Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, Florida.
Division of Nephrology, Hypertension and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, Florida.
Shock. 2022 Jul 1;58(1):20-27. doi: 10.1097/SHK.0000000000001952. Epub 2022 Jul 15.
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.
本研究旨在描述脓毒症患者与无菌性炎症患者早期尿液基因表达差异,并总结出可重现的脓毒症概率评分。
这是一项前瞻性观察队列研究。
研究在一家四级护理学术医院进行。
2015 年 1 月至 2018 年 2 月期间,纳入了 186 例脓毒症患者和 78 例全身炎症反应综合征(SIRS)患者。
对脓毒症患者在脓毒症发病后 12 小时内和大内科手术后 4 小时内采集的尿液进行全基因组转录组分析。
我们确定了 23956 个基因中 422 个(1.7%)在脓毒症和 SIRS 患者之间差异表达。差异表达的探针提供给一组机器学习特征选择模型,以识别区分脓毒症和 SIRS 的聚焦探针集。将这些探针组合起来找到最佳探针集(UrSepsisModel)并计算尿液脓毒症评分(UrSepsisScore),即下调基因的几何平均值减去上调基因的几何平均值。这种方法将所有决定性基因的表达值总结为一个单一的脓毒症评分。UrSepsisModel 和 UrSepsisScore 在验证队列中分别获得 0.91(95%置信区间,0.86-0.96)和 0.80(95%置信区间,0.70-0.88)的受试者工作特征曲线下面积。与脓毒症相关的探针的功能分析表明,代谢失调表现为氧化磷酸化减少、氨基酸代谢减少以及脂质和脂肪酸氧化减少。
对尿液细胞的全基因组转录组分析揭示了可作为区分脓毒症与无菌性 SIRS 的早期诊断工具的聚焦探针组。差异表达基因的功能分析表明,脓毒症存在明显的代谢失调特征。