Integrative "omic" analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes

Raymond J. Langley, Jennifer L. Tipper, Shannon Bruse, Rebecca M. Baron, Ephraim L. Tsalik, James Huntley, Angela J. Rogers, Richard J. Jaramillo, Denise O'Donnell, William M. Mega, Mignon Keaton, Elizabeth Kensicki, Lee Gazourian, Laura E. Fredenburgh, Anthony F. Massaro, Ronny M. Otero, Vance G. Fowler, Emanuel P. Rivers, Chris W. Woods, Stephen F. KingsmoreMohan L. Sopori, Mark A. Perrella, Augustine M.K. Choi, Kevin S. Harrod

Research output: Contribution to journalArticlepeer-review

86 Scopus citations


Rationale: Sepsis is a leading causeofmorbidity andmortality. Currently, early diagnosis and the progression of the disease are difficult to make. The integration of metabolomic and transcriptomic data in a primate model of sepsis may provide a novel molecular signature of clinical sepsis. Objectives: To develop a biomarker panel to characterize sepsis in primates and ascertain its relevance to early diagnosis and progression of human sepsis. Methods: Intravenous inoculation of Macaca fascicularis with Escherichia coli produced mild to severe sepsis, lung injury, and death. Plasma samples were obtained before and after 1, 3, and 5 days of E. coli challenge and at the time of killing. At necropsy, blood, lung, kidney, and spleen samples were collected. An integrative analysis of the metabolomic and transcriptomic datasets was performed to identify a panel of sepsis biomarkers. Measurements and Main Results: The extent of E. coli invasion, respiratory distress, lethargy, and mortality was dependent on the bacterial dose. Metabolomic and transcriptomic changes characterized severe infections and death, and indicated impaired mitochondrial, peroxisomal, and liver functions. Analysis of the pulmonary transcriptome and plasma metabolome suggested impaired fatty acid catabolism regulated by peroxisome-proliferator activated receptor signaling. A representative four-metabolite model effectively diagnosed sepsis in primates (area under the curve, 0.966) and in two human sepsis cohorts (area under the curve, 0.78 and 0.82). Conclusions: A model of sepsis based on reciprocal metabolomic and transcriptomic data was developed in primates and validated in two human patient cohorts. It is anticipated that the identified parameters will facilitate early diagnosis and management of sepsis.

Original languageEnglish (US)
Pages (from-to)445-455
Number of pages11
JournalAmerican journal of respiratory and critical care medicine
Issue number4
StatePublished - Aug 15 2014


  • Bacteremia
  • Metabolomics
  • Mitochondrial dysfunction
  • Nonhuman primates
  • Transcriptomics

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine


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