The Pivotal Role of SOD1 for Prediction of COVID-19 Progression Revealed by Proteomic and Bioinformatic Strategies (preprint)

Background: Severe acute respiratory distress syndrome-associated coronavirus-2 (SARS-CoV-2) is still threatening the whole human population worldwide. Patients infected with SARS-CoV-2 present diverse symptoms regarding to the severity of the disease. Determining the proteome changes associated with diverse symptoms and in different infection stages is beneficial for clinical diagnosis and management. Methods: We performed a TMT labeling proteomic study on the plasma of healthy controls and COVID-19 patients, including those with asymptomatic infection (NS), mild syndrome (MS), and severe syndrome in the early phase (SSEP) and later phase (SSLP). Bioinformatic and ELISA were used for the data analysis and identification. Findings: Hundreds of proteins were dysregulated in the plasma of COVID-19 patients including all the clinical symptoms. Bioinformatics analysis of the dysregulated proteins revealed that oxidative stress, complement activation and glycolysis-related proteins were affected after infection with SARS-CoV-2. ELISA analysis confirmed that SOD1, PRDX2 and LDHA levels were increased along with severe symptoms and did not change after recovery compared with those in healthy controls. Both GSEA and ROC analysis indicated that SOD1 could be a pivotal player in the progression of COVID-19. Interpretation: Our results indicated that plasma proteome changes differed based on symptoms and disease stages and SOD1 could be an important protein for indicating COVID-19 progression. These results may also provide new understanding for COVID-19 diagnosis and treatment. Funding: This project is supported by Shenzhen Bay Laboratory Opening Fund, Shenzhen Key Medical Discipline Construction Fund, Guangdong Natural Science Foundation, Sanming Project of Medicine in Shenzhen.Declaration of Interests: All the authors declared there are no conflicts of interest exist.Ethics Approval Statement: This research was approved by the ethics committee of the Shenzhen Center for Disease Control and Prevention [Approval number: 2020-025A].

Viral RNA level, serum antibody responses, and transmission risk in discharged COVID-19 patients with recurrent positive SARS-CoV-2 RNA test results: a population-based observational cohort study (preprint)

SummaryO_ST_ABSBackgroundC_ST_ABSManaging discharged COVID-19 (DC) patients with recurrent positive (RP) SARS-CoV-2 RNA test results is challenging. We aimed to comprehensively characterize the viral RNA level and serum antibody responses in RP-DC patients and evaluate their viral transmission risk. MethodsA population-based observational cohort study was performed on 479 DC patients discharged from February 1 to May 5, 2020 in Shenzhen, China. We conducted RT-qPCR, antibody assays, neutralisation assays, virus isolation, whole genome sequencing (WGS), and epidemiological investigation of close contacts. FindingsOf 479 DC patients, the 93 (19%) RP individuals, including 36 with multiple RP results, were characterised by young age (median age: 34 years, 95% confidence interval [CI]: 29-38 years). The median discharge-to-RP length was 8 days (95% CI: 7-14 days; maximum: 90 days). After readmission, RP-DC patients exhibited mild (28%) or absent (72%) symptoms, with no disease progression. The viral RNA level in RP-DC patients ranged from 1{middle dot}9-5{middle dot}7 log10 copies/mL (median: 3{middle dot}2, 95% CI: 3{middle dot}1-3{middle dot}5). At RP detection, the IgM, IgG, IgA, total antibody, and neutralising antibody (NAb) seropositivity rates in RP-DC patients were 38% (18/48), 98% (47/48), 63% (30/48), 100% (48/48), and 91% (39/43), respectively. Regarding antibody levels, there was no significant difference between RP-DC and non-RP-DC patients. The antibody level remained constant in RP-DC patients pre- and post-RP detection. Virus isolation of nine representative specimens returned negative results. WGS of six specimens yielded only genomic fragments. No clinical symptoms were exhibited by 96 close contacts of 23 RP-DC patients; their viral RNA (96/96) and antibody (20/20) test results were negative. After full recovery, 60% of patients (n=162, 78 no longer RP RP-DC and 84 non-RP-DC) had NAb titres of [≥]1:32. InterpretationRP may occur in DC patients following intermittent and non-stable excretion of low viral RNA levels. RP-DC patients pose a low risk of transmitting SARS-CoV-2. An NAb titre of [≥] 1:32 may provide a reference indicator for evaluating humoral responses in COVID-19 vaccine clinical trials. FundingSanming Project of Medicine in Shenzhen, China National Science and Technology Major Projects Foundation, Special Foundation of Science and Technology Innovation Strategy of Guangdong Province of China, and Shenzhen Committee of Scientific and Technical Innovation grants.

FDA-approved Pralatrexate identified by virtual drug screening inhibits SARS-CoV-2 replication in vitro (preprint)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic poses serious threats to the global public health and leads to an unprecedented worldwide crisis. Unfortunately, no effective drugs or vaccines are available till now. Since the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a promising therapeutic target, a deep learning and molecular simulation based hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected FDA-approved drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008µM and 9.453 µM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of accurate virtual drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov (preprint)

A novel coronavirus called 2019-nCoV was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. 2019-nCoV spreads more rapidly than SARS-CoV. Unfortunately, there is no drug to combat the virus. It is of high significance to develop a drug that can combat the virus effectively before the situation gets worse. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In this paper, we first collected virus RNA sequences from the GISAID database, translated the RNA sequences into protein sequences, and built a protein 3D model using homology modeling. Coronavirus main protease is considered to be a major therapeutic target, thus this paper focused on drug screening based on the modeled 2019-nCov_main_protease structure. The deep learning based method DFCNN, developed by our group, can identify/rank the protein-ligand interactions with relatively high accuracy. DFCNN is capable of performing virtual screening quickly since no docking or molecular dynamic simulation is needed. DFCNN identifies potential drugs for 2019-nCoV protease by performing drug screening against 4 chemical compound databases. Also, we performed drug screening for all tripeptides against the binding site of 2019-nCov_main_protease since peptides often show better stability, more bio-availability and negligible immune responses. In the end, we provided the list of possible chemical ligands and peptide drugs for experimental validation.