Project description:Background The heterogeneity of sepsis represents a major problem for the development of personalized sepsis therapies. Thus, sepsis subtyping emerged as an important tool to approach this problem, but little progress was made due to insufficient molecular insights. Modern proteomics techniques allow the identification of subtypes and enable molecular and mechanistical insights. Here, we analyzed a prospective multi-center sepsis cohort using plasma proteomics to describe and characterize sepsis plasma proteome subtypes. Methods Plasma samples from 333 patients collected at days 1 and 4 of sepsis were analyzed using liquid-chromatography coupled to tandem mass spectrometry. Plasma proteome subtypes were identified using K-means clustering and were characterized based on clinical routine data, cytokine measurements and proteomics data. A random forest machine learning (ML) classifier was generated to enable the assignment of patients to the subtypes in future. Results Four subtypes with different sepsis severity were identified. Cluster 0 represented the most severe sepsis with 100 % mortality. Cluster 1, 2 and 3 showed a gradual decrease of the median SOFA score, which was reflected by clinical data and cytokine measurements. On the proteome level, the subtypes were characterized by distinct molecular features. We found an alternating immune response with cluster 1 showing prominent activation of the adaptive immune system as indicated by elevated levels of immunoglobulins (Ig) that were verified using orthogonal measurements. Cluster 2 was characterized by acute inflammation and the lowest Ig levels. Cluster 3 represented the sepsis proteome baseline of the investigated cohort. We generated a ML classifier and optimized it for a minimum number of proteins that could realistically be implemented into routine diagnostics. The final model was based on 10 proteins and Ig quantities and allowed the assignment of patients to clusters 1, 2 and 3 with high confidence. Conclusion The identified plasma proteome subtypes provide insights into immune response and disease mechanisms and allow conclusions on appropriate therapeutic measures. Thus, they represent a step forward in the development of targeted therapies and personalized medicine in sepsis.

Project description:Rationale: Sepsis is a life-threatening syndrome that can quickly cause organ failure and death if untreated. Patient variability complicates therapy development, and its molecular mechanisms remain poorly understood. Advancing knowledge of these mechanisms is essential for effective treatments and clearer phenotype definitions. Objectives: To address this issue, we created a large-scale transcriptomic atlas of publicly available adult sepsis patient data with which we performed molecular phenotyping to evaluate patterns of gene expression and identified potential phenotype-specific drug therapies. Methods: We identified studies of bacterial sepsis in three genomic databases (SRA, GEO, and refine.bio) and reviewed metadata from each study to ensure samples met our predetermined inclusion criteria. We combined this data into one large comprehensive data atlas and harmonized gene expression and associated metadata from each sample. Molecular phenotypes of sepsis were identified via clustering analysis of this sepsis transcriptomic data atlas. We then examined clinical correlates of each phenotype and identified gene signatures associated with each. We performed gene set enrichment analysis on those signatures and identified phenotype-specific potential drug repurposing candidates. We also evaluated the associations between computed phenotypes and mortality. Measurements and Main Results: We harmonized data from 3,713 samples (all are included in this dataset) across 28 data sets, of which 2,251 were sepsis patients. Clustering analysis identified four phenotypes within the data. We identified statistically significant phenotype associations with survival, disease, and age. Pathway analysis revealed that MHC class II functions, DNA damage, homeostatic pathways and coagulation may characterize underlying response phenotypes, and may have the potential to guide drug development of sepsis therapeutics. Conclusions: We created the largest transcriptomic sepsis atlas to date, from which we identified four molecular sepsis phenotypes. We described underlying dysregulated molecular mechanisms of these phenotypes, associated clinical covariates, and several potential candidate therapies specific to each phenotype. Future studies should seek to validate such drug-phenotype links to advance sepsis precision medicine.

Project description:Background: Sepsis involves aberrant immune responses to infection, but the exact nature of this immune dysfunction remains poorly defined. Bacterial endotoxins like lipopolysaccharide (LPS) are potent inducers of inflammation, which has been associated with the pathophysiology of sepsis, but repeated exposure can also induce a suppressive effect known as endotoxin tolerance or cellular reprogramming. It has been proposed that endotoxin tolerance might be associated with the immunosuppressive state that was primarily observed during late-stage sepsis. However, this relationship remains poorly characterised. Here we clarify the underlying mechanisms and timing of immune dysfunction in sepsis. Methods: We defined a gene expression signature characteristic of endotoxin tolerance. Gene-set test approaches were used to correlate this signature with early sepsis, both newly and retrospectively analysing microarrays from 593 patients in 11 cohorts. Then we recruited a unique cohort of possible sepsis patients at first clinical presentation in an independent blinded controlled observational study to determine whether this signature was associated with the development of confirmed sepsis and organ dysfunction. Findings: All sepsis patients presented an expression profile strongly associated with the endotoxin tolerance signature (p < 0.01; AUC 96.1%). Importantly, this signature further differentiated between suspected sepsis patients who did, or did not, go on to develop confirmed sepsis, and predicted the development of organ dysfunction. Interpretation: Our data support an updated model of sepsis pathogenesis in which endotoxin tolerance-mediated immune dysfunction (cellular reprogramming) is present throughout the clinical course of disease and related to disease severity. Thus endotoxin tolerance might offer new insights guiding the development of new therapies and diagnostics for early sepsis. For the RNA-Seq study reported here, 73 patients were recruited with deferred consent at the time of first examination in an emergency ward based on the opinion of physicians that there was a potential for the patient's condition to develop into sepsis. These were retrospectively divided into groups based on clinical features and compared to 11 non-urgent surgical controls.

Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20). This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. The GEO data represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).

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Molecular Diagnostics in Sepsis: from Bedside to Bench

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