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

ABSTRACT: 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.To develop a biomarker panel to characterize sepsis in primates and ascertain its relevance to early diagnosis and progression of human sepsis.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.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).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.

SUBMITTER: Langley RJ

PROVIDER: S-EPMC4214130 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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

Langley Raymond J RJ Tipper Jennifer L JL Bruse Shannon S Baron Rebecca M RM Tsalik Ephraim L EL Huntley James J Rogers Angela J AJ Jaramillo Richard J RJ O'Donnell Denise D Mega William M WM Keaton Mignon M Kensicki Elizabeth E Gazourian Lee L Fredenburgh Laura E LE Massaro Anthony F AF Otero Ronny M RM Fowler Vance G VG Rivers Emanuel P EP Woods Chris W CW Kingsmore Stephen F SF Kingsmore Stephen F SF Sopori Mohan L ML Perrella Mark A MA Choi Augustine M K AM Harrod Kevin S KS

American journal of respiratory and critical care medicine 20140801 4

<h4>Rationale</h4>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.<h4>Objectives</h4>To develop a biomarker panel to characterize sepsis in primates and ascertain its relevance to early diagnosis and progression of human sepsis.<h4>Methods</h4>Intravenous inoculatio ...[more]

PMID: 25054455

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Project description:BackgroundDespite the extensive research approaches applied to characterise malignant melanoma, no specific molecular markers are available that are clearly related to the progression of this disease. In this study, our aims were to define a gene expression signature associated with the clinical outcome of melanoma patients and to provide an integrative interpretation of the gene expression -, copy number alterations -, and promoter methylation patterns that contribute to clinically relevant molecular functional alterations.MethodsGene expression profiles were determined using the Affymetrix U133 Plus2.0 array. The NimbleGen Human CGH Whole-Genome Tiling array was used to define CNAs, and the Illumina GoldenGate Methylation platform was applied to characterise the methylation patterns of overlapping genes.ResultsWE IDENTIFIED TWO SUBCLASSES OF PRIMARY MELANOMA: one representing patients with better prognoses and the other being characteristic of patients with unfavourable outcomes. We assigned 1,080 genes as being significantly correlated with ulceration, 987 genes were downregulated and significantly enriched in the p53, Nf-kappaB, and WNT/beta-catenin pathways. Through integrated genome analysis, we defined 150 downregulated genes whose expression correlated with copy number losses in ulcerated samples. These genes were significantly enriched on chromosome 6q and 10q, which contained a total of 36 genes. Ten of these genes were downregulated and involved in cell-cell and cell-matrix adhesion or apoptosis. The expression and methylation patterns of additional genes exhibited an inverse correlation, suggesting that transcriptional silencing of these genes is driven by epigenetic events.ConclusionUsing an integrative genomic approach, we were able to identify functionally relevant molecular hotspots characterised by copy number losses and promoter hypermethylation in distinct molecular subtypes of melanoma that contribute to specific transcriptomic silencing and might indicate a poor clinical outcome of melanoma.

Project description:Expression data from CD8+ T cells and CD68+ monocytes from patients with hemophagocytic lymphohistiocytosis, sepsis, and persistent systemic inflammatory response syndrome Hemophagocytic lymphohistiocytosis (HLH) is a syndrome characterized by pathologic immune activation in which prompt recognition and initiation of immune suppression is essential for survival. Children with HLH have many overlapping clinical features with critically ill children with sepsis and persistent systemic inflammatory response syndrome (SIRS) in whom alternative therapies are indicated. To determine if plasma biomarkers could differentiate HLH from other inflammatory conditions and to better define a ‘core inflammatory signature’ of HLH, concentrations of inflammatory plasma proteins were compared in 40 patients with HLH to 47 pediatric patients with severe sepsis or SIRS. Seventeen of 135 analytes were significantly different in HLH plasma compared to SIRS/sepsis, including increased interferon-gamma (IFNg)-regulated chemokines CXCL9, CXCL10 and CXCL11. Further, a 5-analyte plasma protein classifier including these chemokines was able to differentiate HLH from SIRS/sepsis. Gene expression in CD8+ T cells and CD68+ monocytes from blood were also enriched for IFNg pathway signatures in peripheral blood cells from patients with HLH compared to SIRS/sepsis. This study identifies differential expression of inflammatory proteins as a diagnostic strategy to identify critically ill children with HLH. Further, comprehensive unbiased analysis of inflammatory plasma proteins and global gene expression demonstrates that IFNg signaling is uniquely elevated in HLH. In addition to demonstrating the ability of diagnostic criteria for HLH, sepsis and SIRS to identify groups with distinct inflammatory patterns, results from this study support the potential for prospective evaluation of inflammatory biomarkers to aid in diagnosis of and optimizing therapeutic strategies for children with distinctive hyperinflammatory syndromes.

Dataset Information

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

Publications

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

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