Context-specific emergence and growth of the SARS-CoV-2 Delta variant

Discovery of adapalene and dihydrotachysterol as antiviral agents for the Omicron variant of SARS-CoV-2 through computational drug repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been significantly paralyzing the societies, economies and health care systems around the globe. The mutations on the genome of SARS-CoV-2 led to the emergence of new variants, some of which are classified as "variant of concern" due to their increased transmissibility and better viral fitness. The Omicron variant, as the latest variant of concern, dominated the current COVID-19 cases all around the world. Unlike the previous variants of concern, the Omicron variant has 15 mutations on the receptor-binding domain of spike protein and the changes in the key amino acid residues of S protein can enhance the binding ability of the virus to hACE2, resulting in a significant increase in the infectivity of the Omicron variant. Therefore, there is still an urgent need for treatment and prevention of variants of concern, particularly for the Omicron variant. In this study, an in silico drug repurposing was conducted through the molecular docking of 2890 FDA-approved drugs against the mutant S protein of SARS-CoV-2 for Omicron variant. We discovered promising drug candidates for the inhibition of alarming Omicron variant such as quinestrol, adapalene, tamibarotene, and dihydrotachysterol. The stability of ligands complexed with the mutant S protein was confirmed using MD simulations. The lead compounds were further evaluated for their potential use and side effects based on the current literature. Particularly, adapalene, dihydrotachysterol, levocabastine and bexarotene came into prominence due to their non-interference with the normal physiological processes. Therefore, this study suggests that these approved drugs can be considered as drug candidates for further in vitro and in vivo studies to develop new treatment options for the Omicron variant of SARS-CoV-2.

Early detection of emerging SARS-CoV-2 variants of interest for experimental evaluation

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has demonstrated its ability to rapidly and continuously evolve, leading to the emergence of thousands of different sequence variants, many with distinctive phenotypic properties. Fortunately, the broad application of next generation sequencing (NGS) across the globe has produced a wealth of SARS-CoV-2 genome sequences, offering a comprehensive picture of how this virus is evolving so that accurate diagnostics, reliable therapeutics, and prophylactic vaccines against COVID-19 can be developed and maintained. The millions of SARS-CoV-2 sequences deposited into genomic sequencing databases, including GenBank, BV-BRC, and GISAID, are annotated with the dates and geographic locations of sample collection, and can be aligned to and compared with the Wuhan-Hu-1 reference genome to extract their constellation of nucleotide and amino acid substitutions. By aggregating these data into concise datasets, the spread of variants through space and time can be assessed. Variant tracking efforts have initially focused on the Spike protein due to its critical role in viral tropism and antibody neutralization. To identify emerging variants of concern as early as possible, we developed a computational pipeline to process the genomic data and assign risk scores based on both epidemiological and functional parameters. Epidemiological dynamics are used to identify variants exhibiting substantial growth over time and spread across geographical regions. Experimental data that quantify Spike protein regions targeted by adaptive immunity and critical for other virus characteristics are used to predict variants with consequential immunogenic and pathogenic impacts. The growth assessment and functional impact scores are combined to produce a Composite Score for any set of Spike substitutions detected. With this systematic method to routinely score and rank emerging variants, we have established an approach to identify threatening variants early and prioritize them for experimental evaluation.

A critical overview of current progress for COVID-19: development of vaccines, antiviral drugs, and therapeutic antibodies

The novel coronavirus disease (COVID-19) pandemic remains a global public health crisis, presenting a broad range of challenges. To help address some of the main problems, the scientific community has designed vaccines, diagnostic tools and therapeutics for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The rapid pace of technology development, especially with regard to vaccines, represents a stunning and historic scientific achievement. Nevertheless, many challenges remain to be overcome, such as improving vaccine and drug treatment efficacies for emergent mutant strains of SARS-CoV-2. Outbreaks of more infectious variants continue to diminish the utility of available vaccines and drugs. Thus, the effectiveness of vaccines and drugs against the most current variants is a primary consideration in the continual analyses of clinical data that supports updated regulatory decisions. The first two vaccines granted Emergency Use Authorizations (EUAs), BNT162b2 and mRNA-1273, still show more than 60% protection efficacy against the most widespread current SARS-CoV-2 variant, Omicron. This variant carries more than 30 mutations in the spike protein, which has largely abrogated the neutralizing effects of therapeutic antibodies. Fortunately, some neutralizing antibodies and antiviral COVID-19 drugs treatments have shown continued clinical benefits. In this review, we provide a framework for understanding the ongoing development efforts for different types of vaccines and therapeutics, including small molecule and antibody drugs. The ripple effects of newly emergent variants, including updates to vaccines and drug repurposing efforts, are summarized. In addition, we summarize the clinical trials supporting the development and distribution of vaccines, small molecule drugs, and therapeutic antibodies with broad-spectrum activity against SARS-CoV-2 strains.

Regional importation and asymmetric within-country spread of SARS-CoV-2 variants of concern in the Netherlands

Background:

Variants of concern (VOCs) of SARS-CoV-2 have caused resurging waves of infections worldwide. In the Netherlands, the Alpha, Beta, Gamma, and Delta VOCs circulated widely between September 2020 and August 2021. We sought to elucidate how various control measures, including targeted flight restrictions, had impacted the introduction and spread of these VOCs in the Netherlands.

Methods:

We performed phylogenetic analyses on 39,844 SARS-CoV-2 genomes collected under the Dutch national surveillance program.

Results:

We found that all four VOCs were introduced before targeted flight restrictions were imposed on countries where the VOCs first emerged. Importantly, foreign introductions, predominantly from other European countries, continued during these restrictions. After their respective introductions into the Netherlands, the Alpha and Delta VOCs largely circulated within more populous regions of the country with international connections before asymmetric bidirectional transmissions occurred with the rest of the country and the VOC became the dominant circulating lineage.

Conclusions:

Our findings show that flight restrictions had limited effectiveness in deterring VOC introductions due to the strength of regional land travel importation risks. As countries consider scaling down SARS-CoV-2 surveillance efforts in the post-crisis phase of the pandemic, our results highlight that robust surveillance in regions of early spread is important for providing timely information for variant detection and outbreak control.

Funding:

None.

Identifying SARS-CoV-2 regional introductions and transmission clusters in real time

The unprecedented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic heuristic that quickly and efficiently identifies newly introduced strains in a region, resulting in clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and yields results largely congruent with more sophisticated Bayesian phylogeographic modeling approaches. We also introduce Cluster-Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization across the USA. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from the transmission of the virus between two geographic areas by travelers, streamlining public health tracking of local viral diversity and emerging infection clusters. The site is open-source and designed to be easily configured to analyze any chosen region, making it a useful resource globally. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely sampled pathogens.

Induction of high affinity monoclonal antibodies against SARS-CoV-2 variant infection using a DNA prime-protein boost strategy

BACKGROUND

Calls for the coronavirus to be treated as an endemic illness, such as the flu, are increasing. After achieving high coverage of COVID-19 vaccination, therapeutic drugs have become important for future SARS-CoV-2 variant outbreaks. Although many monoclonal antibodies have been approved for emergency use as treatments for SARS-CoV-2 infection, some monoclonal antibodies are not authorized for variant treatment. Broad-spectrum monoclonal antibodies are unmet medical needs.

METHODS

We used a DNA prime-protein boost approach to generate high-quality monoclonal antibodies. A standard ELISA was employed for the primary screen, and spike protein-human angiotensin-converting enzyme 2 blocking assays were used for the secondary screen. The top 5 blocking clones were selected for further characterization, including binding ability, neutralization potency, and epitope mapping. The therapeutic effects of the best monoclonal antibody against SARS-CoV-2 infection were evaluated in a hamster infection model.

RESULTS

Several monoclonal antibodies were selected that neutralize different SARS-CoV-2 variants of concern (VOCs). These VOCs include Alpha, Beta, Gamma, Delta, Kappa and Lambda variants. The high neutralizing antibody titers against the Beta variant would be important to treat Beta-like variants. Among these monoclonal antibodies, mAb-S5 displays the best potency in terms of binding affinity and neutralizing capacity. Importantly, mAb-S5 protects animals from SARS-CoV-2 challenge, including the Wuhan strain, D614G, Alpha and Delta variants, although mAb-S5 exhibits decreased neutralization potency against the Delta variant. Furthermore, the identified neutralizing epitopes of monoclonal antibodies are all located in the receptor-binding domain (RBD) of the spike protein but in different regions.

CONCLUSIONS

Our approach generates high-potency monoclonal antibodies against a broad spectrum of VOCs. Multiple monoclonal antibody combinations may be the best strategy to treat future SARS-CoV-2 variant outbreaks.

Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19

Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 ('Delta plus') is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group framework.