A comprehensive analysis of the mutational landscape of the newly emerging Omicron (B.1.1.529) variant and comparison of mutations with VOCs and VOIs.

The Omicron variant is spreading rapidly throughout several countries. Thus, we comprehensively analyzed Omicron's mutational landscape and compared mutations with VOC/VOI. We analyzed SNVs throughout the genome, and AA variants (NSP and SP) in VOC/VOI, including Omicron. We generated heat maps to illustrate the AA variants with high mutation prevalence (> 75% frequency) of Omicron, which demonstrated eight mutations with > 90% prevalence in ORF1a and 29 mutations with > 75% prevalence in S-glycoprotein. A scatter plot for Omicron and VOC/VOI's cluster evaluation was computed. We performed a risk analysis of the antibody-binding risk among four mutations (L452, F490, P681, D614) and observed three mutations (L452R, F490S, D614G) destabilized antibody interactions. Our comparative study evaluated the properties of 28 emerging mutations of the S-glycoprotein of Omicron, and the ΔΔG values. Our results showed K417N with minimum and Q954H with maximum ΔΔG value. Furthermore, six important RBD mutations (G339D, S371L, N440K, G446S, T478K, Q498R) were chosen for comprehensive analysis for stabilizing/destabilizing properties and molecular flexibility. The G339D, S371L, N440K, and T478K were noted as stable mutations with 0.019 kcal/mol, 0.127 kcal/mol, 0.064 kcal/mol, and 1.009 kcal/mol. While, G446S and Q498R mutations showed destabilizing results. Simultaneously, among six RBD mutations, G339D, G446S, and Q498R mutations increased the molecular flexibility of S-glycoprotein. This study depicts the comparative mutational pattern of Omicron and other VOC/VOI, which will help researchers to design and deploy novel vaccines and therapeutic antibodies to fight against VOC/VOI, including Omicron.

Human ACE2 Genetic Polymorphism Affecting SARS-CoV and SARS-CoV-2 Entry into Cells.

Severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 have a single envelope glycoprotein (S protein) that binds to human angiotensin-converting enzyme 2 (ACE2) on the host cell membrane. Previous mutational scanning studies have suggested that some substitutions corresponding to single nucleotide variants (SNVs) in human ACE2 affect the binding affinity to the receptor binding domain (RBD) of the SARS-CoV-2 S protein. However, the importance of these substitutions in actual virus infection is still unclear. In this study, we investigated the effects of the reported ACE2 SNV substitutions on the entry of SARS-CoV and SARS-CoV-2 into cells, using vesicular stomatitis Indiana virus (VSIV) pseudotyped with S proteins of these coronaviruses (CoVs). HEK293T cells transfected with plasmids expressing ACE2 having each SNV substitution were infected with the pseudotyped VSIVs and relative infectivities were determined compared to the cells expressing wild-type ACE2. We found that some of the SNV substitutions positively or negatively affected the infectivities of the pseudotyped viruses. Particularly, the H505R substitution significantly enhanced the infection with the pseudotyped VSIVs, including those having the substitutions found in the S protein RBD of SARS-CoV-2 variants of concern. Our findings suggest that human ACE2 SNVs may potentially affect cell susceptibilities to SARS-CoV and SARS-CoV-2. IMPORTANCE SARS-CoV and SARS-CoV-2 are known to cause severe pneumonia in humans. The S protein of these CoVs binds to the ACE2 molecule on the plasma membrane and mediates virus entry into cells. The interaction between the S protein and ACE2 is thought to be important for host susceptibility to these CoVs. Although previous studies suggested that some SNV substitutions in ACE2 might affect the binding to the S protein, it remains elusive whether these SNV substitutions actually alter the efficiency of the entry of SARS CoVs into cells. We analyzed the impact of the ACE2 SNVs on the cellular entry of SARS CoVs using pseudotyped VSIVs having the S protein on the viral surface. We found that some of the SNV substitutions positively or negatively affected the infectivities of the viruses. Our data support the notion that genetic polymorphisms of ACE2 may potentially influence cell susceptibilities to SARS CoVs.

Genetic epidemiology using whole genome sequencing and haplotype networks revealed the linkage of SARS-CoV-2 infection in nosocomial outbreak.

BACKGROUND: A characteristic feature of SARS-CoV-2 is its ability to transmit from pre- or asymptomatic patients, complicating the tracing of infection pathways and causing outbreaks. Despite several reports that whole genome sequencing (WGS) and haplotype networks are useful for epidemiologic analysis, little is known about their use in nosocomial infections. AIM: We aimed to demonstrate the advantages of genetic epidemiology in identifying the link in nosocomial infection by comparing single nucleotide variations (SNVs) of isolates from patients associated with an outbreak in Showa University Hospital. METHODS: We used specimens from 32 patients in whom COVID-19 had been diagnosed using clinical reverse transcription-polymerase chain reaction tests. RNA of SARS-CoV-2 from specimens was reverse-transcribed and analysed using WGS. SNVs were extracted and used for lineage determination, phylogenetic tree analysis, and median-joining analysis. FINDINGS: The lineage of SARS-CoV-2 that was associated with outbreak in Showa University Hospital was B.1.1.214, which was consistent with that found in the Kanto metropolitan area during the same period. Consistent with canonical epidemiological observations, haplotype network analysis was successful for the classification of patients. Additionally, phylogenetic tree analysis revealed three independent introductions of the virus into the hospital during the outbreak. Further, median-joining analysis indicated that four patients were directly infected by any of the others in the same cluster. CONCLUSION: Genetic epidemiology with WGS and haplotype networks is useful for tracing transmission and optimizing prevention strategies in nosocomial outbreaks.

Identification and characterization of SARS-CoV-2 clusters in the EU/EEA in the first pandemic wave: additional elements to trace the route of the virus.

A high-quality dataset of 3289 complete SARS-CoV-2 genomes collected in Europe and European Economic Area (EAA) in the early phase of the first wave of the pandemic was analyzed. Among all single nucleotide mutations, 41 had a frequency ≥ 1%, and the phylogenetic analysis showed at least 6 clusters with a specific mutational profile. These clusters were differentially distributed in the EU/EEA, showing a statistically significant association with the geographic origin. The analysis highlighted that the mutations C14408T and C14805T played an important role in clusters selection and further virus spread. Moreover, the molecular analysis suggests that the SARS-CoV-2 strain responsible for the first Italian confirmed COVID-19 case was already circulating outside the country.

Evolution of SARS-CoV-2 genome from December 2019 to late March 2020: Emerged haplotypes and informative Tag nucleotide variations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes serious disease in humans. First identified in November/December 2019 in China, it has rapidly spread worldwide. We analyzed 2790 SARS-CoV-2 genome sequences from 56 countries that were available on April 2, 2020, to assess the evolution of the virus during this early phase of its expansion. We aimed to assess sequence variations that had evolved in virus genomes, giving the greatest attention to the S gene. We also aimed to identify haplotypes that the variations may define and consider their geographic and chronologic distribution. Variations at 1930 positions that together cause 1203 amino acid changes were identified. The frequencies of changes normalized to the lengths of genes and encoded proteins were relatively high in ORF3a and relatively low in M. A variation that causes an Asp614Gly near the receptor-binding domain of S were found at a high frequency, and it was considered that this may contribute to the rapid spread of viruses with this variation. Our most important findings relate to haplotypes. Sixty-six haplotypes that constitute thirteen haplotype groups (H1-H13) were identified, and 84.6% of the 2790 sequences analyzed were associated with these haplotypes. The majority of the sequences (75.1%) were associated with haplotype groups H1-H3. The distribution pattern of the haplotype groups differed in various geographic regions. A few were country/territory specific. The location and time of emergence of some haplotypes are discussed. Importantly, nucleotide variations that define the various haplotypes and Tag/signature variations for most of the haplotypes are reported. The practical applications of these variations are discussed.

Insights on early mutational events in SARS-CoV-2 virus reveal founder effects across geographical regions.

Here we aim to describe early mutational events across samples from publicly available SARS-CoV-2 sequences from the sequence read archive and GenBank repositories. Up until 27 March 2020, we downloaded 50 illumina datasets, mostly from China, USA (WA State) and Australia (VIC). A total of 30 datasets (60%) contain at least a single founder mutation and most of the variants are missense (over 63%). Five-point mutations with clonal (founder) effect were found in USA next-generation sequencing samples. Sequencing samples from North America in GenBank (22 April 2020) present this signature with up to 39% allele frequencies among samples (n = 1,359). Australian variant signatures were more diverse than USA samples, but still, clonal events were found in these samples. Mutations in the helicase, encoded by the ORF1ab gene in SARS-CoV-2 were predominant, among others, suggesting that these regions are actively evolving. Finally, we firmly urge that primer sets for diagnosis be carefully designed, since rapidly occurring variants would affect the performance of the reverse transcribed quantitative PCR (RT-qPCR) based viral testing.