| Literature DB >> 36008511 |
Deena Jalal1, Mariam G Elzayat1, Hend E El-Shqanqery1, Aya A Diab1, Abdelrahman Yahia1, Omar Samir1, Usama Bakry2, Khaled Amer2, Mostafa ElNaqeeb2, Wael Hassan2, Hala S Talat3, Hala M Farawela4, Mona S Hamdy4, May S Soliman4, Maha H El Sissy4, Moushira H Ezzelarab4, Sara M El Khateeb4, Lamyaa H Soliman4, Sara E Haddad4, Ashraf Hatem5, Mohamed S Ismail5, Maha Hossam6, Tarek Mansour7,8, Lobna Shalaby9,10, Sonia Soliman8,11, Reem Hassan4,12, Mahmoud Hammad10,13, Ibrahim Abdo14, Sameh Magdeldin15,16, Alaa ElHaddad10,13, Sherif Abouelnaga10,13, Ahmed A Sayed17,18.
Abstract
A serious global public health emergency emerged late November 2019 in Wuhan City, China, by a new highly pathogenic virus, SARS-CoV-2. The virus evolution spread has been tracked by three developing databases: GISAID, Nextstrain and PANGO to understand its circulating variants. In this study, 110 diagnosed positive COVID-19 patient's samples, were collected from Kasr Al-Aini Hospital and the Children Cancer Hospital Egypt 57357 between May 2020 and January 2021, with clinical severity ranging from mild to severe. The viral genomes were sequenced by next generation sequencing, and phylogenetic analysis was performed to understand viral transmission dynamics. According to Nextstrain clades, most of our sequenced samples belonged to clades 20A and 20D, which in addition to clade 20B were present from the beginning of sample collection in May 2020. Clades 19A and 19B, on the other hand, appeared in the mid and late 2020 respectively, followed by the disappearance of clade 20B at the end of 2020. We identified a relatively high prevalence of the D614G spike protein variant and novel patterns of mutations associated together and with different clades. We also identified four mutations, spike H49Y, ORF3a H78Y, ORF8 E64stop and nucleocapsid E378V, associated with higher disease severity. Altogether, our study contributes genetic, phylogenetic, and clinical correlation data about the spread of the SARS-CoV-2 pandemic in Egypt.Entities:
Mesh:
Year: 2022 PMID: 36008511 PMCID: PMC9403952 DOI: 10.1038/s41598-022-18644-4
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.996
Demographic data of patients from which SARS-CoV-2 samples were isolated.
| Clinical variables | Groups | N (%) |
|---|---|---|
| Gender | Male | 60 (54.5) |
| Female | 50 (45.5) | |
| Age (adult) | Mean = 39.6 (SD = 15.4) | 88 (80) |
| Age (pediatric) | Mean = 10.2 (SD = 6.3) | 22 (20) |
| Clinical severity | Mild | 67 (60.9) |
| Moderate | 27 (24.6) | |
| Severe | 16 (14.5) | |
| Hospitalisation | Yes | 36 (32.7) |
| No | 74 (67.3) | |
| Status | Alive | 107 (97.3) |
| Died | 3 (2.7) |
Figure 1Phylogenetic analysis of 110 SARS-CoV-2 samples included in this study. (a) Maximum likelihood phylogenetic tree of 110 SARS-CoV-2 sequences and Wuhan-Hu-1 reference sequence. Visualization of the tree was done using ggtree R package[28]. For each sample, date of sample collection, clinical severity, hospitalization, nextstrain clade and PANGO lineage are indicated by the circular color strip around the tree according to the legend. (b) 110 sequenced samples included in this study were placed on Nextclade https://clades.nextstrain.org/[26]. The circles represent the sequences from our study in comparison with published sequences from all over the world. Nextstrain clades are broken down according to the indicated color codes.
Figure 2Bar charts showing mutation types and mutations affecting protein. (a) Shows different types of mutations detected in the samples. (b) Mutations affecting coding regions; nonsynonymous coding and stop codon gain, are divided according to affected protein.
Figure 3Complex heatmap showing mutations in each sample and clinical data of patients from which samples were isolated.
Figure 4Correlations between different mutations shown by linkage disequilibrium.