EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION. Issue 1 (15th July 2022)

• Record Type: Journal Article
• Authors: Bandyopadhyay, Sabyasachi; Loftus, Tyler J.; Peng, Ying-Chih; Lopez, Maria-Cecilia; Baker, Henry V.; Segal, Mark S.; Graim, Kiley; Ozrazgat-Baslanti, Tezcan; Rashidi, Parisa; Bihorac, Azra
• Is Part Of: Shock. Volume 58:Issue 1(2022)
• Page Start: 20
• Page End: 27
• Publication Date: 2022-07-15
• Subjects: Gene expression -- machine learning -- RNA -- sepsis -- SIRS -- urine; Shock -- Periodicals; Shock -- Periodicals; Choc (Pathologie) -- Périodiques; Shock; Periodicals; 616.0475
• DOI: 10.1097/SHK.0000000000001952
• Languages: English
• ISSNs: 1073-2322

Abstract

ABSTRACT: Objective: The aim of this study was to characterize early urinary gene expression differences between patients with sepsis and patients with sterile inflammation and summarize in terms of a reproducible sepsis probability score. Design: This was a prospective observational cohort study. Setting: The study was conducted in a quaternary care academic hospital. Patients: One hundred eighty-six sepsis patients and 78 systemic inflammatory response syndrome (SIRS) patients enrolled between January 2015 and February 2018. Interventions: Whole-genome transcriptomic analysis of RNA was extracted from urine obtained from sepsis patients within 12 hours of sepsis onset and from patients with surgery-acquired SIRS within 4 hours after major inpatient surgery. Measurements and Main Results: We identified 422 of 23, 956 genes (1.7%) that were differentially expressed between sepsis and SIRS patients. Differentially expressed probes were provided to a collection of machine learning feature selection models to identify focused probe sets that differentiate between sepsis and SIRS. These probe sets were combined to find an optimal probe set (UrSepsisModel) and calculate a urinary sepsis score (UrSepsisScore), which is the geometric mean of downregulated genes subtracted from the geometric mean of upregulated genes. This approach summarizes the expression values of all decisive genes as a single sepsis score. The UrSepsisModel and UrSepsisScore achieved area under the receiver operating characteristic curves 0.91 (95% confidence interval, 0.86–0.96) and 0.80 (95% confidence interval, 0.70–0.88) on the validation cohort, respectively. Functional analyses of probes associated with sepsis demonstrated metabolic dysregulation manifest as reduced oxidative phosphorylation, decreased amino acid metabolism, and decreased oxidation of lipids and fatty acids. Conclusions: Whole-genome transcriptomic profiling of urinary cells revealed focused probe panels that can function as an early diagnostic tool for differentiating sepsis from sterile SIRS. Functional analysis of differentially expressed genes demonstrated a distinct metabolic dysregulation signature in sepsis.