Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Nov 1;11(1):21352.
doi: 10.1038/s41598-021-00459-4.

Association between prognostic factors and the outcomes of patients infected with SARS-CoV-2 harboring multiple spike protein mutations

Affiliations

Association between prognostic factors and the outcomes of patients infected with SARS-CoV-2 harboring multiple spike protein mutations

Gunadi et al. Sci Rep. .

Abstract

The outcome of SARS-CoV-2 infection is determined by multiple factors, including the viral, host genetics, age, and comorbidities. This study investigated the association between prognostic factors and disease outcomes of patients infected by SARS-CoV-2 with multiple S protein mutations. Fifty-one COVID-19 patients were recruited in this study. Whole-genome sequencing of 170 full-genomes of SARS-CoV-2 was conducted with the Illumina MiSeq sequencer. Most patients (47%) had mild symptoms of COVID-19 followed by moderate (19.6%), no symptoms (13.7%), severe (4%), and critical (2%). Mortality was found in 13.7% of the COVID-19 patients. There was a significant difference between the age of hospitalized patients (53.4 ± 18 years) and the age of non-hospitalized patients (34.6 ± 19) (p = 0.001). The patients' hospitalization was strongly associated with hypertension, diabetes, and anticoagulant and were strongly significant with the OR of 17 (95% CI 2-144; p = 0.001), 4.47 (95% CI 1.07-18.58; p = 0.039), and 27.97 (95% CI 1.54-507.13; p = 0.02), respectively; while the patients' mortality was significantly correlated with patients' age, anticoagulant, steroid, and diabetes, with OR of 8.44 (95% CI 1.5-47.49; p = 0.016), 46.8 (95% CI 4.63-472.77; p = 0.001), 15.75 (95% CI 2-123.86; p = 0.009), and 8.5 (95% CI 1.43-50.66; p = 0.019), respectively. This study found the clade: L (2%), GH (84.3%), GR (11.7%), and O (2%). Besides the D614G mutation, we found L5F (18.8%), V213A (18.8%), and S689R (8.3%). No significant association between multiple S protein mutations and the patients' hospitalization or mortality. Multivariate analysis revealed that hypertension and anticoagulant were the significant factors influencing the hospitalization and mortality of patients with COVID-19 with an OR of 17.06 (95% CI 2.02-144.36; p = 0.009) and 46.8 (95% CI 4.63-472.77; p = 0.001), respectively. Moreover, the multiple S protein mutations almost reached a strong association with patients' hospitalization (p = 0.07). We concluded that hypertension and anticoagulant therapy have a significant impact on COVID-19 outcomes. This study also suggests that multiple S protein mutations may impact the COVID-19 outcomes. This further emphasized the significance of monitoring SARS-CoV-2 variants through genomic surveillance, particularly those that may impact the COVID-19 outcomes.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The evolutionary history was inferred using the Neighbor-Joining method. The optimal tree is shown. The percentage of replicate trees in which associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Kimura 2-parameter method and are in the units of the number of base substitutions per site. This analysis involved 170 nucleotide sequences. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There was a total of 29,563 positions in the final dataset. Evolutionary analyses were conducted in MEGA10.

Similar articles

  • Molecular epidemiology of SARS-CoV-2 isolated from COVID-19 family clusters.
    Gunadi, Wibawa H, Hakim MS, Marcellus, Trisnawati I, Khair RE, Triasih R, Irene, Afiahayati, Iskandar K, Siswanto, Anggorowati N, Daniwijaya EW, Supriyati E, Nugrahaningsih DAA, Budiono E, Retnowulan H, Puspadewi Y, Puspitawati I, Sianipar O, Afandy D, Simanjaya S, Widitjiarso W, Puspitarani DA, Fahri F, Riawan U, Fauzi AR, Kalim AS, Ananda NR, Setyati A, Setyowireni D, Laksanawati IS, Arguni E, Nuryastuti T, Wibawa T; the Yogyakarta-Central Java COVID-19 study group. Gunadi, et al. BMC Med Genomics. 2021 Jun 1;14(1):144. doi: 10.1186/s12920-021-00990-3. BMC Med Genomics. 2021. PMID: 34074255 Free PMC article.
  • Persistence of SARS-CoV-2 infection and viral intra- and inter-host evolution in COVID-19 hospitalized patients.
    Pavia G, Quirino A, Marascio N, Veneziano C, Longhini F, Bruni A, Garofalo E, Pantanella M, Manno M, Gigliotti S, Giancotti A, Barreca GS, Branda F, Torti C, Rotundo S, Lionello R, La Gamba V, Berardelli L, Gullì SP, Trecarichi EM, Russo A, Palmieri C, De Marco C, Viglietto G, Casu M, Sanna D, Ciccozzi M, Scarpa F, Matera G. Pavia G, et al. J Med Virol. 2024 Jun;96(6):e29708. doi: 10.1002/jmv.29708. J Med Virol. 2024. PMID: 38804179
  • Whole-genome sequencing of SARS-CoV-2 reveals the detection of G614 variant in Pakistan.
    Umair M, Ikram A, Salman M, Khurshid A, Alam M, Badar N, Suleman R, Tahir F, Sharif S, Montgomery J, Whitmer S, Klena J. Umair M, et al. PLoS One. 2021 Mar 23;16(3):e0248371. doi: 10.1371/journal.pone.0248371. eCollection 2021. PLoS One. 2021. PMID: 33755704 Free PMC article.
  • Characterization of SARS-CoV-2 different variants and related morbidity and mortality: a systematic review.
    SeyedAlinaghi S, Mirzapour P, Dadras O, Pashaei Z, Karimi A, MohsseniPour M, Soleymanzadeh M, Barzegary A, Afsahi AM, Vahedi F, Shamsabadi A, Behnezhad F, Saeidi S, Mehraeen E, Shayesteh Jahanfar. SeyedAlinaghi S, et al. Eur J Med Res. 2021 Jun 8;26(1):51. doi: 10.1186/s40001-021-00524-8. Eur J Med Res. 2021. PMID: 34103090 Free PMC article.
  • Comorbidities in SARS-CoV-2 Patients: a Systematic Review and Meta-Analysis.
    Ng WH, Tipih T, Makoah NA, Vermeulen JG, Goedhals D, Sempa JB, Burt FJ, Taylor A, Mahalingam S. Ng WH, et al. mBio. 2021 Feb 9;12(1):e03647-20. doi: 10.1128/mBio.03647-20. mBio. 2021. PMID: 33563817 Free PMC article.

Cited by

References

    1. World Health Organization. https://www.who.int/news/item/29-06-2020-covidtimeline Accessed on July 2, 2021.
    1. Phelan AL, Katz R, Gostin LO. The novel coronavirus originating in Wuhan, China: Challenges for global health governance. JAMA. 2020;323:709–710. doi: 10.1001/jama.2020.1097. - DOI - PubMed
    1. World Health Organization. https://covid19.who.int/table Accessed on July 7, 2021.
    1. Brodin P. Immune determinants of COVID-19 disease presentation and severity. Nat. Med. 2021;27(1):28–33. doi: 10.1038/s41591-020-01202-8. - DOI - PubMed
    1. Awortwe C, Cascorbi I. Meta-analysis on outcome-worsening comorbidities of COVID-19 and related potential drug-drug interactions. Pharmacol. Res. 2020;161:105250. doi: 10.1016/j.phrs.2020.105250. - DOI - PMC - PubMed

Publication types

MeSH terms

Substances