Detection of a cluster of Omicron's BA.4 sublineage in Northern Senegal and identification of the first XAS recombinant variant in Senegal

In Senegal, since its first detection in early March 2020, genomic surveillance of SARS-CoV-2 isolates has led to the identification of the emergence of the Omicron BA.4 and BA.5 sublineages from early June 2022. To investigate the origin of a cluster of cases in Northern Senegal on July 2022, isolates were analysed using Next-generation sequencing and phylogeny. Our data provided evidence of the origin of the cluster of BA.4 cases from a XAS recombinant, that is to date, the first reported sequence of this variant from Senegal. Continuous genomic surveillance of positive SARS-CoV-2 samples is a crucial need.

Genetic diversity and evolutionary dynamics of the Omicron variant of SARS-CoV-2 in Morocco

Among the numerous variants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that have been reported worldwide, the emergence of the Omicron variant has drastically changed the landscape of the coronavirus disease (COVID-19) pandemic. Here, we analyzed the genetic diversity of Moroccan SARS-CoV-2 genomes with a focus on Omicron variant after one year of its detection in Morocco in order to understand its genomic dynamics, features and its potential introduction sources. From 937 Omicron genomes, we identified a total of 999 non-unique mutations distributed across 92 Omicron lineages, of which 13 were specific to the country. Our findings suggest multiple introductory sources of the Omicron variant to Morocco. In addition, we found that four Omicron clades are more infectious in comparison to other Omicron clades. Remarkably, a clade of Omicron is particularly more transmissible and has become the dominant variant worldwide. Moreover, our assessment of Receptor-Binding Domain (RBD) mutations showed that the Spike K444T and N460K mutations enabled a clade higher ability of immune vaccine escape. In conclusion, our analysis highlights the unique genetic diversity of the Omicron variant in Moroccan SARS-CoV-2 genomes, with multiple introductory sources and the emergence of highly transmissible clades. The distinctiveness of the Moroccan strains compared to global ones underscores the importance of ongoing surveillance and understanding of local genomic dynamics for effective response strategies in the evolving COVID-19 pandemic.

A tale of two waves: Delineating diverse genomic and transmission landscapes driving the COVID-19 pandemic in Pune, India

BACKGROUND

Modern response to pandemics, critical for effective public health measures, is shaped by the availability and integration of diverse epidemiological outbreak data. Tracking variants of concern (VOC) is integral to understanding the evolution of SARS-CoV-2 in space and time, both at the local level and global context. This potentially generates actionable information when integrated with epidemiological outbreak data.

METHODS

A city-wide network of researchers, clinicians, and pathology diagnostic laboratories was formed for genome surveillance of COVID-19 in Pune, India. The genomic landscapes of 10,496 sequenced samples of SARS-CoV-2 driving peaks of infection in Pune between December-2020 to March-2022, were determined. As a modern response to the pandemic, a "band of five" outbreak data analytics approach was used. This integrated the genomic data (Band 1) of the virus through molecular phylogenetics with key outbreak data including sample collection dates and case numbers (Band 2), demographics like age and gender (Band 3-4), and geospatial mapping (Band 5).

RESULTS

The transmission dynamics of VOCs in 10,496 sequenced samples identified B.1.617.2 (Delta) and BA(x) (Omicron formerly known as B.1.1.529) variants as drivers of the second and third peaks of infection in Pune. Spike Protein mutational profiling during pre and post-Omicron VOCs indicated differential rank ordering of high-frequency mutations in specific domains that increased the charge and binding properties of the protein. Time-resolved phylogenetic analysis of Omicron sub-lineages identified a highly divergent BA.1 from Pune in addition to recombinant X lineages, XZ, XQ, and XM.

CONCLUSIONS

The band of five outbreak data analytics approach, which integrates five different types of data, highlights the importance of a strong surveillance system with high-quality meta-data for understanding the spatiotemporal evolution of the SARS-CoV-2 genome in Pune. These findings have important implications for pandemic preparedness and could be critical tools for understanding and responding to future outbreaks.

SARS-CoV-2 molecular epidemiology in Slovenia, January to September 2021

BackgroundSequencing of SARS-CoV-2 PCR-positive samples was introduced in Slovenia in January 2021. Our surveillance programme comprised three complementary schemes: (A) non-targeted sequencing of at least 10% of samples, (B) sequencing of samples positive after PCR screening for variants of concern (VOC) and (C) sequencing as per epidemiological indication.AimWe present the analysis of cumulative data of the non-targeted surveillance of SARS-CoV-2 and variant-dependent growth kinetics for the five most common variants in Slovenia for the first 9 months of 2021.MethodsSARS-CoV-2 PCR-positive samples, from January to September 2021, were selected for sequencing according to the national surveillance plan. Growth kinetics studies were done on Vero E6 cells.ResultsAltogether 15,175 genomes were sequenced and 64 variants were detected, of which three successively prevailed. Variant B.1.258.17 was detected in ca 80% of samples in January and was replaced, within 9 weeks, by the Alpha variant. The number of cases decreased substantially during the summer of 2021. However, the introduction of the Delta variant caused a fourth wave and completely outcompeted other variants. Other VOC were only detected in small numbers. Infection of Vero E6 cells showed higher replication rates for the variants Alpha and Delta, compared with B.1.258.17, B.1.258, and B.1.1.70, which dominated in Slovenia before the introduction of the Alpha and Delta variants.ConclusionInformation on SARS-CoV-2 variant diversity provided context to the epidemiological data of PCR-positive cases, contributed to control of the initial spread of known VOC and influenced epidemiological measures.

Analysis of SARS-CoV-2 genomic surveillance data during the Delta and Omicron waves at a Saudi tertiary referral hospital

BACKGROUND

Studying the genomic evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may help determine outbreak clusters and virus transmission advantages to aid public health efforts during the pandemic. Thus, we tracked the evolution of SARS-CoV-2 by variant epidemiology, breakthrough infection, and patient characteristics as the virus spread during the Delta and Omicron waves. We also conducted phylogenetic analyses to assess modes of transmission.

METHODS

Nasopharyngeal samples were collected from a cohort of 900 patients with positive polymerase chain reaction (PCR) test results confirming COVID-19 disease. Samples underwent real-time PCR detection using TaqPath assays. Sequencing was performed with Ion GeneStudio using the Ion AmpliSeq™ SARS-CoV-2 panel. Variant calling was performed with Torrent Suite™ on the Torrent Server. For phylogenetic analyses, the MAFFT tool was used for alignment and the maximum likelihood method with the IQ-TREE tool to build the phylogenetic tree. Data were analyzed using SAS statistical software. Analysis of variance or t tests were used to assess continuous variables, and χ² tests were used to assess categorical variables. Univariate and multivariate logistic regression analyses were preformed to estimate odds ratios (ORs).

RESULTS

The predominant variants in our cohort of 900 patients were non-variants of concern (11.1 %), followed by Alpha (4.1 %), Beta (5.6 %), Delta (21.2 %), and Omicron (58 %). The Delta wave had more male than female cases (112 vs. 78), whereas the Omicron wave had more female than male cases (311 vs. 208). The oldest patients (mean age, 43.4 years) were infected with non-variants of concern; the youngest (mean age, 33.7 years), with Omicron. Younger patients were mostly unvaccinated, whereas elderly patients were mostly vaccinated, a statistically significant difference. The highest risk for breakthrough infection by age was for patients aged 30-39 years (OR = 12.4, CI 95 %: 6.6-23.2), followed by patients aged 40-49 years (OR = 11.2, CI 95 %: 6.1-23.1) and then 20-29 years (OR = 8.2, CI 95 %: 4.4-15.4). Phylogenetic analyses suggested the interaction of multiple cases related to outbreaks for breakthrough infections, healthcare workers, and intensive care unit admission.

CONCLUSION

The findings of this study highlighted several major public health ramifications, including the distribution of variants over a wide range of demographic and clinical variables and by vaccination status.