Dataset Information

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages.

ABSTRACT: The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

SUBMITTER: Mostefai F

PROVIDER: S-EPMC8899026 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages.

Mostefai Fatima F Gamache Isabel I N'Guessan Arnaud A Pelletier Justin J Huang Jessie J Murall Carmen Lia CL Pesaranghader Ahmad A Gaonac'h-Lovejoy Vanda V Hamelin David J DJ Poujol Raphaël R Grenier Jean-Christophe JC Smith Martin M Caron Etienne E Craig Morgan M Wolf Guy G Krishnaswamy Smita S Shapiro B Jesse BJ Hussin Julie G JG

Frontiers in medicine 20220221

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to bett ...[more]

PMID: 35265640

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Project description:Wastewater-based epidemiology (WBE) is an effective way of tracking the appearance and spread of SARS-COV-2 lineages through communities. Beginning in early 2021, we implemented a targeted approach to amplify and sequence the receptor binding domain (RBD) of SARS-COV-2 to characterize viral lineages present in sewersheds. Over the course of 2021, we reproducibly detected multiple SARS-COV-2 RBD lineages that have never been observed in patient samples in 9 sewersheds located in 3 states in the USA. These cryptic lineages contained between 4 to 24 amino acid substitutions in the RBD and were observed intermittently in the sewersheds in which they were found for as long as 14 months. Many of the amino acid substitutions in these lineages occurred at residues also mutated in the Omicron variant of concern (VOC), often with the same substitution. One of the sewersheds contained a lineage that appeared to be derived from the Alpha VOC, but the majority of the lineages appeared to be derived from pre-VOC SARS-COV-2 lineages. Specifically, several of the cryptic lineages from New York City appeared to be derived from a common ancestor that most likely diverged in early 2020. While the source of these cryptic lineages has not been resolved, it seems increasingly likely that they were derived from immunocompromised patients or animal reservoirs. Our findings demonstrate that SARS-COV-2 genetic diversity is greater than what is commonly observed through routine SARS-CoV-2 surveillance. Wastewater sampling may more fully capture SARS-CoV-2 genetic diversity than patient sampling and could reveal new VOCs before they emerge in the wider human population.Author summaryDuring the COVID-19 pandemic, wastewater-based epidemiology has become an effective public health tool. Because many infected individuals shed SARS-CoV-2 in feces, wastewater has been monitored to reveal infection trends in the sewersheds from which the samples were derived. Here we report novel SARS-CoV-2 lineages in wastewater samples obtained from 3 different states in the USA. These lineages appeared in specific sewersheds intermittently over periods of up to 14 months, but generally have not been detected beyond the sewersheds in which they were initially found. Many of these lineages may have diverged in early 2020. Although these lineages share considerable overlap with each other, they have never been observed in patients anywhere in the world. While the wastewater lineages have similarities with lineages observed in long-term infections of immunocompromised patients, animal reservoirs cannot be ruled out as a potential source.

Project description:A total of two lineages of Mycobacterium tuberculosis var. africanum (Maf), L5 and L6, which are members of the Mycobacterium tuberculosis complex (MTBC), are responsible for causing tuberculosis in West Africa. Regions of difference (RDs) are usually used for delineation of MTBC. With increased data availability, single nucleotide polymorphisms (SNPs) promise to provide better resolution. Publicly available 380 Maf samples were analyzed for identification of "core-cluster-specific-SNPs," while additional 270 samples were used for validation. RD-based methods were used for lineage-assignment, wherein 31 samples remained unidentified. The genetic diversity of Maf was estimated based on genome-wide SNPs using phylogeny and population genomics approaches. Lineage-based clustering (L5 and L6) was observed in the whole genome phylogeny with distinct sub-clusters. Population stratification using both model-based and de novo approaches supported the same observations. L6 was further delineated into three sub-lineages (L6.1-L6.3), whereas L5 was grouped as L5.1 and L5.2 based on the occurrence of RD711. L5.1 and L5.2 were further divided into two (L5.1.1 and L5.1.2) and four (L5.2.1-L5.2.4) sub-clusters, respectively. Unassigned samples could be assigned to definite lineages/sub-lineages based on clustering observed in phylogeny along with high-confidence posterior membership scores obtained during population stratification. Based on the (sub)-clusters delineated, "core-cluster-specific-SNPs" were derived. Synonymous SNPs (137 in L5 and 128 in L6) were identified as biomarkers and used for validation. Few of the cluster-specific missense variants in L5 and L6 belong to the central carbohydrate metabolism pathway which include His6Tyr (Rv0946c), Glu255Ala (Rv1131), Ala309Gly (Rv2454c), Val425Ala and Ser112Ala (Rv1127c), Gly198Ala (Rv3293) and Ile137Val (Rv0363c), Thr421Ala (Rv0896), Arg442His (Rv1248c), Thr218Ile (Rv1122), and Ser381Leu (Rv1449c), hinting at the differential growth attenuation. Genes harboring multiple (sub)-lineage-specific "core-cluster" SNPs such as Lys117Asn, Val447Met, and Ala455Val (Rv0066c; icd2) present across L6, L6.1, and L5, respectively, hinting at the association of these SNPs with selective advantage or host-adaptation. Cluster-specific SNPs serve as additional markers along with RD-regions for Maf delineation. The identified SNPs have the potential to provide insights into the genotype-phenotype correlation and clues for endemicity of Maf in the African population.

Dataset Information

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages.

Publications

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages.

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