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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Metabolomic characterization of COVID-19 survivors in Jilin province
Panyang Xu1, Lei Zeng2,3, Chunyu Wang4
1Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, China.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
