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Talanta ; 242: 123297, 2022 May 15.
Article in English | MEDLINE | ID: covidwho-1671185


The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for more than a year and has undergone several mutations and evolutions. Due to the lack of effective therapeutics and long-active vaccines, accurate and large-scale screening and early diagnosis of infected individuals are crucial to control the pandemic. Nevertheless, the current widely used RT-qPCR-based methods suffer from complicated temperature control, long processing time and the risk of false-negative results. Herein, we present a three-way junction induced exponential rolling circle amplification (3WJ-eRCA) combined MALDI-TOF MS assay for SARS-CoV-2 detection. The assay can detect simultaneously the target nucleocapsid (N) and open reading frame 1 ab (orf1ab) genes of SARS-CoV-2 in a single test within 30 min, with an isothermal process (55 °C). High specificity to discriminate SARS-CoV-2 from other coronaviruses, like SARS-CoV, MERS-CoV and bat SARS-like coronavirus (bat-SL-CoVZC45), was observed. We have further used the method to detect pseudovirus of SARS-CoV-2 in various matrices, e.g. water, saliva and urine. The results demonstrated a great potential of the method for large scale screening of COVID-19, which is an important part of the pandemic control.

COVID-19 , SARS-CoV-2 , COVID-19/diagnosis , Gene Amplification , Humans , Nucleic Acid Amplification Techniques/methods , RNA, Viral/genetics , SARS-CoV-2/genetics , Sensitivity and Specificity , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Front Immunol ; 12: 748566, 2021.
Article in English | MEDLINE | ID: covidwho-1463474


Coronavirus disease 2019 (COVID-19) remains a major health challenge globally. Previous studies have suggested that changes in the glycosylation of IgG are closely associated with the severity of COVID-19. This study aimed to compare the profiles of IgG N-glycome between COVID-19 patients and healthy controls. A case-control study was conducted, in which 104 COVID-19 patients and 104 age- and sex-matched healthy individuals were recruited. Serum IgG N-glycome composition was analyzed by hydrophilic interaction liquid chromatography with the ultra-high-performance liquid chromatography (HILIC-UPLC) approach. COVID-19 patients have a decreased level of IgG fucosylation, which upregulates antibody-dependent cell cytotoxicity (ADCC) in acute immune responses. In severe cases, a low level of IgG sialylation contributes to the ADCC-regulated enhancement of inflammatory cytokines. The decreases in sialylation and galactosylation play a role in COVID-19 pathogenesis via the activation of the lectin-initiated alternative complement pathway. IgG N-glycosylation underlines the complex clinical phenotypes of SARS-CoV-2 infection.

COVID-19/metabolism , Immunoglobulin G/metabolism , SARS-CoV-2/physiology , Adult , Antibody-Dependent Cell Cytotoxicity , Case-Control Studies , Chromatography, High Pressure Liquid , Complement Pathway, Mannose-Binding Lectin , Female , Glycosylation , Humans , Male , Middle Aged , Phenotype
Anal Chem ; 93(11): 4782-4787, 2021 03 23.
Article in English | MEDLINE | ID: covidwho-1114675


The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.

COVID-19/diagnosis , Peptides/blood , SARS-CoV-2/metabolism , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods , Area Under Curve , COVID-19/metabolism , COVID-19/virology , Case-Control Studies , Discriminant Analysis , High-Throughput Screening Assays , Humans , Least-Squares Analysis , Machine Learning , Principal Component Analysis , ROC Curve , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Tuberculosis/metabolism , Tuberculosis/pathology