Biomarker Validation on the ‘Analysis of geNe Expression and bioMarkers fOr poiNt-of-care dEcision support in Sepsis’ (ANEMONES) study
Ontology highlight
ABSTRACT: Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. Here, we describe identification of transcriptional mRNA biomarkers able to identify severe systemic inflammation and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study. All patients were recruited in Intensive Care Units (ICUs) from multiple UK hospitals including 59 patients with abdominal sepsis, 84 patients with pulmonary sepsis, 42 SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), at four time points including 30 healthy control donors. Multiple clinical parameters were measured, including SOFA score etc., with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using PBL mRNA microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools. Nineteen select high-performance, differentially-expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed ‘indicators of inflammation’ (IoI), including CD177, FAM20A and OLAH. Combinations of these were trialed. Best-performing minimal panels e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (ROC/AUC>0.99). Twenty select entities were differentially-expressed between sepsis and SIRS (FC>2.0, p-value<0.05), termed ‘SIRS or Sepsis’ (SoS) biomarkers. Panels of biomarkers able to differentiate sepsis from SIRS were also identified and performance assessed using AUCROC. The best performing panel was CMTM5/CETP/PLA2G7/MIA/MPP3 using our dataset (AUCROC=0.9758). The IoI and SoS signatures were evaluated on other independent gene expression datasets, with some reduced performance observed, which maybe in part due to study/platform technical variation.
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.
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.
Project description:Expression data from CD8+ T cells and CD68+ monocytes from patients with hemophagocytic lymphohistiocytosis, sepsis, and persistent systemic inflammatory response syndrome Hemophagocytic lymphohistiocytosis (HLH) is a syndrome characterized by pathologic immune activation in which prompt recognition and initiation of immune suppression is essential for survival. Children with HLH have many overlapping clinical features with critically ill children with sepsis and persistent systemic inflammatory response syndrome (SIRS) in whom alternative therapies are indicated. To determine if plasma biomarkers could differentiate HLH from other inflammatory conditions and to better define a ‘core inflammatory signature’ of HLH, concentrations of inflammatory plasma proteins were compared in 40 patients with HLH to 47 pediatric patients with severe sepsis or SIRS. Seventeen of 135 analytes were significantly different in HLH plasma compared to SIRS/sepsis, including increased interferon-gamma (IFNg)-regulated chemokines CXCL9, CXCL10 and CXCL11. Further, a 5-analyte plasma protein classifier including these chemokines was able to differentiate HLH from SIRS/sepsis. Gene expression in CD8+ T cells and CD68+ monocytes from blood were also enriched for IFNg pathway signatures in peripheral blood cells from patients with HLH compared to SIRS/sepsis. This study identifies differential expression of inflammatory proteins as a diagnostic strategy to identify critically ill children with HLH. Further, comprehensive unbiased analysis of inflammatory plasma proteins and global gene expression demonstrates that IFNg signaling is uniquely elevated in HLH. In addition to demonstrating the ability of diagnostic criteria for HLH, sepsis and SIRS to identify groups with distinct inflammatory patterns, results from this study support the potential for prospective evaluation of inflammatory biomarkers to aid in diagnosis of and optimizing therapeutic strategies for children with distinctive hyperinflammatory syndromes.
Project description:A microarray analysis involving whole blood samples isolated from critically ill patients in the medical intensive care unit at Brigham and Women's Hospital. Four groups of intubated subjects undergoing mechanical ventilation were recruited for the study: those with sepsis alone (Sepsis), those with sepsis + ARDS (se/ARDS), those with SIRS (SIRS), and those whithout sepsis, SIRS, or ARDS (untreated). Blood was obtained from patients on the day of admission (day 0) and 7 days later. RNA was isolated from the whole blood samples and microarrays were prepared to determine differential gene expression between the four groups. Total RNA obtained from whole blood samples of critically ill patients
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.
Project description:A microarray analysis involving whole blood samples isolated from critically ill patients in the medical intensive care unit at Brigham and Women's Hospital. Four groups of intubated subjects undergoing mechanical ventilation were recruited for the study: those with sepsis alone (Sepsis), those with sepsis + ARDS (se/ARDS), those with SIRS (SIRS), and those whithout sepsis, SIRS, or ARDS (untreated). Blood was obtained from patients on the day of admission (day 0) and 7 days later. RNA was isolated from the whole blood samples and microarrays were prepared to determine differential gene expression between the four groups.
Project description:Timely and reliable distinction of non-infectious systemic inflammatory response syndrome (SIRS), common in critically ill patients, from sepsis to support adequate antimicrobial therapy safes lives but is clinically challenging. Expeditious sepsis biomarkers are thus urgently sought. Blood transcriptional profiling provides insights into sepsis pathophysiology, but variability in leukocyte subtype composition complicates profile interpretation, and reliable reference genes to normalize gene expression in sepsis are lacking. Here, we identified AKIRIN1 as a reference gene, specifically, in peripheral NK cells and granulocytes for differential gene expression analysis between patients with SIRS and septic shock on intensive care unit admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells, namely, AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. For in vitro priming of NK cells, GUSB however was confirmed as reference gene of choice. Initially, no candidate genes could be validated in granulocytes, an additional rescreen of known reference genes by RT-PCR included. By serendipity, we could determine equal AKIRIN1 expression levels also in SIRS and septic shock granulocytes and no change by in vitro challenge of granulocytes with LPS. Inspection of four external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. Invariable AKIRIN1 expression in peripheral NK cells and granulocytes needs further validation in sepsis and other infectious and inflammatory diseases. As a reference gene in these cells and conditions, AKIRIN1 may further our understanding of innate immunity and lead to new biomarkers.
Project description:Timely and reliable distinction of non-infectious systemic inflammatory response syndrome (SIRS), common in critically ill patients, from sepsis to support adequate antimicrobial therapy safes lives but is clinically challenging. Expeditious sepsis biomarkers are thus urgently sought. Blood transcriptional profiling provides insights into sepsis pathophysiology, but variability in leukocyte subtype composition complicates profile interpretation, and reliable reference genes to normalize gene expression in sepsis are lacking. Here, we identified AKIRIN1 as a reference gene, specifically, in peripheral NK cells and granulocytes for differential gene expression analysis between patients with SIRS and septic shock on intensive care unit admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells, namely, AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. For in vitro priming of NK cells, GUSB however was confirmed as reference gene of choice. Initially, no candidate genes could be validated in granulocytes, an additional rescreen of known reference genes by RT-PCR included. By serendipity, we could determine equal AKIRIN1 expression levels also in SIRS and septic shock granulocytes and no change by in vitro challenge of granulocytes with LPS. Inspection of four external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. Invariable AKIRIN1 expression in peripheral NK cells and granulocytes needs further validation in sepsis and other infectious and inflammatory diseases. As a reference gene in these cells and conditions, AKIRIN1 may further our understanding of innate immunity and lead to new biomarkers.
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.
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