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Title: Integrative ‘omic analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from SIRS      
availability:
available
aggregation:
instance of dataset
privacy:
not applicable
refinement:
curated
dateReleased:
08-19-2015
ID:
E-GEOD-59075
description:
Rationale: Sepsis is a leading cause of morbidity and mortality; early diagnosis and prediction of progression is difficult to determine. The integration of metabolomic and transcriptomic data in an experimental model of sepsis may be a novel method to identify molecular signatures of clinical sepsis. Objectives: Develop a biomarker panel for earlier diagnosis and prognostic characterization of sepsis patients to inform personalized clinical management and improve understanding of the pathophysiology of sepsis progression. Methods: Mild to severe sepsis, lung injury and death was recapitulated in Macaca fascicularis by intravenous inoculation of Escherichia coli. Plasma samples were obtained at time of challenge and at one, three, and five days later or time of euthanasia. Necropsy was performed and blood, lung, kidney and spleen samples were obtained. An integrative analysis of comprehensive metabolomic and transcriptomic datasets was performed to identify and parameterize a biomarker panel. Measurements and Main Results: Pathogen invasion, respiratory distress, lethargy and mortality was dose dependent. Severe infection and death were associated with metabolomic and transcriptomic changes indicative of mitochondrial, peroxisomal and liver dysfunction. Analysis of reciprocal pulmonary transcriptome and plasma metabolome data revealed an integrated host response that suggested dysregulated fatty acid catabolism resulting from peroxisome-proliferator activated receptor signaling. A representative 4-metabolite model effectively diagnosed sepsis in primates (AUC 0.966) and in two human sepsis cohorts (AUC=0.78 and 0.82). Conclusion: A model to guide early management of patients with sepsis was developed by analysis of reciprocal metabolomic and transcriptomic data in primates that diagnosed sepsis in humans. Transcriptomic analysis of lungs from Cynomolgus macaques challenged with E. coli
keywords:
RNA-seq of coding RNA
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HTML
storedIn:
Array Express
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accessType:
landing page
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none
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primary:
true
accessURL: https://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-59075
format:
JSON
storedIn:
OmicsDI
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not compressed
accessType:
download
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none
authentication:
none
primary:
false
accessURL: www.omicsdi.org/ws/dataset/arrayexpress-repository/E-GEOD-59075.json
format:
XML
storedIn:
OmicsDI
qualifier:
not compressed
accessType:
download
authorization:
none
authentication:
none
primary:
false
accessURL: http://www.omicsdi.org/ws/dataset/arrayexpress-repository/E-GEOD-59075.xml
ID:
SCR:014747
name:
Omics Discovery Index
abbreviation:
OmicsDI
homePage: http://www.omicsdi.org/