Unknown,Transcriptomics,Genomics,Proteomics

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Transcriptomic profiling of the inflammatory response after trauma

ABSTRACT: The study was aimed to identify mechanisms linked to complicated courses after severe trauma by a systems biology approach. In severe trauma, overwhelming systemic inflammation can result in adverse events and the development of complications, including sepsis. In a prospective study, RNA samples from circulating leukocytes from patients with multiple injury (injury severity score â¥ 17) were analyzed for dynamic changes in gene expression over a period of 21 days by whole genome screening. Based on their clinical presentation, patients were divided into two subgroups: patients with secondary sepsis after trauma (n=5) and patients with systemic inflammation without infection (n=5). Expression cluster were defined by correlating gene expression data with clinical outcome parameters. Using unsupervised clustering, patients with systemic inflammation only and patients with sepsis showed a distinct expression pattern and the discrimination of clinical presentation was reflected by clustering of the samples. Explorative gene set analysis revealed robust upregulation of genes related to âhemoglobin metabolism/oxygen transportâ and âpathogenic E.coli infectionâ. 10 patients with multi-system trauma (ISS â¥ 17 points) admitted to the Division of Trauma Surgery at the University Hospital Zurich were included. Whole blood from trauma patients was collected within the first 6 h after trauma (day 0) and on days 1, 2, 3, 5, 7, 10, 14, and 21. Total cellular RNA from circulating leukocytes was isolated using PaxGene Blood RNA Kit (PreAnalytix) for transcriptome profiling. RNA from blood of trauma patients was extracted and subjected to microarray analysis for comparison of longitudinal transcriptomic responses of patients. RNA samples of circulating leukocytes covering time points directly after admission (D0) and on the consecutive days (D1-D21) were subject to multifactorial microarray data analysis: Differences in dynamics of transcripts were assessed by contrasting time- and individual-resolved changes for sepsis and systemic inflammation without infection.

ORGANISM(S): Homo sapiens

SUBMITTER: S. Lambeck

PROVIDER: E-GEOD-70311 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:BackgroundM-bM-^@M-^TDiagnosis and pronostic assessment are challenging in infective endocarditis (IE). To investigate the host response during IE and identify potential biomarkers, we determined the circulating gene expression profile through a whole genome microarray analysis. Methods and ResultsM-bM-^@M-^TA transcriptomic case-control study was performed on blood samples from patients with native valve IE (n=39), excluded IE after an initial suspicion (n=10) at patientM-bM-^@M-^Ys admission, and age-matched healthy controls (n=10). The whole genome microarray analysis showed that patients with IE exhibited a specific transcriptional program with a predominance of gene categories associated with cell activation, innate immune and inflammatory responses. These categories were organized in a dense network from which arose numerous subnetworks including major histocompatibily complex and natural killer cell network, type 1 interferon pathway and intracellular traffic. Quantitative real-time RT-PCR performed on a selection of highly modulated genes showed that the expression of the gene encoding S100 calcium binding protein A11 (S100A11) was significantly increased in patients with IE in comparison with controls (P<0.001) and patients with excluded IE (P<0.05). Interestingly, the upregulated expression of S100A11 gene was more pronounced in staphylococcal IE than in streptococcal IE (P<0.01). These results were confirmed by serum concentrations of the S100A11 protein. Finally, we showed that, in patients with IE, the upregulation of aquaporin-9 gene (AQP9) was significantly related to the occurrence of acute heart failure (P=0.02). ConclusionsM-bM-^@M-^T Using transcriptional signatures of blood samples, we identified S100A11 as a potential diagnostic marker of IE. In addition, the determination of AQP9 may improve the prognostic assessment of IE. The transcriptomic case-control study was performed in 39 consecutive patients with native valve IE (IE group) diagnosed by a multidisciplinary team who applied the modified Duke criteria,12 10 patients admitted for a suspicion of IE but with a final excluded IE diagnosis. Ten IE patients and five controls were arbitrary selected and investigated with microarrays.

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Transcriptomic profiling of the inflammatory response after trauma

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