Phylogenetic supertree reveals detailed evolution of SARS-CoV-2

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs ~5 and ~25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders

The COVID-19 pandemic is marked by the successive emergence of new SARS-CoV-2 variants, lineages, and sublineages that outcompete earlier strains, largely due to factors like increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system, to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute >10% of all the viral sequences added to the GISAID, a public database supporting viral genetic sequence sharing, in a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of ~4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01%-3%), with median lead times of 4-17 weeks, and predicts FDLs between ~5 and ~25 times better than a baseline approach. For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness and may provide significant insights for the optimization of public health 'pre-emptive' intervention strategies.

NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model

Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD's performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .

Predicting potential SARS-CoV-2 spillover and spillback in animals

BACKGROUND

The COVID-19 pandemic is spreading rapidly around the world, causing countries to impose lockdowns and efforts to develop vaccines on a global scale. However, human-to-animal and animal-to-human transmission cannot be ignored, as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread rapidly in farmed and wild animals. This could create a worrying cycle of SARS-CoV-2 spillover from humans to animals and spillback of new strains back into humans, rendering vaccines ineffective.

METHOD

This study provides a key indicator of animals that may be potential susceptible hosts for SARS-CoV-2 and coronavirus infections by analysing the phylogenetic distance between host angiotensin-converting enzyme 2 and the coronavirus spike protein. Crucially, our analysis identifies animals that are at elevated risk from a spillover and spillback incident.

RESULTS

One group of animals has been identified as potentially susceptible to SARS-CoV-2 by harbouring a parasitic coronavirus spike protein similar to the SARS-CoV-2 spike protein. These animals may serve as amplification hosts in spillover events from zoonotic reservoirs. This group consists of a mixture of animals infected internally and naturally: minks, dogs, cats, tigers. Additionally, no internal or natural infections have been found in masked palm civet.

CONCLUSION

Tracing interspecies transmission in multi-host environments based solely on in vitro and in vivo examinations of animal susceptibility or serology is a time-consuming task. This approach allows rapid identification of high-risk animals to prioritize research and assessment of the risk of zoonotic disease transmission in the environment. It is a tool to rapidly identify zoonotic species that may cause outbreaks or participate in expansion cycles of coexistence with their hosts. This prevents the spread of coronavirus infections between species, preventing spillover and spillback incidents from occurring.

Organoids in COVID-19: can we break the glass ceiling?

COVID-19 emerged in September 2020 as a disease caused by the virus SARS-CoV-2. The disease presented as pneumonia at first but later was shown to cause multisystem infections and long-term complications. Many efforts have been put into discovering the exact pathogenesis of the disease. In this review, we aim to discuss an emerging tool in disease modeling, organoids, in the investigation of COVID-19. This review will introduce some methods and breakthroughs achieved by organoids and the limitations of this system.

An ncRNA transcriptomics-based approach to design siRNA molecules against SARS-CoV-2 double membrane vesicle formation and accessory genes

BACKGROUND

The corona virus SARS-CoV-2 is the causative agent of recent most global pandemic. Its genome encodes various proteins categorized as non-structural, accessory, and structural proteins. The non-structural proteins, NSP1-16, are located within the ORF1ab. The NSP3, 4, and 6 together are involved in formation of double membrane vesicle (DMV) in host Golgi apparatus. These vesicles provide anchorage to viral replicative complexes, thus assist replication inside the host cell. While the accessory genes coded by ORFs 3a, 3b, 6, 7a, 7b, 8a, 8b, 9b, 9c, and 10 contribute in cell entry, immunoevasion, and pathological progression.

METHODS

This in silico study is focused on designing sequence specific siRNA molecules as a tool for silencing the non-structural and accessory genes of the virus. The gene sequences of NSP3, 4, and 6 along with ORF3a, 6, 7a, 8, and 10 were retrieved for conservation, phylogenetic, and sequence logo analyses. siRNA candidates were predicted using siDirect 2.0 targeting these genes. The GC content, melting temperatures, and various validation scores were calculated. Secondary structures of the guide strands and siRNA-target duplexes were predicted. Finally, tertiary structures were predicted and subjected to structural validations.

RESULTS

This study revealed that NSP3, 4, and 6 and accessory genes ORF3a, 6, 7a, 8, and 10 have high levels of conservation across globally circulating SARS-CoV-2 strains. A total of 71 siRNA molecules were predicted against the selected genes. Following rigorous screening including binary validations and minimum free energies, final siRNAs with high therapeutic potential were identified, including 7, 2, and 1 against NSP3, NSP4, and NSP6, as well as 3, 1, 2, and 1 targeting ORF3a, ORF7a, ORF8, and ORF10, respectively.

CONCLUSION

Our novel in silico pipeline integrates effective methods from previous studies to predict and validate siRNA molecules, having the potential to inhibit viral replication pathway in vitro. In total, this study identified 17 highly specific siRNA molecules targeting NSP3, 4, and 6 and accessory genes ORF3a, 7a, 8, and 10 of SARS-CoV-2, which might be used as an additional antiviral treatment option especially in the cases of life-threatening urgencies.

Applying the digital data and the bioinformatics tools in SARS-CoV-2 research

Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Temporal Trend of COVID-19 Clinical Severity and the Ethnic/Racial Disparity: A Report from the Maryland Study Group

OBJECTIVES

This study aimed to evaluate the temporal trend of novel coronavirus disease 2019 (COVID-19) symptoms and severity of clinical outcomes among pregnant women over a calendar year in the State of Maryland and compare clinical outcomes between different ethnic and racial groups.

STUDY DESIGN

We conducted a retrospective, multicenter observational study of the temporal trend of COVID-19 clinical presentation during pregnancy in the State of Maryland. We reviewed consecutive charts of adult pregnant females, aged 18 to 55 years, with laboratory-confirmed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection between March 1, 2020, and February 28, 2021, and managed within the University of Maryland Medical System and Johns Hopkins Medicine. We excluded cases with insufficient data for assessing the COVID-19 diagnosis, pregnancy status, or clinical outcomes. We evaluated the evolution of COVID-19 symptoms at the time of presentation. Also, we compared COVID-19 infection rate, hospitalization rate, oxygen use, and intensive care unit (ICU) admission rates between different ethnic and racial groups.

RESULTS

We included 595 pregnant women with laboratory-confirmed COVID-19 over the study period. The prevalence of respiratory and systemic symptoms decreased over time with incidence rate ratios (IRRs) of 0.91 per month (95% confidence interval [CI]: 0.88-0.95) and 0.87 per month (95% CI: 0.83-0.95), respectively. The prevalence of hospitalization, O₂ requirement, and ICU admission decreased over time with IRRs of 0.86 per month (95% CI: 0.82-0.91), 0.91 per month (95% CI: 0.84-0.98), and 0.70 per month (95% CI: 0.57-0.85), respectively. The Hispanic and Black populations had a higher COVID-19 infection rate and hospitalization rate than the non-Hispanic White population (p = 0.004, < 0.001, and < 0.001, respectively).

CONCLUSION

Understanding the concepts of viral evolution could potentially help the fight against pandemics like COVID-19. Moreover, this might improve the knowledge of how pandemics affect disadvantaged populations and help close the gap in health care inequities.

KEY POINTS

· A trade-off between virulence and transmissibility is determined by the natural selection of viruses.. · Understanding the concepts of viral evolution can help the fight against pandemics like COVID-19.. · Evolution of SARS-CoV-2 over time resulted in decreased virulence and increased infectivity..

SARS-CoV-2 Amino Acid Mutations Detection in Greek Patients Infected in the First Wave of the Pandemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel virus that belongs to the Coronoviridae family, emerged in December 2019, causing the COVID-19 pandemic in March 2020. Unlike previous SARS and Middle East respiratory syndrome (MERS) outbreaks, this virus has a higher transmissibility rate, albeit a lower case fatality rate, which results in accumulation of a significant number of mutations and a faster evolution rate. Genomic studies on the mutation rate of the virus, as well as the identification of mutations that prevail and their impact on disease severity, are of great importance for pandemic surveillance and vaccine and drug development. Here, we aim to identify mutations on the SARS-CoV-2 viral genome and their effect on the proteins they are located in, in Greek patients infected in the first wave of the pandemic. To this end, we perform SARS-CoV-2 amplicon-based NGS sequencing on nasopharyngeal swab samples from Greek patients and bioinformatic analysis of the results. Although SARS-CoV-2 is considered genetically stable, we discover a variety of mutations on the viral genome. In detail, 18 mutations are detected in total on 10 SARS-CoV-2 isolates. The mutations are located on ORF1ab, S protein, M protein, ORF3a and ORF7a. Sixteen are also detected in patients from other regions around the world, and two are identified for the first time in the present study. Most of them result in amino acid substitutions. These substitutions are analyzed using computational tools, and the results indicate minor or major impact on the proteins’ structural stability, which could probably affect viral transmissibility and pathogenesis. The correlation of these variations with the viral load levels is examined, and their implication for disease severity and the biology of the virus are discussed.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Continent-wide evolutionary trends of emerging SARS-CoV-2 variants: dynamic profiles from Alpha to Omicron

The ongoing SARS-CoV-2 evolution process has generated several variants due to its continuous mutations, making pandemics more critical. The present study illustrates SARS-CoV-2 evolution and its emerging mutations in five directions. First, the significant mutations in the genome and S-glycoprotein were analyzed in different variants. Three linear models were developed with the regression line to depict the mutational load for S-glycoprotein, total genome excluding S-glycoprotein, and whole genome. Second, the continent-wide evolution of SARS-CoV-2 and its variants with their clades and divergence were evaluated. It showed the region-wise evolution of the SARS-CoV-2 variants and their clustering event. The major clades for each variant were identified. One example is clade 21K, a major clade of the Omicron variant. Third, lineage dynamics and comparison between SARS-CoV-2 lineages across different countries are also illustrated, demonstrating dominant variants in various countries over time. Fourth, gene-wise mutation patterns and genetic variability of SARS-CoV-2 variants across various countries are illustrated. High mutation patterns were found in the ORF10, ORF6, S, and low mutation pattern E genes. Finally, emerging AA point mutations (T478K, L452R, N501Y, S477N, E484A, Q498R, and Y505H), their frequencies, and country-wise occurrence were identified, and the highest event of two mutations (T478K and L452R) was observed.

Anurans against SARS-CoV-2: A review of the potential antiviral action of anurans cutaneous peptides

At the end of 2019, in China, clinical signs and symptoms of unknown etiology have been reported in several patients whose sample sequencing revealed pneumonia caused by the SARS-CoV-2 virus. COVID-19 is a disease triggered by this virus, and in 2020, the World Health Organization declared it a pandemic. Since then, efforts have been made to find effective therapeutic agents against this disease. Identifying novel natural antiviral drugs can be an alternative to treatment. For this reason, antimicrobial peptides secreted by anurans' skin have gained attention for showing a promissory antiviral effect. Hence, this review aimed to elucidate how and which peptides secreted by anurans' skin can be considered therapeutic agents to treat or prevent human viral infectious diseases. Through a literature review, we attempted to identify potential antiviral frogs' peptides to combat COVID-19. As a result, the Magainin-1 and -2 peptides, from the Magainin family, the Dermaseptin-S9, from the Dermaseptin family, and Caerin 1.6 and 1.10, from the Caerin family, are molecules that already showed antiviral effects against SARS-CoV-2 in silico. In addition to these peptides, this review suggests that future studies should use other families that already have antiviral action against other viruses, such as Brevinins, Maculatins, Esculentins, Temporins, and Urumins. To apply these peptides as therapeutic agents, experimental studies with peptides already tested in silico and new studies with other families not tested yet should be considered.

Variations of SARS-CoV-2 in the Iranian population and candidate putative drug-like compounds to inhibit the mutated proteins

The first cases of the novel coronavirus, SARS-CoV-2, were detected in December 2019 in Wuhan, China. Nucleotide substitutions and mutations in the SARS-CoV-2 sequence can result in the evolution of the virus and its rapid spread across the world. Therefore, understanding genetic variants of SARS-CoV-2 and targeting the conserved elements responsible for viral replication have great benefits for detecting its infection sources and diagnosing and treating COVID-19. In this study, we used the SARS-CoV-2 sequence isolated from a 59-year-old man in Ardabil, Iran, in April 2020 and sequenced using Oxford Nanopore technology. A meta-analysis comparing the sequence under study with other sequences from Iran indicated long nucleotide insertions/deletions (indels) that code for NSP15, the NSP14-NSP10 complex, open reading frame ORF9b, and ORF1ab polyproteins. In addition, replicating the NSP8 protein in the study sequence is another topic that can affect viral replication. Then using the DNA structure of NSP8, NSP15, NSP14-NSP10 complex, and ORF1ab as a genetic target can help find drug-like compounds for COVID-19. Potential drug-like compounds reported in this study for their mechanism of action and interactions with SARS-CoV-2 genes using drug repurposing are resveratrol, erythromycin, chloramphenicol, indomethacin, ciclesonide, and PDE4 inhibitor. Ciclesonide appears to show the best results when docked with chosen viral proteins. Therefore, different proteins isolated from nucleotide mutations in the virus sequence can indicate distinct inducers for antibodies and are important in vaccine design.

Human Alphacoronavirus Universal Primers for Genome Amplification and Sequencing

Rapid and accurate sequencing covering the entire genome is essential to identify genetic variations of viral pathogens. However, due to the low viral titers in clinical samples, certain amplification steps are required for viral genome sequencing. At present, there are no universal primers available for alphacoronaviruses and that, since these viruses have diverse strains, new primers specific to the target strain must be continuously developed for sequencing. Thus, in this study, we aimed to develop a universal primer set valid for all human alphacoronaviruses and applicable to samples containing trace amounts of the virus. To this aim, we designed overlapping primer pairs capable of amplifying the entire genome of all known human alphacoronaviruses. The selected primers, named the AC primer set, were composed of 10 primer pairs stretching over the entire genome of alphacoronaviruses, and produced PCR products of the expected size (3-5 kb) from both the HCoV-229E and HCoV-NL63 strains. After genome amplification, an evaluation using various sequencing platforms was carried out. The amplicon library sequencing data were assembled into complete genome sequences in all sequencing strategies examined in this study. The sequencing accuracy varied depending on the sequencing technology, but all sequencing methods showed a sequencing error of less than 0.01%. In the mock clinical specimen, the detection limit was 10^-3 PFU/ml (10² copies/ml). The AC primer set and experimental procedure optimized in this study may enable the fast diagnosis of mutant alphacoronaviruses in future epidemics.

PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences

The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic-an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime-in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models.

A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis

In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.

Unique Evolution of SARS-CoV-2 in the Second Large Cruise Ship Cluster in Japan

In the initial phase of the novel coronavirus disease (COVID-19) pandemic, a large-scale cluster on the cruise ship Diamond Princess (DP) emerged in Japan. Genetic analysis of the DP strains has provided important information for elucidating the possible transmission process of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on a cruise ship. However, genome-based analyses of SARS-CoV-2 detected in large-scale cruise ship clusters other than the DP cluster have rarely been reported. In the present study, whole-genome sequences of 94 SARS-CoV-2 strains detected in the second large cruise ship cluster, which emerged on the Costa Atlantica (CA) in Japan, were characterized to understand the evolution of the virus in a crowded and confined place. Phylogenetic and haplotype network analysis indicated that the CA strains were derived from a common ancestral strain introduced on the CA cruise ship and spread in a superspreading event-like manner, resulting in several mutations that might have affected viral characteristics, including the P681H substitution in the spike protein. Moreover, there were significant genetic distances between CA strains and other strains isolated in different environments, such as cities under lockdown. These results provide new insights into the unique evolution patterns of SARS-CoV-2 in the CA cruise ship cluster.

Dynamics of SARS-CoV-2 variants of concern (VOC) in Bangladesh during the first half of 2021

Bangladesh is the second-worst-affected country in South Asia by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The aim of this study is to examine genome sequences from Bangladesh from January 2021 to June 2021 in order to monitor the SARS-CoV-2 VOC and the clades or lineages that are prevalent in the country. Within the study timeframe, at least eight Nextstrain clades were found: 20A, 20B, 20C, 20H (Beta, V2), 20I (Alpha, V1), 20 J (Gamma, V3), 21A (Delta), 21D (Eta), and six GISAID clades: four main (G, GH, GR, GRY) and two minors (GV, O) with an introduction of VOC B.1.1.7/Alpha, B.1.351/Beta and B.1.617.2/Delta. The introduction and recent occurrence of VOCs with substantial alterations in the receptor binding site of spike protein (K417 N, K417T, L452R, T478K, E484K, S494P, N501Y) are of particular importance. Specifically, VOC B.1.617.2/Delta has surpassed all prior VOCs in Bangladesh, posing a challenge to the existing disease management.

Genetic drift in the genome of SARS COV-2 and its global health concern

The outbreak of the current coronavirus disease (COVID-19) occurred in late 2019 and quickly spread all over the world. The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) belongs to a genetically diverse group that mutates continuously leading to the emergence of multiple variants. Although a few antiviral agents and anti-inflammatory medicines are available, thousands of individuals have passed away due to emergence of new viral variants. Thus, proper surveillance of the SARS-CoV-2 genome is needed for the rapid identification of developing mutations over time, which are of the major concern if they occur specifically in the surface spike proteins of the virus (neutralizing analyte). This article reviews the potential mutations acquired by the SARS-CoV2 since the pandemic began and their significant impact on the neutralizing efficiency of vaccines and validity of the diagnostic assays.

SARS-CoV-2 spike protein and RNA dependent RNA polymerase as targets for drug and vaccine development: A review

The present pandemic has posed a crisis to the economy of the world and the health sector. Therefore, the race to expand research to understand some good molecular targets for vaccine and therapeutic development for SARS-CoV-2 is inevitable. The newly discovered coronavirus 2019 (COVID-19) is a positive sense, single-stranded RNA, and enveloped virus, assigned to the beta CoV genus. The virus (SARS-CoV-2) is more infectious than the previously detected coronaviruses (MERS and SARS). Findings from many studies have revealed that S protein and RdRp are good targets for drug repositioning, novel therapeutic development (antibodies and small molecule drugs), and vaccine discovery. Therapeutics such as chloroquine, convalescent plasma, monoclonal antibodies, spike binding peptides, and small molecules could alter the ability of S protein to bind to the ACE-2 receptor, and drugs such as remdesivir (targeting SARS-CoV-2 RdRp), favipir, and emetine could prevent SASR-CoV-2 RNA synthesis. The novel vaccines such as mRNA1273 (Moderna), 3LNP-mRNAs (Pfizer/BioNTech), and ChAdOx1-S (University of Oxford/Astra Zeneca) targeting S protein have proven to be effective in combating the present pandemic. Further exploration of the potential of S protein and RdRp is crucial in fighting the present pandemic.

What we know and what we need to know about the origin of SARS-CoV-2

Since the appearance of the first cases of COVID-19 in 2019, an unprecedented number of documents on that disease have been published in a short space of time. The current available information covers a large number of topics related with COVID-19 and/or the coronavirus (SARS-CoV-2) responsible of the disease. However, only a limited number of publications have been focused on a controversial issue: the origin of the SARS-CoV-2. In this paper, the scientific literature on the origin of SARS-CoV-2 has been reviewed. Documents published during 2020 and 2021 (January 1-July 19) in journals that are indexed in PubMed and/or Scopus has been considered. The revised studies were grouped according to these two potential origins: natural and unnatural. The analyses of the conclusions of the different documents here assessed show that even considering the zoonotic hypothesis as the most likely, with bats and pangolins being possibly in the origin of the coronavirus, today's date the intermediate source species of SARS-CoV-2 has not been confirmed yet. On the other hand, some researchers point to an unnatural origin of this coronavirus, but their conclusions are not strongly supported by a clear scientific evidence. Given the tremendous severity of the current pandemic, investigations to establish clearly and definitively the origin of SARS-CoV-2, are basic and essential in order to prevent potential future pandemics of similar nature.

Multi-Organ Histopathological Changes in a Mouse Hepatitis Virus Model of COVID-19

Infection with SARS-CoV-2, the virus responsible for the global COVID-19 pandemic, causes a respiratory illness that can severely impact other organ systems and is possibly precipitated by cytokine storm, septic shock, thrombosis, and oxidative stress. SARS-CoV-2 infected individuals may be asymptomatic or may experience mild, moderate, or severe symptoms with or without pneumonia. The mechanisms by which SARS-CoV-2 infects humans are largely unknown. Mouse hepatitis virus 1 (MHV-1)-induced infection was used as a highly relevant surrogate animal model for this study. We further characterized this animal model and compared it with SARS-CoV-2 infection in humans. MHV-1 inoculated mice displayed death as well as weight loss, as reported earlier. We showed that MHV-1-infected mice at days 7-8 exhibit severe lung inflammation, peribronchiolar interstitial infiltration, bronchiolar epithelial cell necrosis and intra-alveolar necrotic debris, alveolar exudation (surrounding alveolar walls have capillaries that are dilated and filled with red blood cells), mononuclear cell infiltration, hyaline membrane formation, the presence of hemosiderin-laden macrophages, and interstitial edema. When compared to uninfected mice, the infected mice showed severe liver vascular congestion, luminal thrombosis of portal and sinusoidal vessels, hepatocyte degeneration, cell necrosis, and hemorrhagic changes. Proximal and distal tubular necrosis, hemorrhage in interstitial tissue, and the vacuolation of renal tubules were observed. The heart showed severe interstitial edema, vascular congestion, and dilation, as well as red blood cell extravasation into the interstitium. Upon examination of the MHV-1 infected mice brain, we observed congested blood vessels, perivascular cavitation, cortical pericellular halos, vacuolation of neuropils, darkly stained nuclei, pyknotic nuclei, and associated vacuolation of the neuropil in the cortex, as well as acute eosinophilic necrosis and necrotic neurons with fragmented nuclei and vacuolation in the hippocampus. Our findings suggest that the widespread thrombotic events observed in the surrogate animal model for SARS-CoV-2 mimic the reported findings in SARS-CoV-2 infected humans, representing a highly relevant and safe animal model for the study of the pathophysiologic mechanisms of SARS-CoV-2 for potential therapeutic interventions.

Potential In Vitro Inhibition of Selected Plant Extracts against SARS-CoV-2 Chymotripsin-Like Protease (3CLPro) Activity

Antiviral treatments inhibiting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication may represent a strategy complementary to vaccination to fight the ongoing Coronavirus disease 19 (COVID-19) pandemic. Molecules or extracts inhibiting the SARS-CoV-2 chymotripsin-like protease (3CLPro) could contribute to reducing or suppressing SARS-CoV-2 replication. Using a targeted approach, we identified 17 plant products that are included in current and traditional cuisines as promising inhibitors of SARS-CoV-2 3CLPro activity. Methanolic extracts were evaluated in vitro for inhibition of SARS-CoV-2 3CLPro activity using a quenched fluorescence resonance energy transfer (FRET) assay. Extracts from turmeric (Curcuma longa) rhizomes, mustard (Brassica nigra) seeds, and wall rocket (Diplotaxis erucoides subsp. erucoides) at 500 µg mL-1 displayed significant inhibition of the 3CLPro activity, resulting in residual protease activities of 0.0%, 9.4%, and 14.9%, respectively. Using different extract concentrations, an IC50 value of 15.74 µg mL-1 was calculated for turmeric extract. Commercial curcumin inhibited the 3CLPro activity, but did not fully account for the inhibitory effect of turmeric rhizomes extracts, suggesting that other components of the turmeric extract must also play a main role in inhibiting the 3CLPro activity. Sinigrin, a major glucosinolate present in mustard seeds and wall rocket, did not have relevant 3CLPro inhibitory activity; however, its hydrolysis product allyl isothiocyanate had an IC50 value of 41.43 µg mL-1. The current study identifies plant extracts and molecules that can be of interest in the search for treatments against COVID-19, acting as a basis for future chemical, in vivo, and clinical trials.

Evolutionary selectivity of amino acid is inspired from the enhanced structural stability and flexibility of the folded protein

AIM

The present study attempts to decipher the site-specific amino acid alterations at certain positions experiencing preferential selectivity and their effect on proteins' stability and flexibility. The study examines the selection preferences by considering pair-wise non-bonded interaction energies of adjacent and interacting amino acids present at the interacting site, along with their evolutionary history.

MATERIALS AND METHODS

For the study, variations in the interacting residues of spike protein (S-Protein) receptor-binding domain (RBD) of different coronaviruses were examined. The MD simulation trajectory analysis revealed that, though all the variants studied were structurally stable at their native and bound confirmations, the RBD of 2019-nCoV/SARS-CoV-2 was found to be more flexible and more dynamic. Furthermore, a noticeable change observed in the non-bonded interaction energies of the amino acids interacting with the receptor corroborated their selection at respective positions.

KEY FINDINGS

The conformational changes exerted by the altered amino acids could be the reason for a broader range of interacting receptors among the selected proteins.

SIGNIFICANCE

The results envisage a strong indication that the residue selection at certain positions is governed by a well-orchestrated feedback mechanism, which follows increased stability and flexibility in the folded structure compared to its evolutionary predecessor.

Unlocking capacities of viral genomics for the COVID-19 pandemic response

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.

A comprehensive SARS-CoV-2 genomic analysis identifies potential targets for drug repurposing

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is a novel human coronavirus strain (HCoV) was initially reported in December 2019 in Wuhan City, China. This acute infection caused pneumonia-like symptoms and other respiratory tract illness. Its higher transmission and infection rate has successfully enabled it to have a global spread over a matter of small time. One of the major concerns involving the SARS-COV-2 is the mutation rate, which enhances the virus evolution and genome variability, thereby making the design of therapeutics difficult. In this study, we identified the most common haplotypes from the haplotype network. The conserved genes and population level variants were analysed. Non-Structural Protein 10 (NSP10), Nucleoprotein, Papain-like protease (Plpro or NSP3) and 3-Chymotrypsin like protease (3CLpro or NSP5), which were conserved at the highest threshold, were used as drug targets for molecular dynamics simulations. Darifenacin, Nebivolol, Bictegravir, Alvimopan and Irbesartan are among the potential drugs, which are suggested for further pre-clinical and clinical trials. This particular study provides a comprehensive targeting of the conserved genes. We also identified the mutation frequencies across the viral genome.

SARS-CoV-2 Molecular Transmission Clusters and Containment Measures in Ten European Regions during the First Pandemic Wave

Background: The spatiotemporal profiling of molecular transmission clusters (MTCs) using viral genomic data can effectively identify transmission networks in order to inform public health actions targeting SARS-CoV-2 spread. Methods: We used whole genome SARS-CoV-2 sequences derived from ten European regions belonging to eight countries to perform phylogenetic and phylodynamic analysis. We developed dedicated bioinformatics pipelines to identify regional MTCs and to assess demographic factors potentially associated with their formation. Results: The total number and the scale of MTCs varied from small household clusters identified in all regions, to a super-spreading event found in Uusimaa-FI. Specific age groups were more likely to belong to MTCs in different regions. The clustered sequences referring to the age groups 50–100 years old (y.o.) were increased in all regions two weeks after the establishment of the lockdown, while those referring to the age group 0–19 y.o. decreased only in those regions where schools’ closure was combined with a lockdown. Conclusions: The spatiotemporal profiling of the SARS-CoV-2 MTCs can be a useful tool to monitor the effectiveness of the interventions and to reveal cryptic transmissions that have not been identified through contact tracing.