- Poster presentation
- Open Access
Gene expression in sepsis is independent of the center-associated effects and indicates a tight regulation of the inflammatory process
© BioMed Central Ltd., 2006
- Published: 21 March 2006
- Gene Expression Profile
- False Discovery Rate
- Severe Sepsis
- cDNA Microarrays
- Consensus Conference
Sepsis remains the leading cause of death in noncardiologic ICUs. Genetic predispositions of patients play an important role in the control of inflammatory response. We have recently reported that cDNA microarrays can be used to identify typical gene expression profiles in patients with severe sepsis despite strong interindividual differences. The aim of the present study was therefore to evaluate whether center-dependent effects on the gene expression pattern exist, whether diagnostically relevant gene expression profile can be identified, and which genes are important during the systemic inflammatory response.
Twenty-nine patients were enrolled from one German and three Czech hospitals. The ACCP/SCCM consensus conference definition was applied to predict the severity of sepsis in ICU patients. As controls we used 18 post spinal or bypass surgery patients, respectively, without signs of inflammation. Gene expression was measured using the inhouse research microarray of SIRS-Lab GmbH Jena (Germany), which comprised probes for 5226 human genes relevant to inflammation, immune response and related processes. The experiments were performed according to MIAME guidelines.
In order to reveal genes differentially expressed during sepsis and to assess the effects sample collection from different centres have on the data, we applied, gene by gene, the two-way analysis of variance to the normalised expression data. Furthermore, the q-value was estimated, thus controlling the false discovery rate (FDR) occurring in multiple comparisons.
A set of 213 genes was obtained, for which the gene expression significantly varied between sepsis and control patients, similarly in both centers (FDR < 0.1). In this set, 88 genes were upregulated and 125 genes were downregulated in sepsis patients compared with the controls.
The present data indicate that microarray technology is suitable for systematically identifying those genes that underlie the attenuated inflammatory response in sepsis. Gene expression profiles were able to distinguish between infectious and non-infectious systemic inflammatory response, despite a magnitude of center-associated effects. The participation of genes involving in the control of inflammatory response indicated a necessity of tight control of inflammatory response and has a potential impact for future diagnosis and treatment of sepsis.