Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking.

The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.

Genomic evidence for divergent co-infections of co-circulating SARS-CoV-2 lineages.

Co-infection of RNA viruses may contribute to their recombination and cause severe clinical symptoms. However, the tracking and identification of SARS-CoV-2 co-infection persist as challenges. Due to the lack of methods for detecting co-infected samples in a large amount of deep sequencing data, the lineage composition, spatial-temporal distribution, and frequency of SARS-CoV-2 co-infection events in the population remains unclear. Here, we propose a hypergeometric distribution-based method named Cov2Coinfect with the ability to decode the lineage composition from 50,809 deep sequencing data. By resolving the mutational patterns in each sample, Cov2Coinfect can precisely determine the co-infected SARS-CoV-2 variants from deep sequencing data. Results from two independent and parallel projects in the United States achieved a similar co-infection rate of 0.3-0.5 % in SARS-CoV-2 positive samples. Notably, all co-infected variants were highly consistent with the co-circulating SARS-CoV-2 lineages in the regional epidemiology, demonstrating that the co-circulation of different variants is an essential prerequisite for co-infection. Overall, our study not only provides a robust method to identify the co-infected SARS-CoV-2 variants from sequencing samples, but also highlights the urgent need to pay more attention to co-infected patients for better disease prevention and control.

Changes in endemic patterns of respiratory syncytial virus infection in pediatric patients under the pressure of nonpharmaceutical interventions for COVID-19 in Beijing, China.

A series of nonpharmaceutical interventions (NPIs) was launched in Beijing, China, on January 24, 2020, to control coronavirus disease 2019. To reveal the roles of NPIs on the respiratory syncytial virus (RSV), respiratory specimens collected from children with acute respiratory tract infection between July 2017 and Dec 2021 in Beijing were screened by capillary electrophoresis-based multiplex PCR (CEMP) assay. Specimens positive for RSV were subjected to a polymerase chain reaction (PCR) and genotyped by G gene sequencing and phylogenetic analysis using iqtree v1.6.12. The parallel and fixed (paraFix) mutations were analyzed with the R package sitePath. Clinical data were compared using SPSS 22.0 software. Before NPIs launched, each RSV endemic season started from October/November to February/March of the next year in Beijing. After that, the RSV positive rate abruptly dropped from 31.93% in January to 4.39% in February 2020; then, a dormant state with RSV positive rates ≤1% from March to September, a nearly dormant state in October (2.85%) and November (2.98%) and a delayed endemic season in 2020, and abnormal RSV positive rates remaining at approximately 10% in summer until September 2021 were detected. Finally, an endemic RSV season returned in October 2021. There was a game between Subtypes A and B, and RSV-A replaced RSV-B in July 2021 to become the dominant subtype. Six RSV-A and eight RSV-B paraFix mutations were identified on G. The percentage of severe pneumonia patients decreased to 40.51% after NPIs launched. NPIs launched in Beijing seriously interfered with the endemic season of RSV.

TEMPO: A transformer-based mutation prediction framework for SARS-CoV-2 evolution.

The widespread of SARS-CoV-2 presents a significant threat to human society, as well as public health and economic development. Extensive efforts have been undertaken to battle against the pandemic, whereas effective approaches such as vaccination would be weakened by the continuous mutations, leading to considerable attention being attracted to the mutation prediction. However, most previous studies lack attention to phylogenetics. In this paper, we propose a novel and effective model TEMPO for predicting the mutation of SARS-CoV-2 evolution. Specifically, we design a phylogenetic tree-based sampling method to generate sequence evolution data. Then, a transformer-based model is presented for the site mutation prediction after learning the high-level representation of these sequence data. We conduct experiments to verify the effectiveness of TEMPO, leveraging a large-scale SARS-CoV- 2 dataset. Experimental results show that TEMPO is effective for mutation prediction of SARS- CoV-2 evolution and outperforms several state-of-the-art baseline methods. We further perform mutation prediction experiments of other infectious viruses, to explore the feasibility and robustness of TEMPO, and experimental results verify its superiority. The codes and datasets are freely available at https://github.com/ZJUDataIntelligence/TEMPO.

covSampler: A subsampling method with balanced genetic diversity for large-scale SARS-CoV-2 genome data sets.

Phylogenetic analysis has been widely used to describe, display, and infer the evolutionary patterns of viruses. The unprecedented accumulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to post-subsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users' requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.

covSampler: A Subsampling Method with Balanced Genetic Diversity for Large-Scale SARS-CoV-2 Genome Data Sets

Phylogenetic analysis has been widely used to describe, display and infer the evolutionary patterns of viruses. The unprecedented accumulation of SARS-CoV-2 genomes has provided valuable materials for the real-time study of SARS-CoV-2 evolution. However, the large number of SARS-CoV-2 genome sequences also poses great challenges for data analysis. Several methods for subsampling these large data sets have been introduced. However, current methods mainly focus on the spatiotemporal distribution of genomes without considering their genetic diversity, which might lead to postsubsampling bias. In this study, a subsampling method named covSampler was developed for the subsampling of SARS-CoV-2 genomes with consideration of both their spatiotemporal distribution and their genetic diversity. First, covSampler clusters all genomes according to their spatiotemporal distribution and genetic variation into groups that we call divergent pathways. Then, based on these divergent pathways, two kinds of subsampling strategies, representative subsampling and comprehensive subsampling, were provided with adjustable parameters to meet different users’ requirements. Our performance and validation tests indicate that covSampler is efficient and stable, with an abundance of options for user customization. Overall, our work has developed an easy-to-use tool and a webserver (https://www.covsampler.net) for the subsampling of SARS-CoV-2 genome sequences.

Potential intervariant and intravariant recombination of Delta and Omicron variants.

Among numerous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concerns, Omicron is more infectious and immune-escaping, while Delta is more pathogenic. Here, we provide evidence for both intervariant and intravariant recombination of the rapidly evolving new SARS-CoV-2 genomes, including XD/XE/XF and BA.3, raising concerns of potential more infectious, immune-escaping, and disease-causing Omicron and Delta-Omicron variants.

Detecting Potentially Adaptive Mutations from the Parallel and Fixed Patterns in SARS-CoV-2 Evolution.

Early identification of adaptive mutations could provide timely help for the control and prevention of the COVID-19 pandemic. The fast accumulation of SARS-CoV-2 sequencing data provides important support, while also raising a great challenge for the recognition of adaptive mutations. Here, we proposed a computational strategy to detect potentially adaptive mutations from their fixed and parallel patterns in the phylogenetic trajectory. We found that the biological meanings of fixed substitution and parallel mutation are highly complementary, and can reasonably be integrated as a fixed and parallel (paraFix) mutation, to identify potentially adaptive mutations. Tracking the dynamic evolution of SARS-CoV-2, 37 sites in spike protein were identified as having experienced paraFix mutations. Interestingly, 70% (26/37) of them have already been experimentally confirmed as adaptive mutations. Moreover, most of the mutations could be inferred as paraFix mutations one month earlier than when they became regionally dominant. Overall, we believe that the concept of paraFix mutations will help researchers to identify potentially adaptive mutations quickly and accurately, which will provide invaluable clues for disease control and prevention.

SARS-CoV-2 Continuous Genetic Divergence and Changes in Multiplex RT-PCR Detection Pattern on Positive Retesting Median 150 Days after Initial Infection.

Being in the epicenter of the COVID-19 pandemic, our lab tested 193,054 specimens for SARS-CoV-2 RNA by diagnostic multiplex reverse transcription polymerase chain reaction (mRT-PCR) starting in March 2020, of which 17,196 specimens resulted positive. To investigate the dynamics of virus molecular evolution and epidemiology, whole genome amplification (WGA) and Next Generation Sequencing (NGS) were performed on 9516 isolates. 7586 isolates with a high quality were further analyzed for the mutation frequency and spectrum. Lastly, we evaluated the utility of the mRT-PCR detection pattern among 26 reinfected patients with repeat positive testing three months after testing negative from the initial infection. Our results show a continuation of the genetic divergence in viral genomes. Furthermore, our results indicate that independent mutations in the primer and probe regions of the nucleocapsid gene amplicon and envelope gene amplicon accumulate over time. Some of these mutations correlate with the changes of detection pattern of viral targets of mRT-PCR. Our data highlight the significance of a continuous genetic divergence on a gene amplification-based assay, the value of the mRT-PCR detection pattern for complementing the clinical diagnosis of reinfection, and the potential for WGA and NGS to identify mutation hotspots throughout the entire viral genome to optimize the design of the PCR-based gene amplification assay.

Detecting Potentially Adaptive Mutations from the Parallel and Fixed Patterns in SARS-CoV-2 Evolution

Early identification of adaptive mutations could provide timely help for the control and prevention of the COVID-19 pandemic. The fast accumulation of SARS-CoV-2 sequencing data provides important support, while also raising a great challenge for the recognition of adaptive mutations. Here, we proposed a computational strategy to detect potentially adaptive mutations from their fixed and parallel patterns in the phylogenetic trajectory. We found that the biological meanings of fixed substitution and parallel mutation are highly complementary, and can reasonably be integrated as a fixed and parallel (paraFix) mutation, to identify potentially adaptive mutations. Tracking the dynamic evolution of SARS-CoV-2, 37 sites in spike protein were identified as having experienced paraFix mutations. Interestingly, 70% (26/37) of them have already been experimentally confirmed as adaptive mutations. Moreover, most of the mutations could be inferred as paraFix mutations one month earlier than when they became regionally dominant. Overall, we believe that the concept of paraFix mutations will help researchers to identify potentially adaptive mutations quickly and accurately, which will provide invaluable clues for disease control and prevention.

Comparison of the Efficacy and Safety of PARP Inhibitors as a Monotherapy for Platinum-Sensitive Recurrent Ovarian Cancer: A Network Meta-Analysis.

BACKGROUND: The present COVID-19 pandemic has tended toward normality. To provide convenient, safe, and effective home treatment programs for patients with recurrent ovarian cancer (ROC), the clinical efficacy and safety of poly (ADP-ribose) polymerase inhibitor (PARPi) (including olaparib, niraparib, and rucaparib) monotherapy as a maintenance treatment for platinum-sensitive ROC were systematically evaluated. METHODS: Numerous electronic databases were systematically searched for randomized controlled trials (RCTs) of PARPi maintenance treatment for ROC that were published before June 2021. The primary endpoints were overall survival (OS) and progression-free survival (PFS), and the secondary endpoint was grade 3-4 adverse effects (AEs). After data extraction and the quality evaluation of the included studies, Bayesian network meta-analysis (NMA) was performed using R software. The ability of each treatment was ranked using the surface under the cumulative ranking (SUCRA) curve. RESULTS: The analysis included five studies and 1390 patients. The NMA results demonstrated that compared with the placebo, olaparib and niraparib exhibited significant benefits in the gBRCA-mutated population, and respectively reduced the risk of death by 31% (HR = 0.69, 95% CI: 0.53-0.90) and 34% (HR = 0.66, 95% CI: 0.44-0.99). Olaparib, niraparib, and rucaparib were all found to be very effective in prolonging PFS in patients with ROC. All three PARPi treatments increased the number of grade 3-4 AEs in patients with ROC as compared with the placebo. CONCLUSIONS: Overall, olaparib and niraparib maintenance treatment can significantly prolong the OS of patients with gBRCA mutations. Furthermore, the three investigated PARPi monotherapy maintenance treatments can prolong PFS regardless of BRCA mutation status. Although the incidence of AEs in the treatment groups was found to be significantly higher than that in the placebo group, the patients in the treatment group tolerated the treatment. Home oral PARPi treatment can balance tumor treatment and pandemic prevention and control, and is the most convenient, safe, and effective home treatment method available against the background of the current COVID-19 pandemic. SYSTEMATIC REVIEW REGISTRATION: https://inplasy.com/inplasy-2021-6-0033/.

Impact of the COVID-19 Lockdown on Quality of Life in Pregnant Women.

BACKGROUND: The COVID-19 pandemic has been associated with significant impacts on mental health and well-being of populations worldwide. However, little is known about its significant impact on psychological aspects of vulnerable population groups such as pregnant women. Therefore, the aim of the study was to determine the psychological impact of the COVID-19 pandemic among pregnant women in mainland China. METHODS: A cross-sectional survey was performed between July and August 2020 using a modified validated 40-item questionnaire which consisted of sociodemographics, attitude, lifestyle changes and the Impact of Event Scale (IES) toward COVID-19 using snowball and convenience samplings. RESULTS: A total of 1,078 participants were included in the study. The mean age of participants was 29.4 ± 4.0 years. Overall, the mean IES of participants was 30.6 ± 12.8 (i.e., moderate-to-severe stressful impact) amidst the COVID-19 pandemic, with 63.9% of participants had an IES score ≥26. Despite increased family and social support, more than half of participants also reported increased feeling of being horrified, apprehensive and helpless. CONCLUSIONS: The COVID-19 pandemic has several psychological impacts on pregnant women. Therefore, based on these valuable data of pregnant women collected, we recommend that a thoughtful planning and time preparation by the government would definitely help to reduce the negative impacts caused by the COVID-19 pandemic and restore the quality of life among pregnant women. Further research is needed to identify vulnerable groups including pregnant women to better adapt and inform mental health interventions and policies by health authorities.

Thymosin-α1 binds with ACE and downregulates the expression of ACE2 in human respiratory epithelia.

BACKGROUND: Thymosin-α1 has been implicated into the treatment of novel respiratory virus Coronavirus Disease 2019 (COVID-19), but the underlying mechanisms are still disputable. AIM: Herein we aimed to reveal a previously unrecognized mechanism that thymosin-α1 prevents COVID-19 by binding with angiotensin-converting enzyme (ACE), which was inspired from the tool of network pharmacology. METHODS: KEGG pathway enrichment of thymosin-α1 treating COVID-19 was analyzed by Database of Functional Annotation Bioinformatics Microarray Analysis, then core targets were validated by ligand binding kinetics assay and fluorometric detection of ACE and ACE2 enzymatic activity. The production of angiotensin I, angiotensin II, angiotensin (1-7) and angiotensin (1-9) were detected by enzyme linked immunosorbent assay. RESULTS: We found that thymosin-α1 impaired the expressions of angiotensin-converting enzyme 2 and angiotensin (1-7) of human lung epithelial cells in a dose-dependent way (p < 0.001). In contrast, thymosin-α1 had no impact on their ACE and angiotensin (1-9) expressions but significantly inhibited the enzymatic activity of ACE (p > 0.05). CONCLUSION: The bioinformatic findings of network pharmacology and the corresponding pharmacological validations have revealed that thymosin-α1 treatment could decrease ACE2 expression in human lung epithelial cells, which strengthens the potential clinical applications of thymosin-α1 to prevent severe acute respiratory syndrome coronavirus 2 infection.

Rare disasters, exchange rates, and macroeconomic policy: Evidence from COVID-19☆

The rapid spread of the novel coronavirus (COVID-19) has had a dramatic effect on financial markets worldwide. This paper explores the association between rare disasters, macroeconomic policy, and the exchange rate, using COVID-19 as an example. Analysis of data from 27 advanced and emerging economies reveals a strong correlation between COVID-19 and time-varying risk premiums in the foreign exchange market. Moreover, the spread of COVID-19 significantly depreciates the domestic exchange rate in emerging markets, but not in advanced countries. During the COVID-19 crisis, expansionary fiscal policies and unconventional monetary policies led to an appreciation of local currencies. However, conventional expansionary monetary policies had the opposite effect, indicating that the traditional effect of monetary policy on the exchange rate takes precedence even in the event of a rare disaster.

One year of SARS-CoV-2 evolution.

Since the outbreak of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, the viral genome has acquired numerous mutations with the potential to increase transmission. One year after its emergence, we now further analyze emergent SARS-CoV-2 genome sequences in an effort to understand the evolution of this virus.

Activation of Nurr1 with Amodiaquine Protected Neuron and Alleviated Neuroinflammation after Subarachnoid Hemorrhage in Rats

Background. Nurr1, a member of the nuclear receptor 4A family (NR4A), played a role in neuron protection, anti-inflammation, and antioxidative stress in multidiseases. We explored the role of Nurr1 on subarachnoid hemorrhage (SAH) progression and investigated the feasibility of its agonist (amodiaquine, AQ) as a treatment for SAH. Methods. SAH rat models were constructed by the endovascular perforation technique. AQ was administered intraperitoneally at 2 hours after SAH induction. SAH grade, mortality, weight loss, neurological performance tests, brain water content, western blot, immunofluorescence, Nissl staining, and qPCR were assessed post-SAH. In vitro, hemin was introduced into HT22 cells to develop a model of SAH. Results. Stimulation of Nurr1 with AQ improved the outcomes and attenuated brain edema. Nurr1 was mainly expressed in neuron, and administration of AQ alleviated neuron injury in vivo and enhanced the neuron viability and inhibited neuron apoptosis and necrosis in vitro. Besides, AQ reduced the amount of IL-1β+Iba-1+ cells and inhibited the mRNA level of proinflammatory cytokines (IL-1β and TNF-α) and the M1-like phenotype markers (CD68 and CD86). AQ inhibited the expression of MMP9 in HT22 cells. Furthermore, AQ reduced the expression of nuclear NF-κB and Nurr1 while increased cytoplasmic Nurr1 in vivo and in vitro. Conclusion. Pharmacological activation of Nurr1 with AQ alleviated the neuron injury and neuroinflammation. The mechanism of antineuroinflammation may be associated with the Nurr1/NF-κB/MMP9 pathway in the neuron. The data supported that AQ might be a promising treatment strategy for SAH.

The m6A methylome of SARS-CoV-2 in host cells.

The newly identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has resulted in a global health emergency because of its rapid spread and high mortality. The molecular mechanism of interaction between host and viral genomic RNA is yet unclear. We demonstrate herein that SARS-CoV-2 genomic RNA, as well as the negative-sense RNA, is dynamically N6-methyladenosine (m6A)-modified in human and monkey cells. Combined RIP-seq and miCLIP analyses identified a total of 8 m6A sites at single-base resolution in the genome. Especially, epidemic strains with mutations at these identified m6A sites have emerged worldwide, and formed a unique cluster in the US as indicated by phylogenetic analysis. Further functional experiments showed that m6A methylation negatively regulates SARS-CoV-2 infection. SARS-CoV-2 infection also triggered a global increase in host m6A methylome, exhibiting altered localization and motifs of m6A methylation in mRNAs. Altogether, our results identify m6A as a dynamic epitranscriptomic mark mediating the virus-host interaction.

A potential ex vivo infection model of human induced pluripotent stem cell-3D organoids beyond coronavirus disease 2019.

The novel coronavirus disease 2019 (COVID-19) outbreak began in the city of Wuhan, whereupon it rapidly spread throughout China and subsequently across the world. Rapid transmission of COVID-19 has caused wide-spread panic. Many established medications have been used to treat the disease symptoms; however, no specific drugs or vaccines have been developed. Organoids derived from human induced pluripotent stem cells (iPSCs) may serve as suitable infection models for ex vivo mimicking of the viral life cycle and drug screening. Human iPSC-3D organoids, self-organised tissues with multiple cell environments, have a similar structure and function as real human organs; hence, these organoids allow greater viral infection efficiency, mimic the natural host-virus interactions, and are suitable for long-term experimentation. Here, we suggest the use of a functional human iPSC-organoid that could act as a reliable and feasible ex vivo infection model for investigation of the virus. This approach will provide much needed insight into the underlying molecular dynamics of COVID-19 for the development of novel treatment and prevention strategies.