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A network-based analysis of systemic inflammation in humans

Author

Listed:
  • Steve E. Calvano

    (UMDNJ-Robert Wood Johnson Medical School)

  • Wenzhong Xiao

    (Stanford Genome Technology Center)

  • Daniel R. Richards

    (Ingenuity Systems Inc)

  • Ramon M. Felciano

    (Ingenuity Systems Inc)

  • Henry V. Baker

    (University of Florida College of Medicine
    University of Florida College of Medicine)

  • Raymond J. Cho

    (Ingenuity Systems Inc)

  • Richard O. Chen

    (Ingenuity Systems Inc)

  • Bernard H. Brownstein

    (Washington University in St Louis)

  • J. Perren Cobb

    (Washington University in St Louis)

  • S. Kevin Tschoeke

    (University of Florida College of Medicine)

  • Carol Miller-Graziano

    (University of Rochester School of Medicine)

  • Lyle L. Moldawer

    (University of Florida College of Medicine)

  • Michael N. Mindrinos

    (Stanford Genome Technology Center)

  • Ronald W. Davis

    (Stanford Genome Technology Center)

  • Ronald G. Tompkins

    (Massachusetts General Hospital, Harvard Medical School)

  • Stephen F. Lowry

    (UMDNJ-Robert Wood Johnson Medical School)

Abstract

Oligonucleotide and complementary DNA microarrays are being used to subclassify histologically similar tumours, monitor disease progress, and individualize treatment regimens1,2,3,4,5. However, extracting new biological insight from high-throughput genomic studies of human diseases is a challenge, limited by difficulties in recognizing and evaluating relevant biological processes from huge quantities of experimental data. Here we present a structured network knowledge-base approach to analyse genome-wide transcriptional responses in the context of known functional interrelationships among proteins, small molecules and phenotypes. This approach was used to analyse changes in blood leukocyte gene expression patterns in human subjects receiving an inflammatory stimulus (bacterial endotoxin). We explore the known genome-wide interaction network to identify significant functional modules perturbed in response to this stimulus. Our analysis reveals that the human blood leukocyte response to acute systemic inflammation includes the transient dysregulation of leukocyte bioenergetics and modulation of translational machinery. These findings provide insight into the regulation of global leukocyte activities as they relate to innate immune system tolerance and increased susceptibility to infection in humans.

Suggested Citation

  • Steve E. Calvano & Wenzhong Xiao & Daniel R. Richards & Ramon M. Felciano & Henry V. Baker & Raymond J. Cho & Richard O. Chen & Bernard H. Brownstein & J. Perren Cobb & S. Kevin Tschoeke & Carol Mille, 2005. "A network-based analysis of systemic inflammation in humans," Nature, Nature, vol. 437(7061), pages 1032-1037, October.
    • Handle: RePEc:nat:nature:v:437:y:2005:i:7061:d:10.1038_nature03985
    DOI: 10.1038/nature03985
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    Citations

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    Cited by:

    1. Yau-Hua Yu & Hsu-Ko Kuo & Kuo-Wei Chang, 2008. "The Evolving Transcriptome of Head and Neck Squamous Cell Carcinoma: A Systematic Review," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-11, September.
    2. Yoram Vodovotz & Marie Csete & John Bartels & Steven Chang & Gary An, 2008. "Translational Systems Biology of Inflammation," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-6, April.
    3. Dolores Wolfram & Ravi Starzl & Hubert Hackl & Derek Barclay & Theresa Hautz & Bettina Zelger & Gerald Brandacher & W P Andrew Lee & Nadine Eberhart & Yoram Vodovotz & Johann Pratschke & Gerhard Piere, 2014. "Insights from Computational Modeling in Inflammation and Acute Rejection in Limb Transplantation," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    4. Patricia Severino & Eliézer Silva & Giovana Lotici Baggio-Zappia & Milena Karina Coló Brunialti & Laura Alejandra Nucci & Otelo Rigato Jr. & Ismael Dale Cotrim Guerreiro da Silva & Flávia Ribeiro Mach, 2014. "Patterns of Gene Expression in Peripheral Blood Mononuclear Cells and Outcomes from Patients with Sepsis Secondary to Community Acquired Pneumonia," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-8, March.
    5. Junhee Seok & Ronald W Davis & Wenzhong Xiao, 2015. "A Hybrid Approach of Gene Sets and Single Genes for the Prediction of Survival Risks with Gene Expression Data," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-15, May.
    6. Herman A van Wietmarschen & Theo H Reijmers & Anita J van der Kooij & Jan Schroën & Heng Wei & Thomas Hankemeier & Jacqueline J Meulman & Jan van der Greef, 2011. "Sub-Typing of Rheumatic Diseases Based on a Systems Diagnosis Questionnaire," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    7. Jonathan E McDunn & Kareem D Husain & Ashoka D Polpitiya & Anton Burykin & Jianhua Ruan & Qing Li & William Schierding & Nan Lin & David Dixon & Weixiong Zhang & Craig M Coopersmith & W Michael Dunne , 2008. "Plasticity of the Systemic Inflammatory Response to Acute Infection during Critical Illness: Development of the Riboleukogram," PLOS ONE, Public Library of Science, vol. 3(2), pages 1-14, February.
    8. Kakajan Komurov & Michael A White & Prahlad T Ram, 2010. "Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-10, August.

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