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    Impacts of 203/204: RG>KR mutation in the N protein PROTEIN of SARS-CoV-2

    Authors: Tomoyuki Nishikawa; Chin Yang Chang; Jiayu A Tai; Hiroki Hayashi; Jiao Sun; Shiho Torii; Chikako Ono; Yoshiharu Matsuura; Ryoko Ide; Junichi Mineno; Miwa Sasai; Masahiro Yamamoto; Hironori Nakagami; Kunihiko Yamashita; Sankar Bhattacharyya; Samreen Siddiqui; Akansha Tyagi; Sujeet Jha; Rajesh Pandey; Somnath Dutta; Rajesh P. Ringe; Raghavan Varadarajan; Louis-Marie Bloyet; Fabio Benigni; Elisabetta Cameroni; Johan Neyts; Agostino Riva; Gyorgy Snell; Amalio Telenti; Sean PJ Whelan; Herbert W Virgin; Davide Corti; Matteo Samuele Pizzuto; David Veesler

    doi:10.1101/2021.01.14.426726 Date: 2021-01-14 Source: bioRxiv

    We present a structure-based model of phosphorylation-dependent binding and sequestration of SARS-CoV-2 nucleocapsid protein PROTEIN and the impact of two consecutive amino acid changes R203K and G204R. Additionally, we studied how mutant strains affect HLA-specific antigen presentation and correlated these findings with HLA allelic population frequencies. We discovered RG>KR mutated SARS-CoV-2 expands the ability for differential expression of the N protein PROTEIN epitope on Major Histocompatibility Complexes (MHC) of varying Human Leukocyte Antigen (HLA) origin. The N protein PROTEIN LKR region K203, R204 of wild type ( SARS-CoVs MESHD) and (SARS-CoV-2) observed HLA-A HGNC*30:01 and HLA-A HGNC*30:21, but mutant SARS-CoV-2 observed HLA-A HGNC*31:01 and HLA-A HGNC*68:01. Expression of HLA-A HGNC genotypes associated with the mutant strain occurred more frequently in all populations studied. ImportanceThe novel coronavirus known as SARS-CoV-2 causes a disease renowned as 2019-nCoV (or COVID-19 MESHD). HLA allele frequencies worldwide could positively correlate with the severity of coronavirus cases and a high number of deaths MESHD.


    Authors: Luca Elli; Federica Facciotti; Vincenza Lombardo; Alice Scricciolo; David S Sanders; Valentina Vaira; Donatella Barisani; Maurizio Vecchi; Andrea Costantino; Lucia Scaramella; Bernardo Dell'Osso; Luisa Doneda; Leda Roncoroni

    doi:10.1101/2020.12.15.20248039 Date: 2020-12-16 Source: medRxiv

    Objective. The SARS-CoV-2 pandemic has spread across the world causing a dramatic number of infections and deaths MESHD. No data are available about the effects of an infection in patients affected by celiac disease ( CD MESHD) in terms of the development of related symptoms and antibodies. We aimed to investigate the impact of the SARS-CoV-2 pandemic in celiac patients. Design. During a lockdown, the celiac patients living in the Milan area were contacted and interviewed about the development of COVID-19 MESHD symptoms as well as adherence to an anti-virus lifestyle and a gluten-free diet (GFD). They were also given a stress questionnaire to fill in. The development of anti-SARS-CoV-2 IgG and IgA (anti-RBD and N proteins PROTEIN) and the expression of the duodenal ACE2 HGNC receptor were investigated. When available, duodenal histology, anti-tissue transglutaminase IgA (tTGA), presence of immunologic comorbidities and adherence to the GFD were analysed as possible risk factors. Results. 362 celiac patients have been interviewed and 42 (11%) presented with COVID-19 MESHD symptoms. The presence of symptoms was not influenced by tTGA positivity, presence of duodenal atrophy MESHD or adherence to GFD. 37% of the symptomatic patients presented anti-SARS-CoV-2 immunoglobulins (Ig). Globally, 18% of celiac patients showed anti-SARS-CoV-2 Ig vs 25% of the non-celiac control (p=0.18). The values of anti-RBD IgG/IgA and anti-N IgG did not differ from the non-celiac controls. Celiac patients had a significant lower level of anti-N IgA. The ACE2 HGNC receptor was detected in the non-atrophic duodenal mucosa of celiac patients; atrophy MESHD was associated with a lower expression of the ACE2 HGNC receptor. Conclusion. CD MESHD patients have an anti-SARS-CoV-2 Ig positiveness and profile similar to non-celiac controls, except for anti-N IgA. The main celiac parameters and adherence to the GFD do not influence the development of a different Ig profile.

    SARS-CoV-2 N promotes the NLRP3 inflammasome activation to induce hyperinflammation

    Authors: Pan Pan; Miaomiao Shen; Zhenyang Yu; Weiwei Ge; Keli Chen; Mingfu Tian; Feng Xiao; Geng Li; Zhenwei Wang; Jun Wang; Yaling Jia; Wenbiao Wang; Pin Wan; Jing Zhang; Weijie Chen; Zhiwei Lei; Xin Chen; Zhen Luo; Qiwei Zhang; Meng Xu; Yongkui Li; Jianguo Wu

    doi:10.21203/rs.3.rs-101224/v1 Date: 2020-10-31 Source: ResearchSquare

    Excessive inflammatory responses induced upon SARS-CoV-2 infection MESHD interlocks with severe symptoms and acute lung injury MESHD in patients with Severe Coronavirus Disease MESHD Coronavirus Disease 2019 MESHD ( COVID-19 MESHD). Revealing the mechanism underlying the control of SARS-CoV-2-triggered immune-inflammatory responses would help us to understand the pathological process and guide clinical treatment. However, the effect of the NLRP3 inflammasome on regulating SARS-CoV-2-induced inflammatory responses has not been reported. Here, we revealed a distinct mechanism by which SARS-CoV-2 nucleocapsid (N) protein PROTEIN promotes the NLRP3 inflammasome activation to induce hyperinflammation. We demonstrated that N protein PROTEIN facilitates the maturation of proinflammatory cytokines IL-1β and IL-6 and induces proinflammatory responses in cultured cells and mice tissues. In team of molecular mechanism, N protein PROTEIN interacts directly with NLRP3 protein, promotes the binding of NLRP3 with ASC, and facilitates the assemble of the inflammasome complex. More importantly, N protein PROTEIN aggravates lung injury MESHD, accelerated death MESHD in sepsis MESHD and acute inflammation MESHD mouse models, and promotes IL-1β and IL-6 activation in mice. Notably, N-induced lung injury MESHD and cytokine production were blocked by Ac-YVAD-cmk, an inhibitor of the NLRP3 inflammasome. Therefore, this study revealed a distinct mechanism by which SARS-CoV-2 N MESHD protein promotes the NLRP3 inflammasome activation and induces excessive inflammatory responses.

    Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains

    Authors: Anastasis Oulas; Maria Zanti; Marios Tomazou; Margarita Zachariou; George Minadakis; Marilena M Bourdakou; Pavlos Pavlidis; George M Spyrou; Hui Zhuang; Yi-Hua Zhou; Yi-Gang Tong; Kuanhui Xiang; Lennart Koepke; Christina Martina Stuerzel; Christiane Schueler; Saskia Stenzel; Elisabeth Braun; Johanna Weiss; Daniel Sauter; Jan Muench; Steffen Stenger; Kei Sato; Alexander Kleger; Christine Goffinet; Konstantin Maria Johannes Sparrer; Frank Kirchhoff; Austin D. Swafford; Karsten Zengler; Susan Cheng; Michael Inouye; Teemu Niiranen; Mohit Jain; Veikko Salomaa; Jeffrey D. Esko; Nathan E Lewis; Rob Knight

    doi:10.1101/2020.08.17.253484 Date: 2020-08-18 Source: bioRxiv

    This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize, genetic data from all strains available from GISAID and countries regional information such as deaths MESHD and cases per million as well as covid-19 MESHD-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N PROTEIN (P13L) and ORF3a PROTEIN (Q57H). The former appears to be significantly associated with decreased deaths MESHD and cases per million according to our models, while the latter shows an opposing association with decreased deaths MESHD and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

    Structure/epitope-based immunoinformatics analysis of structural proteins of 2019 novel coronavirus

    Authors: Yuwei Li; Mi Mao; Liteng Yang; Xizhuo Sun; Nanshan Zhong; Zhigang Liu

    doi:10.21203/rs.3.rs-50740/v1 Date: 2020-07-29 Source: ResearchSquare

    The newly identified 2019 novel coronavirus (2019-nCoV) has caused more than 81,400 laboratory-confirmed human infections, including 3261 deaths MESHD, posing a serious threat to human health. Currently, however, there is no specific antiviral treatment or vaccine. To identify immunodominant peptides for designing global peptide vaccine for combating the infections caused by 2019-nCoV, the structure and immunogenicity of 2019-nCoV structural protein were analyzed by bioinformatics tools. 33 B-cell epitopes and 39 T-cell epitopes were determined in four structural proteins via different immunoinformatic tools in which include spike protein PROTEIN (22 B-cell epitopes, 25 T-cell epitopes ), nucleocapsid protein PROTEIN (7 B-cell epitopes, 6 T-cell epitopes), membrane protein (2 B-cell epitopes, 7 T-cell epitopes), and envelope protein PROTEIN (2 B-cell epitopes, 1T-cell epitopes), respectively. The proportion of epitope residues in primary sequence was used to determine the antigenicity and immunogenicity of proteins. The envelope protein PROTEIN has the largest antigenicity in which residue coverage of B-cell epitopes is 24%. The membrane protein possesses the largest immunogenicity in which residue coverage of T-cell epitopes is 55.86%. The reason that immune storm was caused by 2019-nCoV maybe that the membrane and envelope protein PROTEIN expressed plentifully in cell infected. Further, studies involving experimental validation of these predicted epitopes is warranted to ensure the potential of B-cells and T-cells stimulation for their effective use as vaccine candidates. These findings provide the basis for starting further studies on the pathogenesis, and optimizing the design of diagnostic, antiviral and vaccination strategies for this emerging infection MESHD.

    Antibody Testing Documents the Silent Spread of SARS-CoV-2in New York Prior to the First Reported Case

    Authors: Kathrine Meyers; Lihong Liu; Wen-Hsuan Lin; Yang Luo; Michael Yin; Yumeng Wu; Sandeep Wontakal; Alex Rai; Francesca La Carpia; Sebastian Fernando; Mitra Dowlatshahi; Elad Elkayam; Ankur Garg; Leemor Joshua-Tor; John Wolk; Barbara Alpert; Marie-Laure Romney; Brianna Costabile; Edoardo Gelardi; Francesca Vallese; Oliver Clarke; Filippo Mancia; Anne-Catrin Uhlemann; Magdalena Sobieszczyk; Alan Perelson; Yaoxing Huang; Eldad Hod; David Ho

    doi:10.21203/rs.3.rs-39880/v1 Date: 2020-07-02 Source: ResearchSquare

    We developed and validated serologic assays to determine SARS-CoV-2 seroprevalence in select patient populations in greater New York City area early during the epidemic. We tested “discarded” serum samples from February 24 to March 29 for antibodies against SARS-CoV-2 spike PROTEIN trimer and nucleocapsid protein PROTEIN. Using known durations for antibody development, incubation period, serial interval, and reproductive ratio for this pandemic, we determined that introduction of SARS-CoV-2 into New York likely occurred between January 23 and February 4, 2020. SARS-CoV-2 spread silently for 4–5 weeks before the first community acquired infection was reported. A novel coronavirus emerged in December 2019 in Wuhan, China1,2 and devasted Hubei Province in early 2020 before spreading to every province within China and nearly every country in the world3. This pathogen, now termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a global pandemic, with ~ 10 million cases and over 500,000 deaths MESHD reported through June 30, 20203. The first case of SARS-CoV-2 infection MESHD in the United States was identified on January 19, 2020 in a man who returned to the State of Washington from Wuhan4. In the ensuing months, the U.S. has become a hotspot of the pandemic, presently accounting for almost one third of the total caseload and over one fourth of the deaths3. The first confirmed case in New York was reported on March 1 HGNC in a traveler recently returned from Iran. The first community-acquired SARS-CoV-2 infection MESHD was diagnosed on March 3 HGNC in a 50-year-old male who lived in New Rochelle and worked in New York City (https://www1.nyc.gov/site/doh/covid/ covid-19 MESHD-data-archive.page.) In the ensuing 18 weeks, New York City has suffered a peak daily infection number of ~ 4,500 (Fig. 1a) and a cumulative caseload of ~ 400,000 to date. The time period when SARS-CoV-2 gained entry into this epicenter of the pandemic remains unclear.

    End-to-end COVID-19 MESHD screening with 3D deep learning on chest computed tomography

    Authors: Kun Yang; Xinfeng Liu; Yingli Yang; Xiangjun Liao; Rongpin Wang; Xianchun Zeng; Yuxiang Wang; Mudan Zhang; Tijiang Zhang

    doi:10.21203/rs.3.rs-36433/v1 Date: 2020-06-19 Source: ResearchSquare

    The outbreak of an acute respiratory syndrome MESHD (called novel coronavirus pneumonia MESHD, NCP PROTEIN) caused by SARS-CoV-2 virus has now progressed to a pandemic, and became the most common threat to public death MESHD worldwide[i],[ii]. COVID-19 MESHD screening using computed tomography (CT) can perform a quick diagnosis and identify high-risk NCP PROTEIN patients[iii]. Automated screening using CT volumes is a challenging task owing to inter-grader variability and high false-positive and false-negative rates. We propose a three dimensional (3D) deep learning convolutional neural networks (CNN) that use a patient’s CT volume to predict the risk of COVID-19 MESHD, trained end-to-end from CT volumes directly, using only images and disease labels as inputs. Our model achieves a state-of-the-art performance (95.78% overall accuracy, 99.4% area under the curve) on a dataset of 1,684 COVID-19 MESHD patients, nearly twice larger than previous datasets3, and performs similarly on an independent clinical validation set of 121 cases. We tested its performance against six radiologists on clinical confirmed patient’ CT volumes, our model outperformed all six radiologists with absolute reductions of 7% in false positives and 35.9% in false negatives, demonstrating artificial intelligence (AI) capable to optimize the COVID-19 MESHD screening process via computer assistance and automation with a level of competence comparable to radiologists. While the vast majority of patients remain unscreened, we show the potential for AI to increase the accuracy and consistency of COVID-19 MESHD screening with CT.

    Mapping the Immunodominance Landscape of SARS-CoV-2 Spike PROTEIN Protein for the Design of Vaccines against COVID-19 MESHD

    Authors: Bao-zhong Zhang; Ye-fan Hu; Lin-lei Chen; Yi-gang Tong; Jing-chu Hu; Jian-piao Cai; Kwok-Hung Chan; Ying Dou; Jian Deng; Hua-rui Gong; Chaiyaporn Kuwentrai; Wenjun Li; Xiao-lei Wang; Hin Chu; Can-hui Su; Ivan Fan-Ngai Hung; Thomas Chung Cheung Yau; Kelvin Kai-Wang To; Kwok Yung Yuen; Jian-Dong Huang

    doi:10.1101/2020.04.23.056853 Date: 2020-04-24 Source: bioRxiv

    The ongoing coronavirus disease MESHD coronavirus disease 2019 MESHD ( COVID-19 MESHD) pandemic is a serious threat to global public health, and imposes severe burdens on the entire human society. The severe acute respiratory syndrome MESHD (SARS) coronavirus-2 (SARS-CoV-2) can cause severe respiratory illness MESHD and death MESHD. Currently, there are no specific antiviral drugs that can treat COVID-19 MESHD. Several vaccines against SARS-CoV-2 are being actively developed by research groups around the world. The surface S (spike) protein PROTEIN and the highly expressed internal N (nucleocapsid) protein PROTEIN of SARS-CoV-2 are widely considered as promising candidates for vaccines. In order to guide the design of an effective vaccine, we need experimental data on these potential epitope candidates. In this study, we mapped the immunodominant (ID) sites of S protein PROTEIN using sera samples collected from recently discharged COVID-19 MESHD patients. The SARS-CoV-2 S protein PROTEIN-specific antibody levels in the sera of recovered COVID-19 MESHD patients were strongly correlated with the neutralising antibody titres. We used epitope mapping to determine the landscape of ID sites of S protein PROTEIN, which identified nine linearized B cell ID sites. Four out of the nine ID sites were found in the receptor-binding domain (RBD). Further analysis showed that these ID sites are potential high-affinity SARS-CoV-2 antibody binding sites. Peptides containing two out of the nine sites were tested as vaccine candidates against SARS-CoV-2 in a mouse model. We detected epitope-specific antibodies and SARS-CoV-2-neutralising activity in the immunised mice. This study for the first time provides human serological data for the design of vaccines against COVID-19 MESHD.

    Epidemic Situation of Novel Coronavirus Pneumonia in China mainland

    Authors: Liu youbin; Gong Liming; Li bao hong

    doi:10.1101/2020.02.17.20024034 Date: 2020-02-18 Source: medRxiv

    [Objective] Analyze the occurrence of novel coronavirus pneumonia MESHD( NCP PROTEIN) in China mainland, explore the epidemiological rules, and evaluate the effect of prevention and control. [Methods] From December 1, 2019 to March 4 HGNC, 2020, Analysis of 80,409 confirmed cases of NCP PROTEIN in China mainland. [Results] From December 1, 2019 to March 4 HGNC, 2020, a total of 80,409 cases of NCP PROTEIN were confirmed in China mainland, a total of 67,466 cases were confirmed in Hubei Province, a total of 49,671 cases were confirmed in Wuhan city. From December 1, 2019 to March 4 HGNC, 2020, a total of 3,012 cases of NCP deaths MESHD NCP deaths PROTEIN in China mainland, the mortality was 3.75% (3012/80,409); A total of 52045 cases of cured in China mainland; The turning point of the epidemic have been reached since February 18.2020 in China mainland; The spread index of NCP PROTEIN gradually declined since January 27. 2020, and the extinction index of NCP PROTEIN rose little by little since January 29, 2020. [Conclusion] From December 1, 2019 to March 4 HGNC, 2020, NCP PROTEIN is under control, and the trend of the epidemic will eventually disappearThe turning point of an epidemic that I've created is a great indicator that can calculate the turning date of an outbreak and provide a basis for scientific prevention.

    Can Search Query Forecast successfully in China's 2019-nCov pneumonia?

    Authors: Li Xiaoxuan; Wu Qi; Lv Benfu

    doi:10.1101/2020.02.12.20022400 Date: 2020-02-18 Source: medRxiv

    Recently the novel coronavirus (2019-nCov) pneumonia MESHD outbreak in China then the world, and the Number of infections and death MESHD continues to increases. Search Query performs well in forecasting the epidemics. It is still a question whether search engine data can forecast the drift and the inflexion in 2019-nCov pneumonia MESHD. Based on the Baidu Search Index, we propose three prediction models: composite Index, composite Index with filtering and suspected NCP PROTEIN(Novel Coronavirus Pneumonia). The result demonstrates that the predictive model of composite index with filtering performs the best while the model of suspected NCP PROTEIN has the highest forecast error. We further predict the out-of-the-set NCP PROTEIN confirmed cases and monitor that the next peak of new diagnoses will occur on February 16th and 17th.

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MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins

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