Altered O-glycosylation Level of SARS-CoV-2 Spike Protein by Host O-glycosyltransferase Strengthens Its Trimeric Structure (preprint)

The trimeric spike protein (S) mediates host-cell entry and membrane fusion of SARS-CoV-2. S protein is highly glycosylated, whereas its O-glycosylation is still poorly understood. Herein, we site-specifically examine the O-glycosylation of S protein through a mass spectrometric approach with HCD-triggered-ETD model. We identify 15 high-confidence O-glycosites and at least 10 distinct O-glycan structures on S protein. Peptide microarray assays prove that human ppGalNAc-T6 actively participates in O-glycosylation of S protein. Importantly, the upregulation of ppGalNAc-T6 expression can profoundly enhance the O-glycosylation level by generating new O-glycosites and increasing both O-glycan heterogeneity and intensities. Further molecular dynamics simulations reveal that the O-glycosylation on the protomer-interface regions, which are mainly modified by ppGalNAc-T6, can potentially stabilize the trimeric S protein structure. Our work provides deep molecular insights of how viral infection harnesses the host O-glycosyltransferases to dynamically regulate the O-glycosylation level of the viral envelope protein responsible for membrane fusion.

Spatially Explicit Modeling of 2019-nCoV Epidemic Trend based on Mobile Phone Data in Mainland China (preprint)

IAs of February 11, 2020, all prefecture-level cities in mainland China have reported confirmed cases of 2019 novel coronavirus (2019-nCoV), but the city-level epidemical dynamics is unknown. The aim of this study is to model the current dynamics of 2019-nCoV at city level and predict the trend in the next 30 days under three possible scenarios in mainland China. We developed a spatially explicit epidemic model to consider the unique characteristics of the virus transmission in individual cities. Our model considered that the rate of virus transmission among local residents is different from those with Wuhan travel history due to the self-isolation policy. We introduced a decay rate to quantify the effort of each city to gradually control the disease spreading. We used mobile phone data to obtain the number of individuals in each city who have travel history to Wuhan. This city-level model was trained using confirmed cases up to February 10, 2020 and validated by new confirmed cases on February 11, 2020. We used the trained model to predict the future dynamics up to March 12, 2020 under different scenarios: the current trend maintained, control efforts expanded, and person-to-person contact increased due to work resuming. We estimated that the total infections in mainland China would be 72172, 54348, and 149774 by March 12, 2020 under each scenario respectively. Under the current trend, all cities will show the peak point of daily new infections by February 21. This date can be advanced to February 14 with control efforts expanded or postponed to February 26 under pressure of work resuming. Except Wuhan that cannot eliminate the disease by March 12, our model predicts that 95.4%, 100%, and 75.7% cities will have no new infections by the end of February under three scenarios. The spatial pattern of our prediction could help the government allocate resources to cities that have a more serious epidemic in the next 30 days.