Intracellular expression of granzymes A, B, K and M in blood lymphocyte subsets of critically ill patients with or without sepsis.
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ABSTRACT:
SUBMITTER: Garcia-Laorden MI
PROVIDER: S-EPMC8274148 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
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Intracellular expression of granzymes A, B, K and M in blood lymphocyte subsets of critically ill patients with or without sepsis.
García-Laorden M Isabel MI Hoogendijk Arie J AJ Wiewel Maryse A MA van Vught Lonneke A LA Schultz Marcus J MJ Bovenschen Niels N de Vos Alex F AF van der Poll Tom T
Clinical and experimental immunology 20210527 2
Sepsis is a complex syndrome related to an infection-induced exaggerated inflammatory response, which is associated with a high mortality. Granzymes (Gzm) are proteases mainly found in cytotoxic lymphocytes that not only have a role in target cell death, but also as mediators of infection and inflammation. In this study we sought to analyse the intracellular expression of GzmA, B, M and K by flow cytometry in diverse blood lymphocyte populations from 22 sepsis patients, 12 non-infected intensive ...[more]
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Project description:BackgroundB lymphocytes play a key role in immunosuppression. This study investigated the prognostic value of B cell subsets in sepsis.MethodsFlow cytometry was used to assess peripheral B cell subsets from patients with sepsis on the first and seventh days following admission, as well as 111 healthy controls. The patients were divided into survivors and non-survivors, based on 28-day prognosis.ResultsThe analysis showed abnormal distribution and selective depletion of B cells and its subsets in the early stages of sepsis. On day 1, compared with survivors, non-survivors showed significant decreases in the proportion and absolute count of transitional (Tr) B cells, reductions in the proportion of CD5+ B cells, and increases in the proportion of double-negative (DN) B cells. On day 7, the proportions and absolute counts of Tr and CD5+ B cells significantly decreased whereas the proportion of DN B cells significantly increased in non-survivors. Ninety-four survivors and 15 non-survivors were included in our paired-sample rank-sum test. Compared to day 1, only the survivors showed significant increases in absolute B, Tr B, and CD5+ B cell counts by day 7. Multivariate Cox regression analysis showed that the proportion of DN B cells on day 1 (hazard ratio = 1.092 [95% confidence interval: 1.035-1.152], P = 0.001) was a risk factor for mortality, and Kaplan-Meier survival curve analysis showed that patients with proportions of DN B cells > 11.81% on day 1 had poorer prognoses. Receiver operating characteristic curve analysis showed that B cell subset parameters could predict mortality (area under the receiver operating characteristic curve [AUC], 0.741) and enhanced the prognostic value of the Acute Physiology and Chronic Health Evaluation II score (AUC, 0.840).ConclusionOur study revealed that deficiencies of B, Tr B, and CD5+ B cells, as well as a persistent increase in the proportion of DN B cells, were associated with poor prognosis-and that B cell subsets showed predictive value to mortality. These results provide new insights into the roles of B cell subsets in sepsis, as well as ways to better manage its progression and predict its course.
| S-EPMC11445034 | biostudies-literature
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 SeptiCyte® 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; SeptiCyte® 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 SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® 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
2015-10-24 | E-GEOD-74224 | biostudies-arrayexpress
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: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 SeptiCyte® 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; SeptiCyte® 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 SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® 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
2015-10-24 | GSE74224 | 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. Keywords: disease state analysis, gram-positive sepsis, gram-negative sepsis
2007-12-01 | GSE6535 | GEO
Project description:Sepsis is a frequent complication in critically ill patients and highly heterogeneous that is associated with high morbidity and mortality rates, especially in the elderly population. Utilizing RNA-Sequencing (RNA-Seq) to analysis biological pathways is widely used in clinical and molecular genetics studies, but in elderly patients with sepsis are still lacking. Hence, we aim to investigate the mortality-relevant biological features and transcriptomic features in elderly patients who were admitted to intensive care unit (ICU) for sepsis.
2022-11-03 | GSE216902 | GEO
Project description:ObjectiveThere is a high incidence of delirium among patients with organ dysfunction undergoing cardiac surgery who need critical care. This study aimed to explore the risk factors for delirium in critically ill patients undergoing cardiac surgery and the predictive value of related risk factors.MethodsWe conducted a prospective observational study on adult critically ill patients who underwent cardiac surgery between January 2019 and August 2021. Patients were consecutively assigned to delirium and non-delirium groups. Univariate analysis and multivariate logistic analysis were used to determine the risk factors for delirium. Receiver operating characteristic curves and a nomogram were used to identify the predictive value of related risk factors.ResultsDelirium developed in 242 of 379 (63.9%) participants. Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores were 14.2 ± 5.6 and 18 ± 8.4, respectively. Patients with delirium had longer cardiopulmonary bypass time (149.6 ± 59.1 vs. 126.7 ± 48.5 min, p < 0.001) and aortic cross-clamp time (98.7 ± 51.5 vs. 86.1 ± 41.6 min, p = 0.010) compared with the non-delirium group. The area under the curve was 0.824 for CD4+ T cell count and 0.862 for CD4/CD8 ratio. Multivariate analysis demonstrated that age [odds ratio (OR) 1.030, p = 0.038], duration of physical restraint (OR 1.030, p < 0.001), interleukin-6 (OR 1.001, p = 0.025), CD19+ B cell count (OR 0.996, p = 0.016), CD4+ T cell count (OR 1.005, p < 0.001) and CD4/CD8 ratio (OR 5.314, p < 0.001) were independent risk factors for delirium. A nomogram revealed that age, cardiopulmonary bypass duration, CD4+ T cell count and CD4/CD8 ratio were independent predictors of delirium.ConclusionAge, duration of physical restraint, CD4+ T cell count and CD4/CD8 ratio were reliable factors for predicting delirium in critically ill patients after cardiac surgery. The receiver operating characteristic curves and nomogram suggested a potential role for CD4+ T cells in mediating potential neuroinflammation of delirium.
| S-EPMC9477480 | biostudies-literature
Project description:BackgroundSepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minimal set of streaming physiological data in real time.Methods and findingsA total of 29,552 adult patients were admitted to the intensive care unit across five regional hospitals in Memphis, Tenn, over 18 months from January 2017 to July 2018. From these, 5,958 patients were selected after filtering for continuous (minute-by-minute) physiological data availability. A total of 617 (10.4%) patients were identified as sepsis cases, using the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Physiomarkers, a set of signal processing features, were derived from five physiological data streams including heart rate, respiratory rate, and blood pressure (systolic, diastolic, and mean), captured every minute from the bedside monitors. A support vector machine classifier was used for classification. The model accurately predicted sepsis up to a mean and 95% confidence interval of 17.4 ± 0.22 h before sepsis onset, with an average test accuracy of 83.0% (average sensitivity, specificity, and area under the receiver operating characteristics curve of 0.757, 0.902, and 0.781, respectively).ConclusionsThis study demonstrates that salient physiomarkers derived from continuous bedside monitoring are temporally and differentially expressed in septic patients. Using this information, minimalistic artificial intelligence models can be developed to predict sepsis earlier in critically ill patients.
| S-EPMC8352046 | biostudies-literature
Project description:Sepsis is a syndromic illness that has traditionally been defined by a set of broad, highly sensitive clinical parameters. As a result, numerous distinct pathophysiologic states may meet diagnostic criteria for sepsis, leading to syndrome heterogeneity. The existence of biologically distinct sepsis subtypes may in part explain the lack of actionable evidence from clinical trials of sepsis therapies. We used microarray-based gene expression data from adult patients with sepsis in order to identify molecularly distinct sepsis subtypes.We used partitioning around medoids (PAM) and hierarchical clustering of gene expression profiles from neutrophils taken from a cohort of septic patients in order to identify distinct subtypes. Using the medoids learned from this cohort, we then clustered a second independent cohort of septic patients, and used the resulting class labels to evaluate differences in clinical parameters, as well as the expression of relevant pharmacogenes.We identified two sepsis subtypes based on gene expression patterns. Subtype 1 was characterized by increased expression of genes involved in inflammatory and Toll receptor mediated signaling pathways, as well as a higher prevalence of severe sepsis. There were differences between subtypes in the expression of pharmacogenes related to hydrocortisone, vasopressin, norepinephrine, and drotrecogin alpha.Sepsis subtypes can be identified based on different gene expression patterns. These patterns may generate hypotheses about the underlying pathophysiology of sepsis and suggest new ways of classifying septic patients both in clinical practice, and in the design of clinical trials.
| S-EPMC3682285 | biostudies-literature
Project description:Background and objectiveABO blood types are determined by antigen modifications on glycoproteins and glycolipids and associated with altered plasma levels of inflammatory and endothelial injury markers implicated in AKI pathogenesis. We sought to determine the association of ABO blood types with AKI risk in critically ill patients with trauma or sepsis.Design, setting, participants, & measurementsWe conducted two prospective cohort studies at an urban, academic, level I trauma center and tertiary referral center; 497 patients with trauma admitted to the surgical intensive care unit between 2005 and 2010 with an injury severity score >15 and 759 patients with severe sepsis admitted to the medical intensive care unit between 2008 and 2013 were followed for 6 days for the development of incident AKI. AKI was defined by Acute Kidney Injury Network creatinine and dialysis criteria.ResultsOf 497 patients with trauma, 134 developed AKI (27%). In multivariable analysis, blood type A was associated with higher AKI risk relative to type O among patients of European descent (n=229; adjusted risk, 0.28 versus 0.14; risk difference, 0.14; 95% confidence interval, 0.03 to 0.24; P=0.02). Of 759 patients with sepsis, AKI developed in 326 (43%). Blood type A again conferred higher AKI risk relative to type O among patients of European descent (n=437; adjusted risk, 0.53 versus 0.40; risk difference, 0.14; 95% confidence interval, 0.04 to 0.23; P=0.01). Findings were similar when analysis was restricted to those patients who did not develop acute respiratory distress syndrome or were not transfused. We did not detect a significant association between blood type and AKI risk among individuals of African descent in either cohort.ConclusionsBlood type A is independently associated with AKI risk in critically ill patients with trauma or severe sepsis of European descent, suggesting a role for ABO glycans in AKI susceptibility.
| S-EPMC4633800 | biostudies-literature