An mRNA-based T-cell-inducing antigen strengthens COVID-19 vaccine against SARS-CoV-2 variants.

Herd immunity achieved through mass vaccination is an effective approach to prevent contagious diseases. Nonetheless, emerging SARS-CoV-2 variants with frequent mutations largely evaded humoral immunity induced by Spike-based COVID-19 vaccines. Herein, we develop a lipid nanoparticle (LNP)-formulated mRNA-based T-cell-inducing antigen, which targeted three SARS-CoV-2 proteome regions that enriched human HLA-I epitopes (HLA-EPs). Immunization of HLA-EPs induces potent cellular responses to prevent SARS-CoV-2 infection in humanized HLA-A*02:01/DR1 and HLA-A*11:01/DR1 transgenic mice. Of note, the sequences of HLA-EPs are highly conserved among SARS-CoV-2 variants of concern. In humanized HLA-transgenic mice and female rhesus macaques, dual immunization with the LNP-formulated mRNAs encoding HLA-EPs and the receptor-binding domain of the SARS-CoV-2 B.1.351 variant (RBDbeta) is more efficacious in preventing infection of SARS-CoV-2 Beta and Omicron BA.1 variants than single immunization of LNP-RBDbeta. This study demonstrates the necessity to strengthen the vaccine effectiveness by comprehensively stimulating both humoral and cellular responses, thereby offering insight for optimizing the design of COVID-19 vaccines.

Repurposing CRISPR/Cas to Discover SARS-CoV-2 Detecting and Neutralizing Aptamers.

RNA aptamers provide useful biological probes and therapeutic agents. New methodologies to screen RNA aptamers will be valuable by complementing the traditional Systematic Evolution of Ligands by Exponential Enrichment (SELEX). Meanwhile, repurposing clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated systems (Cas) has expanded their utility far beyond their native nuclease function. Here, CRISmers, a CRISPR/Cas-based novel screening system for RNA aptamers based on binding to a chosen protein of interest in a cellular context, is presented. Using CRISmers, aptamers are identified specifically targeting the receptor binding domain (RBD) of the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Two aptamer leads enable sensitive detection and potent neutralization of SARS-CoV-2 Delta and Omicron variants in vitro. Intranasal administration of one aptamer, further modified with 2'-fluoro pyrimidines (2'-F), 2'-O-methyl purines (2'-O), and conjugation with both cholesterol and polyethylene glycol of 40 kDa (PEG40K), achieves effective prophylactic and therapeutic antiviral activity against live Omicron BA.2 variants in vivo. The study concludes by demonstrating the robustness, consistency, and potential broad utility of CRISmers using two newly identified aptamers but switching CRISPR, selection marker, and host species.

Comparative susceptibility of SARS-CoV-2, SARS-CoV, and MERS-CoV across mammals.

Exploring wild reservoirs of pathogenic viruses is critical for their long-term control and for predicting future pandemic scenarios. Here, a comparative in vitro infection analysis was first performed on 83 cell cultures derived from 55 mammalian species using pseudotyped viruses bearing S proteins from SARS-CoV-2, SARS-CoV, and MERS-CoV. Cell cultures from Thomas's horseshoe bats, king horseshoe bats, green monkeys, and ferrets were found to be highly susceptible to SARS-CoV-2, SARS-CoV, and MERS-CoV pseudotyped viruses. Moreover, five variants (del69-70, D80Y, S98F, T572I, and Q675H), that beside spike receptor-binding domain can significantly alter the host tropism of SARS-CoV-2. An examination of phylogenetic signals of transduction rates revealed that closely related taxa generally have similar susceptibility to MERS-CoV but not to SARS-CoV and SARS-CoV-2 pseudotyped viruses. Additionally, we discovered that the expression of 95 genes, e.g., PZDK1 and APOBEC3, were commonly associated with the transduction rates of SARS-CoV, MERS-CoV, and SARS-CoV-2 pseudotyped viruses. This study provides basic documentation of the susceptibility, variants, and molecules that underlie the cross-species transmission of these coronaviruses.

A PEG-lipid-free COVID-19 mRNA vaccine triggers robust immune responses in mice.

COVID-19 mRNA vaccines represent a completely new category of vaccines and play a crucial role in controlling the COVID-19 pandemic. In this study, we have developed a PEG-lipid-free two-component mRNA vaccine (PFTCmvac) by formulating mRNA encoding the receptor binding domain (RBD) of SARS-CoV-2 into lipid-like nanoassemblies. Without using polyethylene glycol (PEG)-lipids, the self-assembled PFTCmvac forms thermostable nanoassemblies and exhibits a dose-dependent cellular uptake and membrane disruption, eventually leading to high-level protein expression in both mammalian cells and mice. Vaccination with PFTCmvac elicits strong humoral and cellular responses in mice, without evidence of significant adverse reactions. In addition, the vaccine platform does not trigger complement activation in human serum, even at a high serum concentration. Collectively, the PEG-lipid-free two-component nanoassemblies provide an alternative delivery technology for COVID-19 mRNA vaccines and opportunities for the rapid production of new mRNA vaccines against emerging infectious diseases.

Emoji use in China: popularity patterns and changes due to COVID-19.

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users' usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information: The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model.

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

Emoji use in China: popularity patterns and changes due to COVID-19

Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users’ usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage;i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency. Supplementary Information The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

Improving sentiment analysis accuracy with emoji embedding

Due to the diversity and variability of Chinese syntax and semantics, accurately identifying and distinguishing individual emotions from online texts is challenging. To overcome this limitation, we incorporate a new source of individual sentiment, emojis, which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations. We examined popular sentiment analysis algorithms, including rule-based and classification algorithms, to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance. Emojis were also translated into corresponding sentiment words when constructing features for comparison with those directly generated from emoji label words. In addition, considering different functions of emojis in texts, we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance. We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms, and the algorithm performance can be further increased by taking different emoji usages into consideration. In this study, we developed an improved emoji-embedding model based on Bi-LSTM (namely, CEmo-LSTM), which achieves the highest accuracy (around 0.95) when analyzing online Chinese texts. We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1, 2019 to March 20, 2020 to understand the sentiment evolution of online users during the COVID-19 pandemic. We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions (e.g., horror and sadness). Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.

Orthogonal genome-wide screens of bat cells identify MTHFD1 as a target of broad antiviral therapy.

Bats are responsible for the zoonotic transmission of several major viral diseases, including those leading to the 2003 SARS outbreak and likely the ongoing COVID-19 pandemic. While comparative genomics studies have revealed characteristic adaptations of the bat innate immune system, functional genomic studies are urgently needed to provide a foundation for the molecular dissection of the viral tolerance in bats. Here we report the establishment of genome-wide RNA interference (RNAi) and CRISPR libraries for the screening of the model megabat, Pteropus alecto. We used the complementary RNAi and CRISPR libraries to interrogate P. alecto cells for infection with two different viruses: mumps virus and influenza A virus, respectively. Independent screening results converged on the endocytosis pathway and the protein secretory pathway as required for both viral infections. Additionally, we revealed a general dependence of the C1-tetrahydrofolate synthase gene, MTHFD1, for viral replication in bat cells and human cells. The MTHFD1 inhibitor, carolacton, potently blocked replication of several RNA viruses, including SARS-CoV-2. We also discovered that bats have lower expression levels of MTHFD1 than humans. Our studies provide a resource for systematic inquiry into the genetic underpinnings of bat biology and a potential target for developing broad-spectrum antiviral therapy.

Analysis of public opinion evolution of COVID-19 based on LDA-ARMA hybrid model.

The aim of this study was to explore a method for developing an emotional evolution classification model for large-scale online public opinion of events such as Coronavirus Disease 2019 (COVID-19), in order to guide government departments to adopt differentiated forms of emergency management and to correctly guide online public opinion for severely afflicted areas such as Wuhan and those afflicted elsewhere in China. We propose the LDA-ARMA deep neural network for dynamic presentation and fine-grained categorization of a public opinion events. This was applied to a huge quantity of online public opinion texts in a complicated setting and integrated the proposed sentiment measurement algorithm. To begin, the Latent Dirichlet Allocation (LDA) was employed to extract information about the topic of comments. The autoregressive moving average model (ARMA) was then utilized to perform multidimensional sentiment analysis and evolution prediction on large-scale textual data related to COVID-19 published by netizens from Wuhan and other countries on Sina Weibo. The results show that Wuhan netizens paid more attention to the development of the situation, treatment measures, and policies related to COVID-19 than other issues, and were under greater emotional pressure, whereas netizens in the rest of the country paid more attention to the overall COVID-19 prevention and control, and were more positive and optimistic with the assistance of the government and NGOs. The average error in predicting public opinion sentiment was less than 5.64%, demonstrating that this approach may be effectively applied to the analysis of large-scale online public sentiment evolution.

Profiling CD8+ T cell epitopes of COVID-19 convalescents reveals reduced cellular immune responses to SARS-CoV-2 variants.

Cellular immunity is important in determining the disease severity of COVID-19 patients. However, current understanding of SARS-CoV-2 epitopes mediating cellular immunity is limited. Here we apply T-Scan, a recently developed method, to identify CD8+ T cell epitopes from COVID-19 patients of four major HLA-A alleles. Several identified epitopes are conserved across human coronaviruses, which might mediate pre-existing cellular immunity to SARS-CoV-2. In addition, we identify and validate four epitopes that were mutated in the newly circulating variants, including the Delta variant. The mutations significantly reduce T cell responses to the epitope peptides in convalescent and vaccinated samples. We further determine the crystal structure of HLA-A∗02:01/HLA-A∗24:02 in complex with the epitope KIA_S/NYN_S, respectively, which reveals the importance of K417 and L452 of the spike protein for binding to HLA. Our data suggest that evading cellular immunity might contribute to the increased transmissibility and disease severity associated with the new SARS-CoV-2 variants.

Current Strategies of Antiviral Drug Discovery for COVID-19.

SARS-CoV-2 belongs to the family of enveloped, single-strand RNA viruses known as Betacoronavirus in Coronaviridae, first reported late 2019 in China. It has since been circulating world-wide, causing the COVID-19 epidemic with high infectivity and fatality rates. As of the beginning of April 2021, pandemic SARS-CoV-2 has infected more than 130 million people and led to more than 2.84 million deaths. Given the severity of the epidemic, scientists from academia and industry are rushing to identify antiviral strategies to combat the disease. There are several strategies in antiviral drugs for coronaviruses including empirical testing of known antiviral drugs, large-scale phenotypic screening of compound libraries and target-based drug discovery. To date, an increasing number of drugs have been shown to have anti-coronavirus activities in vitro and in vivo, but only remdesivir and several neutralizing antibodies have been approved by the US FDA for treating COVID-19. However, remdesivir's clinical effects are controversial and new antiviral drugs are still urgently needed. We will discuss the current status of the drug discovery efforts against COVID-19 and potential future directions. With the ever-increasing movability of human population and globalization of world economy, emerging and reemerging viral infectious diseases seriously threaten public health. Particularly the past and ongoing outbreaks of coronaviruses cause respiratory, enteric, hepatic and neurological diseases in infected animals and human (Woo et al., 2009). The human coronavirus (HCoV) strains (HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1) usually cause common cold with mild, self-limiting upper respiratory tract infections. By contrast, the emergence of three deadly human betacoronaviruses, middle east respiratory syndrome coronavirus (MERS) (Zaki et al., 2012), severe acute respiratory syndrome coronavirus (SARS-CoV) (Lee et al., 2003), the SARS-CoV-2 (Jin et al., 2020a) highlight the need to identify new treatment strategies for viral infections. SARS-CoV-2 is the etiological agent of COVID-19 disease named by World Health Organization (WHO) (Zhu N. et al., 2020). This disease manifests as either an asymptomatic infection or a mild to severe pneumonia. This pandemic disease causes extent morbidity and mortality in the whole world, especially regions out of China. Similar to SARS and MERS, the SARS CoV-2 genome encodes four structural proteins, sixteen non-structural proteins (nsp) and accessory proteins. The structural proteins include spike (S), envelope (E), membrane (M), nucleoprotein (N). The spike glycoprotein directly recognizes and engages cellular receptors during viral entry. The four non-structural proteins including papain-like protease (PLpro), 3-chymotrypsin-like protease (3CLpro), helicase, and RNA-dependent RNA polymerase (RdRp) are key enzymes involved in viral transcription and replication. The spike and the four key enzymes were considered attractive targets to develop antiviral agents (Zumla et al., 2016). The catalytic sites of the four enzymes of SARS-CoV2 share high similarities with SARS CoV and MERS in genomic sequences (Morse et al., 2020). Besides, the structures of the key drug-binding pockets are highly conserved among the three coronaviruses (Morse et al., 2020). Therefore, it follows naturally that existing anti-SARS-CoV and anti-MERS drugs targeting these enzymes can be repurposed for SARS-CoV-2. Based on previous studies in SARS-CoV and MERS-CoV, it is anticipated a number of therapeutics can be used to control or prevent emerging infectious disease COVID-19 (Li and de Clercq, 2020; Wang et al., 2020c; Ita, 2021), these include small-molecule drugs, peptides, and monoclonal antibodies. Given the urgency of the SARS-CoV-2 outbreak, here we discuss the discovery and development of new therapeutics for SARS-CoV-2 infection based on the strategies from which the new drugs are derived.

Epidemic trend of corona virus disease 2019 (COVID-19) in mainland China / 中华预防医学杂志

Objective@#In order to master the epidemic trend of corona virus disease 2019 (COVID-19) and evaluate the effect of prevention and control, we evaluate the epidemic dynamics of COVID-19 in mainland China, Hubei province, Wuhan city and other provinces outside Hubei from January 16 to February 14, 2020.@*Methods@#We collected the daily number of new confirmed COVID-19 cases by nucleic acid detection reported by the National Health Commission from January 16, 2020 to February 14, 2020. The analysis includes the epidemic curve of the new confirmed cases, multiple of the new confirmed cases for period-over-period, multiple of the new confirmed cases for fixed-base, and the period-over-period growth rate of the new confirmed cases.@*Results@#From January 16 to February 14, 2020, the cumulative number of new confirmed cases of COVID-19 in mainland China was 50 031, including 37 930 in Hubei province, 22 883 in Wuhan city and 12 101 in other provinces outside Hubei. The peak of the number of new confirmed cases in other provinces outside Hubei was from January 31 to February 4, 2020, and the peak of new confirmed cases in Wuhan city and Hubei province was from February 5 to February 9, 2020. The number of new confirmed cases in other provinces outside Hubei showed a significant decline (23% compared with the peak) from February 5 to February 9, 2020, while the number of new confirmed cases in Wuhan city (30% compared with the peak) and Hubei Province (37% compared with the peak) decreased significantly from February 10 to February 14, 2020.@*Conclusion@#The epidemic prevention and control measures taken by the state and governments at all levels have shown very significant effects, effectively curbing the spread of the COVID-19 epidemic in China.