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Lineage-defining SNPs of lineage B.1.620 Only SNPs that differentiate B.1.620 (genomes outlined with a dashed line) from the reference (GenBank accession NC_045512) and that are shared by at least two B.1.620 genomes are shown in the condensed SNP alignment. Sites identical to the reference are shown in grey, changes from the reference are indicated and coloured by nucleotide (green for thymidine, red for adenosine, blue for cytosine, yellow for guanine, dark grey for ambiguities, black for gaps). The first 100 and the last 50 nucleotides are not included in the figure but were used to infer the phylogeny. If a mutation results in an amino acid change, the column label indicates the gene, reference amino acid, amino acid site, and amino acid change in brackets. The phylogeny (branch lengths in the number of mutations) on the right shows the relationships between depicted genomes and was rooted on the reference sequence with coloured circles at the tips indicating the country from which the genome came. Posterior probabilities of nodes leading up to lineage B.1.620 are shown near each node with the long branch leading to lineage B.1.620 labelled as ‘B.1.620’.
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Distinct SARS-CoV-2 lineages, discovered through various genomic surveillance initiatives, have emerged during the pandemic following unprecedented reductions in worldwide human mobility. We here describe a SARS-CoV-2 lineage - designated B.1.620 - discovered in Lithuania and carrying many mutations and deletions in the spike protein shared with wi...
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The ongoing SARS-CoV-2 pandemic has seen an unprecedented amount of rapidly generated genome data. These data have revealed the emergence of lineages with mutations associated to transmissibility and antigenicity, known as variants of concern (VOCs). A striking aspect of VOCs is that many of them involve an unusually large number of defining mutati...
Citations
... The Omicron variant, first identified in Botswana, a country in South Africa, at the end of 2021, is rapidly spreading through more than 170 countries due to its exceptionally strong infectivity (4 Omicron-helixes have numerous new mutations in the receptor binding domain (RBD) of the S protein that strongly enhance the binding affinity between the RBD and the hACE2 complex (5). Therefore, the elderly individuals have the most obvious suffering from Omicron infection, which has led to a high mortality rate during the epidemic (6). ...
Coronavirus disease 2019 (COVID-19), which is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a global pandemic. The Omicron variant (B.1.1.529) was first discovered in November 2021 in specimens collected from Botswana, South Africa. Omicron has become the dominant variant worldwide, and several sublineages or subvariants have been identified recently. Compared to those of other mutants, the Omicron variant has the most highly expressed amino acid mutations, with almost 60 mutations throughout the genome, most of which are in the spike (S) protein, especially in the receptor-binding domain (RBD). These mutations increase the binding affinity of Omicron variants for the ACE2 receptor, and Omicron variants may also lead to immune escape. Despite causing milder symptoms, epidemiological evidence suggests that Omicron variants have exceptionally higher transmissibility, higher rates of reinfection and greater spread than the prototype strain as well as other preceding variants. Additionally, overwhelming amounts of data suggest that the levels of specific neutralization antibodies against Omicron variants decrease in most vaccinated populations, although CD4⁺ and CD8⁺ T-cell responses are maintained. Therefore, the mechanisms underlying Omicron variant evasion are still unclear. In this review, we surveyed the current epidemic status and potential immune escape mechanisms of Omicron variants. Especially, we focused on the potential roles of viral epitope mutations, antigenic drift, hybrid immunity, and “original antigenic sin” in mediating immune evasion. These insights might supply more valuable concise information for us to understand the spreading of Omicron variants.
... Moreover, a 50% increase in weekly infection numbers was observed from December 29, 2020 to January 25, 2021 [8] [9] [10]. The Central African Republic (CAR), like other bordering countries, Cameroon and the Democratic Republic of Congo, experienced two waves from April 27 th 2020 to June 6 th 2021 and April 2020 to September 2021 [11]- [18], respectively. ...
... As the molecular tests have been conducted solely in antigen positive patients some positive results may have been missed because antigenic tests are less sensitive than the molecular ones [22] [23]: their number is likelty limited and estimated as not more than 20. The network collaboration of sub-regional [11] [24] laboratories have made possible the characterization of the circulating variants of SARS-CoV-2 in the sub-region and identified the strains responsible of the variants that influenced the associated with the second wave of COVID19 .Thus despite limited resources it is possible to establish a local genomic surveillance useful for the fight against this disease [25]. ...
... The 501Y variant, V2, initially identified in South Africa, is predominant and fueling a record number of cases in South Africa and the sub-region. This variant has been found in Botswana, Ghana, Kenya, the French Indian Ocean region of Mayotte, Zambia and 24 non-African countries [7] [8] [9] [10] [11].The variant initially detected in the UK was found in Gambia and Nigeria.More research was therefore needed to determine whether the new strain causes more severe disease[8] [10].Faced with new variants detected in the United Kingdom, Brazil and SouthThe Central African Republic, after the detection of its first case on March 14, 2020, had then experienced two successive waves, the most recent of which were devastating due to the circulation of variants B.1.1.7 (commonly known as the British variant) and B.1.135 (South African variant) highlighted thanks to the multilateral efforts deployed during the mass campaign launched on March 17, 2021 and coordinated by the technical team of the National Laboratory of Clinical Biology and Health Public [17] ...
Context and objective: The COVID-19 pandemic has become a major public health problem and has mobilized many innovative means of diagnosis. The Central African Republic is not spared. The emergence of variants and their impact require health monitoring despite the obligation of vaccination. The purpose of this campaign was to determine the circulation of pending second-wave variants. Patients and Methods: A second mass screening campaign took place from 02 to 22 July 2021 in the main land and river entry points of Bangui (Exit North-PK12, Exit South-PK9, Port Beach) and at the LNBCSP. Antigenic and RT-PCR tests carried out on nasopharyngeal samples made it possible to select strains which were finally sequenced. Results: Of 2687 participants included in the study, 53 (1.97%) were positive for SARS-CoV-2. Thirteen (1.53%) were male and 40 (2.18%) female. The analyses carried out on the LumiraDx analyzer were positive for 109 samples against 53 on the How to cite this paper: Rafaï, C.
... Interestingly, B.1.620 SARS-CoV-2 lineage was identified as originating specifically in the CAR, which brings mutations (like D614G on spike protein) and deletions already encountered in Variants of Concern (VoCs), increasing infectivity and resistance to the immune response [16]. Considered as a Variant Under Monitoring (VUM), B.1.620 ...
Since its outbreak, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread rapidly, causing the Coronavirus Disease 19 (COVID-19) pandemic. Even with the vaccines’ administration, the virus continued to circulate due to inequal access to prevention and therapeutic measures in African countries. Information about COVID-19 in Africa has been limited and contradictory, and thus regional studies are important. On this premise, we conducted a genomic surveillance study about COVID-19 lineages circulating in Bangui, Central African Republic (CAR). We collected 2687 nasopharyngeal samples at four checkpoints in Bangui from 2 to 22 July 2021. Fifty-three samples tested positive for SARS-CoV-2, and viral genomes were sequenced to look for the presence of different viral strains. We performed phylogenetic analysis and described the lineage landscape of SARS-CoV-2 circulating in the CAR along 15 months of pandemics and in Africa during the study period, finding the Delta variant as the predominant Variant of Concern (VoC). The deduced aminoacidic sequences of structural and non-structural genes were determined and compared to reference and reported isolates from Africa. Despite the limited number of positive samples obtained, this study provides valuable information about COVID-19 evolution at the regional level and allows for a better understanding of SARS-CoV-2 circulation in the CAR.
... Comparing the original and subsampled datasets to epidemiological data could be a solution to the present issue in sampling. However, this type of data also often suffers from biases, includ-ing those created by under-sampling in low-and middle-income countries or are not documented particularly for the early dynamics of the epidemics (Dudas et al. 2021;Zeller et al. 2021). We made an effort to obtain retrospective epidemiological data documenting the number of patients infected with HIV-1 Subtype C, but the epidemiological data does not report nor is sorted by subtype, which further complicated this endeavor. ...
Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence of unbalanced sampled distribution by collection date, location, and risk group of HIV-1 subtype C using a comprehensive subsampling strategy, and assess their impact on the reconstruction of the viral spatial and risk group dynamics using phylogenetic comparative methods. Our study shows that a most suitable dataset for ancestral trait reconstruction can be obtained through subsampling by all available traits, particularly using multigene datasets. We also demonstrate that sampling bias is inflated when considerable information for a given trait is unavailable or of poor quality, as we observed for the trait risk group. In conclusion, we suggest that, even if traits are not well recorded, including them deliberately optimizes the representativeness of the original dataset rather than completely excluding them. Therefore, we advise the inclusion of as many traits as possible with the aid of subsampling approaches in order to optimize the dataset for phylodynamic analysis while reducing the computational burden. This will benefit research communities investigating the evolutionary and spatiotemporal patterns of infectious diseases.
... These variants contribute to the continuation of the COVID-19 pandemic. The term "variant of concern" (VOC) for SARS-CoV-2 refers to viral variants that have mutations in their spike protein receptor-binding domain (RBD), which improve the binding affinity of RBD with thehACE2 complex causing the faster spread of the virus in populations [4]. The World Health Organization (WHO) has considered Alpha, Beta, Gamma, Delta, and Omicron [5][6][7] as variants of concern. ...
During replication, some mutations occur in SARS-CoV-2, the causal agent of COVID-19, leading to the emergence of different variants of the virus. The mutations that accrue in different variants of the virus, influence the virus' ability to bind to human cell receptors and ability to evade the human immune system, the rate of viral transmission, and effectiveness of vaccines. Some of these mutations occur in the receptor binding domain (RBD) of the spike protein that may change the affinity of the virus for the ACE2 receptor. In this study, several in silico techniques, such as MD and SMD simulations, were used to perform comparative studies to deeply understand the effect of mutation on structural and functional details of the interaction of the spike glycoprotein of SARS-CoV-2, with the ACE2 receptor. According to our results, the mutation in the RBD associated with the Omicron variant increase binding affinity of the virus to ACE2 when compared to wild type and Delta variants. We also observed that the flexibility of the spike protein of the Omicron variant was lower than in comparison to other variants. In summary, different mutations in variants of the virus can have an effect on the binding mechanism of the receptor binding domain of the virus with ACE2.
... The unprecedented amount of genomic data generated through worldwide genomic surveillance of SARS-CoV-2 has enabled valuable insights into the dispersal dynamics of the virus and its different variants. In that context, numerous phylogeographic investigations have been conducted at global [28][29][30][31] and local [32][33][34][35] scales. By placing phylogenetic trees in geographic space and time, phylogeographic reconstructions provide information on the mode and pace of viral lineage circulation across spatial scales. ...
Since the latter part of 2020, SARS-CoV-2 evolution has been characterised by the emergence of viral variants associated with distinct biological characteristics. While the main research focus has centred on the ability of new variants to increase in frequency and impact the effective reproductive number of the virus, less attention has been placed on their relative ability to establish transmission chains and to spread through a geographic area. Here, we describe a phylogeographic approach to estimate and compare the introduction and dispersal dynamics of the main SARS-CoV-2 variants-Alpha, Iota, Delta, and Omicron-that circulated in the New York City area between 2020 and 2022. Notably, our results indicate that Delta had a lower ability to establish sustained transmission chains in the NYC area and that Omicron (BA.1) was the variant fastest to disseminate across the study area. The analytical approach presented here complements non-spatially-explicit analytical approaches that seek a better understanding of the epidemiological differences that exist among successive SARS-CoV-2 variants of concern.
... 37 variants of concern (VOC) based on the Pango SARS-CoV-2 lineage nomenclature, which implies viral variant with mutations in their RBD altering the binding to hACE2 receptor. 41,42 According to WHO, the rapidly spreading viral variants are categorized as Alfa, Beta, Gamma, Delta and Omicron. 37,43 ...
Infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a growing concern to the global well-being of the public at present. Different amino acid mutations alter the biological and epidemiological characteristics, as well as immune resistance of SARS-CoV-2. The virus-induced pulmonary impairment and inflammatory cytokine storm are directly related to its clinical manifestations. But, the fundamental mechanisms of inflammatory responses are found to be the reason for the death of immune cells which render the host immune system failure. Apoptosis of immune cells is one of the most common forms of programmed cell death induced by the virus for its survival and virulence property. ORF3a, a SARS-CoV-2 accessory viral protein, induces apoptosis in host cells and suppress the defense mechanism. This suggests, inhibiting SARS-CoV-2 ORF3a protein is a good therapeutic strategy for the treatment in COVID-19 infection by promoting the host immune defense mechanism
... B.1.525 which may enhance IFN antagonism [53][54][55]. ...
Host immunity can exert a complex array of selective pressures on a pathogen, which can drive highly mutable RNA viruses towards viral escape. The plasticity of a virus depends on its rate of mutation, as well as the balance of fitness cost and benefit of mutations, including viral adaptations to the host’s immune response. Since its emergence, SARS-CoV-2 has diversified into genetically distinct variants, which are characterised often by clusters of mutations that bolster its capacity to escape human innate and adaptive immunity. Such viral escape is well documented in the context of other pandemic RNA viruses such as the human immunodeficiency virus (HIV) and influenza virus. This review describes the selection pressures the host’s antiviral immunity exerts on SARS-CoV-2 and other RNA viruses, resulting in divergence of viral strains into more adapted forms. As RNA viruses obscure themselves from host immunity, they uncover weak points in their own armoury that can inform more comprehensive, long-lasting, and potentially cross-protective vaccine coverage.
... Omicron BA.2.35 (12.3%), and B.1.620 (discovered in Lithuania [36], 7.7%) lineages, and of the XB recombinant (7.3%); these genomes having been mostly obtained in USA, United Kingdom, South Korea, and Germany. G29000A was predominantly detected in genomes of Alpha (22.2%), ...
The tremendous majority of SARS-CoV-2 genomic data so far neglected intra-host genetic diversity. Here, we studied SARS-CoV-2 quasispecies based on data generated by next-generation sequencing (NGS) of complete genomes. SARS-CoV-2 raw NGS data had been generated for nasopharyngeal samples collected between March 2020 and February 2021 by the Illumina technology on a MiSeq instrument, without prior PCR amplification. To analyze viral quasispecies, we designed and implemented an in-house Excel file (“QuasiS”) that can characterize intra-sample nucleotide diversity along the genomes using data of the mapping of NGS reads. We compared intra-sample genetic diversity and global genetic diversity available from Nextstrain. Hierarchical clustering of all samples based on the intra-sample genetic diversity was performed and visualized with the Morpheus web application. NGS mapping data from 110 SARS-CoV-2-positive respiratory samples characterized by a mean depth of 169 NGS reads/nucleotide position and for which consensus genomes that had been obtained were classified into 15 viral lineages were analyzed. Mean intra-sample nucleotide diversity was 0.21 ± 0.65%, and 5357 positions (17.9%) exhibited significant (>4%) diversity, in ≥2 genomes for 1730 (5.8%) of them. ORF10, spike, and N genes had the highest number of positions exhibiting diversity (0.56%, 0.34%, and 0.24%, respectively). Nine hot spots of intra-sample diversity were identified in the SARS-CoV-2 NSP6, NSP12, ORF8, and N genes. Hierarchical clustering delineated a set of six genomes of different lineages characterized by 920 positions exhibiting intra-sample diversity. In addition, 118 nucleotide positions (0.4%) exhibited diversity at both intra- and inter-patient levels. Overall, the present study illustrates that the SARS-CoV-2 consensus genome sequences are only an incomplete and imperfect representation of the entire viral population infecting a patient, and that quasispecies analysis may allow deciphering more accurately the viral evolutionary pathways.
