The Influence of Developmental Age on the Early Transcriptomic Response of Children with Septic Shock

Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, United States of America.
Molecular Medicine (Impact Factor: 4.51). 07/2011; 17(11-12):1146-56. DOI: 10.2119/molmed.2011.00169
Source: PubMed


Septic shock is a frequent and costly problem among patients in the pediatric intensive care unit (PICU) and is associated with high mortality and devastating survivor morbidity. Genome-wide expression patterns can provide molecular granularity of the host response and offer insight into why large variations in outcomes exist. We derived whole-blood genome-wide expression patterns within 24 h of PICU admission from children with septic shock. We compared the transcriptome between septic shock developmental-age groups defined as neonates (≤ 28 d, n = 17), infants (1 month to 1 year, n = 62), toddlers (2-5 years, n = 54) and school-age (≥ 6 years, n = 47) and age-matched controls. Direct intergroup comparisons demonstrated profound changes in neonates, relative to older children. Neonates with septic shock demonstrated reduced expression of genes representing key pathways of innate and adaptive immunity. In contrast to the largely upregulated transcriptome in all other groups, neonates exhibited a predominantly downregulated transcriptome when compared with controls. Neonates and school-age subjects had the most uniquely regulated genes relative to controls. Age-specific studies of the host response are necessary to identify developmentally relevant translational opportunities that may lead to improved sepsis outcomes.

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Available from: Thomas P Shanley, Mar 10, 2014
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    • "Genome-level expression patterns in children with septic shock strongly support this concept of immune suppression (2, 11–17). Specifically, pediatric septic shock is characterized by wide spread repression of gene programs corresponding to various major components of the adaptive immune system, including the T cell receptor signaling pathway, T cell function, B cell function, and the MHC antigen presentation pathway. "
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    ABSTRACT: For nearly a decade, our research group has had the privilege of developing and mining a multi-center, microarray-based, genome-wide expression database of critically ill children (≤ 10 years of age) with septic shock. Using bioinformatic and systems biology approaches, the expression data generated through this discovery-oriented, exploratory approach have been leveraged for a variety of objectives, which will be reviewed. Fundamental observations include wide spread repression of gene programs corresponding to the adaptive immune system, and biologically significant differential patterns of gene expression across developmental age groups. The data have also identified gene expression-based subclasses of pediatric septic shock having clinically relevant phenotypic differences. The data have also been leveraged for the discovery of novel therapeutic targets, and for the discovery and development of novel stratification and diagnostic biomarkers. Almost a decade of genome-wide expression profiling in pediatric septic shock is now demonstrating tangible results. The studies have progressed from an initial discovery-oriented and exploratory phase, to a new phase where the data are being translated and applied to address several areas of clinical need.Pediatric Research (2013); doi:10.1038/pr.2013.11.
    Pediatric Research 01/2013; 73. DOI:10.1038/pr.2013.11 · 2.31 Impact Factor
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    • "Reuse of samples in several experiments: this is for instance the case in the five experiments, used as the core data of five papers, GSE9692 (9), GSE26378 (10), GSE8121 (11), GSE13904 (12), and GSE26440 (13). It appears that over the 101 samples that we have annotated from these experiments, 72 were reused several times: 15 were duplicated in four experiments (60 annotated samples, GSE9692, GSE8121, GSE13904, GSE26440); three were duplicated in two experiments (six annotated samples, GSE13904 and GSE26440), leading these experiments to have a total of 18 samples in common; yet, three others in two experiments (six annotated samples, GSE26378 and GSE26440). "
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    ABSTRACT: As part of the development of the database Bgee (a dataBase for Gene Expression Evolution), we annotate and analyse expression data from different types and different sources, notably Affymetrix data from GEO and ArrayExpress, and RNA-Seq data from SRA. During our quality control procedure, we have identified duplicated content in GEO and ArrayExpress, affecting ∼14% of our data: fully or partially duplicated experiments from independent data submissions, Affymetrix chips reused in several experiments, or reused within an experiment. We present here the procedure that we have established to filter such duplicates from Affymetrix data, and our procedure to identify future potential duplicates in RNA-Seq data.Database URL:
    Database The Journal of Biological Databases and Curation 01/2013; 2013:bat010. DOI:10.1093/database/bat010 · 3.37 Impact Factor
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    • "All patients with microarray data in the current study were previously reported in studies addressing hypotheses 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. "
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    ABSTRACT: Introduction: Differentiating between sterile inflammation and bacterial infection in critically ill patients with fever and other signs of the systemic inflammatory response syndrome (SIRS) remains a clinical challenge. The objective of our study was to mine an existing genome-wide expression database for the discovery of candidate diagnostic biomarkers to predict the presence of bacterial infection in critically ill children. Methods: Genome-wide expression data were compared between patients with SIRS having negative bacterial cultures (n = 21) and patients with sepsis having positive bacterial cultures (n = 60). Differentially expressed genes were subjected to a leave-one-out cross-validation (LOOCV) procedure to predict SIRS or sepsis classes. Serum concentrations of interleukin-27 (IL-27) and procalcitonin (PCT) were compared between 101 patients with SIRS and 130 patients with sepsis. All data represent the first 24 hours of meeting criteria for either SIRS or sepsis. Results: Two hundred twenty one gene probes were differentially regulated between patients with SIRS and patients with sepsis. The LOOCV procedure correctly predicted 86% of the SIRS and sepsis classes, and Epstein-Barr virus-induced gene 3 (EBI3) had the highest predictive strength. Computer-assisted image analyses of gene-expression mosaics were able to predict infection with a specificity of 90% and a positive predictive value of 94%. Because EBI3 is a subunit of the heterodimeric cytokine, IL-27, we tested the ability of serum IL-27 protein concentrations to predict infection. At a cut-point value of ≥5 ng/ml, serum IL-27 protein concentrations predicted infection with a specificity and a positive predictive value of >90%, and the overall performance of IL-27 was generally better than that of PCT. A decision tree combining IL-27 and PCT improved overall predictive capacity compared with that of either biomarker alone. Conclusions: Genome-wide expression analysis has provided the foundation for the identification of IL-27 as a novel candidate diagnostic biomarker for predicting bacterial infection in critically ill children. Additional studies will be required to test further the diagnostic performance of IL-27. The microarray data reported in this article have been deposited in the Gene Expression Omnibus under accession number GSE4607.
    Critical Care 10/2012; 16(5):R213. DOI:10.1186/cc11847 · 4.48 Impact Factor
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