Nanobodies against SARS-CoV-2 RBD from a Two-step Phage Screening of Universal and Focused Synthetic Libraries (preprint)

Coronavirus disease 2019 (COVID-19) is an evolving global pandemic, and nanobody (Nb) is recognized as a potential diagnostic and therapeutic tool for infectious disease. Here, we designed and synthesized a humanized and highly diverse phage Nbs library hsNb-U (Humanized synthetic Nbs Library - Universal). We expressed and purified the SARS-CoV-2 receptor-binding domain (RBD), and screened this univeral library against the RBD protein target. Then, the CDR1 and CDR2 sequences of five leads obtained from the hsNb-U phage panning were combined with randomly mutated CDR3 to construct a targeted (focused) phage display library, hsNb-RBD, for subsequent phage panning and screening. From the obtained sequences, we expressed 45 unique anti-RBD candidate Nbs. Among the selected Nbs, eight were found to be highly expressed, and five of these show high-affinity to RBD (EC50 less than 100nM). Finally, we found that Nb39 can compete with angiotensin converting enzyme 2 (ACE2) for binding to RBD. Overall, this two-step strategy of synthetic phage display libraries enables rapid selection of SARS-CoV-2 RBD nanobodies with potential therapeutic activity, and this two-step strategy can potentially be used for rapid discovery of Nbs against other targets.

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].

Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: a single-center retrospective study (preprint)

Background: Management of high mortality risk due to significant progression requires prior assessment of time-to-progression. However, few related methods are available for COVID-19 pneumonia. Methods: We retrospectively enrolled 338 adult patients admitted to one hospital between Jan 11, 2020 to Feb 29, 2020. The final follow-up date was March 8, 2020. We compared characteristics between patients with severe and non-severe outcome, and used multivariate survival analyses to assess the risk of progression to severe conditions. Results: A total of 76 (31.9%) patients progressed to severe conditions and 3 (0.9%) died. The mean time from hospital admission to severity onset is 3.7 days. Age, body mass index (BMI), fever symptom on admission, co-existing hypertension or diabetes are associated with severe progression. Compared to non-severe group, the severe group already demonstrated, at an early stage, abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen and coagulation function. The cohort is characterized with increasing cumulative incidences of severe progression up to 10 days after admission. Competing risks survival model incorporating CT imaging and baseline information showed an improved performance for predicting severity onset (mean time-dependent AUC = 0.880). Conclusions: Multiple predisposition factors can be utilized to assess the risk of progression to severe conditions at an early stage. Multivariate survival models can reasonably analyze the progression risk based on early-stage CT images that would otherwise be misjudged by artificial analysis.