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The world has been dealing with the SARS-COV-2 pandemic since December 2019 and a lot of effort has focused on tracking the spread of the virus by gathering information regarding testing statistics and generating viral genomic sequences. Unfortunately, there is neither a single comprehensive resource with global historical testing data nor a centralized database with summary statistics of the identified genomic variants. We merged different pre-aggregated historical testing data and complemented them with our manually extracted ones, which consist of 6852 historical test statistics from 76 countries/states unreported in any other dataset, at the date of submission, making our dataset the most comprehensive to date. We also analyzed all publicly deposited SARS-CoV-2 genomic sequences in GISAID and annotated their variants. Both datasets can be accessed through our interactive dashboard which also provides important insights on different outbreak trends across countries and states. The dashboard is available at https://bioinfo.lau.edu.lb/gkhazen/covid19 . A daily updated version of the datasets can be downloaded from github.com/KhazenLab/covid19-data.

During the COVID-19 pandemic, immunosuppressed patients showed prolonged SARS-CoV-2 infections, with several studies reporting the accumulation of mutations in the viral genome. The weakened immune system present in these individuals, along with the effect of antiviral therapies, are thought to create a favourable environment for intra-host viral evolution and have been linked to the emergence of new viral variants which strongly challenged containment measures and some therapeutic treatments. To assess whether impaired immunity could lead to the increased instability of viral genomes, longitudinal nasopharyngeal swabs were collected from eight immunocompromised patients and fourteen non-immunocompromised subjects, all undergoing SARS-CoV-2 infection. Intra-host viral evolution was compared between the two groups through deep sequencing, exploiting a probe-based enrichment method to minimise the possibility of artefactual mutations commonly generated in amplicon-based methods, which heavily rely on PCR amplification. Although, as expected, immunocompromised patients experienced significantly longer infections, the acquisition of novel intra-host viral mutations was similar between the two groups. Moreover, a thorough analysis of viral quasispecies showed that the variability of viral populations in the two groups is comparable not only at the consensus level, but also when considering low-frequency mutations. This study suggests that a compromised immune system alone does not affect SARS-CoV-2 within-host genomic variability.

Background Tracking the genetic variability of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is a crucial challenge. Mainly to identify target sequences in order to generate robust vaccines and neutralizing monoclonal antibodies, but also to track viral genetic temporal and geographic evolution and to mine for variants associated with reduced or increased disease severity. Several online tools and bioinformatic phylogenetic analyses have been released, but the main interest lies in the Spike protein, which is the pivotal element of current vaccine design, and in the Receptor Binding Domain, that accounts for most of the neutralizing the antibody activity. Methods Here, we present an open-source bioinformatic protocol, and a web portal focused on SARS-CoV-2 single mutations and minimal consensus sequence building as a companion vaccine design tool. Furthermore, we provide immunogenomic analyses to understand the impact of the most frequent RBD variations. Results Results on the whole GISAID sequence dataset at the time of the writing (October 2020) reveals an emerging mutation, S477N, located on the central part of the Spike protein Receptor Binding Domain, the Receptor Binding Motif. Immunogenomic analyses revealed some variation in mutated epitope MHC compatibility, T-cell recognition, and B-cell epitope probability for most frequent human HLAs. Conclusions This work provides a framework able to track down SARS-CoV-2 genomic variability.

Background The continuous emergence of SARS-CoV-2 variants and subvariants poses significant public health challenges. The latest designated subvariant JN.1, with all its descendants, shows more than 30 mutations in the spike gene. JN.1 has raised concerns due to its genomic diversity and its potential to enhance transmissibility and immune evasion. This study aims to analyse the molecular characteristics of JN.1-related lineages (JN.1*) identified in Italy from October 2023 to April 2024 and to evaluate the neutralization activity against JN.1 of a subsample of sera from individuals vaccinated with XBB.1.5 mRNA. Methods The genomic diversity of the spike gene of 794 JN.1* strain was evaluated and phylogenetic analysis was conducted to compare the distance to XBB.1.5. Moreover, serum neutralization assays were performed on a subsample of 19 healthcare workers (HCWs) vaccinated with the monovalent XBB.1.5 mRNA booster to assess neutralizing capacity against JN.1. Results Sequence analysis displayed high spike variability between JN.1* and phylogenetic investigation confirmed a substantial differentiation between JN.1* and XBB.1.5 spike regions with 29 shared mutations, of which 17 were located within the RBD region. Pre-booster neutralization activity against JN.1 was observed in 42% of HCWs sera, increasing significantly post-booster, with all HCWs showing neutralization capacity three months after vaccination. A significant correlation was found between anti-trimeric Spike IgG levels and neutralizing titers against JN.1. Conclusions The study highlights the variability of JN.1* in Italy. Results on a subsample of sera from HCWs vaccinated with XBB.1.5 mRNA booster vaccine suggested enhanced neutralization activity against JN.1.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that caused coronavirus disease 2019 (COVID-19). Whole-genome tracking of SARS-CoV-2 enhanced our understanding of the mechanism of the disease, control, and prevention of COVID-19. Methods: we analyzed 3368 SARS-CoV-2 protein sequences from Iran and compared them with 15.6 million global sequences in the GISAID database, using the Wuhan-Hu-1 strain as a reference. Results: Our investigation revealed that NSP12-P323L, ORF9c-G50N, NSP14-I42V, membrane-A63T, Q19E, and NSP3-G489S were found to be the most frequent mutations among Iranian SARS-CoV-2 sequences. Furthermore, it was observed that more than 94% of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutations when compared to the Wuhan-Hu-1 strain. Finally, our data indicated that the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be responsible factors for the surge in the SARS-CoV-2 Omicron variant wave in Iran. Conclusions: real-time genomic surveillance is crucial for detecting new SARS-CoV-2 variants, updating diagnostic tools, designing vaccines, and understanding adaptation to new environments.

Differences in SARS-CoV-2-specific immune responses have been observed between individuals following natural infection or vaccination. In addition to already known factors, such as age, sex, COVID-19 severity, comorbidity, vaccination status, hybrid immunity, and duration of infection, inter-individual variations in SARS-CoV-2 immune responses may, in part, be explained by structural differences brought about by genetic variation in the human leukocyte antigen (HLA) molecules responsible for the presentation of SARS-CoV-2 antigens to T effector cells. While dendritic cells present peptides with HLA class I molecules to CD8+ T cells to induce cytotoxic T lymphocyte responses (CTLs), they present peptides with HLA class II molecules to T follicular helper cells to induce B cell differentiation followed by memory B cell and plasma cell maturation. Plasma cells then produce SARS-CoV-2-specific antibodies. Here, we review published data linking HLA genetic variation or polymorphisms with differences in SARS-CoV-2-specific antibody responses. While there is evidence that heterogeneity in antibody response might be related to HLA variation, there are conflicting findings due in part to differences in study designs. We provide insight into why more research is needed in this area. Elucidating the genetic basis of variability in the SARS-CoV-2 immune response will help to optimize diagnostic tools and lead to the development of new vaccines and therapeutics against SARS-CoV-2 and other infectious diseases.

Background SARS-CoV-2 is responsible for the ongoing global pandemic, and the continuous emergence of novel variants threatens fragile populations, such as immunocompromised patients. This subgroup of patients seems to be seriously affected by intrahost viral changes, as the pathogens, which are keen to cause replication inefficiency, affect the impaired immune system, preventing efficient clearance of the virus. Therefore, these patients may represent an optimal reservoir for the development of new circulating SARS-CoV-2 variants. The following study aimed to investigate genomic changes in SARS-CoV-2-positive immunocompromised patients over time. Methods SARS-CoV-2-positive nasopharyngeal swabs were collected at different time points for each patient (patient A and patient B), extracted and then analyzed through next-generation sequencing (NGS). The resulting sequences were examined to determine mutation frequencies, describing viral evolution over time. Case presentation Patient A was a 53-year-old patient with onco-hematological disease with prolonged infection lasting for 51 days from May 28th to July 18th, 2022. Three confirmed SARS-CoV-2-positive samples were collected on May 28th, June 15th and July 4th. Patient B was 75 years old and had onco-hematological disease with prolonged infection lasting for 146 days. Two confirmed positive SARS-CoV-2 samples were collected at the following time points: May 21st and August 18th. Conclusion Heat map construction provided evidence of gain and/or loss of mutations over time for both patients, suggesting within-host genomic evolution of the virus. In addition, mutation polymorphisms and changes in the SARS-CoV-2 lineage were observed in Patient B. Sequence analysis revealed high mutational pattern variability, reflecting the high complexity of viral replication dynamics in fragile patients.

Effective systems for the analysis of molecular data are fundamental for monitoring the spread of infectious diseases and studying pathogen evolution. The rapid identification of emerging viral strains, and/or genetic variants potentially associated with novel phenotypic features is one of the most important objectives of genomic surveillance of human pathogens and represents one of the first lines of defense for the control of their spread. During the COVID 19 pandemic, several taxonomic frameworks have been proposed for the classification of SARS-Cov-2 isolates. These systems, which are typically based on phylogenetic approaches, represent essential tools for epidemiological studies as well as contributing to the study of the origin of the outbreak. Here, we propose an alternative, reproducible, and transparent phenetic method to study changes in SARS-CoV-2 genomic diversity over time. We suggest that our approach can complement other systems and facilitate the identification of biologically relevant variants in the viral genome. To demonstrate the validity of our approach, we present comparative genomic analyses of more than 175,000 genomes. Our method delineates 22 distinct SARS-CoV-2 haplogroups, which, based on the distribution of high-frequency genetic variants, fall into four major macrohaplogroups. We highlight biased spatiotemporal distributions of SARS-CoV-2 genetic profiles and show that seven of the 22 haplogroups (and of all of the four haplogroup clusters) showed a broad geographic distribution within China by the time the outbreak was widely recognized—suggesting early emergence and widespread cryptic circulation of the virus well before its isolation in January 2020. General patterns of genomic variability are remarkably similar within all major SARS-CoV-2 haplogroups, with UTRs consistently exhibiting the greatest variability, with s2m, a conserved secondary structure element of unknown function in the 3′-UTR of the viral genome showing evidence of a functional shift. Although several polymorphic sites that are specific to one or more haplogroups were predicted to be under positive or negative selection, overall our analyses suggest that the emergence of novel types is unlikely to be driven by convergent evolution and independent fixation of advantageous substitutions, or by selection of recombined strains. In the absence of extensive clinical metadata for most available genome sequences, and in the context of extensive geographic and temporal biases in the sampling, many questions regarding the evolution and clinical characteristics of SARS-CoV-2 isolates remain open. However, our data indicate that the approach outlined here can be usefully employed in the identification of candidate SARS-CoV-2 genetic variants of clinical and epidemiological importance.

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

Since its emergence, SARS-CoV-2 Omicron clade has shown a marked degree of variability and different clinical presentation compared with previous clades. Here we demonstrate that at least four Omicron lineages circulated in children since December 2021, and studied until November 2022: BA.1 (33.6%), BA.2 (40.6%), BA.5 (23.7%) and BQ.1 (2.1%). At least 70% of infections concerned children under 1 year, most of them being infected with BA.2 lineages (n = 201, 75.6%). Looking at SARS-CoV-2 genetic variability, 69 SNPs were found to be significantly associated in pairs, (phi <  − 0.3 or > 0.3 and p-value < 0.001). 16 SNPs were involved in 4 distinct clusters (bootstrap > 0.75). One of these clusters (A23040G, A27259C, T23617G, T23620G) was also positively associated with moderate/severe COVID-19 presentation (AOR [95% CI] 2.49 [1.26–4.89] p-value: 0.008) together with comorbidities (AOR [95% CI] 2.67 [1.36–5.24] p-value: 0.004). Overall, these results highlight the extensive SARS-CoV-2 Omicron circulation in children, mostly aged < 1 year, and provide insights on viral diversification even considering low-abundant SNPs, finally suggesting the potential contribution of viral diversification in affecting disease severity.