Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
Project description:Sepsis remains a diagnostic challenge with no gold-standard test. Urine provides a readily available, non-invasive biofluid with significant diagnostic potential. Urinary gene expression has been previously used for diagnosis and prognosis of urological malignancies and transplant allograft rejections, but remains unutilized for sepsis diagnosis. In this study, the authors use urinary gene expression profiles to both diagnose sepsis and characterize its pathophysiology. By using differential expression augmented with machine learning ensembles, the authors identify a collection of cellular mRNA from 239 genes in patient urine which show exceptional power in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes the disrupted biological pathways in early sepsis and additionally reveals key molecular networks driving its pathogenesis. This study serves a pioneering step towards expanding the clinical potential of urinary molecular profiles for application to systemic diseases.
Project description:We assayed leukocyte global gene expression for a prospective discovery cohort of 106 adult patients admitted to UK intensive care units with sepsis due to community acquired pneumonia or faecal peritonitis. We assigned all samples to sepsis response signature groups after performing unsupervised analysis of the transcriptomic data.
Project description:To identify signature genes that help distinguish (1) sepsis from non-infectious causes of systemic inflammatory response syndrome, (2) between Gram-positive and Gram-negative sepsis. Experiment Overall Design: A total of 70 critically ill patients (46 sepsis and 24 control) were enrolled in a single-centre observational study. Gene-expression profiling was performed using Affymetrix microarray (U133plus2) with 54,675 transcript. Data was divided into a training set (n=35) and a validation set (n=35). A molecular signature was developed in the training set and was then validated in the validation set.
Project description:We assayed leukocyte global gene expression for a prospective validation cohort of 221 adult patients admitted to UK intensive care units with sepsis due to community acquired pneumonia or faecal peritonitis. 10 samples from patients scheduled for elective cardiac surgery were also assayed as non-septic controls. We assigned all samples to sepsis response signature groups after performing unsupervised analysis of the transcriptomic data.