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1.
J Virol ; 96(4): e0160021, 2022 02 23.
Article in English | MEDLINE | ID: covidwho-1759291

ABSTRACT

A comprehensive study of the B cell response against SARS-CoV-2 could be significant for understanding the immune response and developing therapeutical antibodies and vaccines. To define the dynamics and characteristics of the antibody repertoire following SARS-CoV-2 infection, we analyzed the mRNA transcripts of immunoglobulin heavy chain (IgH) repertoires of 24 peripheral blood samples collected between 3 and 111 days after symptom onset from 10 COVID-19 patients. Massive clonal expansion of naive B cells with limited somatic hypermutation (SHM) was observed in the second week after symptom onset. The proportion of low-SHM IgG clones strongly correlated with spike-specific IgG antibody titers, highlighting the significant activation of naive B cells in response to a novel virus infection. The antibody isotype switching landscape showed a transient IgA surge in the first week after symptom onset, followed by a sustained IgG elevation that lasted for at least 3 months. SARS-CoV-2 infection elicited poly-germ line reactive antibody responses. Interestingly, 17 different IGHV germ line genes recombined with IGHJ6 showed significant clonal expansion. By comparing the IgH repertoires that we sequenced with the 774 reported SARS-CoV-2-reactive monoclonal antibodies (MAbs), 13 shared spike-specific IgH clusters were found. These shared spike-specific IgH clusters are derived from the same lineage of several recently published neutralizing MAbs, including CC12.1, CC12.3, C102, REGN10977, and 4A8. Furthermore, identical spike-specific IgH sequences were found in different COVID-19 patients, suggesting a highly convergent antibody response to SARS-CoV-2. Our analysis based on sequencing antibody repertoires from different individuals revealed key signatures of the systemic B cell response induced by SARS-CoV-2 infection. IMPORTANCE Although the canonical delineation of serum antibody responses following SARS-CoV-2 infection has been well established, the dynamics of antibody repertoire at the mRNA transcriptional level has not been well understood, especially the correlation between serum antibody titers and the antibody mRNA transcripts. In this study, we analyzed the IgH transcripts and characterized the B cell clonal expansion and differentiation, isotype switching, and somatic hypermutation in COVID-19 patients. This study provided insights at the repertoire level for the B cell response after SARS-CoV-2 infection.


Subject(s)
Antibodies, Neutralizing/genetics , Antibodies, Viral/genetics , B-Lymphocytes/immunology , COVID-19/genetics , Immunoglobulin G/genetics , Receptors, Antigen, B-Cell/genetics , SARS-CoV-2/immunology , Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , COVID-19/immunology , Humans , Immunoglobulin G/immunology , Receptors, Antigen, B-Cell/immunology
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 3705-3708, 2021 11.
Article in English | MEDLINE | ID: covidwho-1566210

ABSTRACT

The coronavirus disease 2019 (COVID-19) has become a global pandemic. The segmentation of COVID-19 pneumonia lesions from CT images is important in quantitative evaluation and assessment of the infection. Though many deep learning segmentation methods have been proposed, the performance is limited when pixel-level annotations are hard to obtain. In order to alleviate the performance limitation brought by the lack of pixel-level annotation in COVID-19 pneumonia lesion segmentation task, we construct a denoising self-supervised framework, which is composed of a pretext denoising task and a downstream segmentation task. Through the pretext denoising task, the semantic features from massive unlabelled data are learned in an unsupervised manner, so as to provide additional supervisory signal for the downstream segmentation task. Experimental results showed that our method can effectively leverage unlabelled images to improve the segmentation performance, and outperformed reconstruction-based self-supervised learning when only a small set of training images are annotated.Clinical relevance-The proposed method can effectively leverage unlabelled images to improve the performance for COVID-19 pneumonia lesion segmentation when only a small set of CT images are annotated.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Tomography, X-Ray Computed
3.
Front Immunol ; 12: 664619, 2021.
Article in English | MEDLINE | ID: covidwho-1325524

ABSTRACT

Recent studies have highlighted observations regarding re-tested positivity (RP) of SARS-CoV-2 RNA in discharged COVID-19 patients, however, the immune mechanisms underlying SARS-CoV-2 RNA RP in immunocompetent patients remain elusive. Herein, we describe the case of an immunocompetent COVID-19 patient with moderate symptoms who was twice re-tested as positive for SARS-CoV-2 RNA, and the period between first and third viral RNA positivity was 95 days, longer than previously reported (18-25 days). The chest computed tomography findings, plasma anti-SARS-CoV-2 antibody, neutralizing antibodies (NAbs) titer, and whole blood transcriptic characteristics in the viral RNA RP patient and other COVID-19 patients were analyzed. During the SARS-CoV-2 RNA RP period, new lung lesions were observed. The COVID-19 patient with viral RNA RP had delayed seroconversion of anti-spike/receptor-binding domain (RBD) IgA antibody and NAbs and were accompanied with disappearance of the lung lesions. Further experimental data validated that NAbs titer was significantly associated with anti-RBD IgA and IgG, and anti-spike IgG. The RP patient had lower interferon-, T cells- and B cell-related genes expression than non-RP patients with mild-to-moderate symptoms, and displayed lower cytokines and chemokines gene expression than severe patients. Interestingly, the RP patient had low expression of antigen presentation-related genes and low B cell counts which might have contributed to the delayed anti-RBD specific antibody and low CD8+ cell response. Collectively, delayed antigen presentation-related gene expression was found related to delayed adaptive immune response and contributed to the SARS-CoV-2 RNA RP in this described immunocompetent patient.


Subject(s)
COVID-19/immunology , COVID-19/virology , RNA, Viral/isolation & purification , Adaptive Immunity , Antibodies, Neutralizing/blood , Antibodies, Neutralizing/immunology , Antibodies, Viral/blood , Antibodies, Viral/immunology , COVID-19/diagnosis , Coronavirus Nucleocapsid Proteins/immunology , Gene Expression Profiling , Humans , Immunity, Innate , Male , Middle Aged , Phosphoproteins/immunology , SARS-CoV-2/immunology , SARS-CoV-2/isolation & purification , Seroconversion , Spike Glycoprotein, Coronavirus/immunology
4.
Front Endocrinol (Lausanne) ; 12: 642452, 2021.
Article in English | MEDLINE | ID: covidwho-1302108

ABSTRACT

Background: We investigated if the concentration and "rangeability" of cystatin C (CysC) influenced the prognosis of coronavirus disease 2019 (COVID-19) in patients suffering from, or not suffering from, type 2 diabetes mellitus (T2DM). Methods: A total of 675 T2DM patients and 572 non-T2DM patients were divided into "low" and "high" CysC groups and low and high CysC-rangeability groups according to serum CysC level and range of change of CysC level, respectively. Demographic characteristics, clinical data, and laboratory results of the four groups were analyzed. Results: COVID-19 patients with a high level and rangeability of CysC had more organ damage and a higher risk of death compared with those with a low level or low rangeability of CysC. Patients with a higher level and rangeability of CysC had more blood lymphocytes and higher levels of C-reactive protein, alanine aminotransferase, and aspartate aminotransferase. After adjustment for possible confounders, multivariate analysis revealed that CysC >0.93 mg/dL was significantly associated with the risk of heart failure (OR = 2.231, 95% CI: 1.125-5.312) and all-cause death (2.694, 1.161-6.252). CysC rangeability >0 was significantly associated with all-cause death (OR = 4.217, 95% CI: 1.953-9.106). These associations were stronger in patients suffering from T2DM than in those not suffering from T2DM. Conclusions: The level and rangeability of CysC may influence the prognosis of COVID-19. Special care and appropriate intervention should be undertaken in COVID-19 patients with an increased CysC level during hospitalization and follow-up, especially for those with T2DM.


Subject(s)
Biomarkers/blood , COVID-19/diagnosis , COVID-19/mortality , Cystatin C/blood , Diabetes Mellitus, Type 2/physiopathology , SARS-CoV-2/isolation & purification , Aged , COVID-19/blood , COVID-19/virology , Case-Control Studies , Female , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Factors
5.
Emerg Microbes Infect ; 10(1): 1097-1111, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1214429

ABSTRACT

Monoclonal antibodies (mAbs) encoded by IGHV3-53 (VH3-53) targeting the spike receptor-binding domain (RBD) have been isolated from different COVID-19 patients. However, the existence and prevalence of shared VH3-53-encoded antibodies in the antibody repertoires is not clear. Using antibody repertoire sequencing, we found that the usage of VH3-53 increased after SARS-CoV-2 infection. A highly shared VH3-53-J6 clonotype was identified in 9 out of 13 COVID-19 patients. This clonotype was derived from convergent gene rearrangements with few somatic hypermutations and was evolutionary conserved. We synthesized 34 repertoire-deduced novel VH3-53-J6 heavy chains and paired with a common IGKV1-9 light chain to produce recombinant mAbs. Most of these recombinant mAbs (23/34) possess RBD binding and virus-neutralizing activities, and recognize ACE2 binding site via the same molecular interface. Our computational analysis, validated by laboratory experiments, revealed that VH3-53 antibodies targeting RBD are commonly present in COVID-19 patients' antibody repertoires, indicating many people have germline-like precursor sequences to rapidly generate SARS-CoV-2 neutralizing antibodies. Moreover, antigen-specific mAbs can be digitally obtained through antibody repertoire sequencing and computational analysis.


Subject(s)
Antibodies, Monoclonal/blood , Antibodies, Neutralizing/blood , Antibodies, Viral/blood , COVID-19/immunology , SARS-CoV-2/immunology , Adult , Aged , Aged, 80 and over , Antibodies, Monoclonal/immunology , Base Sequence , COVID-19/blood , Case-Control Studies , Epitopes, B-Lymphocyte , Female , HEK293 Cells , Humans , Male , Middle Aged , Models, Molecular , Phylogeny , Protein Conformation , Receptors, Antigen, B-Cell/genetics
6.
BMJ Open ; 10(12): e042085, 2020 12 18.
Article in English | MEDLINE | ID: covidwho-991829

ABSTRACT

INTRODUCTION: To date, no specific antivirus drugs or vaccines have been available to prevent or treat the COVID-19 pandemic. Mesenchymal stem cell (MSC) therapy may be a promising therapeutic approach that reduces the high mortality in critical cases. This protocol is proposed for a systematic review and meta-analysis that aims to evaluate the efficacy and safety of MSC therapy on patients with COVID-19. METHODS AND ANALYSIS: Ten databases including PubMed, EMBASE, Cochrane Library, CINAHL, Web of Science, Chinese National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP), Wanfang database, China Biomedical Literature Database (CBM) and Chinese Biomedical Literature Service System (SinoMed) will be searched from inception to 1 December 2020. All published randomised controlled trials, clinical controlled trials and case series that meet the prespecified eligibility criteria will be included. The primary outcomes include mortality, incidence and severity of adverse events, respiratory improvement, days from ventilator, duration of fever, progression rate from mild or moderate to severe, improvement of such serious symptoms as difficulty breathing or shortness of breath, chest pain or pressure, and loss of speech or movement, biomarkers of laboratory examination and changes in CT. The secondary outcomes include dexamethasone doses and quality of life. Two reviewers will independently perform study selection, data extraction and assessment of bias risk. Data synthesis will be conducted using RevMan software (V.5.3.5). If necessary, subgroup and sensitivity analysis will be performed. Grading of Recommendations Assessment, Development and Evaluation system will be used to assess the strength of evidence. ETHICS AND DISSEMINATION: Ethical approval is not necessary since no individual patient or privacy data have been collected. The results of this review will be disseminated in a peer-reviewed journal or an academic conference presentation. PROSPERO REGISTRATION NUMBER: CRD42020190079.


Subject(s)
COVID-19/therapy , Mesenchymal Stem Cell Transplantation/methods , Humans , Meta-Analysis as Topic , Research Design , SARS-CoV-2/isolation & purification , Systematic Reviews as Topic , Treatment Outcome
7.
Nat Commun ; 11(1): 4207, 2020 08 21.
Article in English | MEDLINE | ID: covidwho-724410

ABSTRACT

The rapid spread of coronavirus SARS-CoV-2 greatly threatens global public health but no prophylactic vaccine is available. Here, we report the generation of a replication-incompetent recombinant serotype 5 adenovirus, Ad5-S-nb2, carrying a codon-optimized gene encoding Spike protein (S). In mice and rhesus macaques, intramuscular injection with Ad5-S-nb2 elicits systemic S-specific antibody and cell-mediated immune (CMI) responses. Intranasal inoculation elicits both systemic and pulmonary antibody responses but weaker CMI response. At 30 days after a single vaccination with Ad5-S-nb2 either intramuscularly or intranasally, macaques are protected against SARS-CoV-2 challenge. A subsequent challenge reveals that macaques vaccinated with a 10-fold lower vaccine dosage (1 × 1010 viral particles) are also protected, demonstrating the effectiveness of Ad5-S-nb2 and the possibility of offering more vaccine dosages within a shorter timeframe. Thus, Ad5-S-nb2 is a promising candidate vaccine and warrants further clinical evaluation.


Subject(s)
Betacoronavirus/immunology , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Viral Vaccines/administration & dosage , Adenoviridae/genetics , Animals , Antibodies, Neutralizing/blood , Antibodies, Viral/blood , COVID-19 , COVID-19 Vaccines , Coronavirus Infections/immunology , Dose-Response Relationship, Immunologic , Female , HEK293 Cells , Humans , Immunity, Cellular , Macaca mulatta , Male , Mice , Mice, Inbred BALB C , Pneumonia, Viral/immunology , Respiratory System/pathology , Respiratory System/virology , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/immunology , Vaccines, Synthetic/administration & dosage
8.
IEEE Trans Med Imaging ; 39(8): 2653-2663, 2020 08.
Article in English | MEDLINE | ID: covidwho-691238

ABSTRACT

Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss that is a generalization of Dice loss for segmentation and Mean Absolute Error (MAE) loss for robustness against noise, then propose a novel COVID-19 Pneumonia Lesion segmentation network (COPLE-Net) to better deal with the lesions with various scales and appearances. The noise-robust Dice loss and COPLE-Net are combined with an adaptive self-ensembling framework for training, where an Exponential Moving Average (EMA) of a student model is used as a teacher model that is adaptively updated by suppressing the contribution of the student to EMA when the student has a large training loss. The student model is also adaptive by learning from the teacher only when the teacher outperforms the student. Experimental results showed that: (1) our noise-robust Dice loss outperforms existing noise-robust loss functions, (2) the proposed COPLE-Net achieves higher performance than state-of-the-art image segmentation networks, and (3) our framework with adaptive self-ensembling significantly outperforms a standard training process and surpasses other noise-robust training approaches in the scenario of learning from noisy labels for COVID-19 pneumonia lesion segmentation.


Subject(s)
Coronavirus Infections/diagnostic imaging , Deep Learning , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Algorithms , Betacoronavirus , COVID-19 , Humans , Lung/diagnostic imaging , Pandemics , SARS-CoV-2
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