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Analysis of Serum Inflammatory Mediators Identifies Unique Dynamic Networks Associated with Death and Spontaneous Survival in Pediatric Acute Liver Failure

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Listed:
  • Nabil Azhar
  • Cordelia Ziraldo
  • Derek Barclay
  • David A Rudnick
  • Robert H Squires
  • Yoram Vodovotz
  • for the Pediatric Acute Liver Failure Study Group

Abstract

Background: Tools to predict death or spontaneous survival are necessary to inform liver transplantation (LTx) decisions in pediatric acute liver failure (PALF), but such tools are not available. Recent data suggest that immune/inflammatory dysregulation occurs in the setting of acute liver failure. We hypothesized that specific, dynamic, and measurable patterns of immune/inflammatory dysregulation will correlate with outcomes in PALF. Methods: We assayed 26 inflammatory mediators on stored serum samples obtained from a convenience sample of 49 children in the PALF study group (PALFSG) collected within 7 days after enrollment. Outcomes were assessed within 21 days of enrollment consisting of spontaneous survivors, non-survivors, and LTx recipients. Data were subjected to statistical analysis, patient-specific Principal Component Analysis (PCA), and Dynamic Bayesian Network (DBN) inference. Findings: Raw inflammatory mediator levels assessed over time did not distinguish among PALF outcomes. However, DBN analysis did reveal distinct interferon-gamma-related networks that distinguished spontaneous survivors from those who died. The network identified in LTx patients pre-transplant was more like that seen in spontaneous survivors than in those who died, a finding supported by PCA. Interpretation: The application of DBN analysis of inflammatory mediators in this small patient sample appears to differentiate survivors from non-survivors in PALF. Patterns associated with LTx pre-transplant were more like those seen in spontaneous survivors than in those who died. DBN-based analyses might lead to a better prediction of outcome in PALF, and could also have more general utility in other complex diseases with an inflammatory etiology.

Suggested Citation

  • Nabil Azhar & Cordelia Ziraldo & Derek Barclay & David A Rudnick & Robert H Squires & Yoram Vodovotz & for the Pediatric Acute Liver Failure Study Group, 2013. "Analysis of Serum Inflammatory Mediators Identifies Unique Dynamic Networks Associated with Death and Spontaneous Survival in Pediatric Acute Liver Failure," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-8, November.
  • Handle: RePEc:plo:pone00:0078202
    DOI: 10.1371/journal.pone.0078202
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    References listed on IDEAS

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    1. Qi Mi & Gregory Constantine & Cordelia Ziraldo & Alexey Solovyev & Andres Torres & Rajaie Namas & Timothy Bentley & Timothy R Billiar & Ruben Zamora & Juan Carlos Puyana & Yoram Vodovotz, 2011. "A Dynamic View of Trauma/Hemorrhage-Induced Inflammation in Mice: Principal Drivers and Networks," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-12, May.
    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.
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    Cited by:

    1. 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.
    2. Ruben Zamora & Sebastian Korff & Qi Mi & Derek Barclay & Lukas Schimunek & Riccardo Zucca & Xerxes D Arsiwalla & Richard L Simmons & Paul Verschure & Timothy R Billiar & Yoram Vodovotz, 2018. "A computational analysis of dynamic, multi-organ inflammatory crosstalk induced by endotoxin in mice," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-16, November.

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