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1.
Front Immunol ; 12: 807134, 2021.
Article in English | MEDLINE | ID: covidwho-1604257

ABSTRACT

ORF8 is a viral immunoglobulin-like (Ig-like) domain protein encoded by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA genome. It tends to evolve rapidly and interfere with immune responses. However, the structural characteristics of various coronavirus ORF8 proteins and their subsequent effects on biological functions remain unclear. Herein, we determined the crystal structures of SARS-CoV-2 ORF8 (S84) (one of the epidemic isoforms) and the bat coronavirus RaTG13 ORF8 variant at 1.62 Å and 1.76 Å resolution, respectively. Comparison of these ORF8 proteins demonstrates that the 62-77 residues in Ig-like domain of coronavirus ORF8 adopt different conformations. Combined with mutagenesis assays, the residue Cys20 of ORF8 is responsible for forming the covalent disulfide-linked dimer in crystal packing and in vitro biochemical conditions. Furthermore, immune cell-binding assays indicate that various ORF8 (SARS-CoV-2 ORF8 (L84), ORF8 (S84), and RaTG13 ORF8) proteins have different interaction capabilities with human CD14+ monocytes in human peripheral blood. These results provide new insights into the specific characteristics of various coronavirus ORF8 and suggest that ORF8 variants may influence disease-related immune responses.

2.
Microbiol Spectr ; 9(2): e0135221, 2021 10 31.
Article in English | MEDLINE | ID: covidwho-1526454

ABSTRACT

The emerging new lineages of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have marked a new phase of coronavirus disease 2019 (COVID-19). Understanding the recognition mechanisms of potent neutralizing monoclonal antibodies (NAbs) against the spike protein is pivotal for developing new vaccines and antibody drugs. Here, we isolated several monoclonal antibodies (MAbs) against the SARS-CoV-2 spike protein receptor-binding domain (S-RBD) from the B cell receptor repertoires of a SARS-CoV-2 convalescent. Among these MAbs, the antibody nCoV617 demonstrates the most potent neutralizing activity against authentic SARS-CoV-2 infection, as well as prophylactic and therapeutic efficacies against the human angiotensin-converting enzyme 2 (ACE2) transgenic mouse model in vivo. The crystal structure of S-RBD in complex with nCoV617 reveals that nCoV617 mainly binds to the back of the "ridge" of RBD and shares limited binding residues with ACE2. Under the background of the S-trimer model, it potentially binds to both "up" and "down" conformations of S-RBD. In vitro mutagenesis assays show that mutant residues found in the emerging new lineage B.1.1.7 of SARS-CoV-2 do not affect nCoV617 binding to the S-RBD. These results provide a new human-sourced neutralizing antibody against the S-RBD and assist vaccine development. IMPORTANCE COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has posed a serious threat to global health and the economy, so it is necessary to find safe and effective antibody drugs and treatments. The receptor-binding domain (RBD) in the SARS-CoV-2 spike protein is responsible for binding to the angiotensin-converting enzyme 2 (ACE2) receptor. It contains a variety of dominant neutralizing epitopes and is an important antigen for the development of new coronavirus antibodies. The significance of our research lies in the determination of new epitopes, the discovery of antibodies against RBD, and the evaluation of the antibodies' neutralizing effect. The identified antibodies here may be drug candidates for the development of clinical interventions for SARS-CoV-2.


Subject(s)
Antibodies, Neutralizing/therapeutic use , Antibodies, Viral/therapeutic use , COVID-19/therapy , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Angiotensin-Converting Enzyme 2/antagonists & inhibitors , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/immunology , Animals , Antibodies, Monoclonal/immunology , Antibodies, Monoclonal/therapeutic use , Antibodies, Neutralizing/immunology , Antibodies, Neutralizing/metabolism , Antibodies, Viral/immunology , Antibodies, Viral/metabolism , Binding Sites/immunology , COVID-19 Vaccines/immunology , Crystallography, X-Ray , Disease Models, Animal , Female , Humans , Immunization, Passive/methods , Immunoglobulin G/blood , Mice , Mice, Inbred C57BL , Mice, Transgenic , Protein Interaction Domains and Motifs/immunology , Viral Load/drug effects
3.
Preprint in English | EuropePMC | ID: ppcovidwho-291175

ABSTRACT

The Coronavirus Disease (COVID-19) has affected 1.8 million people and resulted in more than 110,000 deaths as of April 12, 2020. Several studies have shown that tomographic patterns seen on chest Computed Tomography (CT), such as ground-glass opacities, consolidations, and crazy paving pattern, are correlated with the disease severity and progression. CT imaging can thus emerge as an important modality for the management of COVID-19 patients. AI-based solutions can be used to support CT based quantitative reporting and make reading efficient and reproducible if quantitative biomarkers, such as the Percentage of Opacity (PO), can be automatically computed. However, COVID-19 has posed unique challenges to the development of AI, specifically concerning the availability of appropriate image data and annotations at scale. In this paper, we propose to use synthetic datasets to augment an existing COVID-19 database to tackle these challenges. We train a Generative Adversarial Network (GAN) to inpaint COVID-19 related tomographic patterns on chest CTs from patients without infectious diseases. Additionally, we leverage location priors derived from manually labeled COVID-19 chest CTs patients to generate appropriate abnormality distributions. Synthetic data are used to improve both lung segmentation and segmentation of COVID-19 patterns by adding 20% of synthetic data to the real COVID-19 training data. We collected 2143 chest CTs, containing 327 COVID-19 positive cases, acquired from 12 sites across 7 countries. By testing on 100 COVID-19 positive and 100 control cases, we show that synthetic data can help improve both lung segmentation (+6.02% lesion inclusion rate) and abnormality segmentation (+2.78% dice coefficient), leading to an overall more accurate PO computation (+2.82% Pearson coefficient).

4.
Adv Exp Med Biol ; 1304: 53-71, 2021.
Article in English | MEDLINE | ID: covidwho-1237448

ABSTRACT

Innate immunity is the first defense line of the host against various infectious pathogens, environmental insults, and other stimuli causing cell damages. Upon stimulation, pattern recognition receptors (PRRs) act as sensors to activate innate immune responses, containing NF-κB signaling, IFN response, and inflammasome activation. Toll-like receptors (TLRs), retinoic acid-inducible gene I-like receptors (RLRs), NOD-like receptors (NLRs), and other nucleic acid sensors are involved in innate immune responses. The activation of innate immune responses can facilitate the host to eliminate pathogens and maintain tissue homeostasis. However, the activity of innate immune responses needs to be tightly controlled to ensure the optimal intensity and duration of activation under various contexts. Uncontrolled innate immune responses can lead to various disorders associated with aberrant inflammatory response, including pulmonary diseases such as COPD, asthma, and COVID-19. In this chapter, we will have a broad overview of how innate immune responses function and the regulation and activation of innate immune response at molecular levels as well as their contribution to various pulmonary diseases. A better understanding of such association between innate immune responses and pulmonary diseases may provide potential therapeutic strategies.


Subject(s)
COVID-19 , Humans , Immunity, Innate , Receptors, Pattern Recognition , SARS-CoV-2 , Toll-Like Receptors/genetics
5.
Int J Biol Sci ; 17(6): 1486-1496, 2021.
Article in English | MEDLINE | ID: covidwho-1206432

ABSTRACT

The pandemic of Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome 2 coronavirus (SARS-CoV-2) continues to be a global health crisis. Fundamental studies at genome, transcriptome, proteome, and interactome levels have revealed many viral and host targets for therapeutic interventions. Hundreds of antibodies for treating COVID-19 have been developed at preclinical and clinical stages in the format of polyclonal antibodies, monoclonal antibodies, and cocktail antibodies. Four products, i.e., convalescent plasma, bamlanivimab, REGN-Cov2, and the cocktail of bamlanivimab and etesevimab have been authorized by the U.S. Food and Drug Administration (FDA) for emergency use. Hundreds of relevant clinical trials are ongoing worldwide. Therapeutic antibody therapies have been a very active and crucial part of COVID-19 treatment. In this review, we focus on the progress of therapeutic COVID-19 antibody development and application, discuss corresponding problems and challenges, suggesting new strategies and solutions.


Subject(s)
Antibodies, Monoclonal/therapeutic use , Antibodies, Neutralizing/therapeutic use , COVID-19/therapy , SARS-CoV-2/immunology , Antibodies, Monoclonal/immunology , Antibodies, Neutralizing/immunology , COVID-19/virology , Humans , Immunization, Passive
6.
J Proteome Res ; 19(12): 4735-4746, 2020 12 04.
Article in English | MEDLINE | ID: covidwho-1065786

ABSTRACT

According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.


Subject(s)
Proteome , Proteomics , Databases, Protein , Genome, Human , Humans , Mass Spectrometry , Proteome/genetics
7.
Invest Radiol ; 56(8): 471-479, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1043316

ABSTRACT

OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. MATERIALS AND METHODS: We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT. The resulting 3-dimensional masks were projected into 2-dimensional anterior-posterior DRR to compute area-based AD percentage (POa). A CNN was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD, and quantifying POa on CXR. The CNN POa results were compared with POa quantified on CXR by 2 expert readers and to the POv ground truth, by computing correlations and mean absolute errors. RESULTS: Bootstrap mean absolute error and correlations between POa and POv were 11.98% (11.05%-12.47%) and 0.77 (0.70-0.82) for average of expert readers and 9.56% to 9.78% (8.83%-10.22%) and 0.78 to 0.81 (0.73-0.85) for the CNN, respectively. CONCLUSIONS: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of AD on CXR in patients with positive reverse transcriptase-polymerase chain reaction test results for COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Image Processing, Computer-Assisted/methods , Radiography, Thoracic , Radiologists , Tomography, X-Ray Computed , Cohort Studies , Humans , Lung/diagnostic imaging , Male , Retrospective Studies
8.
ArXiv ; 2020 Apr 02.
Article in English | MEDLINE | ID: covidwho-823485

ABSTRACT

PURPOSE: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. MATERIALS AND METHODS: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobewise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April, 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth. RESULTS: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO(P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations. CONCLUSION: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

9.
Preprint | ArXiv | ID: ppcovidwho-477

ABSTRACT

PURPOSE: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. MATERIALS AND METHODS: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deelearning and deereinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobewise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April, 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth. RESULTS: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (< .001), 0.97 for PHO(< .001), 0.91 for LSS (< .001), 0.90 for LHOS (< .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations. CONCLUSION: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

10.
Radiol Artif Intell ; 2(4): e200048, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-731125

ABSTRACT

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations. Materials and Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning. The first measure of (PO, PHO) is global, while the second of (LSS, LHOS) is lobe-wise. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States collected between 2002-Present (April 2020). Ground truth is established by manual annotations of lesions, lungs, and lobes. Correlation and regression analyses were performed to compare the prediction to the ground truth. Results: Pearson correlation coefficient between method prediction and ground truth for COVID-19 cases was calculated as 0.92 for PO (P < .001), 0.97 for PHO (P < .001), 0.91 for LSS (P < .001), 0.90 for LHOS (P < .001). 98 of 100 healthy controls had a predicted PO of less than 1%, 2 had between 1-2%. Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations. Conclusion: A new method segments regions of CT abnormalities associated with COVID-19 and computes (PO, PHO), as well as (LSS, LHOS) severity scores.

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