Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium

Jason E. McDermott, Ronald C. Taylor, Hyunjin Yoon, Fred Heffron

    Research output: Contribution to journalArticle

    42 Citations (Scopus)

    Abstract

    Recent advances in experimental methods have provided sufficient data to consider systems as large networks of interconnected components. High-throughput determination of protein-protein interaction networks has led to the observation that topological bottlenecks, proteins defined by high centrality in the network, are enriched in proteins with systems-level phenotypes such as essentiality. Global transcriptional profiling by microarray analysis has been used extensively to characterize systems, for example, examining cellular response to environmental conditions and effects of genetic mutations. These transcriptomic datasets have been used to infer regulatory and functional relationship networks based on co-regulation. We use the context likelihood of relatedness (CLR) method to infer networks from two datasets gathered from the pathogen Salmonella typhimurium: one under a range of environmental culture conditions and the other from deletions of 15 regulators found to be essential in virulence. Bottleneck and hub genes were identified from these inferred networks, and we show for the first time that these genes are significantly more likely to be essential for virulence than their non-bottleneck or non-hub counterparts. Networks generated using simple similarity metrics (correlation and mutual information) did not display this behavior. Overall, this study demonstrates that topology of networks inferred from global transcriptional profiles provides information about the systems-level roles of bottleneck genes. Analysis of the differences between the two CLR-derived networks suggests that the bottleneck nodes are either mediators of transitions between system states or sentinels that reflect the dynamics of these transitions.

    Original languageEnglish (US)
    Pages (from-to)169-180
    Number of pages12
    JournalJournal of Computational Biology
    Volume16
    Issue number2
    DOIs
    StatePublished - 2009

    Fingerprint

    Salmonella
    Salmonella typhimurium
    Virulence
    Proteins
    Genes
    Data Display
    Protein Interaction Maps
    Microarray Analysis
    Information Systems
    Pathogens
    Microarrays
    Gene
    Phenotype
    Likelihood
    Mutation
    Throughput
    Topology
    Protein
    Functional Relationship
    Protein Interaction Networks

    Keywords

    • Bottlenecks
    • Network inference
    • Salmonella typhimurium
    • Virulence

    ASJC Scopus subject areas

    • Molecular Biology
    • Genetics
    • Computational Mathematics
    • Modeling and Simulation
    • Computational Theory and Mathematics

    Cite this

    Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. / McDermott, Jason E.; Taylor, Ronald C.; Yoon, Hyunjin; Heffron, Fred.

    In: Journal of Computational Biology, Vol. 16, No. 2, 2009, p. 169-180.

    Research output: Contribution to journalArticle

    McDermott, Jason E. ; Taylor, Ronald C. ; Yoon, Hyunjin ; Heffron, Fred. / Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. In: Journal of Computational Biology. 2009 ; Vol. 16, No. 2. pp. 169-180.
    @article{06f027395a144c4798ea57721fed59fb,
    title = "Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium",
    abstract = "Recent advances in experimental methods have provided sufficient data to consider systems as large networks of interconnected components. High-throughput determination of protein-protein interaction networks has led to the observation that topological bottlenecks, proteins defined by high centrality in the network, are enriched in proteins with systems-level phenotypes such as essentiality. Global transcriptional profiling by microarray analysis has been used extensively to characterize systems, for example, examining cellular response to environmental conditions and effects of genetic mutations. These transcriptomic datasets have been used to infer regulatory and functional relationship networks based on co-regulation. We use the context likelihood of relatedness (CLR) method to infer networks from two datasets gathered from the pathogen Salmonella typhimurium: one under a range of environmental culture conditions and the other from deletions of 15 regulators found to be essential in virulence. Bottleneck and hub genes were identified from these inferred networks, and we show for the first time that these genes are significantly more likely to be essential for virulence than their non-bottleneck or non-hub counterparts. Networks generated using simple similarity metrics (correlation and mutual information) did not display this behavior. Overall, this study demonstrates that topology of networks inferred from global transcriptional profiles provides information about the systems-level roles of bottleneck genes. Analysis of the differences between the two CLR-derived networks suggests that the bottleneck nodes are either mediators of transitions between system states or sentinels that reflect the dynamics of these transitions.",
    keywords = "Bottlenecks, Network inference, Salmonella typhimurium, Virulence",
    author = "McDermott, {Jason E.} and Taylor, {Ronald C.} and Hyunjin Yoon and Fred Heffron",
    year = "2009",
    doi = "10.1089/cmb.2008.04TT",
    language = "English (US)",
    volume = "16",
    pages = "169--180",
    journal = "Journal of Computational Biology",
    issn = "1066-5277",
    publisher = "Mary Ann Liebert Inc.",
    number = "2",

    }

    TY - JOUR

    T1 - Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium

    AU - McDermott, Jason E.

    AU - Taylor, Ronald C.

    AU - Yoon, Hyunjin

    AU - Heffron, Fred

    PY - 2009

    Y1 - 2009

    N2 - Recent advances in experimental methods have provided sufficient data to consider systems as large networks of interconnected components. High-throughput determination of protein-protein interaction networks has led to the observation that topological bottlenecks, proteins defined by high centrality in the network, are enriched in proteins with systems-level phenotypes such as essentiality. Global transcriptional profiling by microarray analysis has been used extensively to characterize systems, for example, examining cellular response to environmental conditions and effects of genetic mutations. These transcriptomic datasets have been used to infer regulatory and functional relationship networks based on co-regulation. We use the context likelihood of relatedness (CLR) method to infer networks from two datasets gathered from the pathogen Salmonella typhimurium: one under a range of environmental culture conditions and the other from deletions of 15 regulators found to be essential in virulence. Bottleneck and hub genes were identified from these inferred networks, and we show for the first time that these genes are significantly more likely to be essential for virulence than their non-bottleneck or non-hub counterparts. Networks generated using simple similarity metrics (correlation and mutual information) did not display this behavior. Overall, this study demonstrates that topology of networks inferred from global transcriptional profiles provides information about the systems-level roles of bottleneck genes. Analysis of the differences between the two CLR-derived networks suggests that the bottleneck nodes are either mediators of transitions between system states or sentinels that reflect the dynamics of these transitions.

    AB - Recent advances in experimental methods have provided sufficient data to consider systems as large networks of interconnected components. High-throughput determination of protein-protein interaction networks has led to the observation that topological bottlenecks, proteins defined by high centrality in the network, are enriched in proteins with systems-level phenotypes such as essentiality. Global transcriptional profiling by microarray analysis has been used extensively to characterize systems, for example, examining cellular response to environmental conditions and effects of genetic mutations. These transcriptomic datasets have been used to infer regulatory and functional relationship networks based on co-regulation. We use the context likelihood of relatedness (CLR) method to infer networks from two datasets gathered from the pathogen Salmonella typhimurium: one under a range of environmental culture conditions and the other from deletions of 15 regulators found to be essential in virulence. Bottleneck and hub genes were identified from these inferred networks, and we show for the first time that these genes are significantly more likely to be essential for virulence than their non-bottleneck or non-hub counterparts. Networks generated using simple similarity metrics (correlation and mutual information) did not display this behavior. Overall, this study demonstrates that topology of networks inferred from global transcriptional profiles provides information about the systems-level roles of bottleneck genes. Analysis of the differences between the two CLR-derived networks suggests that the bottleneck nodes are either mediators of transitions between system states or sentinels that reflect the dynamics of these transitions.

    KW - Bottlenecks

    KW - Network inference

    KW - Salmonella typhimurium

    KW - Virulence

    UR - http://www.scopus.com/inward/record.url?scp=59649108558&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=59649108558&partnerID=8YFLogxK

    U2 - 10.1089/cmb.2008.04TT

    DO - 10.1089/cmb.2008.04TT

    M3 - Article

    C2 - 19178137

    AN - SCOPUS:59649108558

    VL - 16

    SP - 169

    EP - 180

    JO - Journal of Computational Biology

    JF - Journal of Computational Biology

    SN - 1066-5277

    IS - 2

    ER -