Project description:Background: As early and appropriate care of severe septic patients is associated with better outcome, understanding of very first events in the disease process is needed. Pan-genomic analyses offer an interesting opportunity to study global genomic response within the very first hours after sepsis. The objective of this study was to investigate the systemic genomic response in severe Intensive Care Unit (ICU) patients and determine whether patterns of gene expression could be associated with clinical severity evaluated by severity score. Methods: Twenty-eight ICU patients were enrolled at the onset of septic shock. Blood samples were collected within 30 minutes, 24 and 48 hours after shock and genomic response was evaluated using microarrays. The genome wide expression pattern of blood leukocytes was sequentially compared to healthy volunteers and after stratification based on SAPSII score to identify potential mechanisms of dysregulation. Results: Septic shock induces a global reprogramming of the whole leukocyte transcriptome affecting multiple functions and pathways (> 71% of the whole genome was modified). Most altered pathways were not significantly different between SAPSII-high and SAPSII-low groups of patients. However the magnitude and the duration of these alterations were different between these two groups. Importantly, we observed that the more severe patients did not exhibit the strongest modulation. This indicates that some regulation mechanisms leading to recovery seem to take place at early stage. Conclusion: In conclusion, both pro- and anti-inflammatory processes, measured at the transcriptomic level, are induced within the very first hours after septic shock. Interestingly, the more severe patients did not exhibit the strongest modulation. This highlights that, not only the responses mechanisms by themself but mainly their early and appropriate regulation, are crucial for patient recovery. This reinforces the idea that an immediate and tailored aggressive care of patients, aimed at restoring an appropriately regulated immune response, may have a beneficial impact on outcome. Twenty-eight ICU patients were enrolled at the onset of septic shock. Blood samples were collected within 30 minutes, 24 and 48 hours after septic shock and compared to twenty-five healthy volunteers

Project description:Goal of the experiment: To identify correlated genes, pathways and groups of patients with systemic inflammatory response syndrome and septic shock that is indicative of biologically important processes active in these patients. Background: We measured gene expression levels and profiles of children with systemic inflammatory response syndrome (SIRS) and septic shock as a means for discovering patient sub-groups and gene signatures that are active in disease-affected individuals and potentially in patients with poor outcomes. Methods: Microarray and bioinformatics analyses of 123 microarray chips representing whole blood derived RNA from controls, children with SIRS, and children with septic shock. Results: A discovery-based filtering approach was undertaken to identify genes whose expression levels were altered in patients with SIRS or septic shock. Clustering of these genes identified 3 Major and several minor sub-groups of patients with SIRS or septic shock. The three groups differed with respect to incidence of septic shock and trended toward differences in mortality. Statistical analyses demonstrated that 6,435 gene probes were differentially regulated between the three patient sub-groups (false discovery rate < 0.001%). Of these gene probes, 623 gene probes within 7 major gene ontologies accounted for the majority of group differentiation. Network analyses of these 623 gene probes demonstrated 5 major gene networks that were differentially expressed between the 3 groups. Statistical comparison of septic shock survivors and non-survivors identified one major gene network that was under expressed in a high fraction of the non-survivors and identified potential biomarkers for poor outcome. Conclusions: This is the first genome-level demonstration of pediatric patient sub-groups with SIRS and septic shock. The sub-groups differ clinically and differentially express 5 major gene networks. We have identified gene signatures and potential biomarkers associated with poor outcome in children with septic shock. These data represent a major advancement in our genome-level understanding of pediatric SIRS and septic shock. Experiment Overall Design: Children < 10 years of age admitted to the pediatric intensive care unit and meeting the criteria for either SIRS or septic shock were eligible for the study. SIRS and septic shock were defined based on pediatric-specific criteria. We did not use separate categories of "sepsis" or "severe sepsis". Patients meeting criteria for "sepsis" or "severe sepsis" were placed in the categories of SIRS and septic shock, respectively, for study purposes. Control patients were recruited from the outpatient or inpatient departments of the participating institutions using the following exclusion criteria: a recent febrile illness (within 2 weeks), recent use of anti-inflammatory medications (within 2 weeks), or any history of chronic or acute disease associated with inflammation. Experiment Overall Design: After obtaining informed consent, blood samples were obtained on Day 1 of the study, and when possible on Day 3 of the study. Blood samples were divided for RNA extraction and isolation of serum. Severity of illness was calculated based on the PRISM III score. Organ failure was defined based on pediatric-specific criteria. Annotated clinical and laboratory data were collected daily while in the intensive care unit. Study patients were placed in the study categories of SIRS or Septic Shock on Day 1 of the study. On Day 3 of the study, patients were classified as SIRS, Septic Shock, or SIRS resolved (no longer meeting criteria for SIRS). All study patients were followed for 28 days to determine mortality or survival. Clinical, laboratory, and biological data were entered and stored using a web-based data base developed locally.

Dataset Information

Gene expression analysis in platelets of septic patients

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets