title: Integrative "omic" analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes.
abstract: BACKGROUND
  Sepsis is a leading cause of morbidity and mortality. 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.
  
  
  OBJECTIVE
  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.
  
  
  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.
authors: 1 Respiratory Immunology Program.
authors: Raymond J
authors: RJ
authors: Langley
authors: Jennifer L
authors: JL
authors: Tipper
authors: Shannon
authors: S
authors: Bruse
authors: Rebecca M
authors: RM
authors: Baron
authors: Ephraim L
authors: EL
authors: Tsalik
authors: James
authors: J
authors: Huntley
authors: Angela J
authors: AJ
authors: Rogers
authors: Richard J
authors: RJ
authors: Jaramillo
authors: Denise
authors: D
authors: O'Donnell
authors: William M
authors: WM
authors: Mega
authors: Mignon
authors: M
authors: Keaton
authors: Elizabeth
authors: E
authors: Kensicki
authors: Lee
authors: L
authors: Gazourian
authors: Laura E
authors: LE
authors: Fredenburgh
authors: Anthony F
authors: AF
authors: Massaro
authors: Ronny M
authors: RM
authors: Otero
authors: Vance G
authors: VG
authors: Fowler
authors: Emanuel P
authors: EP
authors: Rivers
authors: Chris W
authors: CW
authors: Woods
authors: Stephen F
authors: SF
authors: Kingsmore
authors: Mohan L
authors: ML
authors: Sopori
authors: Mark A
authors: MA
authors: Perrella
authors: Augustine M K
authors: AM
authors: Choi
authors: Kevin S
authors: KS
authors: Harrod
mesh_terms: D000818:Animals; D016470:Bacteremia / Q000097:blood* / Q000175:diagnosis*; D015415:Biomarkers / Q000097:blood; D015331:Cohort Studies; D004195:Disease Models, Animal; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008251:Macaca; D008297:Male; D055432:Metabolomics / Q000379:methods*; D018746:Systemic Inflammatory Response Syndrome / Q000097:blood* / Q000175:diagnosis*; D059467:Transcriptome / Q000502:physiology*
chemical_list: D015415:Biomarkers
keywords: bacteremia; metabolomics; mitochondrial dysfunction; nonhuman primates; transcriptomics