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EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION

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: Th...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391290/
https://www.ncbi.nlm.nih.gov/pubmed/35904146
http://dx.doi.org/10.1097/SHK.0000000000001952
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author 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
author_facet 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
author_sort Bandyopadhyay, Sabyasachi
collection PubMed
description 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.
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spelling pubmed-93912902022-08-26 EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION 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 Shock Clinical Science Aspects 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. Lippincott Williams & Wilkins 2022-07 2022-07-15 /pmc/articles/PMC9391290/ /pubmed/35904146 http://dx.doi.org/10.1097/SHK.0000000000001952 Text en Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Clinical Science Aspects
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
EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION
title EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION
title_full EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION
title_fullStr EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION
title_full_unstemmed EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION
title_short EARLY DIFFERENTIATION BETWEEN SEPSIS AND STERILE INFLAMMATION VIA URINARY GENE SIGNATURES OF METABOLIC DYSREGULATION
title_sort early differentiation between sepsis and sterile inflammation via urinary gene signatures of metabolic dysregulation
topic Clinical Science Aspects
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391290/
https://www.ncbi.nlm.nih.gov/pubmed/35904146
http://dx.doi.org/10.1097/SHK.0000000000001952
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