Control samples were obtained from healthy children in the ambulatory departments of participating institutions by using previously published inclusion and exclusion criteria [18].All patients with microarray data in the current study were previously reported in studies addressing hypotheses tech support entirely different from that of the current report [7,9-16,18,20]. For the current study, all patients in the sepsis and septic-shock cohorts had clinical microbiology laboratory confirmation of a bacterial pathogen from blood cultures or other normally sterile body fluids, whereas all patients in the SIRS cohort had negative bacterial cultures.RNA extraction, microarray hybridization, and microarray analysisTotal RNA was isolated from whole-blood samples by using the PaxGene Blood RNA System (PreAnalytiX; Qiagen/Becton Dickinson, Valencia, CA, USA) according the manufacturer’s specifications.
Microarray hybridization was performed by the Affymetrix Gene Chip Core facility at Cincinnati Children’s Hospital Research Foundation, as previously described, by using the Human Genome U133 Plus 2.0 GeneChip (Affymetrix, Santa Clara, CA, USA) [18].Analyses were performed by using one patient sample per chip. Image files were captured by using an Affymetrix GeneChip Scanner 3000. Raw data files were subsequently preprocessed by using Robust Multiple-array Average (RMA) normalization with GeneSpring GX 7.3 software (Agilent Technologies, Palo Alto, CA, USA) [21]. All chips were then normalized to the respective median values of normal, age-matched controls, as previously described [18].
Differences in mRNA abundance between patient samples were determined by using GeneSpring GX 7.3. All statistical analyses used corrections for multiple comparisons. The specific statistical and filtering approaches for identifying differentially regulated genes are provided in the Results section because of their relevance to data interpretation. All microarray data have been deposited in the Gene Expression Omnibus [22] under accession number “type”:”entrez-geo”,”attrs”:”text”:”GSE4607″,”term_id”:”4607″GSE4607.Generation of gene-expression mosaicsGene-expression mosaics were generated by using the Gene Expression Dynamics Inspector (GEDI) platform. GEDI is a publicly available gene-expression analysis program developed by the Ingber Laboratory at Harvard University [23,24].
The signature graphic outputs of GEDI are gene-expression mosaics that give microarray data a “face” that is intuitively recognizable by human pattern recognition [10,11]. The underlying algorithm for creating the mosaics is a self-organizing map (SOM).Computer-assisted image analysisIndividual patient mosaics were Brefeldin_A compared with SIRS and sepsis reference mosaics by using a publicly available image-analysis platform (ImageJ), as previously described [10].