Gene expression profiling of Gram-negative bacteria-induced @entity32 in XXXX whole blood: The role of complement and @entity5750 -mediated innate immune response.

@entity1 @entity32 @entity2244 @entity295 @entity63 @entity5750 @entity526 @entity2242 @entity1850 @entity881 @entity130
Non-sterile pathogen-induced @entity32 like in @entity130 or @entity63 -reperfusion injury may both coincide with the life threatening @entity1850 and @entity881 . Consequently, there is an urgent need for specific biomarkers in order to distinguish @entity295 from sterile conditions. The overall aim of this study was to uncover putative @entity295 biomarkers and biomarker pathways, as well as to test the efficacy of combined inhibition of innate immunity key players complement and Toll-like receptor co-receptor @entity5750 as a possible therapeutic regimen for @entity295 . We performed whole blood gene expression analyses using microarray in order to profile Gram-negative bacteria-induced @entity32 responses in an ex vivo @entity1 whole blood model. The experiments were performed in the presence or absence of inhibitors of complement proteins (C3 and @entity2242 (C5a receptor 1)) and @entity5750 , alone or in combination. In addition, we used blood from a @entity2244 donor. Anti-coagulated whole blood was challenged with heat-inactivated @entity526 for 2  h, total RNA was isolated and microarray analyses were performed on the Affymetrix GeneChip Gene 1.0 ST Array platform. The initial experiments were performed in duplicates using blood from two healthy donors. @entity2244 is very rare, and only one donor could be recruited. In order to increase statistical power, a technical replicate of the @entity2244 samples was run. Subsequently, log2-transformed intensities were processed by robust multichip analysis and filtered using a threshold of four. In total, 73 microarray chips were run and analyzed. The normalized and filtered raw data have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible with GEO Series accession number GSE55537. Linear models for microarray data were applied to estimate fold changes between data sets and the respective multiple testing adjusted p-values (FDR q-values). The interpretation of the data has been published by Lau et al. in an open access article entitled " @entity5750 and Complement Crosstalk and Largely Mediate the Transcriptional Response to @entity526 in @entity1 Whole Blood as revealed by DNA Microarray" (Lau et al., 2015).
