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1.
researchsquare; 2020.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-60563.v1

ABSTRACT

ObjectivesWe aimed to explore the association between dynamic antibody responses and the clinical severity of COVID-19. MethodsWe collected complete follow-up data of 777 pathogen-confirmed COVID-19 patients with corresponding IgG/IgM testing results. ResultsWe found the overall positive rates of IgG and IgM in severe patients were slightly higher than those in non-severe patients. In addition, higher IgG levels were detected in severe patients compared with non-severe patients (P=0.026). Through further analysis, our results showed that the statistical difference in the IgG only significant in serum samples taken ≤14 days from disease onset (P<0.001). In 74 patients who taken detection more than three times, by analyzing the antibody expression levels at different time points, we found that the difference between IgG was more obvious than that of IgM among severe/non-severe patients. In multivariate logistic regression models, after adjusting for cofactors, the higher anti-SARS-CoV-2 IgG level before 14 days from disease onset was independently associated with severe disease in COVID-19 (OR=1.310, 95%CI= 1.137-1.509).ConclusionWe observed differences in antibody responses among COVID-19 patients with different disease severity. A high IgG level in the first 14 days from disease onset might positively associate with severe disease.  


Subject(s)
COVID-19
2.
researchsquare; 2020.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-59060.v1

ABSTRACT

The outbreak of coronavirus disease 2019 (COVID-19) has been causing a global health emergency. Although previous studies investigated COVID-19 at different omics levels, the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here, we presented a trans-omics landscape for COVID-19 based on integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles from blood samples of 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Notably, we found neutrophils heterogeneity existed between asymptomatic and critically ill patients. Expression discordance of inflammatory cytokines at mRNA and protein levels in asymptomatic patients could possibly be explained by post-transcriptional regulation by RNA binding proteins (RBPs) and microRNAs. Neutrophils over-activation, induced arginine depletion, and tryptophan metabolites accumulation contributed to T/NK cell dysfunction in critical patients. Anti-virus interferons were gradually suppressed along with disease severity. Overall, our study systematically revealed multi-omics characteristics of COVID-19, and the data we generated could hopefully help illuminate COVID-19 pathogenesis and provide valuable clues about potential therapeutic strategies for COVID-19.


Subject(s)
COVID-19 , Critical Illness
3.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.07.17.20155150

ABSTRACT

System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment.


Subject(s)
COVID-19 , Critical Illness
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