmonella enterica Serovar Typhimurium , 2009, PLoS pathogens.

[21]  H. Teh,et al.  Specific antiviral activity demonstrated by TGTP, a member of a new family of interferon-induced GTPases. , 1998, Journal of immunology.

[22]  Tao Yang,et al.  Multiple Preconditioning Paradigms Converge on Interferon Regulatory Factor-Dependent Signaling to Promote Tolerance to Ischemic Brain Injury , 2011, The Journal of Neuroscience.

[23]  Lee Ann McCue,et al.  A model of cyclic transcriptomic behavior in the cyanobacterium Cyanothece sp. ATCC 51142. , 2011, Molecular bioSystems.

[24]  Scott L. Zeger,et al.  The Analysis of Gene Expression Data: Methods and Software , 2013 .

[25]  John D. Storey,et al.  A network-based analysis of systemic inflammation in humans , 2005, Nature.

[26]  Hyunjin Yoon,et al.  Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium , 2009, J. Comput. Biol..

[27]  Y. Benjamini,et al.  More powerful procedures for multiple significance testing. , 1990, Statistics in medicine.

[28]  T. Ohtsuki,et al.  Lipopolysaccharide pre-treatment induces resistance against subsequent focal cerebral ischemic damage in spontaneously hypertensive rats , 1997, Brain Research.

[29]  Courtney Corley,et al.  Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis , 2012, BMC Systems Biology.

[30]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[31]  Bin Li,et al.  Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics , 2008, PLoS Comput. Biol..

[32]  G. Sen,et al.  Induction and mode of action of the viral stress-inducible murine proteins, P56 and P54. , 2005, Virology.

[33]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[34]  S. Brown,et al.  GADD34 Gene Restores Virulence in Viral Vector Used in Experimental Stroke Study , 2008, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[35]  I. Autenrieth,et al.  Forced IFIT-2 expression represses LPS induced TNF-alpha expression at posttranscriptional levels , 2008, BMC Immunology.

[36]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.