Large scale genomic analysis of 3067 SARS-CoV-2 genomes reveals a clonal geo-distribution and a rich genetic variations of hotspots mutations

Laamarti, Meriem; Alouane, Tarek; Kartti, Souad; Chemao-Elfihri, Mohammed Walid; Hakmi, Mohammed; Essabbar, Abdelomunim; Laamart, Mohamed; Hlali, Haitam; Allam, Loubna; Hafidi, Naïma El; Jaoudi, Rachid El; Allali, Imane; Marchoudi, Nabila; Fekkak, Jamal; Benrahma, Houda; Nejjari, Chakib; Amzazi, Saaïd; Belyamani, Lahcen; Ibrahimi, Azeddine

doi:10.1101/2020.05.03.074567

Cited by 53 publications

(78 citation statements)

References 37 publications

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“…In addition to their influence on phylogenetic inference, recurrent systematic errors can also lead to erroneous inferences about viral mutation processes, recombination and selection. For example, artefactual biases in mutational processes could confound signatures of mutational hotspots [28][29][30][31][32][33] . The issue of whether or not recombination has occurred during the outbreak is critical to the immunological battle against the virus and is under intense debate [6,[34][35][36][37][38][39][40] .…”

Section: Figure 1: Effect Of Recurrent Sequencing Mutations On Phylogmentioning

confidence: 99%

“…Because many tests of recombination assume that all mutations can only occur once at each site, recurrent mutation and systematic errors can confound signatures of recombination [6,26,35] . Finally, recurrent mutations have been identified as a possible signatures of elevated mutation rates and natural selection in SARS-CoV-2 [8,13,[24][25][26]29,33,35,41] , but some of these apparent instances of selection may be due systematic errors in the sequences. Confusion about recurrent mutations and recombination affects our understanding of host response and influences our decisions about which viral molecular processes or specific immune epitopes we might want to target in vaccine development.…”

Section: Figure 1: Effect Of Recurrent Sequencing Mutations On Phylogmentioning

confidence: 99%

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Stability of SARS-CoV-2 Phylogenies

Turakhia

Thornlow

Gozashti

et al. 2020

Preprint

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The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral samples and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab-specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation and/or recombination among viral lineages. We suggest how samples can be screened and problematic mutations removed. We also develop tools for comparing and visualizing differences among phylogenies and we show that consistent clade-and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.Foreword:

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Section: Figure 1: Effect Of Recurrent Sequencing Mutations On Phylogmentioning

confidence: 99%

Section: Figure 1: Effect Of Recurrent Sequencing Mutations On Phylogmentioning

confidence: 99%

Stability of SARS-CoV-2 Phylogenies

Turakhia

Thornlow

Gozashti

et al. 2020

Preprint

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“…These polyproteins are cleaved into five structural structural proteins, including spike protein (S), membrane protein (M), envelope protein (E), nucleocapsid protein (N) and 26 non-structural proteins. There are also nine putative ORFs (ORF3a, ORF3b, ORF4, ORF5, ORF6, ORF7a, ORF7b, ORF8 and ORF10) predicted as hypothetical proteins [1].Characterization of viral mutations can provide valuable information for assessing the mechanisms linked to pathogenesis, immune evasion and viral drug resistance. In addition, viral mutation studies can be crucial for the design of new vaccines, antiviral drugs and diagnostic tests.…”

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confidence: 99%

mentioning

confidence: 99%

Temporal evolution and adaptation of SARS-COV-2 codon usage

Posani

Dilucca

Forcelloni

et al. 2020

Preprint

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The outbreak of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has caused an unprecedented pandemic. Since the first sequenced whole-genome of SARS-CoV-2 on January 2020, the identification of its genetic variants has become crucial in tracking and evaluating their spread across the globe.In this study, we compared 15,259 SARS-CoV-2 genomes isolated from 60 countries since the outbreak of this novel coronavirus with the first sequenced genome in Wuhan to quantify the evolutionary divergence of SARS-CoV-2. Thus, we compared the codon usage patterns, every two weeks, of 13 of SARS-CoV-2 genes encoding for the membrane protein (M), envelope (E), spike surface glycoprotein (S), nucleoprotein (N), non-structural 3C-like proteinase (3CLpro), ssRNA-binding protein (RBP), 2-O-ribose methyltransferase (OMT), endoRNase (RNase), helicase, RNA-dependent RNA polymerase (RdRp), Nsp7, Nsp8, and exonuclease ExoN.As a general rule, we find that SARS-CoV-2 genome tends to diverge over time by accumulating mutations on its genome and, specifically, on the coding sequences for proteins N and S. Interestingly, different patterns of codon usage were observed among these genes. Genes S, N sp7, N Sp8, tend to use a norrower set of synonymous codons that are better optimized to the human host. Conversely, genes E and M consistently use a broader set of synonymous codons, which does not vary with respect to the reference genome. We identified key SARS-CoV-2 genes (S, N , ExoN , RN ase, RdRp, N sp7 and N sp8) suggested to be causally implicated in the virus adaptation to the human host.pandemic had affected more than 190 countries and territories, with more than 5,731,956 confirmed cases and 353,854 deaths (https : //www.worldometers.inf o/coronavirus/).The size of the SARS-CoV2 genome is approximately 30 kb and its genomic structure is characteristic of that of known coronaviruses. SARS-CoV-2 genome is translated into two large overlapping polyproteins, ORF1a and ORF1ab. These polyproteins are cleaved into five structural structural proteins, including spike protein (S), membrane protein (M), envelope protein (E), nucleocapsid protein (N) and 26 non-structural proteins. There are also nine putative ORFs (ORF3a, ORF3b, ORF4, ORF5, ORF6, ORF7a, ORF7b, ORF8 and ORF10) predicted as hypothetical proteins [1].Characterization of viral mutations can provide valuable information for assessing the mechanisms linked to pathogenesis, immune evasion and viral drug resistance. In addition, viral mutation studies can be crucial for the design of new vaccines, antiviral drugs and diagnostic tests. Mutation rate of RNA viruses is dramatically higher than their hosts. This high mutation rate is correlated with virulence modulation and evolvability, which are considered beneficial for viral adaptation [2]. Tang and coworkers have recently characterized 13 variation sites in SARS-CoV-2: ORF1ab, S, ORF3a, ORF8 and N regions, among which positions 28144 in ORF8 and 8782 in ORF1a showed a mutation rate of 30.53% and 29.47%, respectively. A previo...

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“…Other key variants observed in present report includes 25563G>T (Q57H) in ORF3a, along with consecutive series of three variants at position 28881 (G>A), 28882 (G>A) and 28883(G>C) in N proteins. The triple site mutation 28881-28883 that brings change in two amino acid 203-204:RG>KR, is known to play a critical role in virion assembly and structure and had been abundantly seen in US strain previously [14], but also observed recently in Spain, Greece, Vietnam and South America, Mexico, Australia, New Zealand, Belgium, Brazil and Peru [15,16]. According to GISAID repository, these mutations in N protein were rst described in isolates from Netherlands (EPI_ISL_413565) with travel history to Italy, whereas the same mutation was detected in one Italian sample from Abruzzo region (EPI_ISL_436718).…”

Section: Frequent Mutations In Turkish Isolatesmentioning

confidence: 99%

Identification of novel mutations in SARS-COV-2 isolates from Turkey

Rehman

Mahmood

Ejaz

et al. 2020

Preprint

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Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), originally emerged from Wuhan, has caused an unprecedented worldwide pandemic in the first half of 2020. Since the first report of SARS-CoV-2 on March 10th, 2020 in Turkey, more than 150,000 people in the country have been infected with this virus. In this study, a total of 80 genomic virulent strains from Turkey which were uploaded in NCBI and GISAID database were analyzed with other genomic sequences from different countries with the aim to characterize notable genomic features of SARS-CoV-2 and to identify some novel mutations. Consistent with other studies, the combination of variants at positions C3037T, C14408T and A23403G were most common mutations (73%), that exist together in isolates from Turkey. Our secondary structure prediction analysis also highlighted 11 unique non-substitutional mutations from viral SARS-COV-2 isolates of Turkey in different regions such as in spike (S) protein and non-structural proteins (Nsp2, Nsp3, NSP4, and NSP12/RdRP). Of these 11 mutations, nine of them have been found to be involved in structural alterations at different sites. 3/9 mutants (A771V, T1238I and G1251V) cause alteration in structure of S protein, while the rest of them induces structural changes in Nsp2 (A206T, R207C, T265I), Nsp3 (A1824V), Nsp4 (M2796I) and NSP12 (A4489V). These mutations identified here might have significant functional implications that needs to be addressed for future studies in the context of vaccine engineering and therapeutic interventions. Moreover, transmission and phylogenetic analysis revealed multiple independent sources of introductions for infection of hCovs in Turkey and close phylogenetic relationship of Turkish strains with Saudi strains.

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Large scale genomic analysis of 3067 SARS-CoV-2 genomes reveals a clonal geo-distribution and a rich genetic variations of hotspots mutations

Cited by 53 publications

References 37 publications

Stability of SARS-CoV-2 Phylogenies

Stability of SARS-CoV-2 Phylogenies

Temporal evolution and adaptation of SARS-COV-2 codon usage

Identification of novel mutations in SARS-COV-2 isolates from Turkey

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