Determine the Whole Genome Sequences of SARS-COV-2 Isolated from Iraqi Patients Using NGS Method

Israa hashem Al-Zubaidy; Saife Al-Ahmer; Mohammed Issa Aldafaee

doi:10.54133/ajms.v6i2.777

Authors

Israa hashem Al-Zubaidy Institute of Genetic Engineering and Biotechnology for Post Graduate Studies, University of Baghdad, Baghdad,Iraq https://orcid.org/0000-0002-1393-2785
Saife Al-Ahmer Institute of Genetic Engineering and Biotechnology for Post Graduate Studies, University of Baghdad, Baghdad, Iraq
Mohammed Issa Aldafaee Medical City, Baghdad, Iraq

DOI:

https://doi.org/10.54133/ajms.v6i2.777

Keywords:

SARS corona virus-2, WGS, NGS

Abstract

Background: Next-generation sequencing (NGS) can monitor the transmission of COVID-19 and viral alterations. Objectives: To provide information about testing techniques and infection control measures, as well as to direct the development of vaccines and treatments for the Corona virus. Methods: Six Iraqi SARS-CoV-2 strains were investigated using whole-genome sequencing using the next-generation sequencing method. The sequencing was carried out with an Illumina MiSeq system, and phylogenetic analysis was carried out for all Iraqi sequences retrieved from GISAID. Results: The analysis of the isolates from this study showed that all the sequences from the most recent wave, which happened in the summer of 2022, were primarily clustered in the 20A clades and the 21K, 21L (Omicron) clades, as determined by the GISAID and Nextclade systems. On the other hand, the PANGO system revealed that six sequences were of the BA.1 lineage in Iraq, while four were of the BA.2 lineage. We found that throughout the country's subsequent pandemic waves, SARS-CoV-2 clades and their lineages exhibited circulation patterns and dominance. Conclusions: NGS continues to supply vital COVID-19 evidence to academics, vaccine and medication makers, and public health regulators.

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References

Sulayman N. Deep learning-based predictive model of mRNA vaccine deterioration: An analysis of the stanford COVID-19 mRNA vaccine dataset. Baghdad Sci J. 2023;20(4 (SI)):1451-1458. doi: 10.21123/bsj.2023.8504. DOI: https://doi.org/10.21123/bsj.2023.8504

Zhu FC, Li YH, Guan XH, Hou LH, Wang WJ, Li JX, et al. Safety, tolerability, and immunogenicity of a recombinant adenovirus type-5 vectored COVID-19 vaccine: a dose-escalation, open-label, non-randomised, first-in-human trial. Lancet. 2020;395(10240):1845-1854. doi: 10.1016/S0140-6736(20)31208-3. DOI: https://doi.org/10.1016/S0140-6736(20)31208-3

Hamid MK. Impact of COVID-19 vaccine on hearing status of young ages (Medical College students as a sample). Baghdad Sci J. 2023;20(4):1498-1506. doi: 10.21123/bsj.2023.8694. DOI: https://doi.org/10.21123/bsj.2023.8694

Yousif MG, Hashim K, Rawaf S. Post COVID-19 effect on medical staff and doctors' productivity analysed by machine learning. Baghdad Sci J. 2023;20(4):1507-1519. doi: 10.21123/bsj.2023.8875. DOI: https://doi.org/10.21123/bsj.2023.8875

Zhou P, Yang X-L, Wang X-G, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. nature. 2020;579(7798):270-273. doi: 10.1038/s41586-020-2012-7. DOI: https://doi.org/10.1038/s41586-020-2012-7

Almashhadani AD, AL-Thwani AN. Determination of angiotensin-converting enzyme 2 (ACE2) receptor level in samples of Iraqi patients infected with COVID-19. Iraqi J Biotechnol. 2022;21(2)178-182.

Auda IG, Auda J, Salih RH. SARS-CoV-2 and other Coronaviruses: A matter of variations. Al-Kindy Coll Med J. 2023;19(1):5-10.doi: 10.47723/kcmj.v19i1.927. DOI: https://doi.org/10.47723/kcmj.v19i1.927

Guo YR, Cao QD, Hong ZS, Tan YY, Chen SD, Jin HJ, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status. Military Med Res. 2020;7:1-10. doi: 10.1186/s40779-020-00240-0. DOI: https://doi.org/10.1186/s40779-020-00240-0

Abduallah ZS, AL-Thwani AN, Almashhadani AD, Mohsin MI. Estimation of CRP and some hematological parameters with COVID-19 patients using ANOVA as a statistical tool. Nat Volatile Essent Oils. 2021;8(5):8915-8919.

Zalzala HH. Diagnosis of COVID-19: facts and challenges. New Microbes New Infect. 2020;38:100761. doi: 10.1016/j.nmni.2020.100761. DOI: https://doi.org/10.1016/j.nmni.2020.100761

Kradi AM, Al-Faisal AHM, Turki AM. Alteration of M and N genes of corona virus 2 (SARS-CoV-2). Iraqi J Biotechnol. 2022;21(2):39-45.

Hoffmann M, Kleine-Weber H, Schroeder S, Krüger N, Herrler T, Erichsen S, et al. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 2020;181(2):271-280. doi: 10.1016/j.cell.2020.02.052. DOI: https://doi.org/10.1016/j.cell.2020.02.052

Oude Munnink BB, Nieuwenhuijse DF, Stein M, O’Toole Á, Haverkate M, Mollers M, et al. Rapid SARS-CoV-2 whole-genome sequencing and analysis for informed public health decision-making in the Netherlands. Nature Med. 2020;26(9):1405-1410. doi: 10.1038/s41591-020-0997-y. DOI: https://doi.org/10.1101/2020.04.21.050633

Alpdagtas S, Ilhan E, Uysal E, Sengor M, Ustundag CB, Gunduz O. Evaluation of current diagnostic methods for COVID-19. APL Bioengineer. 2020;4(4):041506. doi: 10.1063/5.0021554. DOI: https://doi.org/10.1063/5.0021554

Elbe S, Buckland‐Merrett G. Data, disease, and diplomacy: GISAID's innovative contribution to global health. Global Challenges. 2017;1(1):33-46. doi: 10.1002/gch2.1018. DOI: https://doi.org/10.1002/gch2.1018

Shu Y, McCauley J. GISAID: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance. 2017;22(13):30494. doi: 10.2807/1560-7917.ES.2017.22.13.30494. DOI: https://doi.org/10.2807/1560-7917.ES.2017.22.13.30494

Ghazzi JJ, Fadhil HY, Aufi IM. Impact of SARS-COV-2 Variants on the Infection Severity among Iraqi Patients. Iraqi J Sci. 2023:3263-3272. doi: 10.24996/ijs.2023.64.7.7. DOI: https://doi.org/10.24996/ijs.2023.64.7.7

Al-Janabi MK, Al-Ahmer SD, Al-Ganabi AD, Aufi IM, editors. Rapid and accurate molecular detection of adenovirus associated with gastroenteritis children in Baghdad city. AIP Conference Proceedings. 2022; 2398(1). doi: 10.1063/5.0093770. DOI: https://doi.org/10.1063/5.0093770

Andrews S. Fast QC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom; 2010.

Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114-2120. doi: 10.1093/bioinformatics/btu170. DOI: https://doi.org/10.1093/bioinformatics/btu170

Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754-1760. doi: 10.1093/bioinformatics/btu170. DOI: https://doi.org/10.1093/bioinformatics/btp324

Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265-269. doi: 10.1038/s41586-020-2008-3. DOI: https://doi.org/10.1038/s41586-020-2008-3

Database resources of the national genomics data center in 2020. Nucleic Acids Res. 2020;48(D1):D24-D33. doi: 10.1093/nar/gkz913. DOI: https://doi.org/10.1093/nar/gkz913

Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics. 2018;34(23):4121-4123. doi: 10.1093/bioinformatics/bty407. DOI: https://doi.org/10.1093/bioinformatics/bty407

Katoh K, Asimenos G, Toh H. Multiple alignment of DNA sequences with MAFFT. Methods in Molecular Biology (Clifton, N.J.). 2009:537:39-64. doi: 10.1007/978-1-59745-251-9_3. DOI: https://doi.org/10.1007/978-1-59745-251-9_3

Nguyen LT, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evolu. 2015;32(1):268-274. doi: 10.1093/molbev/msu300. DOI: https://doi.org/10.1093/molbev/msu300

Rambaut A, Holmes EC, O’Toole Á, Hill V, McCrone JT, Ruis C, et al. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nature Microbiol. 2020;5(11):1403-1407. doi: 10.1038/s41564-020-0770-5. DOI: https://doi.org/10.1038/s41564-020-0770-5

GISAID. lineage nomenclature aids in genomic epidemiology studies of active hCoV-19 viruses. Clade and Lineage Nomenclature, March 2, 2021. Available at: https://gisaid.org/resources/statements-clarifications/clade-and-lineage-nomenclature-aids-in-genomic-epidemiology-of-active-hcov-19-viruses/

Alhussien TAA, Fadhil HY. Analysis of mutations in conserved and susceptible regions across the whole genome sequencing analysis for SARS-CoV-2 in Iraqi patients. Iraqi J Sci. 2023:56-64. doi: 10.24996/ijs.2023.64.1.6. DOI: https://doi.org/10.24996/ijs.2023.64.1.6

Dearlove B, Lewitus E, Bai H, Li Y, Reeves DB, Joyce MG, et al. A SARS-CoV-2 vaccine candidate would likely match all currently circulating variants. Proc Natl Acad Sci USA. 2020;117(38):23652-23662. doi: 10.1073/pnas.2008281117. DOI: https://doi.org/10.1073/pnas.2008281117

Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J, Abfalterer W, et al. Tracking changes in SARS-CoV-2 spike evidence that D614G increases infectivity of the COVID-19 virus. Cell. 2020;182(4):812-827. doi: 10.1016/j.cell.2020.06.043. DOI: https://doi.org/10.1016/j.cell.2020.06.043

Cleemput S, Dumon W, Fonseca V, Abdool Karim W, Giovanetti M, Alcantara LC, et al. Genome detective coronavirus typing tool for rapid identification and characterization of novel coronavirus genomes. Bioinformatics. 2020;36(11):3552-3555. doi: 10.1093/bioinformatics/btaa145. DOI: https://doi.org/10.1093/bioinformatics/btaa145

Michel CJ, Mayer C, Poch O, Thompson JD. Characterization of accessory genes in coronavirus genomes. Virol J. 2020;17:1-13. doi: 10.1186/s12985-020-01402-1. DOI: https://doi.org/10.1186/s12985-020-01402-1

Mahmood ZS, Fadhil HY, Hussein TAA, Ad'hiah AH. The severity of coronavirus disease 19: Profile of inflammatory markers and ACE (rs4646994) and ACE2 (rs2285666) gene polymorphisms in Iraqi patients. Meta Gene. 2022;31:101014. doi: 10.1016/j.mgene.2022.101014. DOI: https://doi.org/10.1016/j.mgene.2022.101014