Differential Peripheral Blood Glycoprotein Profiles in Symptomatic and Asymptomatic COVID-19 [article]

Chad Pickering, Bo Zhou, Gege Xu, Rachel Rice, Hector Huang, Tho Pham, Jeffrey Shapiro, Xin Cong, Saborni Chakraborty, Karlie Edwards, Srinavasa Reddy, Faheem Giurgis (+3 others)
2022 medRxiv   pre-print
Glycosylation is the most common form of post-translational modification of proteins, critically affecting their structure and function. Using liquid chromatography and mass spectrometry for high-resolution site-specific quantification of glycopeptides coupled with high-throughput artificial intelligence-powered data processing, we analyzed differential protein glyco-isoform distributions of 597 abundant serum glycopeptides and non-glycosylated peptides in 50 individuals who had been seriously
more » ... ll with COVID-19 and in 22 individuals who had recovered after an asymptomatic course of COVID-19. As additional comparison reference phenotypes, we included 12 individuals with a history of infection with a common cold coronavirus, 16 patients with bacterial sepsis, and 15 healthy subjects without history of coronavirus exposure. We found statistically significant differences, at FDR<0.05, for normalized abundances of 374 of the 597 peptides and glycopeptides interrogated, between symptomatic and asymptomatic COVID-19 patients. Similar statistically significant differences were seen when comparing symptomatic COVID-19 patients to healthy controls (350 differentially abundant peptides and glycopeptides) and common cold coronavirus seropositive subjects (353 differentially abundant peptides and glycopeptides). Among healthy controls and sepsis patients, 326 peptides and glycopeptides were found to be differentially abundant, of which 277 overlapped with biomarkers that showed differential expression between symptomatic COVID-19 cases and healthy controls. Among symptomatic COVID-19 cases and sepsis patients, 101 glycopeptide and peptide biomarkers were found to be statistically significantly abundant. Using both supervised and unsupervised machine learning techniques, we found specific glycoprotein profiles to be strongly predictive of symptomatic COVID-19 infection. LASSO-regularized multivariable logistic regression and K-means clustering yielded accuracies of 100% in an independent test set and of 96% overall, respectively. Our findings are consistent with the interpretation that a majority of glycoprotein modifications observed which are shared among symptomatic COVID-19 and sepsis patients likely represent a generic consequence of a severe systemic immune and inflammatory state. However, there are glyco-isoform changes that are specific and particular to severe COVID-19 infection. These may be representative of either COVID-19-specific consequences or of susceptibility to or predisposition for a severe course of the disease. Our findings support the potential value of glycoproteomic biomarkers in the biomedical understanding, and, potentially, the clinical management of serious acute infectious conditions.
doi:10.1101/2022.01.07.21267956 fatcat:235jluylkraj3blb4gisikv6kq