A COVID-19 antibody curbs SARS-CoV-2 nucleocapsid protein-induced complement hyperactivation (preprint)

Although human antibodies elicited by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein are profoundly boosted upon infection, little is known about the function of N-reactive antibodies. Herein, we isolated and profiled a panel of 32 N protein-specific monoclonal antibodies (mAbs) from a quick recovery coronavirus disease-19 (COVID-19) convalescent patient who had dominant antibody responses to the SARS-CoV-2 N protein rather than to the SARS-CoV-2 spike (S) protein. The complex structure of the N protein RNA binding domain with the mAb with the highest binding affinity (nCoV396) revealed changes in the epitopes and antigen’s allosteric regulation. Functionally, a virus-free complement hyper-activation analysis demonstrated that nCoV396 specifically compromises the N protein-induced complement hyper-activation, which is a risk factor for the morbidity and mortality of COVID-19 patients, thus laying the foundation for the identification of functional anti-N protein mAbs.

Structural Insight Reveals SARS-CoV-2 Orf7a as an Immunomodulating Factor for Human CD14+ Monocytes (preprint)

Dysregulated immune cell responses have been linked to the severity of Coronavirus Disease 2019 (COVID-19). However, the specific viral factor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contributing to the immune-dysregulation is currently unclear. Herein, we identified the ectodomain of Ig-like fold viral proteinSARS-CoV-2 Orf7a interacted with CD14+ monocytes at the highest efficiency in human peripheral blood mononuclear cells, but not for the relative highly pathogenic protein SARS-CoV Orf7a. The 2.2 Å resolutioncrystal structure of SARS-CoV-2 Orf7a reveals three remarkable changes in the amphipathic side of the four-strand β-sheet, implying the potential functional interface of the viral protein. Structure-based superimposition of SARS-CoV-2 Orf7a with SARS-CoV Orf7a - LFA1 working model suggests that SARS-CoV-2 Orf7a utilizes different binding patterns to recognize the specific immune cells. Importantly, SARS-CoV-2 Orf7a co-incubation with CD14+ monocytes ex vivo triggers a decrease of HLA-DR/DP/DQ and significant pro-inflammatory cytokines expressions, including IL-6, IL-1β, IL-8, and TNF-α. Our work demonstrates that SARS-CoV-2 Orf7a is an immunomodulating factor for immune cells binding and trigging aberrant inflammatory responses, providing promising therapeutic drug targets in the pandemic disease COVID-19.Funding: This work is supported by the National Key R&D Program of China (2019YFA0110300) granted to J.C.; the Special Fund for Scientific and Technological Innovation Strategy of Guangdong Province of China (2018B030306029), COVID-19 Emerging Prevention Products, Research Special Fund of Zhuhai City (ZH22036302200016PWC) granted to S.C.; the Fundamental Research Funds for the Central Universities (19ykzd36) granted to J.C; and the Science and Technology Program of Guangzhou (202002030069) granted to J.C. Conflict of Interest: The authors declare no conflict of interest.Ethical Approval: The human peripheral blood samples for the experiments were collected through The Health Management Center, The Fifth Affiliated Hospital, Sun Yat-sen University. This study was approved by The Medical Ethics Committee of The Fifth Affiliated Hospital, Sun Yat-sen University (2020-K195-1).

A comparative survey of betacoronavirus strain molecular dynamics identifies key ACE2 binding sites (preprint)

Comparative functional analysis of the binding interactions between various betacoronavirus strains and their potential human target proteins, such as ACE1, ACE2 and CD26, is critical to our future understanding and combating of COVID-19. Here, employing large replicate sets of GPU accelerated molecular dynamics simulations, we statistically compare atom fluctuations of the known human target proteins in both the presence and absence of different strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. We identify a common interaction site between the N-terminal helices of ACE2 and the viral RBD in all strains (hCoV-OC43, hCoV-HKU1, MERS-CoV, SARS-CoV1, and SARS-CoV-2) and a second more dynamically complex RBD interaction site involving the ACE2 amino acid sites K353, Q325, and a novel motif, AAQPFLL (386-392) in the more recent cross-species spillovers (i.e. absent in hCoV-OC43). We use computational mutagenesis to further confirm the functional relevance of these sites. We propose a "one touch/two touch" model of viral evolution potentially involved in functionally facilitating binding interactions in zoonotic spillovers. We also observe these two touch sites governing RBD binding activity in simulations on hybrid models of the suspected viral progenitor, batCoV-HKU4, interacting with both the human SARS target, ACE2, and the human MERS target, CD26. Lastly, we confirm that the presence of a common hypertension drug (lisinopril) within the target site of SARS-CoV-2 bound models of ACE1 and ACE2 acts to enhance the RBD interactions at the same key sites in our proposed model. In the near future, we recommend that our comparative computational analysis identifying these key viral RBD-ACE2 binding interactions be supplemented with comparative studies of site-directed mutagenesis in order to screen for current and future coronavirus strains at high risk of zoonotic transmission to humans. STATEMENT OF SIGNIFICANCEWe generated structural models of the spike glycoprotein receptor binding domain from recent and past betacoronavirus outbreak strains aligned to the angiotensin 1 converting enzyme 2 protein, the primary target protein of the SARS-CoV-2 virus causing COVID 19. We then statistically compared computer simulated molecular dynamics of viral bound and unbound versions of each model to identify locations where interactions with each viral strain have dampened the atom fluctuations during viral binding. We demonstrate that all known strains of betacoronavirus are strongly interactive with the N-terminal helix region of ACE2. We also identify a more complex viral interaction with three novel sites that associates with more recent and deadly SARS strains, and also a bat progenitor strain HKU4.

A COVID-19 antibody curbs SARS-CoV-2 nucleocapsid protein-induced complement hyper-activation (preprint)

Although human antibodies elicited by severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) nucleocapsid (N) protein are profoundly boosted upon infection, little is known about the function of N-directed antibodies. Herein, we isolated and profiled a panel of 32 N protein-specific monoclonal antibodies (mAb) from a quick recovery coronavirus disease-19 (COVID-19) convalescent, who had dominant antibody responses to SARS-CoV-2 N protein rather than to Spike protein. The complex structure of N protein RNA binding domain with the highest binding affinity mAb nCoV396 reveals the epitopes and antigens allosteric changes. Functionally, a virus-free complement hyper-activation analysis demonstrates that nCoV396 specifically compromises N protein-induced complement hyper-activation, a risk factor for morbidity and mortality in COVID-19, thus paving the way for functional anti-N mAbs identification. One Sentence SummaryB cell profiling, structural determination, and protease activity assays identify a functional antibody to N protein.

Establishment of a clinical nomogram model to predict the progression of COVID-19 to severe disease (preprint)

Background Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aimed to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely, efficient manner.Methods This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed with the discrimination curve.Results Risk factors, including demographics data, symptoms, laboratory and image findings were recorded for the 202 patients. Eight of the 52 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. Sensitivity, specificity and AUC were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset.Conclusions We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.

Establishment of a clinical nomogram model to predict the progress to severe COVID-19 (preprint)

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the leading cause of a public health emergency in the world, accompanying with high mortality in severe corona virus disease 2019(COVID-19 ), thereby early detection and stopping the progress to severe COVID-19 is important. Our aim is to establish a clinical nomogram model to calculate and predict the progress to severe COVID-19 timely and efficiently.Methods: In this study, 65 patients with COVID-19 had been included retrospectively in the Fifth Affiliated Hospital of Sun Yat-sen University from January 17, to February 11, 2020. Patients were randomly assigned to train dataset (n=51 with 15 progressing to severe COVID-19) and test dataset (n=14 with 4 progressing to severe COVID-19). Lasso algorithm was applied to filter the most classification relevant clinical factors. Based on selected factors, logistic regression model was fit to predict the severe from mild/common. Meanwhile in nomogram sensitivity, specificity, AUC (Area under Curve), and calibration curve were depicted and calculated by R language, to evaluate the prediction performance to severe COVID-19.Results:High ratio of sever COVID-19 patients (26.5%) had been found in our retrospective study, and 84% of these cases progress to severe or critical after 5 days from their first clinical examination. In these 65 patients with COVID-19, 77 clinical characteristics in first examination were collected and analyzed, and 37 ones had been found different between non-severe and severe COVID-19. But when all these factors were analyzed in establishment of prediction model, six factors are crucial for predicting progress of severe COVID-19 via Lasso algorithm. Based on these six factors, including increased fibrinogen, hyponatremia, decreased PaO2,multiple lung lobes involved, down-regulated CD3（+）T-lymphocyte and fever, a logistic regression model was fit to discriminate severe and common COVID-19 patients. The sensitivity, specificity and AUC were 0.93, 0.86, 0.96 in the train dataset and 0.9, 1.0, 1.0 in test dataset respectively. Nomogram-predicted probability was more consistent with actual probability by R language.Conclusions:In summary, an efficient and reliable clinical nomogram model had been established, which indicate increased fibrinogen, hyponatremia, decreased PaO2, multiple lung lobes involved, down-regulated CD3（+）T-lymphocyte and fever at the first clinical examination, could predict progress of patients to severe COVID-19.

Crystal structure of SARS-CoV-2 nucleocapsid protein RNA binding domain reveals potential unique drug targeting sites (preprint)

The outbreak of coronavirus disease (COVID-19) in China caused by SARS-CoV-2 virus continually lead to worldwide human infections and deaths. It is currently no specific viral protein targeted therapeutics yet. Viral nucleocapsid protein is a potential antiviral drug target, serving multiple critical functions during the viral life cycle. However, the structural information of SARS-CoV-2 nucleocapsid protein is yet to be clear. Herein, we have determined the 2.7 [A] crystal structure of the N-terminal RNA binding domain of SARS-CoV-2 nucleocapsid protein. Although overall structure is similar with other reported coronavirus nucleocapsid protein N-terminal domain, the surface electrostatic potential characteristics between them are distinct. Further comparison with mild virus type HCoV-OC43 equivalent domain demonstrates a unique potential RNA binding pocket alongside the {beta}-sheet core. Complemented by in vitro binding studies, our data provide several atomic resolution features of SARS-CoV-2 nucleocapsid protein N-terminal domain, guiding the design of novel antiviral agents specific targeting to SARS-CoV-2.