CoChat
Sign Up
Cited by 26

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

SARS-CoV-2 variant evolution in the United States: High accumulation of viral mutations over time likely through serial Founder Events and mutational bursts

Tasakis, Rafail Nikolaos, Samaras, Georgios, Jamison, Anna, Lee, Michelle, Paulus, Alexandra, Whitehouse, Gabrielle, Verkoczy, Laurent, Papavasiliou, F. Nina, Diaz, Marilyn·2021·PLOS ONE
DOI 10.1371/journal.pone.0255169PMC PMC8301627

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Since the first case of COVID-19 in December 2019 in Wuhan, China, SARS-CoV-2 has spread worldwide and within a year and a half has caused 3.56 million deaths globally. With dramatically increasing infection numbers, and the arrival of new variants with increased infectivity, tracking the evolution of its genome is crucial for effectively controlling the pandemic and informing vaccine platform development. Our study explores evolution of SARS-CoV-2 in a representative cohort of sequences covering the entire genome in the United States, through all of 2020 and early 2021. Strikingly, we detected many accumulating Single Nucleotide Variations (SNVs) encoding amino acid changes in the SARS-CoV-2 genome, with a pattern indicative of RNA editing enzymes as major mutators of SARS-CoV-2 genomes. We report three major variants through October of 2020. These revealed 14 key mutations that were found in various combinations among 14 distinct predominant signatures. These signatures likely represent evolutionary lineages of SARS-CoV-2 in the U.S. and reveal clues to its evolution such as a mutational burst in the summer of 2020 likely leading to a homegrown new variant, and a trend towards higher mutational load among viral isolates, but with occasional mutation loss. The last quartile of 2020 revealed a concerning accumulation of mostly novel low frequency replacement mutations in the Spike protein, and a hypermutable glutamine residue near the putative furin cleavage site. Finally, end of the year data and 2021 revealed the gradual increase to prevalence of known variants of concern, particularly B.1.1.7, that have acquired additional Spike mutations. Overall, our results suggest that predominant viral genomes are dynamically evolving over time, with periods of mutational bursts and unabated mutation accumulation. This high level of existing variation, even at low frequencies and especially in the Spike-encoding region may become problematic when super-spreader events, akin to serial Founder Events in evolution, drive these rare mutations to prominence.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0255169

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig 1 SARS-CoV-2 viral genomes accumulate specific sets of SNVs over time. (A) Frequency histogram showing the steady increase of SNVs called per viral isolate over time (Collection Date), indicating their accumulation in SARS-CoV-2 genomes. (B) Distribution of substitutions at unique SNVs. C>T and A>G substitutions have been previously associated with APOBEC and ADAR deaminase activities, on the SARS-CoV-2 ssRNA(+) genome or its dsRNA intermediate, respectively. (C) Graphical representation of SNV substitution profiles at various SARS-CoV-2 ORFs, illustrating intrinsic mutational bias for C>T dominating the mutation pattern in some ORF’s (i.e. 1a and 1b), but being masked (likely by selection) in other ORFs like ORF2 encoding Spike region. ORF lengths (kb) are given in parentheses across the x axis.
Fig 1 SARS-CoV-2 viral genomes accumulate specific sets of SNVs over time. (A) Frequency histogram showing the steady increase of SNVs called per viral isolate over time (Collection Date), indicating their accumulation in SARS-CoV-2 genomes. (B) Distribution of substitutions at unique SNVs. C>T and A>G substitutions have been previously associated with APOBEC and ADAR deaminase activities, on the SARS-CoV-2 ssRNA(+) genome or its dsRNA intermediate, respectively. (C) Graphical representation of SNV substitution profiles at various SARS-CoV-2 ORFs, illustrating intrinsic mutational bias for C>T dominating the mutation pattern in some ORF’s (i.e. 1a and 1b), but being masked (likely by selection) in other ORFs like ORF2 encoding Spike region. ORF lengths (kb) are given in parentheses across the x axis.CC BY · PubMed Central
Fig 2 Accumulation of mutations in SARS-CoV-2 genomes and evolution of variants in 2020. (A) Dot plot representation of missense mutations identified in the SARS2-CoV-2 genome, of which fourteen were found in the most abundant SNVs, including Threonine-to-Isoleucine (T85I) and Proline-to-Leucine (P323L) changes in ORF1b (present in about 48.79% and 80.2% of the sequences, respectively) and the well-documented Aspartic-acid-to-Glycine (D614G) change in ORF2 (found in 80.76% of the sequences). A summary further detailing these predominant mutations is provided in the Table 2. (B) Density histograms (showing how the most common mutations from Fig 2A change over time), reveal that the most common mutations can be grouped into four distinct patterns (A-D); these mutational co-occurrences thus indicate the presence of at least three major variants. (C) Unique profiles of co-occurring mutational signatures from the dataset were employed to compile 48 sub-variant putative signatures (s1-s48; S2A Fig), distinct from the original Wuhan viral isolate (s0). 14 signatures and the s0, were found in more than 0.1% of the sequences. A time-scaled phylogenetic tree of those 14 subvariants and s0 (highlighted in red) reveals accumulation of mutations and more complex signatures with an acute burst of mutations in the summer of 2020 likely leading to a novel homegrown variant (s48). The first and last sequences by time profiled (per signature) are denoted with light blue and red dots respectively. The reference genome (Wuhan-Hu-1) is denoted with a dark purple dot. Gain of mutations in the clades is denoted with red letters for each specific mutation, while loss with grey. The most abundant signatures in the end of 2020 and early 2021 are s6, s22 and s48 (also shown in S2B Fig).
Fig 2 Accumulation of mutations in SARS-CoV-2 genomes and evolution of variants in 2020. (A) Dot plot representation of missense mutations identified in the SARS2-CoV-2 genome, of which fourteen were found in the most abundant SNVs, including Threonine-to-Isoleucine (T85I) and Proline-to-Leucine (P323L) changes in ORF1b (present in about 48.79% and 80.2% of the sequences, respectively) and the well-documented Aspartic-acid-to-Glycine (D614G) change in ORF2 (found in 80.76% of the sequences). A summary further detailing these predominant mutations is provided in the Table 2. (B) Density histograms (showing how the most common mutations from Fig 2A change over time), reveal that the most common mutations can be grouped into four distinct patterns (A-D); these mutational co-occurrences thus indicate the presence of at least three major variants. (C) Unique profiles of co-occurring mutational signatures from the dataset were employed to compile 48 sub-variant putative signatures (s1-s48; S2A Fig), distinct from the original Wuhan viral isolate (s0). 14 signatures and the s0, were found in more than 0.1% of the sequences. A time-scaled phylogenetic tree of those 14 subvariants and s0 (highlighted in red) reveals accumulation of mutations and more complex signatures with an acute burst of mutations in the summer of 2020 likely leading to a novel homegrown variant (s48). The first and last sequences by time profiled (per signature) are denoted with light blue and red dots respectively. The reference genome (Wuhan-Hu-1) is denoted with a dark purple dot. Gain of mutations in the clades is denoted with red letters for each specific mutation, while loss with grey. The most abundant signatures in the end of 2020 and early 2021 are s6, s22 and s48 (also shown in S2B Fig).CC BY · PubMed Central
Fig 3 Some low frequency mutations in late 2020 rise to prevalence in 2021. (A) Dot plots showing accumulation of multiple LFSMs (>0.1% of cohort) in ORF2 over time in 2020. The amino acid position per LSFM is shown at the bottom, while quartiles, from the first (Q1) till the last (Q4) are denoted on the right. Mutations in Spike domains are further denoted by shaded areas. A detailed list of all LFSM in 2020 is provided in S3 File. (B) Dot plot representation of missense mutations identified in the SARS2-CoV-2 genome in 2021 and revealing VOC’s arrival. 34 mutations were found in at least 10% of the genomes (dark purple), most of which are common with the ones identified in 2020 (Fig 2A, Table 1), including P323L and D614G, which are present in 100% of the genomes analyzed in 2021. A number of newly predominant mutations in the Spike appeared in 2021 (red labels), include some previously found in 2020 Q4 (red labels with asterisk) as well as novel ones originating from VOC’s. Not all mutations are named but the main ones are S13I, A570D, P681H, W152C, S982A and D1118H. The complete set of mutations found in more than 10% of the sequences in 2021 is shown in S3A Fig.
Fig 3 Some low frequency mutations in late 2020 rise to prevalence in 2021. (A) Dot plots showing accumulation of multiple LFSMs (>0.1% of cohort) in ORF2 over time in 2020. The amino acid position per LSFM is shown at the bottom, while quartiles, from the first (Q1) till the last (Q4) are denoted on the right. Mutations in Spike domains are further denoted by shaded areas. A detailed list of all LFSM in 2020 is provided in S3 File. (B) Dot plot representation of missense mutations identified in the SARS2-CoV-2 genome in 2021 and revealing VOC’s arrival. 34 mutations were found in at least 10% of the genomes (dark purple), most of which are common with the ones identified in 2020 (Fig 2A, Table 1), including P323L and D614G, which are present in 100% of the genomes analyzed in 2021. A number of newly predominant mutations in the Spike appeared in 2021 (red labels), include some previously found in 2020 Q4 (red labels with asterisk) as well as novel ones originating from VOC’s. Not all mutations are named but the main ones are S13I, A570D, P681H, W152C, S982A and D1118H. The complete set of mutations found in more than 10% of the sequences in 2021 is shown in S3A Fig.CC BY · PubMed Central
Fig 4 SARS-CoV-2 viral isolate signature frequencies change over time, but with different patterns across states, showing dynamic evolution by mutation, drift, selection and migration. State-specific ridgeline plots indicate the density of each signature (y axis) over collection date (x axis). In each plot, peak colors gradually change to highlight transition in time (x axis), with the pink-shaded areas corresponding to periods of time where data was not available. States shown were selected only on abundance of sequence data throughout the year (n, number of viral genomes per state). Of note, the reference strain s0 is virtually absent by June 2020, while signature s48 is common from July 2020 through the end of the first quartile of 2021. Several new signatures found predominantly in 2021 (in more than 0.1% of the genomes) reveal a new and complex mutational profile, with a number of them being related to B.1.2 (blue labels), or B.1.1.7 (red labels) lineages (which were introduced by migration), as well as of other VOC lineages (S3A, S4 and S5 Figs).
Fig 4 SARS-CoV-2 viral isolate signature frequencies change over time, but with different patterns across states, showing dynamic evolution by mutation, drift, selection and migration. State-specific ridgeline plots indicate the density of each signature (y axis) over collection date (x axis). In each plot, peak colors gradually change to highlight transition in time (x axis), with the pink-shaded areas corresponding to periods of time where data was not available. States shown were selected only on abundance of sequence data throughout the year (n, number of viral genomes per state). Of note, the reference strain s0 is virtually absent by June 2020, while signature s48 is common from July 2020 through the end of the first quartile of 2021. Several new signatures found predominantly in 2021 (in more than 0.1% of the genomes) reveal a new and complex mutational profile, with a number of them being related to B.1.2 (blue labels), or B.1.1.7 (red labels) lineages (which were introduced by migration), as well as of other VOC lineages (S3A, S4 and S5 Figs).CC BY · PubMed Central
Fig 5 Variants of concern emerging in the United States include novel “add-on” mutations in key Spike protein functional domains. 6 variants of concern (VOCs), B.1.1.7, B1.351, B.1.427, B.1.429 and P.1, were detected in the cohort of sequences we explored, primarily in the last quartile of 2020 and the first of 2021. Clearly VOC genomes are accumulating a diverse set of new spike mutations, in addition to their defining mutations (S3B Fig). Bar plots show the number of sequences (n) of the 6 different VOCs per collection date. Red labels annotate the introduction of new spike mutations in the genomes of the reported VOCs in time in more than 1% of the respective VOC, while less frequent mutations (>0.1% of genomes) are shown in light blue labels. Of note, the most common mutation is L5F, which, in being present robustly throughout time and across variants, suggests it may be a recurrent mutation. Interestingly, other mutations previously seen as LFSMs in 2020 (Fig 3A), including Q677H or Q677R, T859I, E1202Q, V1040F, V1176F are also present in distinct VOCs, suggesting multiple recurrent mutations that may reflect mutational or selection bias.
Fig 5 Variants of concern emerging in the United States include novel “add-on” mutations in key Spike protein functional domains. 6 variants of concern (VOCs), B.1.1.7, B1.351, B.1.427, B.1.429 and P.1, were detected in the cohort of sequences we explored, primarily in the last quartile of 2020 and the first of 2021. Clearly VOC genomes are accumulating a diverse set of new spike mutations, in addition to their defining mutations (S3B Fig). Bar plots show the number of sequences (n) of the 6 different VOCs per collection date. Red labels annotate the introduction of new spike mutations in the genomes of the reported VOCs in time in more than 1% of the respective VOC, while less frequent mutations (>0.1% of genomes) are shown in light blue labels. Of note, the most common mutation is L5F, which, in being present robustly throughout time and across variants, suggests it may be a recurrent mutation. Interestingly, other mutations previously seen as LFSMs in 2020 (Fig 3A), including Q677H or Q677R, T859I, E1202Q, V1040F, V1176F are also present in distinct VOCs, suggesting multiple recurrent mutations that may reflect mutational or selection bias.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Research works printed but not linked: 70

  1. C Wang, 2020, A novel coronavirus outbreak of global health concern, The Lancet, 395, 470, 10.1016/S0140-6736(20)30185-9 · DOI · Paper page
  2. D Cucinotta, 2020, WHO Declares COVID-19 a Pandemic., Acta Bio Medica Atenei Parm, 91, 157
  3. P V’kovski, 2020, Coronavirus biology and replication: implications for SARS-CoV-2, Nat Rev Microbiol, 1
  4. FK Yoshimoto, 2020, The Proteins of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS CoV-2 or n-COV19), the Cause of COVID-19., Protein J, 39, 198, 10.1007/s10930-020-09901-4 · DOI
  5. C Yi, 2020, Key residues of the receptor binding motif in the spike protein of SARS-CoV-2 that interact with ACE2 and neutralizing antibodies, Cell Mol Immunol, 17, 621, 10.1038/s41423-020-0458-z · DOI
  6. AAT Naqvi, 2020, Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach., Biochim Biophys Acta BBA—Mol Basis Dis, 1866, 165878, 10.1016/j.bbadis.2020.165878 · DOI
  7. Y Chen, 2020, Emerging coronaviruses: Genome structure, replication, and pathogenesis, J Med Virol, 92, 418, 10.1002/jmv.25681 · DOI
  8. SG Fang, 2008, Proteolytic processing of polyproteins 1a and 1ab between non-structural proteins 10 and 11/12 of Coronavirus infectious bronchitis virus is dispensable for viral replication in cultured cells, Virology, 379, 175, 10.1016/j.virol.2008.06.038 · DOI
  9. H Slanina, Coronavirus replication–transcription complex: Vital and selective NMPylation of a conserved site in nsp9 by the NiRAN-RdRp subunit, Proc Natl Acad Sci
  10. K Schubert, 2020, SARS-CoV-2 Nsp1 binds the ribosomal mRNA channel to inhibit translation, Nat Struct Mol Biol, 27, 959, 10.1038/s41594-020-0511-8 · DOI
  11. S Angeletti, 2020, COVID-2019: The role of the nsp2 and nsp3 in its pathogenesis., J Med Virol, 92, 584, 10.1002/jmv.25719 · DOI
  12. D Benvenuto, 2020, Evolutionary analysis of SARS-CoV-2: how mutation of Non-Structural Protein 6 (NSP6) could affect viral autophagy., J Infect, 81, e24, 10.1016/j.jinf.2020.03.058 · DOI
  13. RN Kirchdoerfer, 2019, Structure of the SARS-CoV nsp12 polymerase bound to nsp7 and nsp8 co-factors., Nat Commun, 10, 2342, 10.1038/s41467-019-10280-3 · DOI
  14. NS Ogando, 2020, The Enzymatic Activity of the nsp14 Exoribonuclease Is Critical for Replication of MERS-CoV and SARS-CoV-2, J Virol, 94, 10.1128/JVI.01246-20 · DOI
  15. Y Ma, 2015, Structural basis and functional analysis of the SARS coronavirus nsp14–nsp10 complex, Proc Natl Acad Sci, 112, 9436, 10.1073/pnas.1508686112 · DOI
  16. S Klein, 2020, SARS-CoV-2 structure and replication characterized by in situ cryo-electron tomography, Nat Commun, 11, 5885, 10.1038/s41467-020-19619-7 · DOI
  17. P Flaherty, 2012, Ultrasensitive detection of rare mutations using next-generation targeted resequencing, Nucleic Acids Res, 40, e2, 10.1093/nar/gkr861 · DOI
  18. R Wang, 2020, Host Immune Response Driving SARS-CoV-2 Evolution., Viruses, 12, 10.3390/v12101095 · DOI · Paper page
  19. LJ Klimczak, 2020, Similarity between mutation spectra in hypermutated genomes of rubella virus and in SARS-CoV-2 genomes accumulated during the COVID-19 pandemic., PLOS ONE, 15, e0237689, 10.1371/journal.pone.0237689 · DOI · Paper page
  20. SD Giorgio, 2020, Evidence for host-dependent RNA editing in the transcriptome of SARS-CoV-2., Sci Adv, 6, eabb5813, 10.1126/sciadv.abb5813 · DOI · Paper page
  21. M-C Liu, 2018, AID/APOBEC-like cytidine deaminases are ancient innate immune mediators in invertebrates., Nat Commun, 9, 1948, 10.1038/s41467-018-04273-x · DOI
  22. K Nishikura, 2010, Functions and regulation of RNA editing by ADAR deaminases, Annu Rev Biochem, 79, 321, 10.1146/annurev-biochem-060208-105251 · DOI · Paper page
  23. S Stavrou, 2015, APOBEC3 Proteins in Viral Immunity, J Immunol, 195, 4565, 10.4049/jimmunol.1501504 · DOI
  24. BR Rosenberg, 2011, Transcriptome-wide sequencing reveals numerous APOBEC1 mRNA-editing targets in transcript 3’ UTRs, Nat Struct Mol Biol, 18, 230, 10.1038/nsmb.1975 · DOI · Paper page
  25. S Sharma, 2015, APOBEC3A cytidine deaminase induces RNA editing in monocytes and macrophages, Nat Commun, 6, 6881, 10.1038/ncomms7881 · DOI · Paper page
  26. P Jalili, 2020, Quantification of ongoing APOBEC3A activity in tumor cells by monitoring RNA editing at hotspots, Nat Commun, 11, 2971, 10.1038/s41467-020-16802-8 · DOI
  27. S Sharma, 2017, Stem-loop structure preference for site-specific RNA editing by APOBEC3A and APOBEC3G., PeerJ, 5, e4136, 10.7717/peerj.4136 · DOI
  28. WR Gallaher, 2020, A palindromic RNA sequence as a common breakpoint contributor to copy-choice recombination in SARS-COV-2, Arch Virol, 165, 2341, 10.1007/s00705-020-04750-z · DOI
  29. PJK Libin, 2019, VIRULIGN: fast codon-correct alignment and annotation of viral genomes, Bioinformatics, 35, 1763, 10.1093/bioinformatics/bty851 · DOI
  30. Á O’Toole, 2021, Tracking the international spread of SARS-CoV-2 lineages B.1.1.7 and B.1.351/501Y-V2., Wellcome Open Res, 6, 121, 10.12688/wellcomeopenres.16661.1 · DOI · Paper page
  31. BQ Minh, 2020, IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era, Mol Biol Evol, 37, 1530, 10.1093/molbev/msaa015 · DOI · Paper page
  32. H Wickham, 2019, Welcome to the Tidyverse., J Open Source Softw, 4, 1686, 10.21105/joss.01686 · DOI · Paper page
  33. T Galili, 2015, dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering, Bioinforma Oxf Engl, 31, 3718, 10.1093/bioinformatics/btv428 · DOI
  34. U Bodenhofer, 2015, msa: an R package for multiple sequence alignment, Bioinformatics, 31, 3997, 10.1093/bioinformatics/btv494 · DOI
  35. L-G Wang, 2020, Treeio: An R Package for Phylogenetic Tree Input and Output with Richly Annotated and Associated Data, Mol Biol Evol, 37, 599, 10.1093/molbev/msz240 · DOI
  36. G Yu, 2017, ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data, Methods Ecol Evol, 8, 28, 10.1111/2041-210X.12628 · DOI
  37. S Laha, 2020, Characterizations of SARS-CoV-2 mutational profile, spike protein stability and viral transmission, Infect Genet Evol, 85, 104445, 10.1016/j.meegid.2020.104445 · DOI
  38. Y Toyoshima, 2020, SARS-CoV-2 genomic variations associated with mortality rate of COVID-19, J Hum Genet, 65, 1075, 10.1038/s10038-020-0808-9 · DOI
  39. AA Pater, 2021, Emergence and Evolution of a Prevalent New SARS-CoV-2 Variant in the United States., BioRxiv, 2021
  40. YJ Hou, 2020, SARS-CoV-2 D614G variant exhibits efficient replication ex vivo and transmission in vivo, Science, 370, 1464, 10.1126/science.abe8499 · DOI · Paper page
  41. SS Hassan, 2020, Variability of Accessory Proteins Rules the SARS-CoV-2 Pathogenicity., BioRxiv, 2020
  42. E Issa, 2020, SARS-CoV-2 and ORF3a: Nonsynonymous Mutations, Functional Domains, and Viral Pathogenesis., MSystems, 5, 10.1128/mSystems.00266-20 · DOI
  43. R Wang, 2020, Characterizing SARS-CoV-2 mutations in the United States., Res Sq
  44. SJ Arévalo, 2020, Analysis of the Dynamics and Distribution of SARS-CoV-2 Mutations and its Possible Structural and Functional Implications., BioRxiv, 2020
  45. O Saha, 2020, Temporal landscape of mutation accumulation in SARS-CoV-2 genomes from Bangladesh: possible implications from the ongoing outbreak in Bangladesh., BioRxiv, 2020
  46. TC Williams, 2021, SARS-CoV-2 evolution and vaccines: cause for concern?, Lancet Respir Med, 0
  47. L Verkoczy, 2014, Autoreactivity in HIV-1 broadly neutralizing antibodies: implications for their function & induction by vaccination, Curr Opin HIV AIDS, 9, 224, 10.1097/COH.0000000000000049 · DOI
  48. B Korber, 2020, Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus., Cell, 182, 812, 10.1016/j.cell.2020.06.043 · DOI · Paper page
  49. WB Provine, 2004, Ernst Mayr: Genetics and Speciation., Genetics, 167, 1041, 10.1093/genetics/167.3.1041 · DOI
  50. SM Clegg, 2002, Genetic consequences of sequential founder events by an island-colonizing bird, Proc Natl Acad Sci, 99, 8127, 10.1073/pnas.102583399 · DOI
  51. M Shah, 2020, Mutations in the SARS-CoV-2 spike RBD are responsible for stronger ACE2 binding and poor anti-SARS-CoV mAbs cross-neutralization., Comput Struct Biotechnol J, 18, 3402, 10.1016/j.csbj.2020.11.002 · DOI
  52. SE Galloway, 2021, Emergence of SARS-CoV-2 B.1.1.7 Lineage—United States, December 29, 2020–January 12, 2021., Morb Mortal Wkly Rep, 70, 95, 10.15585/mmwr.mm7003e2 · DOI
  53. A Bal, 2021, Two-step strategy for the identification of SARS-CoV-2 variant of concern 202012/01 and other variants with spike deletion H69–V70, France, August to December 2020., Eurosurveillance, 26, 2100008
  54. BB Oude Munnink, 2021, Transmission of SARS-CoV-2 on mink farms between humans and mink and back to humans, Science, 371, 172, 10.1126/science.abe5901 · DOI
  55. J Lin, Genomic monitoring of SARS-CoV-2 uncovers an Nsp1 deletion variant that modulates type I interferon response, Cell Host Microbe, 2021, S1931312821000457
  56. X Wang, 2012, The Cellular Antiviral Protein APOBEC3G Interacts with HIV-1 Reverse Transcriptase and Inhibits Its Function during Viral Replication, J Virol, 86, 3777, 10.1128/JVI.06594-11 · DOI
  57. AM Edwards, 2020, Extracurricular Functions of tRNA Modifications in Microorganisms., Genes, 11, 907, 10.3390/genes11080907 · DOI
  58. B Meyer, 2020, Identification of the 3-amino-3-carboxypropyl (acp) transferase enzyme responsible for acp3U formation at position 47 in Escherichia coli tRNAs., Nucleic Acids Res, 48, 1435, 10.1093/nar/gkz1191 · DOI
  59. M Miladi, 2020, The landscape of SARS-CoV-2 RNA modifications, BioRxiv, 2020
  60. F Poulain, 2020, Footprint of the host restriction factors APOBEC3 on the genome of human viruses, PLOS Pathog, 16, e1008718, 10.1371/journal.ppat.1008718 · DOI
  61. P Simmonds, 2020, Rampant C→U Hypermutation in the Genomes of SARS-CoV-2 and Other Coronaviruses: Causes and Consequences for Their Short- and Long-Term Evolutionary Trajectories., MSphere, 5, e00408, 10.1128/mSphere.00408-20 · DOI · Paper page
  62. J Wise, 2021, Covid-19: The E484K mutation and the risks it poses, BMJ, 372, n359, 10.1136/bmj.n359 · DOI
  63. S Werner, 2020, Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes, Nucleic Acids Res, 48, 3734, 10.1093/nar/gkaa113 · DOI
  64. W Hoepel, 2021, High titers and low fucosylation of early human anti–SARS-CoV-2 IgG promote inflammation by alveolar macrophages, Sci Transl Med, 13, 10.1126/scitranslmed.abf8654 · DOI
  65. LC Katzelnick, 2017, Antibody-dependent enhancement of severe dengue disease in humans, Science, 358, 929, 10.1126/science.aan6836 · DOI
  66. AM Arvin, 2020, A perspective on potential antibody-dependent enhancement of SARS-CoV-2, Nature, 584, 353, 10.1038/s41586-020-2538-8 · DOI · Paper page
  67. WS Lee, 2020, Antibody-dependent enhancement and SARS-CoV-2 vaccines and therapies., Nat Microbiol, 5, 1185, 10.1038/s41564-020-00789-5 · DOI · Paper page
  68. Morris SB. Case Series of Multisystem Inflammatory Syndrome in Adults Associated with SARS-CoV-2 Infection—United Kingdom and United States, March–August 2020. MMWR Morb Mortal Wkly Rep 2020;69. https://doi.org/10.15585/mmwr.mm6940e1. · DOI
  69. M Slatkin, 2012, Serial Founder Effects During Range Expansion: A Spatial Analog of Genetic Drift, Genetics, 191, 171, 10.1534/genetics.112.139022 · DOI
  70. JMJ Travis, 2007, Deleterious Mutations Can Surf to High Densities on the Wave Front of an Expanding Population, Mol Biol Evol, 24, 2334, 10.1093/molbev/msm167 · DOI

Cited by

Sorted by citations

Metadata

Authors
Tasakis, R. N., Samaras, G., Jamison, A., Lee, M., Paulus, A., Whitehouse, G., Verkoczy, L., Papavasiliou, F. N., & Diaz, M.
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2021-07-23
Volume
16
Issue
7
Pages
e0255169
ISSN
1932-6203
DOI
10.1371/journal.pone.0255169
PMC
PMC8301627
Cited by
26
References
4
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review