Derivatization of sialylated glycopeptides plus based sialoglycopeptides enrichment using cation exchange media.

Glycosylation is one of the most important post-translational modifications. However, the characterizations of glycopeptides, especially the negatively charged sialoglycopeptides that are associated with various diseases, remain challenging, due to the co-existence with high abundant peptides and the low ionization efficiency of sialoglycopeptides resulting from the carboxyl groups. Therefore, it is essential to develop an efficient enrichment method for sialoglycopeptides. Here, we present a novel derivatization-based enrichment method that can (i) identify linkage isomers of sialic acids by generating mass difference, (ii) unify the net charge of peptides into zero, and (iii) introduce positive charges to sialoglycopeptides by conjugating quaternary ammonium with sialic acid. The derivatization, termed derivatization of sialylated glycopeptides plus (DOSG+), enables efficient enrichment through electrostatic interaction using weak cation exchange (WCX) media. DOSG+ -based WCX enrichment was validated and optimized with samples derived from bovine fetuin. Peptides were removed efficiently (recovery rate <1%). The signal intensity of a selected model sialoglycopeptide was increased by ∼30% (suggesting recovery rate >100%). The method was employed on human alpha-1 acid glycoprotein (AGP), and recombinant human erythropoietin (EPO), demonstrating the application of DOSG+ -based WCX enrichment on complexed N-linked and O-linked sialoglycopeptides. The method is simple, efficient, and targets small-scale sialoglycopeptide enrichment.

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method.

BACKGROUND: Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. METHODS: A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. RESULTS: The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. CONCLUSIONS: Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

A Comparative Study of the Impacts of Human Capital and Physical Capital on Building Sustainable Economies at Different Stages of Economic Development

This study investigated the contributions of human capital and physical capital to economies at different stages by measuring the economic development with the traditional GDP and green GDP. The traditional GDP stood for the quantity of economic growth, and the green GDP, taking both the energy consumption and environmental pollution into account, was employed to represent the sustainability of economic development. We used a panel data of 143 countries and regions during the period from 1990 to 2014, and results showed that the elasticities of output with respect to human capital were greater compared to physical capital, while green GDP was significantly more sensitive to changes in human capital than the traditional GDP. In particular, considering the unbalanced distribution of economic growth among countries and regions, we employed the quantile regression model to explore the heterogeneous roles of physical and human capital in different stages of economic development, which evidenced not only the significance but also the stability of human capital. As national economic levels grew, countries became less dependent on physical capital, yet human capital maintained its outstanding role at different stages of economic development, particularly for the building of more sustainable economies.

Direct Comparison of N-Glycans and Their Isomers Derived from Spike Glycoprotein 1 of MERS-CoV, SARS-CoV-1, and SARS-CoV-2.

The emergence of COVID-19 pandemic has engaged the scientific community around the globe in the rapid development of effective therapeutics and vaccines. Owing to its crucial role in the invasion of the host cell, spike (S) glycoprotein is one of the major targets in these studies. The S1 subunit of the S protein (S1 protein) accommodates the receptor-binding domain, which enables the initial binding of the virus to the host cell. Being a heavily glycosylated protein, numerous studies have investigated its glycan composition. However, none of the studies have explored the isomeric glycan distribution of this protein. Furthermore, this isomeric glycan distribution has never been compared to that in S1 proteins of other coronaviruses, severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus, which were responsible for past epidemics. This study explores the uncharted territory of the isomeric glycan distribution in the coronaviruses' S1 protein using liquid chromatography coupled to tandem mass spectrometry. We believe that our data would facilitate future investigations to study the role of isomeric glycans in coronavirus viral pathogenesis.

Impact of the 2019 Novel Coronavirus Disease (COVID-19) Epidemic on Radiotherapy-Treated Patients with Cancer: A Single-Center Descriptive Study.

INTRODUCTION: The present study investigated hospitalization data of patients receiving radiotherapy at Anhui Cancer Hospital during the COVID-19 epidemic and analyzed the impact of the epidemic on the clinical data of radiotherapy patients to provide references for the feasibility and safety of radiotherapy at other medical institutions. METHODS: The present study performed a retrospective analysis of hospitalization data of patients undergoing radiotherapy at the Radiation Department (from January 5 to March 19, 2020 according to the Chinese lunar calendar), who were defined as the epidemic group. Hospitalization data for patients undergoing radiotherapy during the same period in 2019 were used as the control group for comparison with the epidemic group in terms of sex, age, distribution of various cancer types, hospitalization costs, average length of stay, completion rate of radiotherapy, treatment mode, and purpose of radiotherapy. RESULTS: A total of 79 and 115 patients received radiotherapy in the epidemic group and control group, respectively. The number of patients who received radiotherapy declined 31.3% during the epidemic period. The number of head and neck cancer patients who received radiotherapy was 36 (45.57%) in the epidemic group and 32 (27.83%) in the control group, which was a significant difference (χ2 =6.476, P=0.011). The proportions of patients with other types of cancer decreased, with no significant difference between the two groups (P>0.05). No significant differences between the two groups were found in terms of other hospitalization data (P>0.05). CONCLUSION: The total number of patients who received radiotherapy decreased during the epidemic period, but the proportion of head and neck cancer increased. The epidemic had no significant effect on other hospitalization data. While strengthening prevention and control measures, we should actively perform radiotherapy to ensure that cancer patients receive timely and safe treatment.