Emergence of genomic diversity and recurrent mutations in SARS-CoV-2

Antibody drugs targeting SARS-CoV-2: Time for a rethink?

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) heavily burdens human health. Multiple neutralizing antibodies (nAbs) have been issued for emergency use or tested for treating infected patients in the clinic. However, SARS-CoV-2 variants of concern (VOC) carrying mutations reduce the effectiveness of nAbs by preventing neutralization. Uncoding the mutation profile and immune evasion mechanism of SARS-CoV-2 can improve the outcome of Ab-mediated therapies. In this review, we first outline the development status of anti-SARS-CoV-2 Ab drugs and provide an overview of SARS-CoV-2 variants and their prevalence. We next focus on the failure causes of anti-SARS-CoV-2 Ab drugs and rethink the design strategy for developing new Ab drugs against COVID-19. This review provides updated information for the development of therapeutic Ab drugs against SARS-CoV-2 variants.

A systematic mutation analysis of 13 major SARS-CoV-2 variants

SARS-CoV-2 evolves constantly with various novel mutations. Due to their enhanced infectivity, transmissibility and immune evasion, a comprehensive understanding of the association between these mutations and the respective functional changes is crucial. However, previous mutation studies of major SARS-CoV-2 variants remain limited. Here, we performed systematic analyses of full-length amino acids mutation, phylogenetic features, protein physicochemical properties, molecular dynamics and immune escape as well as pseudotype virus infection assays among thirteen major SARS-CoV-2 variants. We found that Omicron exhibited the most abundant and complex mutation sites, higher indices of hydrophobicity and flexibility than other variants. The results of molecular dynamics simulation suggest that Omicron has the highest number of hydrogen bonds and strongest binding free energy between the S protein and ACE2 receptor. Furthermore, we revealed 10 immune escape sites in 13 major variants, some of them were reported previously, but four of which (i.e. 339/373/477/496) are first reported to be specific to Omicron, whereas 462 is specific to Epslion. The infectivity of these variants was confirmed by the pseudotype virus infection assays. Our findings may help us understand the functional consequences of the mutations within various variants and the underlying mechanisms of the immune escapes conferred by the S proteins.

Detection and Characterisation of SARS-CoV-2 in Eastern Province of Zambia: A Retrospective Genomic Surveillance Study

Mutations have driven the evolution and development of new variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with potential implications for increased transmissibility, disease severity and vaccine escape among others. Genome sequencing is a technique that allows scientists to read the genetic code of an organism and has become a powerful tool for studying emerging infectious diseases. Here, we conducted a cross-sectional study in selected districts of the Eastern Province of Zambia, from November 2021 to February 2022. We analyzed SARS-CoV-2 samples (n = 76) using high-throughput sequencing. A total of 4097 mutations were identified in 69 SARS-CoV-2 genomes with 47% (1925/4097) of the mutations occurring in the spike protein. We identified 83 unique amino acid mutations in the spike protein of the seven Omicron sublineages (BA.1, BA.1.1, BA.1.14, BA.1.18, BA.1.21, BA.2, BA.2.23 and XT). Of these, 43.4% (36/83) were present in the receptor binding domain, while 14.5% (12/83) were in the receptor binding motif. While we identified a potential recombinant XT strain, the highly transmissible BA.2 sublineage was more predominant (40.8%). We observed the substitution of other variants with the Omicron strain in the Eastern Province. This work shows the importance of pandemic preparedness and the need to monitor disease in the general population.

Morphological analysis for two types of viral particles in vacuoles of SARS-CoV-2-infected cells

In this study, we analyzed the morphological structure of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human cells. We identified the two types of viral particles present within the vacuoles of infected cells. Using transmission electron microscopy, we observed that SARS-CoV-2 particles exhibited both low- and high-electron-density structures, which was further confirmed through three-dimensional reconstruction using electron tomography. The budding of these particles was exclusively observed within these vacuoles. Intriguingly, viral particles with low-electron-density structures were confined to vacuoles, whereas those with high-electron-density structures were found in vacuoles and on the cell membrane surface of infected cells. Notably, high-electron-density particles found within vacuoles exhibited the same morphology as those outside the infected cells. This observation suggests that the two types of viral particles identified in this study had different maturation status. Our findings provide valuable insights into the molecular details of SARS-CoV-2 particles, contributing to our understanding of the virus.

Structural, dynamic behaviour, in-vitro and computational investigations of Schiff's bases of 1,3-diphenyl urea derivatives against SARS-CoV-2 spike protein

The COVID-19 has had a significant influence on people's lives across the world. The viral genome has undergone numerous unanticipated changes that have given rise to new varieties, raising alarm on a global scale. Bioactive phytochemicals derived from nature and synthetic sources possess lot of potential as pathogenic virus inhibitors. The goal of the recent study is to report new inhibitors of Schiff bases of 1,3-dipheny urea derivatives against SARS COV-2 spike protein through in-vitro and in-silico approach. Total 14 compounds were evaluated, surprisingly, all the compounds showed strong inhibition with inhibitory values between 79.60% and 96.00% inhibition. Here, compounds 3a (96.00%), 3d (89.60%), 3e (84.30%), 3f (86.20%), 3g (88.30%), 3h (86.80%), 3k (82.10%), 3l (90.10%), 3m (93.49%), 3n (85.64%), and 3o (81.79%) exhibited high inhibitory potential against SARS COV-2 spike protein. While 3c also showed significant inhibitory potential with 79.60% inhibition. The molecular docking of these compounds revealed excellent fitting of molecules in the spike protein receptor binding domain (RBD) with good interactions with the key residues of RBD and docking scores ranging from - 4.73 to - 5.60 kcal/mol. Furthermore, molecular dynamics simulation for 150 ns indicated a strong stability of a complex 3a:6MOJ. These findings obtained from the in-vitro and in-silico study reflect higher potency of the Schiff bases of 1,3-diphenyl urea derivatives. Furthermore, also highlight their medicinal importance for the treatment of SARS COV-2 infection. Therefore, these small molecules could be a possible drug candidate.

Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics

New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.

The Limits of the Constant-rate Birth-Death Prior for Phylogenetic Tree Topology Inference

Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under the constant-rate birth-death (crBD) model tend to differ from empirical trees, for example, with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between the crBD model and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which the crBD model differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD model trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used Bayesian methods and crBD priors with those that used other non-crBD priors and non-Bayesian methods (i.e., maximum likelihood methods), we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using the crBD model in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios but also highlight that empirically observed phylogenetic imbalance is highly improbable under the crBD model, leading to systematic bias in data sets with limited information content.

Genomic Context of SARS-CoV-2 Outbreaks in Farmed Mink in Spain during Pandemic: Unveiling Host Adaptation Mechanisms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects various mammalian species, with farmed minks experiencing the highest number of outbreaks. In Spain, we analyzed 67 whole genome sequences and eight spike sequences from 18 outbreaks, identifying four distinct lineages: B.1, B.1.177, B.1.1.7, and AY.98.1. The potential risk of transmission to humans raises crucial questions about mutation accumulation and its impact on viral fitness. Sequencing revealed numerous not-lineage-defining mutations, suggesting a cumulative mutation process during the outbreaks. We observed that the outbreaks were predominantly associated with different groups of mutations rather than specific lineages. This clustering pattern by the outbreaks could be attributed to the rapid accumulation of mutations, particularly in the ORF1a polyprotein and in the spike protein. Notably, the mutations G37E in NSP9, a potential host marker, and S486L in NSP13 were detected. Spike protein mutations may enhance SARS-CoV-2 adaptability by influencing trimer stability and binding to mink receptors. These findings provide valuable insights into mink coronavirus genetics, highlighting both host markers and viral transmission dynamics within communities.

A comprehensive analysis of SARS-CoV-2 missense mutations indicates that all possible amino acid replacements in the viral proteins occurred within the first two-and-a-half years of the pandemic

The surveillance of COVID-19 pandemic has led to the determination of millions of genome sequences of the SARS-CoV-2 virus, with the accumulation of a wealth of information never collected before for an infectious disease. Exploring the information retrieved from the GISAID database reporting at that time >13 million genome sequences, we classified the 141,639 unique missense mutations detected in the first two-and-a-half years (up to October 2022) of the pandemic. Notably, our analysis indicates that 98.2 % of all possible conservative amino acid replacements occurred. Even non-conservative mutations were highly represented (73.9 %). For a significant number of residues (3 %), all possible replacements with the other nineteen amino acids have been observed. These observations strongly indicate that, in this time interval, the virus explored all possible alternatives in terms of missense mutations for all sites of its polypeptide chain and that those that are not observed severely affect SARS-CoV-2 integrity. The implications of the present findings go well beyond the structural biology of SARS-CoV-2 as the huge amount of information here collected and classified may be valuable for the elucidation of the sequence-structure-function relationships in proteins.

Accurate Detection of Convergent Mutations in Large Protein Alignments With ConDor

Evolutionary convergences are observed at all levels, from phenotype to DNA and protein sequences, and changes at these different levels tend to be correlated. Notably, convergent mutations can lead to convergent changes in phenotype, such as changes in metabolism, drug resistance, and other adaptations to changing environments. We propose a two-component approach to detect mutations subject to convergent evolution in protein alignments. The "Emergence" component selects mutations that emerge more often than expected, while the "Correlation" component selects mutations that correlate with the convergent phenotype under study. With regard to Emergence, a phylogeny deduced from the alignment is provided by the user and is used to simulate the evolution of each alignment position. These simulations allow us to estimate the expected number of mutations in a neutral model, which is compared to the observed number of mutations in the data studied. In Correlation, a comparative phylogenetic approach, is used to measure whether the presence of each of the observed mutations is correlated with the convergent phenotype. Each component can be used on its own, for example Emergence when no phenotype is available. Our method is implemented in a standalone workflow and a webserver, called ConDor. We evaluate the properties of ConDor using simulated data, and we apply it to three real datasets: sedge PEPC proteins, HIV reverse transcriptase, and fish rhodopsin. The results show that the two components of ConDor complement each other, with an overall accuracy that compares favorably to other available tools, especially on large datasets.

VIPERA: Viral Intra-Patient Evolution Reporting and Analysis

Viral mutations within patients nurture the adaptive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intra-patient SARS-CoV-2 serial samples. Herein, we describe Viral Intra-Patient Evolution Reporting and Analysis (VIPERA), a new software that integrates the evaluation of the intra-patient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection. We have validated it using positive and negative control datasets and have successfully applied it to a new case, which revealed population dynamics and evidence of adaptive evolution. VIPERA is available under a free software license at https://github.com/PathoGenOmics-Lab/VIPERA.

Patient-Related Metadata Reported in Sequencing Studies of SARS-CoV-2: Protocol for a Scoping Review and Bibliometric Analysis

Background

There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.While these repositories include fields reflecting patient-related metadata for a given sequence, inclusion of these demographic and clinical details is scarce. The extent to which patient-related metadata is reported in published sequencing studies and its quality remains largely unexplored.

Methods

The NIH's LitCovid collection will be used for automated classification of articles reporting having deposited SARS-CoV-2 sequences in public repositories, while an independent search will be conducted in PubMed for validation. Data extraction will be conducted using Covidence. The extracted data will be synthesized and summarized to quantify the availability of patient metadata in the published literature of SARS-CoV-2 sequencing studies. For the bibliometric analysis, relevant data points, such as author affiliations and citation metrics will be extracted.

Discussion

This scoping review will report on the extent and types of patient-related metadata reported in genomic viral sequencing studies of SARS-CoV-2, identify gaps in this reporting, and make recommendations for improving the quality and consistency of reporting in this area. The bibliometric analysis will uncover trends and patterns in the reporting of patient-related metadata, including differences in reporting based on study types or geographic regions. Co-occurrence networks of author keywords will also be presented. The insights gained from this study may help improve the quality and consistency of reporting patient metadata, enhancing the utility of sequence metadata and facilitating future research on infectious diseases.

Molecular insights into the adaptive evolution of SARS-CoV-2 spike protein

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has substantially damaged the global economy and human health. The spike (S) protein of coronaviruses plays a pivotal role in viral entry by binding to host cell receptors. Additionally, it acts as the primary target for neutralizing antibodies in those infected and is the central focus for currently utilized or researched vaccines. During the virus's adaptation to the human host, the S protein of SARS-CoV-2 has undergone significant evolution. As the COVID-19 pandemic has unfolded, new mutations have arisen and vanished, giving rise to distinctive amino acid profiles within variant of concern strains of SARS-CoV-2. Notably, many of these changes in the S protein have been positively selected, leading to substantial alterations in viral characteristics, such as heightened transmissibility and immune evasion capabilities. This review aims to provide an overview of our current understanding of the structural implications associated with key amino acid changes in the S protein of SARS-CoV-2. These research findings shed light on the intricate and dynamic nature of viral evolution, underscoring the importance of continuous monitoring and analysis of viral genomes. Through these molecular-level investigations, we can attain deeper insights into the virus's adaptive evolution, offering valuable guidance for designing vaccines and developing antiviral drugs to combat the ever-evolving viral threats.

Identification of new pharmacophore against SARS-CoV-2 spike protein by multi-fold computational and biochemical techniques

COVID-19 appeared as a highly contagious disease after its outbreak in December 2019 by the virus, named SARS-CoV-2. The threat, which originated in Wuhan, China, swiftly became an international emergency. Among different genomic products, spike protein of virus plays a crucial role in the initiation of the infection by binding to the human lung cells, therefore, SARS-CoV-2's spike protein is a promising therapeutic target. Using a combination of a structure-based virtual screening and biochemical assay, this study seeks possible therapeutic candidates that specifically target the viral spike protein. A database of ~ 850 naturally derived compounds was screened against SARS-CoV-2 spike protein to find natural inhibitors. Using virtual screening and inhibitory experiments, we identified acetyl 11-keto-boswellic acid (AKBA) as a promising molecule for spike protein, which encouraged us to scan the rest of AKBA derivatives in our in-house database via 2D-similarity searching. Later 19 compounds with > 85% similarity with AKBA were selected and docked with receptor binding domain (RBD) of spike protein. Those hits declared significant interactions at the RBD interface, best possess and excellent drug-likeness and pharmacokinetics properties with high gastrointestinal absorption (GIA) without toxicity and allergenicity. Our in-silico observations were eventually validated by in vitro bioassay, interestingly, 10 compounds (A3, A4, C3, C6A, C6B, C6C, C6E, C6H, C6I, and C6J) displayed significant inhibitory ability with good percent inhibition (range: > 72-90). The compounds C3 (90.00%), C6E (91.00%), C6C (87.20%), and C6D (86.23%) demonstrated excellent anti-SARS CoV-2 spike protein activities. The docking interaction of high percent inhibition of inhibitor compounds C3 and C6E was confirmed by MD Simulation. In the molecular dynamics simulation, we observed the stable dynamics of spike protein inhibitor complexes and the influence of inhibitor binding on the protein's conformational arrangements. The binding free energy ΔGTOTAL of C3 (-38.0 ± 0.08 kcal/mol) and C6E (-41.98 ± 0.08 kcal/mol) respectively indicate a strong binding affinity to Spike protein active pocket. These findings demonstrate that these molecules particularly inhibit the function of spike protein and, therefore have the potential to be evaluated as drug candidates against SARS-CoV-2.

Analysis of SARS-CoV-2 genome evolutionary patterns

The spread of SARS-CoV-2 virus accompanied by public availability of abundant sequence data provides a window for the determination of viral evolutionary patterns. In this study, SARS-CoV-2 genome sequences were collected from seven countries in the period January 2020-December 2022. The sequences were classified into three phases, namely, pre-vaccination, post-vaccination, and recent period. Comparison was performed between these phases based on parameters like mutation rates, selection pressure (dN/dS ratio), and transition to transversion ratios (Ti/Tv). Similar comparisons were performed among SARS-CoV-2 variants. Statistical significance was tested using Graphpad unpaired t-test. The analysis showed an increase in the percent genomic mutation rates post-vaccination and in recent periods across all countries from the pre-vaccination sequences. Mutation rates were highest in NSP3, S, N, and NSP12b before and increased further after vaccination. NSP4 showed the largest change in mutation rates after vaccination. The dN/dS ratios showed purifying selection that shifted toward neutral selection after vaccination. N, ORF8, ORF3a, and ORF10 were under highest positive selection before vaccination. Shift toward neutral selection was driven by E, NSP3, and ORF7a in the after vaccination set. In recent sequences, the largest dN/dS change was observed in E, NSP1, and NSP13. The Ti/Tv ratios decreased with time. C→U and G→U were the most frequent transitions and transversions. However, U→G was the most frequent transversion in recent period. The Omicron variant had the highest genomic mutation rates, while Delta showed the highest dN/dS ratio. Protein-wise dN/dS ratio was also seen to vary across the different variants.IMPORTANCETo the best of our knowledge, there exists no other large-scale study of the genomic and protein-wise mutation patterns during the time course of evolution in different countries. Analyzing the SARS-CoV-2 evolutionary patterns in view of the varying spatial, temporal, and biological signals is important for diagnostics, therapeutics, and pharmacovigilance of SARS-CoV-2.

Genetic determinants of SARS-CoV-2 and the clinical outcome of COVID-19 in Southern Bangladesh

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has had a severe impact on population health. The genetic determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in southern Bangladesh are not well understood.

METHODS

This study aimed to determine the genomic variation in SARS-CoV-2 genomes that have evolved over 2 years of the pandemic in southern Bangladesh and their association with disease outcomes and virulence of this virus. We investigated demographic variables, disease outcomes of COVID-19 patients and genomic features of SARS-CoV-2.

RESULTS

We observed that the disease severity was significantly higher in adults (85.3%) than in children (14.7%), because the expression of angiotensin-converting enzyme-2 (ACE-2) diminishes with ageing that causes differences in innate and adaptive immunity. The clade GK (n = 66) was remarkable between June 2021 and January 2022. Because of the mutation burden, another clade, GRA started a newly separated clustering in December 2021. The burden was significantly higher in GRA (1.5-fold) highlighted in mild symptoms of COVID-19 patients than in other clades (GH, GK, and GR). Mutations were accumulated mainly in S (22.15 mutations per segment) and ORF1ab segments. Missense (67.5%) and synonymous (18.31%) mutations were highly noticed in adult patients with mild cases rather than severe cases, especially in ORF1ab segments. Moreover, we observed many unique mutations in S protein in mild cases compared to severe, and homology modeling revealed that those might cause more folding in the protein's alpha helix and beta sheets.

CONCLUSION

Our study identifies some risk factors such as age comorbidities (diabetes, hypertension, and renal disease) that are associated with severe COVID-19, providing valuable insight regarding prioritizing vaccination for high-risk individuals and allocating health care and resources. The findings of this work outlined the knowledge and mutational basis of SARS-CoV-2 for the next treatment steps. Further studies are needed to confirm the effects of structural and functional proteins of SARS-CoV-2 in detail for monitoring the emergence of new variants in future.

Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with enhanced transmissibility and immune escape have emerged periodically throughout the coronavirus disease 2019 (COVID-19) pandemic, but the impact of these variants on disease severity has remained unclear. In this single-center, retrospective cohort study, we examined the association between SARS-CoV-2 clade and patient outcome over a two-year period in Chicago, Illinois. Between March 2020 and March 2022, 14,252 residual diagnostic specimens were collected from SARS-CoV-2-positive inpatients and outpatients alongside linked clinical and demographic metadata, of which 2,114 were processed for viral whole-genome sequencing. When controlling for patient demographics and vaccination status, several viral clades were associated with risk for hospitalization, but this association was negated by the inclusion of population-level confounders, including case count, sampling bias, and shifting standards of care. These data highlight the importance of integrating non-virological factors into disease severity and outcome models for the accurate assessment of patient risk.

SARS-CoV-2 variant biology and immune evasion

This chapter discusses the SARS-CoV-2 variants and their immune evasion strategies, shedding light on the dynamic nature of the COVID-19 pandemic. The ecological dynamics and viral evolution of SARS-CoV-2 are explored, considering carriers of infection, individual immunity profiles, and human movement as key factors in the emergence and dissemination of variants. The chapter discusses SARS-CoV-2 mutation, including mutation rate, substitution rate, and recombination, influencing genetic diversity and evolution. Transmission bottlenecks are highlighted as determinants of dominant variants during viral spread. The evolution phases of the pandemic are outlined, from limited early evolution to the emergence of notable changes like the D614G substitution and variants with heavy mutations. Variants of Concern (VOCs), including Alpha, Beta, Gamma, and the recent Omicron variant, are examined, with insights into inter-lineage and intra-lineage dynamics. The origin of VOCs and the Omicron variant is explored, alongside the role of the furin cleavage site (FCS) in variant emergence. The impact of structural and non-structural proteins on viral infectivity is assessed, as well as innate immunity evasion strategies employed by SARS-CoV-2 variants. The chapter concludes by considering future possibilities, including ongoing virus evolution, the need for surveillance, vaccine development, and public health measures.

High connectivity and human movement limits the impact of travel time on infectious disease transmission

The speed of spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the importance of understanding how infections are transmitted in a highly connected world. Prior to vaccination, changes in human mobility patterns were used as non-pharmaceutical interventions to eliminate or suppress viral transmission. The rapid spread of respiratory viruses, various intervention approaches, and the global dissemination of SARS-CoV-2 underscore the necessity for epidemiological models that incorporate mobility to comprehend the spread of the virus. Here, we introduce a metapopulation susceptible-exposed-infectious-recovered model parametrized with human movement data from 340 cities in China. Our model replicates the early-case trajectory in the COVID-19 pandemic. We then use machine learning algorithms to determine which network properties best predict spread between cities and find travel time to be most important, followed by the human movement-weighted personalized PageRank. However, we show that travel time is most influential locally, after which the high connectivity between cities reduces the impact of travel time between individual cities on transmission speed. Additionally, we demonstrate that only significantly reduced movement substantially impacts infection spread times throughout the network.

Intense P.1 (Gamma) diversification followed by rapid Delta substitution in Southern Brazil: a SARS-CoV-2 genomic epidemiology study

The analyses of genetic traits, dispersion patterns and phylogenomics are essential for understanding the evolutionary forces driving SARS-CoV-2 viruses in these three years of COVID-19 pandemics. Brazil is one of the most affected countries in the world and not sufficient genomic studies have been performed. The emergence of P.1 lineage led to one of the most serious public health crises on record. Our study presents the genomic sequencing and characterization of 412 samples from Rio Grande do Sul state, in the Brazilian Southern region, during Gamma and Delta epidemic waves, in 2021. Additionally, molecular evolution tests were performed to identify positively selected sites in Brazil between 2020 and 2022, as well as offer some evolutionary perspective about the maintenance of multiple spike mutations in Omicron lineages. Genomic epidemiology analysis has indicated an intense P.1 (Gamma) diversification followed by rapid Delta substitution in Southern Brazil.

Emerging variants of SARS-CoV-2 NSP10 highlight strong functional conservation of its binding to two non-structural proteins, NSP14 and NSP16

The coronavirus SARS-CoV-2 protects its RNA from being recognized by host immune responses by methylation of its 5' end, also known as capping. This process is carried out by two enzymes, non-structural protein 16 (NSP16) containing 2'-O-methyltransferase and NSP14 through its N7 methyltransferase activity, which are essential for the replication of the viral genome as well as evading the host's innate immunity. NSP10 acts as a crucial cofactor and stimulator of NSP14 and NSP16. To further understand the role of NSP10, we carried out a comprehensive analysis of >13 million globally collected whole-genome sequences (WGS) of SARS-CoV-2 obtained from the Global Initiative Sharing All Influenza Data (GISAID) and compared it with the reference genome Wuhan/WIV04/2019 to identify all currently known variants in NSP10. T12I, T102I, and A104V in NSP10 have been identified as the three most frequent variants and characterized using X-ray crystallography, biophysical assays, and enhanced sampling simulations. In contrast to other proteins such as spike and NSP6, NSP10 is significantly less prone to mutation due to its crucial role in replication. The functional effects of the variants were examined for their impact on the binding affinity and stability of both NSP14-NSP10 and NSP16-NSP10 complexes. These results highlight the limited changes induced by variant evolution in NSP10 and reflect on the critical roles NSP10 plays during the SARS-CoV-2 life cycle. These results also indicate that there is limited capacity for the virus to overcome inhibitors targeting NSP10 via the generation of variants in inhibitor binding pockets.

The Many Mutations of the COVID-19 Variant: Current Perspectives on EG.5/Eris

Viral diseases pose a significant threat to public health around the world. SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) was originally identified in Wuhan, China, in 2019. Throughout the epidemic, SARS-CoV-2 has continually changed genetically, giving rise to variants that are distinct from the original virus. SARS-CoV-2 has a high-frequency mutation rate, resulting in more genetic diversity. EG.5/Eris is a subvariant and descendant of Omicron, which remains the world's most prevalent coronavirus strain of current concern. The percentage of EG.5 recorded has steadily increased across the board. Epidemiological week 29 (17-23 July 2023) saw a 17.4% global prevalence of EG.5. Mutations in the virus's genome can cause false-negative results in molecular detection and cause increased transmissibility, morbidity, and mortality due to a reduction in vaccine efficiency. Furthermore, these changes in S-protein structure alter the neutralising ability of neutralising antibodies (Nabs), resulting in a reduction in vaccine efficiency. Therefore, all countries should take efficient infection prevention and control measures as per the guidelines of the world, continental, and their country's health organisations, along with vaccine and treatment investigations.

Using machine learning probabilities to identify effects of COVID-19

Coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has had extensive economic, social, and public health impacts in the United States and around the world. To date, there have been more than 600 million reported infections worldwide with more than 6 million reported deaths. Retrospective analysis, which identified comorbidities, risk factors, and treatments, has underpinned the response. As the situation transitions to an endemic, retrospective analyses using electronic health records will be important to identify the long-term effects of COVID-19. However, these analyses can be complicated by incomplete records, which makes it difficult to differentiate visits where the patient had COVID-19. To address this issue, we trained a random Forest classifier to assign a probability of a patient having been diagnosed with COVID-19 during each visit. Using these probabilities, we found that higher COVID-19 probabilities were associated with a future diagnosis of myocardial infarction, urinary tract infection, acute renal failure, and type 2 diabetes.

Predicting virus Fitness: Towards a structure-based computational model

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogens. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein (∼380,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

High-precision rapid testing of omicron SARS-CoV-2 variants in clinical samples using AI-nanopore

A digital platform that can rapidly and accurately diagnose pathogenic viral variants, including SARS-CoV-2, will minimize pandemics, public anxiety, and economic losses. We recently reported an artificial intelligence (AI)-nanopore platform that enables testing for Wuhan SARS-CoV-2 with high sensitivity and specificity within five minutes. However, which parts of the virus are recognized by the platform are unknown. Similarly, whether the platform can detect SARS-CoV-2 variants or the presence of the virus in clinical samples needs further study. Here, we demonstrated the platform can distinguish SARS-CoV-2 variants. Further, it identified mutated Wuhan SARS-CoV-2 expressing spike proteins of the delta and omicron variants, indicating it discriminates spike proteins. Finally, we used the platform to identify omicron variants with a sensitivity and specificity of 100% and 94%, respectively, in saliva specimens from COVID-19 patients. Thus, our results demonstrate the AI-nanopore platform is an effective diagnostic tool for SARS-CoV-2 variants.

COVID-19 Treatments: Then and Now

The coronavirus disease 2019 (COVID-19) pandemic has evolved over the past 3+ years, and strategies to prevent illness and treat infection have changed over time. As COVID-19 transitions from a pandemic to an endemic infection, widespread nonpharmaceutical interventions such as mask mandates and governmental policies requiring social distancing have given way to more selective strategies for risk mitigation. Monoclonal antibody therapies used for disease prevention and treatment lost utility owing to the emergence of resistant viral variants. Oral antiviral medications have become the mainstay of treatment in nonhospitalized individuals, whereas systemic corticosteroids remain the cornerstone of therapy in those requiring supplemental oxygen. Emerging literature also supports the use of additional immune-modulating therapies in select admitted patients. Importantly, the COVID-19 pandemic highlighted both unprecedented research and development of medical interventions while also drawing attention to significant pitfalls in the global response. This review provides a comprehensive update in prevention and management of COVID-19.

PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny

The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires objective subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than existing approaches. To demonstrate its utility, we applied parnas to 1) quantify SARS-CoV-2 genetic diversity over time, 2) select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data, and 3) identify gaps in H3N2 human influenza A virus vaccine coverage. We suggest that our method, through the objective selection of representatives in a phylogeny, provides criteria for quantifying genetic diversity that has application in the the rational design of multivalent vaccines and genomic epidemiology. PARNAS is available at https://github.com/flu-crew/parnas.

Broad-spectrum vaccine via combined immunization routes triggers potent immunity to SARS-CoV-2 and its variants

IMPORTANCE

The development of broad-spectrum SARS-CoV-2 vaccines will reduce the global economic and public health stress from the COVID-19 pandemic. The use of conserved T-cell epitopes in combination with spike antigen that induce humoral and cellular immune responses simultaneously may be a promising strategy to further enhance the broad spectrum of COVID-19 vaccine candidates. Moreover, this research suggests that the combined vaccination strategies have the ability to induce both effective systemic and mucosal immunity, which may represent promising strategies for maximizing the protective efficacy of respiratory virus vaccines.

Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty

Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

A fast and accurate method for SARS-CoV-2 genomic tracing

To contain infectious diseases, it is crucial to determine the origin and transmission routes of the pathogen, as well as how the virus evolves. With the development of genome sequencing technology, genome epidemiology has emerged as a powerful approach for investigating the source and transmission of pathogens. In this study, we first presented the rationale for genomic tracing of SARS-CoV-2 and the challenges we currently face. Identifying the most genetically similar reference sequence to the query sequence is a critical step in genome tracing, typically achieved using either a phylogenetic tree or a sequence similarity search. However, these methods become inefficient or computationally prohibitive when dealing with tens of millions of sequences in the reference database, as we encountered during the COVID-19 pandemic. To address this challenge, we developed a novel genomic tracing algorithm capable of processing 6 million SARS-CoV-2 sequences in less than a minute. Instead of constructing a giant phylogenetic tree, we devised a weighted scoring system based on mutation characteristics to quantify sequences similarity. The developed method demonstrated superior performance compared to previous methods. Additionally, an online platform was developed to facilitate genomic tracing and visualization of the spatiotemporal distribution of sequences. The method will be a valuable addition to standard epidemiological investigations, enabling more efficient genomic tracing. Furthermore, the computational framework can be easily adapted to other pathogens, paving the way for routine genomic tracing of infectious diseases.

Remdesivir increases mtDNA copy number causing mild alterations to oxidative phosphorylation

SARS-CoV-2 causes the severe respiratory disease COVID-19. Remdesivir (RDV) was the first fast-tracked FDA approved treatment drug for COVID-19. RDV acts as an antiviral ribonucleoside (adenosine) analogue that becomes active once it accumulates intracellularly. It then diffuses into the host cell and terminates viral RNA transcription. Previous studies have shown that certain nucleoside analogues unintentionally inhibit mitochondrial RNA or DNA polymerases or cause mutational changes to mitochondrial DNA (mtDNA). These past findings on the mitochondrial toxicity of ribonucleoside analogues motivated us to investigate what effects RDV may have on mitochondrial function. Using in vitro and in vivo rodent models treated with RDV, we observed increases in mtDNA copy number in Mv1Lu cells (35.26% increase ± 11.33%) and liver (100.27% increase ± 32.73%) upon treatment. However, these increases only resulted in mild changes to mitochondrial function. Surprisingly, skeletal muscle and heart were extremely resistant to RDV treatment, tissues that have preferentially been affected by other nucleoside analogues. Although our data suggest that RDV does not greatly impact mitochondrial function, these data are insightful for the treatment of RDV for individuals with mitochondrial disease.

Highly-specific aptamer targeting SARS-CoV-2 S1 protein screened on an automatic integrated microfluidic system for COVID-19 diagnosis

Variants of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) have evolved such that it may be challenging for diagnosis and clinical treatment of the pandemic coronavirus disease-19 (COVID-19). Compared with developed SARS-CoV-2 diagnostic tools recently, aptamers may exhibit some advantages, including high specificity/affinity, longer shelf life (vs. antibodies), and could be easily prepared. Herein an integrated microfluidic system was developed to automatically carry out one novel screening process based on the systematic evolution of ligands by exponential enrichment (SELEX) for screening aptamers specific with SARS-CoV-2. The new screening process started with five rounds of positive selection (with the S1 protein of SARS-CoV-2). In addition, including non-target viruses (influenza A and B), human respiratory tract-related cancer cells (adenocarcinoma human alveolar basal epithelial cells and dysplastic oral keratinocytes), and upper respiratory tract-related infectious bacteria (including methicillin-resistant Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae), and human saliva were involved to increase the specificity of the screened aptamer during the negative selection. Totally, all 10 rounds could be completed within 20 h. The dissociation constant of the selected aptamer was determined to be 63.0 nM with S1 protein. Limits of detection for Wuhan and Omicron clinical strains were found to be satisfactory for clinical applications (i.e. 4.80 × 10¹ and 1.95 × 10² copies/mL, respectively). Moreover, the developed aptamer was verified to be capable of capturing inactivated SARS-CoV-2 viruses, eight SARS-CoV-2 pseudo-viruses, and clinical isolates of SARS-CoV-2 viruses. For high-variable emerging viruses, this developed integrated microfluidic system can be used to rapidly select highly-specific aptamers based on the novel SELEX methods to deal with infectious diseases in the future.

SARS-CoV-2 Variants of Concern and Clinical Severity in the Mexican Pediatric Population

The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants of concern (VOCs) presents global heterogeneity, and their relative effect on pediatric severity is still limited. In this study, we associate VOCs with pediatric clinical severity outcomes in Mexico. Bioinformatics methods were used to characterize VOCs and single amino acid (aa) mutations in 75,348 SARS-CoV-2 genetic sequences from February 2020 to October 2022. High-predominance VOCs groups were calculated and subsequently associated with 372,989 COVID-19 clinical pediatric outcomes. We identified 21 high-frequency mutations related to Omicron lineages with an increased prevalence in pediatric sequences compared to adults. Alpha and the other lineages had a significant increase in case fatality rate (CFR), intensive critical unit (ICU) admission, and automated mechanical ventilation (AMV). Furthermore, a logistic model with age-adjusted variables estimated an increased risk of hospitalization, ICU/AMV, and death in Gamma and Alpha, in contrast to the other lineages. We found that, regardless of the VOCs lineage, infant patients presented the worst severity prognoses. Our findings improve the understanding of the impact of VOCs on pediatric patients across time, regions, and clinical outcomes. Enhanced understanding of the pediatric severity for VOCs would enable the development and improvement of public health strategies worldwide.

Building a global community of health for all: A positive discourse analysis of COVID-19 discourse

‘A global community of health for all’ has become a dominant concept in China’s global health governance system. Although this concept has been investigated by several studies in different domains, little attention has been given to its discursive legitimation in China’s media communication from a linguistic perspective. To fill this gap, the present study employs positive discourse analysis to investigate how the aforementioned concept is legitimised via the predominant discourses associated with COVID-19 in state-run Chinese English-language newspapers. The findings show that the Chinese news media attempted to formulate the positive discourses, including cooperation as a win-win solution, people’s lives and well-being as the priority and science as the spirit, though the discourses may not resonate with some countries. The findings shed light on the use of language by the media in promoting official ideologies, projecting China’s national image and improving China’s international relations amid a global health crisis.

COVID-19 annual update: a narrative review

Three and a half years after the pandemic outbreak, now that WHO has formally declared that the emergency is over, COVID-19 is still a significant global issue. Here, we focus on recent developments in genetic and genomic research on COVID-19, and we give an outlook on state-of-the-art therapeutical approaches, as the pandemic is gradually transitioning to an endemic situation. The sequencing and characterization of rare alleles in different populations has made it possible to identify numerous genes that affect either susceptibility to COVID-19 or the severity of the disease. These findings provide a beginning to new avenues and pan-ethnic therapeutic approaches, as well as to potential genetic screening protocols. The causative virus, SARS-CoV-2, is still in the spotlight, but novel threatening virus could appear anywhere at any time. Therefore, continued vigilance and further research is warranted. We also note emphatically that to prevent future pandemics and other world-wide health crises, it is imperative to capitalize on what we have learnt from COVID-19: specifically, regarding its origins, the world's response, and insufficient preparedness. This requires unprecedented international collaboration and timely data sharing for the coordination of effective response and the rapid implementation of containment measures.

Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective

Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure-function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence-structure-function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.

SARS2Mutant: SARS-CoV-2 amino-acid mutation atlas database

The coronavirus disease 19 (COVID-19) is a highly pathogenic viral infection of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulted in the global pandemic of 2020. A lack of therapeutic and preventive strategies has quickly posed significant threats to world health. A comprehensive understanding of SARS-CoV-2 evolution and natural selection, how it impacts host interaction, and phenotype symptoms is vital to develop effective strategies against the virus. The SARS2Mutant database (http://sars2mutant.com/) was developed to provide valuable insights based on millions of high-quality, high-coverage SARS-CoV-2 complete protein sequences. Users of this database have the ability to search for information on three amino acid substitution mutation strategies based on gene name, geographical zone, or comparative analysis. Each strategy is presented in five distinct formats which includes: (i) mutated sample frequencies, (ii) heat maps of mutated amino acid positions, (iii) mutation survivals, (iv) natural selections and (v) details of substituted amino acids, including their names, positions, and frequencies. GISAID is a primary database of genomics sequencies of influenza viruses updated daily. SARS2Mutant is a secondary database developed to discover mutation and conserved regions from the primary data to assist with design for targeted vaccine, primer, and drug discoveries.

Clinical Evaluation of Commercial HARDSON COVID-19 Antigen Rapid Test Kit for Routine COVID-19 Diagnosis

Objective

This study aimed to evaluate the sensitivity, specificity, and accuracy of the commercial HARDSON COVID-19 Antigen Rapid Test Kit for diagnosing COVID-19 among the Iranian population by compared with the results of commercial RT-PCR.

Materials and Methods

Two nasopharyngeal swabs were collected from each patient. One swab was tested with HARDSON COVID-19 Antigen Rapid Test Kit, and the second swab was placed in 3 mL of a virus-transmitted inactivated media for RT-PCR testing. Then, the results of both tests were compared to investigate the diagnostic accuracy of the rapid antigen test.

Results

A total of 275 suspected COVID-19 patients' samples were collected to investigate the diagnostic accuracy of HARDSON COVID-19 Antigen Rapid Test Kit. In this study, 162 positive and 113 negative samples were evaluated. As a result, the sensitivity, specificity, and accuracy of HARDSON COVID-19 Antigen Rapid Test Kit were 90%, 100%, and 94%, respectively.

Conclusion

The diagnostic kit analyzed in this study indicated excellent specificity and a relatively good overall sensitivity for the diagnosis of COVID-19 when compared with the RT-PCR detection kit. Based on the result of this study, COVID-19 Antigen Rapid Test Kit indicated a good sensitivity (96%) in low cycle threshold (Ct) value, and it would be recommended to be integrated into routine diagnostic laboratories and used as an at-home rapid antigen test.

Quantitative Mutation Analysis of Genes and Proteins of Major SARS-CoV-2 Variants of Concern and Interest

Of various SARS-CoV-2 variants, some have drawn special concern or interest because of their heightened disease threat. The mutability of individual SARS-CoV-2 genes/proteins presumably varies. The present study quantified gene/protein mutations in 13 major SARS-CoV-2 variants of concern/interest, and analyzed viral protein antigenicity using bioinformatics. The results from 187 carefully perused genome clones showed significantly higher mean percent mutations in the spike, ORF8, nucleocapsid, and NSP6 than in other viral proteins. The ORF8 and spike proteins also tolerated higher maximal percent mutations. The omicron variant presented more percent mutations in the NSP6 and structural proteins, whereas the delta featured more in the ORF7a. Omicron subvariant BA.2 exhibited more mutations in ORF6, and omicron BA.4 had more in NSP1, ORF6, and ORF7b, relative to omicron BA.1. Delta subvariants AY.4 and AY.5 bore more mutations in ORF7b and ORF8 than delta B.1.617.2. Predicted antigen ratios of SARS-CoV-2 proteins significantly vary (range: 38-88%). To overcome SARS-CoV-2 immune evasion, the relatively conserved, potentially immunogenic NSP4, NSP13, NSP14, membrane, and ORF3a viral proteins may serve as more suitable targets for molecular vaccines or therapeutics than the mutation-prone NSP6, spike, ORF8, or nucleocapsid protein. Further investigation into distinct mutations of the variants/subvariants may help understand SARS-CoV-2 pathogenesis.

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.

SARS-CoV-2 Mpro Protease Variants of Concern Display Altered Viral Substrate and Cell Host Target Galectin-8 Processing but Retain Sensitivity toward Antivirals

The main protease of SARS-CoV-2 (M^pro) is the most promising drug target against coronaviruses due to its essential role in virus replication. With newly emerging variants there is a concern that mutations in M^pro may alter the structural and functional properties of protease and subsequently the potency of existing and potential antivirals. We explored the effect of 31 mutations belonging to 5 variants of concern (VOCs) on catalytic parameters and substrate specificity, which revealed changes in substrate binding and the rate of cleavage of a viral peptide. Crystal structures of 11 M^pro mutants provided structural insight into their altered functionality. Additionally, we show M^pro mutations influence proteolysis of an immunomodulatory host protein Galectin-8 (Gal-8) and a subsequent significant decrease in cytokine secretion, providing evidence for alterations in the escape of host-antiviral mechanisms. Accordingly, mutations associated with the Gamma VOC and highly virulent Delta VOC resulted in a significant increase in Gal-8 cleavage. Importantly, IC50s of nirmatrelvir (Pfizer) and our irreversible inhibitor AVI-8053 demonstrated no changes in potency for both drugs for all mutants, suggesting M^pro will remain a high-priority antiviral drug candidate as SARS-CoV-2 evolves.

Intra-Host Evolution Provides for the Continuous Emergence of SARS-CoV-2 Variants

Variants of concern (VOC) in SARS-CoV-2 refer to viruses whose viral genomes differ from the ancestor virus by ≥3 single-nucleotide variants (SNVs) and that show the potential for higher transmissibility and/or worse clinical progression. VOC have the potential to disrupt ongoing public health measures and vaccine efforts. Still, too little is known regarding how frequently new viral variants emerge and under what circumstances. We report a study to determine the degree of SARS-CoV-2 sequence evolution in 94 patients and to estimate the frequency at which highly diverse variants emerge. Two cases accumulated ≥9 SNVs over a 2-week period and one case accumulated 23 SNVs over 3 weeks, including three nonsynonymous mutations in the spike protein (D138H, E554D, D614G). The remainder of the infected patients did not show signs of intra-host evolution. We estimate that in as much as 2% of hospitalized COVID-19 cases, variants with multiple mutations in the spike glycoprotein emerge in as little as 1 month of persistent intra-host virus replication. This suggests the continued local emergence of variants with multiple nonsynonymous SNVs, even in patients without overt immune deficiency. Surveillance by sequencing for (i) viremic COVID-19 patients, (ii) patients suspected of reinfection, and (iii) patients with diminished immune function may offer broad public health benefits. IMPORTANCE New SARS-CoV-2 variants can potentially disrupt ongoing public health measures and vaccine efforts. Still, little is known regarding how frequently new viral variants emerge and under what circumstances. Based on this study, we estimate that in hospitalized COVID-19 cases, variants with multiple mutations may emerge locally in as little as 1 month, even in patients without overt immune deficiency. Surveillance by sequencing for continuously shedding patients, patients suspected of reinfection, and patients with diminished immune function may offer broad public health benefits.

Vaccines Alone Cannot Slow the Evolution of SARS-CoV-2

The rapid emergence of immune-evading viral variants of SARS-CoV-2 calls into question the practicality of a vaccine-only public-health strategy for managing the ongoing COVID-19 pandemic. It has been suggested that widespread vaccination is necessary to prevent the emergence of future immune-evading mutants. Here, we examined that proposition using stochastic computational models of viral transmission and mutation. Specifically, we looked at the likelihood of emergence of immune escape variants requiring multiple mutations and the impact of vaccination on this process. Our results suggest that the transmission rate of intermediate SARS-CoV-2 mutants will impact the rate at which novel immune-evading variants appear. While vaccination can lower the rate at which new variants appear, other interventions that reduce transmission can also have the same effect. Crucially, relying solely on widespread and repeated vaccination (vaccinating the entire population multiple times a year) is not sufficient to prevent the emergence of novel immune-evading strains, if transmission rates remain high within the population. Thus, vaccines alone are incapable of slowing the pace of evolution of immune evasion, and vaccinal protection against severe and fatal outcomes for COVID-19 patients is therefore not assured.

The evolution of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths and substantial morbidity worldwide. Intense scientific effort to understand the biology of SARS-CoV-2 has resulted in daunting numbers of genomic sequences. We witnessed evolutionary events that could mostly be inferred indirectly before, such as the emergence of variants with distinct phenotypes, for example transmissibility, severity and immune evasion. This Review explores the mechanisms that generate genetic variation in SARS-CoV-2, underlying the within-host and population-level processes that underpin these events. We examine the selective forces that likely drove the evolution of higher transmissibility and, in some cases, higher severity during the first year of the pandemic and the role of antigenic evolution during the second and third years, together with the implications of immune escape and reinfections, and the increasing evidence for and potential relevance of recombination. In order to understand how major lineages, such as variants of concern (VOCs), are generated, we contrast the evidence for the chronic infection model underlying the emergence of VOCs with the possibility of an animal reservoir playing a role in SARS-CoV-2 evolution, and conclude that the former is more likely. We evaluate uncertainties and outline scenarios for the possible future evolutionary trajectories of SARS-CoV-2.

Ginger ring compounds as an inhibitor of spike binding protein of alpha, beta, gamma and delta variants of SARS-CoV-2: An in-silico study

The available drugs against coronavirus disease 2019 (COVOD-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are limited. This study aimed to identify ginger-derived compounds that might neutralize SARS-CoV-2 and prevent its entry into host cells. Ring compounds of ginger were screened against spike (S) protein of alpha, beta, gamma, and delta variants of SARS-CoV-2. The S protein FASTA sequence was retrieved from Global Initiative on Sharing Avian Influenza Data (GISAID) and converted into ".pdb" format using Open Babel tool. A total of 306 compounds were identified from ginger through food and phyto-databases. Out of those, 38 ring compounds were subjected to docking analysis using CB Dock online program which implies AutoDock Vina for docking. The Vina score was recorded, which reflects the affinity between ligands and receptors. Further, the Protein Ligand Interaction Profiler (PLIP) program for detecting the type of interaction between ligand-receptor was used. SwissADME was used to compute druglikeness parameters and pharmacokinetics characteristics. Furthermore, energy minimization was performed by using Swiss PDB Viewer (SPDBV) and energy after minimization was recorded. Molecular dynamic simulation was performed to find the stability of protein-ligand complex and root-mean- square deviation (RMSD) as well as root-mean-square fluctuation (RMSF) were calculated and recorded by using myPresto v5.0. Our study suggested that 17 out of 38 ring compounds of ginger were very likely to bind the S protein of SARS-CoV-2. Seventeen out of 38 ring compounds showed high affinity of binding with S protein of alpha, beta, gamma, and delta variants of SARS-CoV-2. The RMSD showed the stability of the complex was parallel to the S protein monomer. These computer-aided predictions give an insight into the possibility of ginger ring compounds as potential anti-SARS-CoV-2 worthy of in vitro investigations.

Antitarget, Anti-SARS-CoV-2 Leads, Drugs, and the Drug Discovery-Genetics Alliance Perspective

The most advanced antiviral molecules addressing major SARS-CoV-2 targets (Main protease, Spike protein, and RNA polymerase), compared with proteins of other human pathogenic coronaviruses, may have a short-lasting clinical efficacy. Accumulating knowledge on the mechanisms underlying the target structural basis, its mutational progression, and the related biological significance to virus replication allows envisaging the development of better-targeted therapies in the context of COVID-19 epidemic and future coronavirus outbreaks. The identification of evolutionary patterns based solely on sequence information analysis for those targets can provide meaningful insights into the molecular basis of host-pathogen interactions and adaptation, leading to drug resistance phenomena. Herein, we will explore how the study of observed and predicted mutations may offer valuable suggestions for the application of the so-called "synthetic lethal" strategy to SARS-CoV-2 Main protease and Spike protein. The synergy between genetics evidence and drug discovery may prioritize the development of novel long-lasting antiviral agents.

Rooting and Dating Large SARS-CoV-2 Trees by Modeling Evolutionary Rate as a Function of Time

Almost all published rooting and dating studies on SARS-CoV-2 assumed that (1) evolutionary rate does not change over time although different lineages can have different evolutionary rates (uncorrelated relaxed clock), and (2) a zoonotic transmission occurred in Wuhan and the culprit was immediately captured, so that only the SARS-CoV-2 genomes obtained in 2019 and the first few months of 2020 (resulting from the first wave of the global expansion from Wuhan) are sufficient for dating the common ancestor. Empirical data contradict the first assumption. The second assumption is not warranted because mounting evidence suggests the presence of early SARS-CoV-2 lineages cocirculating with the Wuhan strains. Large trees with SARS-CoV-2 genomes beyond the first few months are needed to increase the likelihood of finding SARS-CoV-2 lineages that might have originated at the same time as (or even before) those early Wuhan strains. I extended a previously published rapid rooting method to model evolutionary rate as a linear function instead of a constant. This substantially improves the dating of the common ancestor of sampled SARS-CoV-2 genomes. Based on two large trees with 83,688 and 970,777 high-quality and full-length SARS-CoV-2 genomes that contain complete sample collection dates, the common ancestor was dated to 12 June 2019 and 7 July 2019 with the two trees, respectively. The two data sets would give dramatically different or even absurd estimates if the rate was treated as a constant. The large trees were also crucial for overcoming the high rate-heterogeneity among different viral lineages. The improved method was implemented in the software TRAD.

Thiopurine therapy in inflammatory bowel disease in the pandemic era: Safe or unsafe?

BACKGROUND

Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract. Crohn's disease (CD) and Ulcerative colitis (UC) are the two major types affecting millions across the globe. Various immunomodulatory drugs consisting of small molecules (thiopurines, methotrexate and tofacitinib) and biologics are used to treat IBD. Thiopurines (TP) are widely used in the treatment of IBD and it plays an important role both alone and in combination with anti-TNF agents as IBD maintenance therapy. Although the advent of biologics therapy has significantly advanced the management of IBD, TP remains the mainstay of treatment in resource-limited and low economic settings. However, the recently commenced pandemic has raised uncertainty over the safety of the use of immunosuppressant drugs such as TP among healthcare care providers and patients, as there is a scarcity of data on whether IBD patients are at higher risk of COVID-19 infection or more prone to its severe outcomes.

AIM

This review aims to encapsulate evidence on the risk of COVID-19 infection and its severe prognosis in IBD patients on TP. Additionally, it also evaluates the role of TP in inhibiting the viral protease, a potential drug target, essential for the replication and pathogenesis of the virus.

CONCLUSION

Emerging evidence suggests that TP therapy is safe during the current pandemic and does not carry an elevated risk when used as monotherapy or in combination with other IBD drugs. In-vitro studies demonstrate that TP is a potential therapeutic for present and future betacoronavirus pandemics.

Global landscape of SARS-CoV-2 mutations and conserved regions

BACKGROUND

At the end of December 2019, a novel strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) disease (COVID-19) has been identified in Wuhan, a central city in China, and then spread to every corner of the globe. As of October 8, 2022, the total number of COVID-19 cases had reached over 621 million worldwide, with more than 6.56 million confirmed deaths. Since SARS-CoV-2 genome sequences change due to mutation and recombination, it is pivotal to surveil emerging variants and monitor changes for improving pandemic management.

METHODS

10,287,271 SARS-CoV-2 genome sequence samples were downloaded in FASTA format from the GISAID databases from February 24, 2020, to April 2022. Python programming language (version 3.8.0) software was utilized to process FASTA files to identify variants and sequence conservation. The NCBI RefSeq SARS-CoV-2 genome (accession no. NC_045512.2) was considered as the reference sequence.

RESULTS

Six mutations had more than 50% frequency in global SARS-CoV-2. These mutations include the P323L (99.3%) in NSP12, D614G (97.6) in S, the T492I (70.4) in NSP4, R203M (62.8%) in N, T60A (61.4%) in Orf9b, and P1228L (50.0%) in NSP3. In the SARS-CoV-2 genome, no mutation was observed in more than 90% of nsp11, nsp7, nsp10, nsp9, nsp8, and nsp16 regions. On the other hand, N, nsp3, S, nsp4, nsp12, and M had the maximum rate of mutations. In the S protein, the highest mutation frequency was observed in aa 508-635(0.77%) and aa 381-508 (0.43%). The highest frequency of mutation was observed in aa 66-88 (2.19%), aa 7-14, and aa 164-246 (2.92%) in M, E, and N proteins, respectively.

CONCLUSION

Therefore, monitoring SARS-CoV-2 proteomic changes and detecting hot spots mutations and conserved regions could be applied to improve the SARS-CoV-2 diagnostic efficiency and design safe and effective vaccines against emerging variants.