ABSTRACT
Serum antibodies IgM and IgG are elevated during COVID-19 to defend against viral attack. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their two-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense, and that high titers of IgM might not be favorable to COVID-19 recovery.
Subject(s)
COVID-19ABSTRACT
Objective@#Comparing the benefit of Abidor, lopinavir/ritonavir and recombinant interferon α-2b triple combination antiviral therapy and lopinavir/ritonavir and interferon dual combination antiviral therapy to hospitalized novel coronavirus pneumonia 2019 in Zhejiang province.@*Methods@#A multi-center prospective study was carried out to compare the effect of triple combination antiviral therapy with dual combination antiviral therapy in 15 medical institutions of Zhejiang Province. All patients were treated with recombinant interferon α-2b (5 million U, 2 times/d) aerosol inhalation. 196 patients were treated with abidol (200 mg, 3 times/d) + lopinavir / ritonavir (2 tablets, 1 time/12 h) as the triple combination antiviral treatment group. 41 patients were treated with lopinavir / ritonavir (2 tablets, 1 time/12 h) as the dual combination antiviral treatment group. The patients who received triple combination antiviral therapy were divided into three groups: within 48 hours, 3-5 days and > 5 days after the symptom onset. To explore the therapeutic effects of triple combination antiviral drugs and dual combination antiviral drugs, as well as triple combination antiviral drugs with different antiviral initiate time. SPSS17.0 software was used to analyze the data.@*Results@#The time of virus nucleic acid turning negative was (12.2 ± 4.7) days in the triple combination antiviral drug group, which was shorter than that in the dual combination antiviral drug group [(15.0 ± 5.0) days] (t = 6.159, P < 0.01 ). The length of hospital stay [12 (9, 17) d] in the triple combination antiviral drug group was also shorter than that in the dual combination antiviral drug group [15 (10, 18) d] (H = 2.073, P < 0.05). Comparing the antiviral treatment which was started within 48 hours, 3-5 days and > 5 days after the symptom onset of triple combination antiviral drug group, the time from the symptom onset to the negative of viral shedding was 13 (10,16.8), 17 (13,22) and 21 (18-24) days respectively (Z = 32.983, P < 0.01), and the time from antiviral therapy to the negative of viral shedding was (11.8±3.9) , (13.5±5.1) and (11.2±4.3) d. The differences among the three groups were statistically significant (Z=32.983 and 6.722, P<0.01 or<0.05).@*Conclusions@#The triple combination antiviral therapy of Abidor, Lopinavir/Litonavir and recombinant interferon α-2b showed shorter viral shedding time and hospitalization time compared with the dual combination antiviral therapy. The earlier the time to initiate triple antiviral treatment, the shorter the time of virus shedding.
ABSTRACT
Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.Funding: This work is supported by grants from Westlake Special Program for COVID19 (2020), and Tencent foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04). Conflict of Interest: The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc. C.Z., Z.K., Z.K. and S.Q. are employees of DIAN Diagnostics.
Subject(s)
COVID-19 , Sleep Disorders, Circadian RhythmABSTRACT
Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.