Metabolomic characterization of COVID-19 survivors in Jilin province

IF 4.7 2区医学 Q1 RESPIRATORY SYSTEM

Respiratory Research Pub Date : 2024-09-19 DOI:10.1186/s12931-024-02974-0

Panyang Xu, Lei Zeng, Chunyu Wang, Jiatong Chai, Junguo Yin, Jiancheng Xu

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引用次数: 0

Abstract

The COVID-19 pandemic has escalated into a severe global public health crisis, with persistent sequelae observed in some patients post-discharge. However, metabolomic characterization of the reconvalescent remains unclear. In this study, serum and urine samples from COVID-19 survivors (n = 16) and healthy subjects (n = 16) underwent testing via the non-targeted metabolomics approach using UPLC-MS/MS. Univariate and multivariate statistical analyses were conducted to delineate the separation between the two sample groups and identify differentially expressed metabolites. By integrating random forest and cluster analysis, potential biomarkers were screened, and the differential metabolites were subsequently subjected to KEGG pathway enrichment analysis. Significant differences were observed in the serum and urine metabolic profiles between the two groups. In serum samples, 1187 metabolites were detected, with 874 identified as significant (457 up-regulated, 417 down-regulated); in urine samples, 960 metabolites were detected, with 39 deemed significant (12 up-regulated, 27 down-regulated). Eight potential biomarkers were identified, with KEGG analysis revealing significant enrichment in several metabolic pathways, including arginine biosynthesis. This study offers an overview of the metabolic profiles in serum and urine of COVID-19 survivors, providing a reference for post-discharge monitoring and the prognosis of COVID-19 patients.

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吉林省COVID-19幸存者的代谢组学特征

COVID-19 大流行已升级为严重的全球公共卫生危机，一些患者在出院后会出现持续的后遗症。然而，幸存者的代谢组学特征仍不清楚。在这项研究中，通过使用 UPLC-MS/MS 的非靶向代谢组学方法，对 COVID-19 幸存者（n = 16）和健康受试者（n = 16）的血清和尿液样本进行了检测。我们进行了单变量和多变量统计分析，以确定两组样本之间的差异，并识别差异表达的代谢物。通过整合随机森林和聚类分析，筛选出了潜在的生物标记物，随后对差异代谢物进行了 KEGG 通路富集分析。结果发现，两组样本的血清和尿液代谢谱存在显著差异。在血清样本中，共检测到 1187 个代谢物，其中 874 个被认定为显著差异（457 个上调，417 个下调）；在尿液样本中，共检测到 960 个代谢物，其中 39 个被认定为显著差异（12 个上调，27 个下调）。通过 KEGG 分析发现，包括精氨酸生物合成在内的几种代谢途径中的代谢物明显增加。这项研究概述了 COVID-19 幸存者血清和尿液中的代谢概况，为 COVID-19 患者出院后的监测和预后提供了参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Respiratory Research 医学-呼吸系统

自引率

1.70%

发文量

314

期刊介绍： Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases. As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion. Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.