| Literature DB >> 35051202 |
Tiago Souza Salles1, Andrea Cony Cavalcanti1,2, Fábio Burack da Costa1, Vanessa Zaquieu Dias1, Leandro Magalhães de Souza2, Marcelo Damião Ferreira de Meneses1, José Antônio Suzano da Silva1,3, Cinthya Domingues Amaral1, Jhonatan Ramos Felix1,4, Duleide Alves Pereira1, Stefanella Boatto3, Maria Angélica Arpon Marandino Guimarães5, Davis Fernandes Ferreira1, Renata Campos Azevedo1.
Abstract
The SARS-CoV-2 responsible for the ongoing COVID pandemic reveals particular evolutionary dynamics and an extensive polymorphism, mainly in Spike gene. Monitoring the S gene mutations is crucial for successful controlling measures and detecting variants that can evade vaccine immunity. Even after the costs reduction resulting from the pandemic, the new generation sequencing methodologies remain unavailable to a large number of scientific groups. Therefore, to support the urgent surveillance of SARS-CoV-2 S gene, this work describes a new feasible protocol for complete nucleotide sequencing of the S gene using the Sanger technique. Such a methodology could be easily adopted by any laboratory with experience in sequencing, adding to effective surveillance of SARS-CoV-2 spreading and evolution.Entities:
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Year: 2022 PMID: 35051202 PMCID: PMC8775319 DOI: 10.1371/journal.pone.0262170
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Graphical representation of the SARS-CoV-2 genome.
Highlighted are the S gene and the main mutations described in the variants of concern. As a measure of comparison, the length of the S gene is already equivalent to the one of the whole Dengue virus genome.
Fig 2Lethality [(Reported COVID-19 deaths)/(Reported COVID-19 cases)] per country.
We made use of the GISAID platform’s data to estimate COVID lethality and genome sharing per 105 inhabitants. We obtained the geospatial data for plotting the map in the open-source software library written for the Python programming language, Geopandas. The areas in grey are without reported data.
Fig 3Normalized distribution of the genome shared per 105 inhabitants per country.
The areas in grey are without reported data. We have normalized the data to compare countries of different population sizes. The geospatial data for plotting the map was obtained in the open-source software library written for the Python programming language, Geopandas (source: GISAID platform).
Genome shared and lethality per country.
| Country | Genome shared | Genome shared per 105 inhabitants | Reported COVID-19 cases | Reported COVID-19 deaths | Lethality |
|---|---|---|---|---|---|
| United States of America | 1,732,690 | 523 | 47,802,459 | 771,529 | 1.61% |
| United Kingdom | 1,282,315 | 1,889 | 10,021,501 | 144,433 | 1.44% |
| Germany | 260,519 | 311 | 5,650,170 | 100,476 | 1.78% |
| Denmark | 218,679 | 3,775 | 466,817 | 2.841 | 0.61% |
| Canada | 161,403 | 428 | 1,774,946 | 29,580 | 1,67% |
| France | 121,009 | 185 | 7,285,128 | 116,314 | 1.60% |
| India | 74,279 | 5 | 34,555,431 | 467,468 | 1.35% |
| Italy | 71,623 | 118 | 4,968,341 | 133,486 | 2.69% |
| Brazil | 75,292 | 35 | 22,043,112 | 613,339 | 2.78% |
| Mexico | 38,365 | 30 | 3,872,263 | 293,186 | 7.57% |
| South Africa | 23,634 | 40 | 2,952,500 | 89,771 | 3.04% |
| Russia | 9,982 | 7 | 9,502,879 | 270,292 | 2.84% |
| China | 1,203 | 0,1 | 127,631 | 5,697 | 4.46% |
| Iceland | 9,812 | 2,875 | 17,446 | 35 | 0.20% |
§ [(Reported COVID-19 deaths)/(Reported COVID-19 cases)].
§§Genome sharing data were normalized per 105 habitants to allow comparison between countries of rather different population sizes (source: GISAID platform).
Data collected up to November 28th 2021.
Fig 4Agarose Gel Electrophoresis and schematic representation of the targeted fragments of each set of primers.
Amplification of S gene is visualized in agarose Gel Electrophoresis (A). Schematic representation of the targeted fragments of each set of primers is shown in (B). PR1 represents primer set 1, PR2 primer set 2, PR3 primer set 3, PR4 primer set 4, PR5 primer set 5, PR6 primer set 6, PR7 primer set 7 and PR8 primer set 8.
List of primers used for the S gene amplification of SARS-CoV-2.
| Primer | Sequence (5’–3’) | Coding Position | Product size (pb) |
|---|---|---|---|
| SP1 sense |
| (21551–21574) | 923pb |
| SP2 sense |
| (22190–22211) | 620pb |
| SP3 sense |
| (22751–22775) | 892pb |
| SP4 sense |
| (23445–23466) | 979pb |
| SP5 sense |
| (24355–24377) | 342pb |
| SP6 sense |
| (24610–24632) | 766pb |
| SP7 sense |
| (21492–21511) | 712pb |
| SP8 sense |
| (24610–24632) | 825pb |
# Set primers 7 and 8 flanks the S protein-coding region.
Nucleotide positions are according to the SARS-CoV-2 Wuhan (Genbank accession no.NC_ 045512-Wuhan-HU-1).
RT-PCR cycle conditions.
| Temperature | Time | |
|---|---|---|
| 60°C | 1 minute | Reverse transcription and Transcriptase inactivation |
| 50°C | 45 minutes | |
| 94°C | 2 minutes | |
| 95°C | 15 seconds | 40 cycles of amplification |
| 53°C | 30 seconds | |
| 68°C | 1 minute | |
| 68°C | 7 minutes | Final extension |
Protein S mutations of each VOC and studied sequences.
| Sequences | AA identity | AA changes | Mutations | Accession number |
|---|---|---|---|---|
|
| 99.214% | 10 | H69del, V70del, Y144del, N501Y, A570D, D614G, P681H, T716I, S982A, D1118H | EPI_ISL_601443 |
|
| 99.607% | 5 | D80A, E484K, N501Y, D614G, A701V | EPI_ISL_660613 |
|
| 99.057% | 12 | L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, D614G, H655Y, T1027I, V1176F | |EPI_ISL_906071 |
|
| 99.214% | 10 | T19R, E156G, F157del, R158del, A222V, L452R, T478K, D614G, P681R, D950N | EPI_ISL_2047658 |
|
| 99.057% | 12 | L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, D614G, H655Y, T1027I, V1176F | EPI_ISL_4496739 |
|
| 99.764% | 3 | E484K, D614G, V1176F | EPI_ISL_4497141 |
|
| 99.450% | 7 | E156D, E484K, D614G, D775V, T866P, M869K, V1176F | EPI_ISL_4497286 |
Nucleotide positions are according to the SARS-CoV-2 Wuhan (GenBank accession no. NC_ 045512-Wuhan-HU-1).
Fig 5Sequence alignment showing amino acid substitutions E484K and D614G.