Screening the Potential Biomarkers of COVID-19-Related Thrombosis Through Bioinformatics Analysis.

A high proportion of critically ill patients with coronavirus disease 2019 (COVID-19) experience thrombosis, and there is a strong correlation between anticoagulant therapy and the COVID-19 survival rate, indicating that common COVID-19 and thrombosis targets have potential therapeutic value for severe COVID-19.Gene expression profiling data were downloaded from Gene Expression Omnibus (GEO), and common differentially expressed genes (co-DEGs) were identified. The potential biological functions of these co-DEGs were explored by functional enrichment analysis, and protein-protein interaction (PPI) networks were constructed to elucidate the molecular mechanisms of the co-DEGs. Finally, hub genes in the co-DEG network were identified, and correlation analysis was performed.We identified 8320 upregulated genes and 7651 downregulated genes from blood samples of COVID-19 patients and 368 upregulated genes and 240 downregulated genes from blood samples of thrombosis patients. The enriched cellular component terms were mainly related to cytosolic ribosomes and ribosomal subunits. The enriched molecular function terms were mainly related to structural constituents of ribosomes and electron transfer activity. Construction of the PPI network and identification of hub genes ultimately confirmed that RPS7, IGF1R, DICER1, ERH, MCTS1, and TNPO1 were jointly upregulated hub genes, and FLNA and PXN were jointly downregulated hub genes.The identification of novel potential biomarkers provides new options for treating COVID-19-related thrombosis and reducing the rate of severe COVID-19.

Screening the Potential Biomarkers of COVID-19-Related Thrombosis Through Bioinformatics Analysis

A high proportion of critically ill patients with coronavirus disease 2019 (COVID-19) experience thrombosis, and there is a strong correlation between anticoagulant therapy and the COVID-19 survival rate, indicating that common COVID-19 and thrombosis targets have potential therapeutic value for severe COVID-19.Gene expression profiling data were downloaded from Gene Expression Omnibus (GEO), and common differentially expressed genes (co-DEGs) were identified. The potential biological functions of these co-DEGs were explored by functional enrichment analysis, and protein–protein interaction (PPI) networks were constructed to elucidate the molecular mechanisms of the co-DEGs. Finally, hub genes in the co-DEG network were identified, and correlation analysis was performed.We identified 8320 upregulated genes and 7651 downregulated genes from blood samples of COVID-19 patients and 368 upregulated genes and 240 downregulated genes from blood samples of thrombosis patients. The enriched cellular component terms were mainly related to cytosolic ribosomes and ribosomal subunits. The enriched molecular function terms were mainly related to structural constituents of ribosomes and electron transfer activity. Construction of the PPI network and identification of hub genes ultimately confirmed that RPS7, IGF1R, DICER1, ERH, MCTS1, and TNPO1 were jointly upregulated hub genes, and FLNA and PXN were jointly downregulated hub genes.The identification of novel potential biomarkers provides new options for treating COVID-19-related thrombosis and reducing the rate of severe COVID-19.

Identification of a recombinant equine coronavirus in donkey, China.

Equine coronavirus (ECoV) was first identified in the USA and has been previously described in several countries. In order to test the presence of ECoV in China, we collected 51 small intestinal samples from donkey foals with diarrhoea from a donkey farm in Shandong Province, China between August 2020 and April 2021. Two samples tested positive for ECoV and full-length genome sequences were successfully obtained using next-generation sequencing, one of which was further confirmed by Sanger sequencing. The two strains shared 100% sequence identity at the scale of whole genome. Bioinformatics analyses further showed that the two Chinese strains represent a novel genetic variant of ECoV and shared the highest sequence identity of 97.05% with the first identified ECoV strain - NC99. In addition, it may be a recombinant, with the recombination region around the NS2 gene. To our knowledge, this is the first documented report of ECoV in China, highlighting its risk to horse/donkey breeding. In addition, its potential risk to public health also warrants further investigation.

Structural basis of SARS-CoV-2 polymerase inhibition by Favipiravir (preprint)

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has developed into an unprecedented global pandemic. Nucleoside analogues, such as Remdesivir and Favipiravir, can serve as the first-line broad-spectrum antiviral drugs against the newly emerging viral diseases. Recent clinical trials of these two drugs for SARS-CoV-2 treatment revealed antiviral efficacies as well as side effects with different extents1-4. As a pyrazine derivative, Favipiravir could be incorporated into the viral RNA products by mimicking both adenine and guanine nucleotides, which may further lead to mutations in progeny RNA copies due to the non-conserved base-pairing capacity5. Here, we determined the cryo-EM structure of Favipiravir bound to the replicating polymerase complex of SARS-CoV-2 in the pre-catalytic state. This structure provides a missing snapshot for visualizing the catalysis dynamics of coronavirus polymerase, and reveals an unexpected base-pairing pattern between Favipiravir and pyrimidine residues which may explain its capacity for mimicking both adenine and guanine nucleotides. These findings shed lights on the mechanism of coronavirus polymerase catalysis and provide a rational basis for developing antiviral drugs to combat the SARS-CoV-2 pandemic.

Deep Neural Network Analysis of Clinical Variables Predicts Escalated Care in COVID-19 Patients (preprint)

This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients will need escalated care early on using deep-learning neural networks. Analysis was performed on hospitalized COVID-19 patients between February 7, 2020 and May 4, 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs, and blood gases were collected. We compared data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: i) COVID-19 patients admitted to the general floor (N=1203) versus those directly admitted to ICU (N=104), and ii) patients not upgraded to ICU (N=979) versus those upgraded to the ICU (N=224) from the general floor. A deep neural network algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer, and lymphocyte count showed temporal divergence between patients were upgraded to ICU compared to those were not. The deep learning predictive model ranked essentially the same set of laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782±0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.861±0.018). This study identified a few laboratory tests that were predictive of escalated care. This work could help frontline physicians to anticipate downstream ICU needs to more effectively allocate healthcare resources.

Structural and biochemical characterization of nsp12-nsp7-nsp8 core polymerase complex from COVID-19 virus (preprint)

The ongoing global pandemic of coronavirus disease 2019 (COVID-19) has caused huge number of human deaths. Currently, there are no specific drugs or vaccines available for this virus. The viral polymerase is a promising antiviral target. However, the structure of COVID-19 virus polymerase is yet unknown. Here, we describe the near-atomic resolution structure of its core polymerase complex, consisting of nsp12 catalytic subunit and nsp7-nsp8 cofactors. This structure highly resembles the counterpart of SARS-CoV with conserved motifs for all viral RNA-dependent RNA polymerases, and suggests the mechanism for activation by cofactors. Biochemical studies revealed reduced activity of the core polymerase complex and lower thermostability of individual subunits of COVID-19 virus as compared to that of SARS-CoV. These findings provide important insights into RNA synthesis by coronavirus polymerase and indicate a well adaptation of COVID-19 virus towards humans with relatively lower body temperatures than the natural bat hosts.

Caspofungin and LTX-315 inhibit SARS-CoV-2 replication by targeting the nsp12 polymerase (preprint)

The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously designated as 2019-nCoV) outbreak has caused global concern1. Currently, there are no clinically approved specific drugs or vaccines available for this virus. The viral polymerase is a promising target for developing broad- spectrum antiviral drugs. Here, based on the highly similar structure of SARS- CoV non-structural protein 12 (nsp12) polymerase subunit2, we applied virtual screen for the available compounds, including both the FDA-approved and under- clinic drugs, to identify potential antiviral molecules against SARS-CoV-2. We found two drugs, the clinically approved anti-fungi drug Caspofungin Acetate (Cancidas) and the oncolytic peptide LTX-315, can bind SARS-CoV-2 nsp12 protein to block the polymerase activity in vitro. Further live virus assay revealed that both Caspofungin Acetate and LTX-315 can effectively inhibit SARS-CoV-2 replication in vero cells. These findings present promising drug candidates for treatment of related diseases and would also stimulate the development of pan- coronavirus antiviral agents.Authors Min Wang, Fei Ye, Jiaqi Su, Jingru Zhao, and Bin Yuan contributed equally to this work.