Discovery and Validation of Urinary Molecular Signature of Early Sepsis

Bandyopadhyay, Sabyasachi; Lysak, Nicholas; Adhikari, Lasith; Velez, Laura M.; Sautina, Larysa; Mohandas, Rajesh; Lopez, Maria-Cecilia; Ungaro, Ricardo; Peng, Ying-Chih; Kadri, Ferdous; Efron, Philip; Brakenridge, Scott; Moldawer, Lyle; Moore, Frederick; Baker, Henry V.; Segal, Mark S.; Ozrazgat-Baslanti, Tezcan; Rashidi, Parisa; Bihorac, Azra

OBJECTIVES: Identify alterations in gene expression unique to systemic and kidney-specific pathophysiologic processes using whole-genome analyses of RNA isolated from the urinary cells of sepsis patients. DESIGN: Prospective cohort study. SETTING: Quaternary care academic hospital. PATIENTS: A total of 266 sepsis and 82 control patients enrolled between January 2015 and February 2018. INTERVENTIONS: Whole-genome transcriptomic analysis of messenger RNA isolated from the urinary cells of sepsis patients within 12 hours of sepsis onset and from control subjects. MEASUREMENTS AND MAIN RESULTS: The differentially expressed probes that map to known genes were subjected to feature selection using multiple machine learning techniques to find the best subset of probes that differentiates sepsis from control subjects. Using differential expression augmented with machine learning ensembles, we identified a set of 239 genes in urine, which show excellent effectiveness in classifying septic patients from those with chronic systemic disease in both internal and independent external validation cohorts. Functional analysis indexes disrupted biological pathways in early sepsis and reveal key molecular networks driving its pathogenesis. CONCLUSIONS: We identified unique urinary gene expression profile in early sepsis. Future studies need to confirm whether this approach can complement blood transcriptomic approaches for sepsis diagnosis and prognostication.