Project description:BACKGROUND: Despite best supportive intensive care, the development of severe sepsis and septic shock after trauma is still associated with a high mortality rate in intensive care units (ICU). Incomplete knowledge of the pathophysiological and biochemical mechanisms that lead to perturbations of the host system is a major cause for this clinical entity. This lack of knowledge is mirrored in a delay in both diagnosis of sepsis and stratification of patients at risk. The current diagnostic and classification methods do not provide an early and reliable evidence for the development of sepsis and consequently lead to uncertainty in the classification and a delay in the initiation of an adequate therapy. Here, we present a prospective study performed with a well-defined patient cohort suffering from severe multiple trauma (n=85), in which we aimed to uncover the biological reasons why patients with multiple trauma can have dramatically different outcomes after suffering similar traumatic insults. METHODS: We used peripheral whole blood obtained with the PaxGene system at the time of admission to the ICU, i.e., 12 hours after trauma, for transcriptome analysis and monitored clinically the sequence of events from the admission to the ICU, to the very onset of sepsis and finally to the full development of multiple organ dysfunction syndrome (MODS). Study protocols and standard operating procedures (SOPs) regarding ethics, patient recruitment, logistics in sample acquisition and storage, microarray experiments, data analysis and data management have been developed and provided for a high feasibility and reproducibility of the investigations. FINDINGS: The transcriptome analysis from peripheral blood revealed a strong host response upon severe injury and demonstrated the suitability of whole blood in the PaxGene system as a surrogate marker in clinical gene expression studies. It was evident that patients who developed sepsis in the course of the disease (3-5 days after admission) showed an imbalance in the TH1/TH2 response towards a pronounced TH2 response, soon after trauma. The imbalanced TH1/TH2 shift along with inactivation of T cells, neutrophils, monocytes and macrophages might be critical factors for the initiation of a septic program seen in these patients 12 hours after trauma. INTERPRETATION: First, in this study, we provide protocols for logistics and infrastructure that can be applied generally to successfully integrate gene expression analysis studies in a clinical routine and demonstrate that by following these protocols, dedicated and reproducible results can be obtained. Second, we showed that peripheral whole blood is a suitable surrogate marker for the host response as it contains lots of “information” and is easily accessible. Third, we identified mechanisms involved in the perturbations of the immune system that lead to sepsis. Keywords: Prospective study 159 consecutive traumatized patients were included in the study upon admission to the intensive care unit (ICU), fulfilling all of the following inclusion criteria: severe injuries to at least two body regions or three major fractures, between 18 and 65 years of age, an estimated Injury Severity Score (ISS) of ?15 points after thorough assessment of injuries, <12 hours between the occurrence of the accident and the time of admission to the ICU, and at least greater than 3 days of survival. None of the patients underwent neuro- or cardiac surgery. We excluded patients with severe intracranial head injuries, coagulation abnormalities known at the day of admission to the ICU, acute renal failure, liver failure, malignant disease, or hemofiltration in the patient`s history. The Patients were divided into two groups depending on their posttraumatic course: Patients not complicatd by sepsis (Group: N) and Patients complicated by sepsis (Group: S). Using the CodeLink UniSet Human 10 K Bioarrays (GE Healthcare, Freiburg, Germany), we searched for early differences in the transcriptome of these two groups. Only whole blood samples drawn upon admission to the ICU were used for this investigation. Microarray results were validated by quantification of mRNA by TaqMan® technology. Each patient sample was hybridized in duplicate or triplicate on microarrays (label-extract technical replicate). A total of 72 arrays (36 group N, 36 group S) were subjected to microarray quality analysis. Of these, 2 arrays (0 group N, 2 group S) were excluded from the data set due to quality reasons and a final set of 70 arrays (36 group N and 34 group S) were subjected to microarray analysis. Since the eliminated 2 arrays belonged to patients with three replicates, they were treated as two replicates in the further analysis. The arrays were designated N_xx_yy_z or S_xx_yy_z, with x for the patient-ID (1,2,3,4..) and z for the technical replicate number (1,2 or 3).

Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyteÂ® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyteÂ® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyteÂ® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyteÂ® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyteÂ® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyteÂ® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator

Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.

Dataset Information

Cell-free DNA as biomarker for sepsis by integration of microbial and host information

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure