Dataset Information

SARS-CoV genome polymorphism: a bioinformatics study.

ABSTRACT: A dataset of 103 SARS-CoV isolates (101 human patients and 2 palm civets) was investigated on different aspects of genome polymorphism and isolate classification. The number and the distribution of single nucleotide variations (SNVs) and insertions and deletions, with respect to a "profile", were determined and discussed ("profile" being a sequence containing the most represented letter per position). Distribution of substitution categories per codon positions, as well as synonymous and non-synonymous substitutions in coding regions of annotated isolates, was determined, along with amino acid (a.a.) property changes. Similar analysis was performed for the spike (S) protein in all the isolates (55 of them being predicted for the first time). The ratio Ka/Ks confirmed that the S gene was subjected to the Darwinian selection during virus transmission from animals to humans. Isolates from the dataset were classified according to genome polymorphism and genotypes. Genome polymorphism yields to two groups, one with a small number of SNVs and another with a large number of SNVs, with up to four subgroups with respect to insertions and deletions. We identified three basic nine-locus genotypes: TTTT/TTCGG, CGCC/TTCAT, and TGCC/TTCGT, with four subgenotypes. Both classifications proposed are in accordance with the new insights into possible epidemiological spread, both in space and time.

SUBMITTER: Pavlovic-Lazetic GM

PROVIDER: S-EPMC5172477 | biostudies-literature | 2005 Feb

REPOSITORIES: biostudies-literature

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SARS-CoV genome polymorphism: a bioinformatics study.

Pavlović-Lazetić Gordana M GM Mitić Nenad S NS Tomović Andrija M AM Pavlović Mirjana D MD Beljanski Milos V MV

Genomics, proteomics & bioinformatics 20050201 1

A dataset of 103 SARS-CoV isolates (101 human patients and 2 palm civets) was investigated on different aspects of genome polymorphism and isolate classification. The number and the distribution of single nucleotide variations (SNVs) and insertions and deletions, with respect to a "profile", were determined and discussed ("profile" being a sequence containing the most represented letter per position). Distribution of substitution categories per codon positions, as well as synonymous and non-syno ...[more]

PMID: 16144519

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Project description:BackgroundWe have compared 38 isolates of the SARS-CoV complete genome. The main goal was twofold: first, to analyze and compare nucleotide sequences and to identify positions of single nucleotide polymorphism (SNP), insertions and deletions, and second, to group them according to sequence similarity, eventually pointing to phylogeny of SARS-CoV isolates. The comparison is based on genome polymorphism such as insertions or deletions and the number and positions of SNPs.ResultsThe nucleotide structure of all 38 isolates is presented. Based on insertions and deletions and dissimilarity due to SNPs, the dataset of all the isolates has been qualitatively classified into three groups each having their own subgroups. These are the A-group with "regular" isolates (no insertions / deletions except for 5' and 3' ends), the B-group of isolates with "long insertions", and the C-group of isolates with "many individual" insertions and deletions. The isolate with the smallest average number of SNPs, compared to other isolates, has been identified (TWH). The density distribution of SNPs, insertions and deletions for each group or subgroup, as well as cumulatively for all the isolates is also presented, along with the gene map for TWH. Since individual SNPs may have occurred at random, positions corresponding to multiple SNPs (occurring in two or more isolates) are identified and presented. This result revises some previous results of a similar type. Amino acid changes caused by multiple SNPs are also identified (for the annotated sequences, as well as presupposed amino acid changes for non-annotated ones). Exact SNP positions for the isolates in each group or subgroup are presented. Finally, a phylogenetic tree for the SARS-CoV isolates has been produced using the CLUSTALW program, showing high compatibility with former qualitative classification.ConclusionsThe comparative study of SARS-CoV isolates provides essential information for genome polymorphism, indication of strain differences and variants evolution. It may help with the development of effective treatment.

Project description:BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset.MethodsWe identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study.ResultsThe benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2.DiscussionThe datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines.

Project description: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.

Dataset Information

SARS-CoV genome polymorphism: a bioinformatics study.

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SARS-CoV genome polymorphism: a bioinformatics study.

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