Dataset Information

Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?

ABSTRACT: The outbreak of a novel corona Virus Disease 2019 (COVID-19) in the city of Wuhan, China has resulted in more than 1.7 million laboratory confirmed cases all over the world. Recent studies showed that SARS-CoV-2 was likely originated from bats, but its intermediate hosts are still largely unknown. In this study, we assembled the complete genome of a coronavirus identified in 3 sick Malayan pangolins. The molecular and phylogenetic analyses showed that this pangolin coronavirus (pangolin-CoV-2020) is genetically related to the SARS-CoV-2 as well as a group of bat coronaviruses but do not support the SARS-CoV-2 emerged directly from the pangolin-CoV-2020. Our study suggests that pangolins are natural hosts of Betacoronaviruses. Large surveillance of coronaviruses in pangolins could improve our understanding of the spectrum of coronaviruses in pangolins. In addition to conservation of wildlife, minimizing the exposures of humans to wildlife will be important to reduce the spillover risks of coronaviruses from wild animals to humans.

SUBMITTER: Liu P

PROVIDER: S-EPMC7224457 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?

Liu Ping P Jiang Jing-Zhe JZ Wan Xiu-Feng XF Hua Yan Y Li Linmiao L Zhou Jiabin J Wang Xiaohu X Hou Fanghui F Chen Jing J Zou Jiejian J Chen Jinping J

PLoS pathogens 20200514 5

The outbreak of a novel corona Virus Disease 2019 (COVID-19) in the city of Wuhan, China has resulted in more than 1.7 million laboratory confirmed cases all over the world. Recent studies showed that SARS-CoV-2 was likely originated from bats, but its intermediate hosts are still largely unknown. In this study, we assembled the complete genome of a coronavirus identified in 3 sick Malayan pangolins. The molecular and phylogenetic analyses showed that this pangolin coronavirus (pangolin-CoV-2020 ...[more]

PMID: 32407364

Similar Datasets

Project description:The emergence of 2019 novel coronavirus (2019-nCoV) is of global concern and might have emerged from RNA recombination among existing coronaviruses. CoV spike (S) protein which is crucial for receptor binding, membrane fusion via conformational changes, internalization of the virus, host tissue tropism and comprises crucial targets for vaccine development, remain largely uncharacterized. Therefore, the present study has been planned to determine the sequence variation, structural and antigenic divergence of S glycoprotein which may be helpful for the management of 2019-nCoV infection. The sequences of spike glycoprotein of 2019-nCoV and SARS coronavirus (SARS-CoV) were used for the comparison. The sequence variations were determined using EMBOSS Needle pairwise sequence alignment tools. The variation in glycosylation sites was predicted by NetNGlyc 1.0 and validated by N-GlyDE server. Antigenicity was predicted by NetCTL 1.2 and validated by IEDB Analysis Resource server. The structural divergence was determined by using SuperPose Version 1.0 based on cryo-EM structure of the SARS coronavirus spike glycoprotein. Our data suggests that 2019-nCoV is newly spilled coronavirus into humans in China is closely related to SARS-CoV, which has only 12.8% of difference with SARS-CoV in S protein and has 83.9% similarity in minimal receptor-binding domain with SARS-CoV. Addition of a novel glycosylation sites were observed in 2019-nCoV. In addition, antigenic analysis proposes that great antigenic differences exist between both the viral strains, but some of the epitopes were found to be similar between both the S proteins. In spite of the variation in S protein amino acid composition, we found no significant difference in their structures. Collectively, for the first time our results exhibit the emergence of human 2019-nCoV is closely related to predecessor SARS-CoV and provide the evidence that 2019-nCoV uses various novel glycosylation sites as SARS-CoV and may have a potential to become pandemic owing its antigenic discrepancy. Further, demonstration of novel Cytotoxic T lymphocyte epitopes may impart opportunities for the development of peptide based vaccine for the prevention of 2019-nCoV.

Project description:ObjectivesTo develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia.MethodsFor this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia.ResultsThe radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.ConclusionsThe radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making.Key points• A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.

Dataset Information

Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?

Publications

Are pangolins the intermediate host of the 2019 novel coronavirus (SARS-CoV-2)?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets