Gene Expression Profiles in Children with Suspected Sepsis in the Emergency Department
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE119217 | GEO | 2020/01/01
REPOSITORIES: GEO
Similar Datasets
Project description:Gene Expression Profiles in Children with Suspected Sepsis in the Emergency Department
| PRJNA488482 | ENA
Project description:The temporal evolution of sepsis was monitored by transcriptional profiling of five critically ill children with meningococcal sepsis and sepsis-induced multiple organ failure. Blood was sampled at 6 time points during the first 48 hours of their admission to pediatric intensive care, where the children received standard clinical treatment including organ support and antimicrobial therapy. Striking transcript instability was observed over the 48 hours, with increasing numbers of regulated genes over time. Most notably, proposed biomarkers for sepsis risk stratification also showed expression instability, with varied expression levels over 48 hours. This study demonstrates the extent of the complexity of temporal changes in gene expression that occur during the evolution of sepsis-induced multiple organ failure. Importantly, stratification tools that propose expression of biomarkers must take into account the temporal changes, over the use of single snapshots that may be less informative.
2013-04-03 | E-MEXP-3850 | biostudies-arrayexpress
Project description:Normal children, children with SIRS, children with sepsis, and children with septic shock. Objectives: To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Experiment Overall Design: Prospective observational study involving microarray-based bioinformatics.
2009-01-05 | E-GEOD-13904 | biostudies-arrayexpress
Project description:Normal children, children with SIRS, children with sepsis, and children with septic shock. Objectives: To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Keywords: Normal vs diseased
2008-12-12 | GSE13904 | GEO
Project description:Both sepsis and acute respiratory distress syndrome (ARDS) rely on imprecise clinical definitions leading to heterogeneity, which has contributed to negative trials. Because circulating protein/DNA complexes have been implicated in sepsis and ARDS, we aimed to develop a proteomic signature of DNA-bound proteins to discriminate between children with sepsis with and without ARDS. We performed a prospective case-control study in 12 children with sepsis with ARDS matched to 12 children with sepsis without ARDS on age, severity of illness score, and source of infection. We performed co-immunoprecipitation and downstream proteomics in plasma collected ≤ 24 h of intensive care unit admission. Expression profiles were generated, and a random forest classifier was used on differentially expressed proteins to develop a signature which discriminated ARDS. The classifier was tested in six independent blinded samples. Neutrophil and nucleosome proteins were over-represented in ARDS, including two S100A proteins, superoxide dismutase (SOD), and three histones. Random forest produced a 10-protein signature that accurately discriminated between children with sepsis with and without ARDS. This classifier perfectly assigned six independent blinded samples as having ARDS or not. We validated higher expression of the most informative discriminating protein, galectin-3-binding protein, in children with ARDS. Our methodology has applicability to isolation of DNA-bound proteins from plasma. Our results support the premise of a molecular definition of ARDS, and give preliminary insight into why some children with sepsis, but not others, develop ARDS.
2024-02-14 | PXD049377 | Pride
Project description:1364 plasma proteome samples taken at time-of-admission to the emergency department from patients suspected of sepsis.
Used initially with the following abstract:
Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine.
2024-04-08 | MSV000094486 | MassIVE
Project description:Background: Sepsis, a leading cause of morbidity and mortality, is not a homogeneous disease but rather a syndrome encompassing many heterogeneous pathophysiologies. Patient factors including genetics predispose to poor outcomes, though current clinical characterizations fail to identify those at greatest risk of progression and mortality. Results: The Community Acquired Pneumonia and Sepsis Outcome Diagnostic study enrolled 1,152 subjects with suspected sepsis. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis (infection with SIRS), including 78 sepsis survivors and 28 sepsis nonsurvivors, who had previously undergone plasma proteomic and metabolomic profiling. The expression of 338 genes differed between subjects with SIRS and those with sepsis, primarily reflective of immune activation in sepsis. The expression of 1,238 genes differed with sepsis outcome: Nonsurvivors had lower expression of many immune function-related genes. Functional genetic variants associated with sepsis mortality were sought based on a common disease â rare variant hypothesis. VPS9D1, whose expression was increased in sepsis survivors, had a higher burden of missense variants in sepsis survivors, and these were associated with altered expression of 3,799 genes, primarily reflecting Golgi and endosome biology. Conclusions: Host response in sepsis survivors â activation of immune response-related genes â was muted in sepsis nonsurvivors. The association of sepsis survival with robust immune response and presence of missense variants in VPS9D1 warrants replication and further functional studies. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS, n=23) or sepsis (infection with SIRS), including 78 sepsis survivors and 28 sepsis nonsurvivors, who had previously undergone plasma proteomic and metabolomic profiling.
2015-01-01 | E-GEOD-63042 | biostudies-arrayexpress
Project description:Background: Sepsis, a leading cause of morbidity and mortality, is not a homogeneous disease but rather a syndrome encompassing many heterogeneous pathophysiologies. Patient factors including genetics predispose to poor outcomes, though current clinical characterizations fail to identify those at greatest risk of progression and mortality. Results: The Community Acquired Pneumonia and Sepsis Outcome Diagnostic study enrolled 1,152 subjects with suspected sepsis. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis (infection with SIRS), including 78 sepsis survivors and 28 sepsis nonsurvivors, who had previously undergone plasma proteomic and metabolomic profiling. The expression of 338 genes differed between subjects with SIRS and those with sepsis, primarily reflective of immune activation in sepsis. The expression of 1,238 genes differed with sepsis outcome: Nonsurvivors had lower expression of many immune function-related genes. Functional genetic variants associated with sepsis mortality were sought based on a common disease – rare variant hypothesis. VPS9D1, whose expression was increased in sepsis survivors, had a higher burden of missense variants in sepsis survivors, and these were associated with altered expression of 3,799 genes, primarily reflecting Golgi and endosome biology. Conclusions: Host response in sepsis survivors – activation of immune response-related genes – was muted in sepsis nonsurvivors. The association of sepsis survival with robust immune response and presence of missense variants in VPS9D1 warrants replication and further functional studies.
2015-01-01 | GSE63042 | 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