Transcriptomes of human whole blood cells in sepsis patients
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE294985 | GEO | 2026/03/11
REPOSITORIES: GEO
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Project description:The peripheral whole-blood transcriptome analysis has been used to the identification of biomarkers in different clinical syndromes. The ability to simultaneously measure the gene expression levels of the whole transcriptome is providing a broader view of clinical syndromes such as sepsis. The aim of this study was to assess the gene expression profiles of post-surgical patients with septic shock compared with patients without sepsis.
2014-03-15 | E-MTAB-1548 | biostudies-arrayexpress
Project description:There is currently no reliable tool available to measure immune dysfunction in septic patients in the clinical setting. This proof-of-concept study assesses the potential of gene expression profiling of whole blood as a tool to monitor immune dysfunction in critically ill septic patients. Whole blood samples were collected daily for up to 5 days from patients admitted to the intensive care unit with sepsis. RNA isolated from whole blood samples was assayed on Illumina HT-12 gene expression microarrays consisting of 48,804 probes. Microarray analysis identified 3677 genes as differentially expressed across 5 days between septic patients and healthy controls. Of the 3677 genes, biological pathway analysis identified 86 genes significantly down-regulated in the sepsis patients were present in pathways relating to immune response. These 86 genes correspond to known immune pathways implicated in sepsis including lymphocyte depletion, reduced T lymphocyte activation and deficient antigen presentation. Furthermore, expression levels of these genes correlated with clinical severity, with a significantly greater degree of down-regulation found in non-survivors compared to survivors. The results show that whole blood gene-expression analysis can capture systemic immune dysfunctions in septic patients. Our study provides an experimental basis to support further study on the use of a gene expression based assay, to assess immunosuppression and guide immunotherapy in future clinical trials. Daily PAXgene samples for up to 5 days for sepsis survivors (n=26), sepsis nonsurvivors (n=9), and healthy controls (n=18).
2014-02-04 | E-GEOD-54514 | biostudies-arrayexpress
Project description:Clinical study of critically ill patients with sepsis and sepsis-related ARDS with whole blood RNA collected within the first 24 hours of admission Goal of the study was to determine whether biologically relevant genes were identified to be differentially expressed genes in patients with sepsis alone and sepsis with ARDS Prospective observational study, case cohort design
2015-04-01 | E-GEOD-66890 | biostudies-arrayexpress
Project description:Clinical study of critically ill patients with sepsis and sepsis-related ARDS with whole blood RNA collected within the first 24 hours of admission Goal of the study was to determine whether biologically relevant genes were identified to be differentially expressed genes in patients with sepsis alone and sepsis with ARDS
2015-04-01 | GSE66890 | GEO
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.
2010-05-25 | E-GEOD-6535 | biostudies-arrayexpress
Project description:We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole blood RNA sequencing analysis was performed on preoperative samples taken from 267 patients. These comprised patients who developed postoperative infection with (n=77) or without (n=49) sepsis, non-infectious systemic inflammatory response (n=31), or an uncomplicated postoperative course (n=110). Machine learning classification models built on preoperative transcriptomic signatures predicted postoperative outcomes including sepsis.
2024-08-30 | GSE208581 | GEO
Project description:Sepsis is a syndromic response to infection and is frequently a final common pathway to death from many infectious diseases worldwide. The global burden of sepsis is difficult to ascertain, although a recent scientific publication estimated that there were 48.9 million cases and 11 million sepsis-related deaths worldwide, which accounted for almost 20% of all global deaths. Here we analyzed the whole blood transcriptome of 5 healthy controls and 5 patients with confirmed sepsis from the Department of Emergency, Division of Surgical critical care, Tongji Trauma Center, Tongji Hospital, Tongji Medical College. This research aims to understand the expression of immune cells in sepsis patients and to construct an immune cell landscape of sepsis.
2023-02-05 | GSE224095 | GEO
Project description:Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset.
2021-07-12 | GSE168442 | GEO
Project description:Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset.
2021-07-12 | GSE168440 | GEO
Project description:There is currently no reliable tool available to measure immune dysfunction in septic patients in the clinical setting. This proof-of-concept study assesses the potential of gene expression profiling of whole blood as a tool to monitor immune dysfunction in critically ill septic patients. Whole blood samples were collected daily for up to 5 days from patients admitted to the intensive care unit with sepsis. RNA isolated from whole blood samples was assayed on Illumina HT-12 gene expression microarrays consisting of 48,804 probes. Microarray analysis identified 3677 genes as differentially expressed across 5 days between septic patients and healthy controls. Of the 3677 genes, biological pathway analysis identified 86 genes significantly down-regulated in the sepsis patients were present in pathways relating to immune response. These 86 genes correspond to known immune pathways implicated in sepsis including lymphocyte depletion, reduced T lymphocyte activation and deficient antigen presentation. Furthermore, expression levels of these genes correlated with clinical severity, with a significantly greater degree of down-regulation found in non-survivors compared to survivors. The results show that whole blood gene-expression analysis can capture systemic immune dysfunctions in septic patients. Our study provides an experimental basis to support further study on the use of a gene expression based assay, to assess immunosuppression and guide immunotherapy in future clinical trials.
2014-02-04 | GSE54514 | GEO