Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America.
Proteomics and Metabolomics Facility, Cornell University, Ithaca, New York, United States of America.
PLoS One. 2022 Jul 8;17(7):e0271137. doi: 10.1371/journal.pone.0271137. eCollection 2022.
Sepsis is a diagnostic and therapeutic challenge and is associated with morbidity and a high risk of death. Metabolomic and lipidomic profiling in sepsis can identify alterations in metabolism and might provide useful insights into the dysregulated host response to infection, but investigations in dogs are limited. We aimed to use untargeted metabolomics and lipidomics to characterize metabolic pathways in dogs with sepsis to identify therapeutic targets and potential diagnostic and prognostic biomarkers. In this prospective observational cohort study, we examined the plasma metabolomes and lipidomes of 20 healthy control dogs and compared them with those of 21 client-owned dogs with sepsis. Patient data including signalment, physical exam findings, clinicopathologic data and clinical outcome were recorded. Metabolites were identified using an untargeted mass spectrometry approach and pathway analysis identified multiple enriched metabolic pathways including pyruvaldehyde degradation; ketone body metabolism; the glucose-alanine cycle; vitamin-K metabolism; arginine and betaine metabolism; the biosynthesis of various amino acid classes including the aromatic amino acids; branched chain amino acids; and metabolism of glutamine/glutamate and the glycerophospholipid phosphatidylethanolamine. Metabolites were identified with high discriminant abilities between groups which could serve as potential biomarkers of sepsis including 13,14-Dihydro-15-keto Prostaglandin A2; 12(13)-DiHOME (12,13-dihydroxy-9Z-octadecenoic acid); and 9-HpODE (9-Hydroxyoctadecadienoic acid). Metabolites with higher abundance in samples from nonsurvivors than survivors included 3-(2-hydroxyethyl) indole, indoxyl sulfate and xanthurenic acid. Untargeted lipidomic profiling revealed multiple sphingomyelin species (SM(d34:0)+H; SM(d36:0)+H; SM(d34:0)+HCOO; and SM(d34:1D3)+HCOO); lysophosphatidylcholine molecules (LPC(18:2)+H) and lipophosphoserine molecules (LPS(20:4)+H) that were discriminating for dogs with sepsis. These biomarkers could aid in the diagnosis of dogs with sepsis, provide prognostic information, or act as potential therapeutic targets.
脓毒症是一种诊断和治疗上的挑战,与发病率和高死亡率相关。脓毒症的代谢组学和脂质组学分析可以确定代谢的改变,并可能为失调的宿主对感染的反应提供有用的见解,但在狗中的研究有限。我们的目的是使用非靶向代谢组学和脂质组学来描述脓毒症狗的代谢途径,以确定治疗靶点以及潜在的诊断和预后生物标志物。在这项前瞻性观察性队列研究中,我们检查了 20 只健康对照犬的血浆代谢组和脂质组,并将其与 21 只患有脓毒症的患犬进行了比较。记录了患者数据,包括特征、体格检查结果、临床病理数据和临床结果。使用非靶向质谱法鉴定代谢物,并通过途径分析鉴定了多个富集的代谢途径,包括丙酮酸醛降解;酮体代谢;葡萄糖-丙氨酸循环;维生素 K 代谢;精氨酸和甜菜碱代谢;各种氨基酸类的生物合成,包括芳香族氨基酸;支链氨基酸;以及谷氨酰胺/谷氨酸和甘油磷脂磷脂酰乙醇胺的代谢。在两组之间具有高判别能力的代谢物可以作为脓毒症的潜在生物标志物,包括 13,14-二氢-15-酮前列腺素 A2;12(13)-二 HOMe(12,13-二羟基-9Z-十八碳烯酸);和 9-HpODE(9-羟基十八碳二烯酸)。在幸存者和非幸存者的样本中丰度较高的代谢物包括 3-(2-羟乙基)吲哚、吲哚硫酸和黄尿酸。非靶向脂质组学分析揭示了多种鞘磷脂种类(SM(d34:0)+H;SM(d36:0)+H;SM(d34:0)+HCOO;和 SM(d34:1D3)+HCOO);溶血磷脂酰胆碱分子(LPC(18:2)+H)和脂磷丝氨酸分子(LPS(20:4)+H)可以区分脓毒症犬。这些生物标志物可以帮助诊断脓毒症犬,提供预后信息,或作为潜在的治疗靶点。