Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance

  1. Fares Z Najar
  2. Evan Linde
  3. Chelsea L Murphy
  4. Veniamin A Borin
  5. Haun Wang
  6. Shozeb Haider
  7. Pratul K Agarwal  Is a corresponding author
  1. Oklahoma State University, United States
  2. University College London, United Kingdom

Abstract

COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate, combined with high rates of transmission and fatality can cause a deadly world-wide pandemic in a matter of weeks.1 Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections ('surges') before they occur. We describe here real time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens.

Data availability

All sequences used in this work are available from GenBank. The protocol used for analysis are described in the supporting information.

The following previously published data sets were used

Article and author information

Author details

  1. Fares Z Najar

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
  2. Evan Linde

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2053-6721
  3. Chelsea L Murphy

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
  4. Veniamin A Borin

    High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    Competing interests
    No competing interests declared.
  5. Haun Wang

    School of Pharmacy, Pharmaceutical and Biological Chemistry, University College London, London, United Kingdom
    Competing interests
    No competing interests declared.
  6. Shozeb Haider

    School of Pharmacy, Pharmaceutical and Biological Chemistry, University College London, London, United Kingdom
    Competing interests
    Shozeb Haider, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2650-2925
  7. Pratul K Agarwal

    1High-Performance Computing Center, Oklahoma State University, Stillwater, United States
    For correspondence
    pratul.agarwal@okstate.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3848-9492

Funding

No external funding was received for this work.

Copyright

© 2023, Najar et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Fares Z Najar
  2. Evan Linde
  3. Chelsea L Murphy
  4. Veniamin A Borin
  5. Haun Wang
  6. Shozeb Haider
  7. Pratul K Agarwal
(2023)
Future COVID19 surges prediction based on SARS-CoV-2 mutations surveillance
eLife 12:e82980.
https://doi.org/10.7554/eLife.82980

Share this article

https://doi.org/10.7554/eLife.82980

Further reading

    1. Epidemiology and Global Health
    Jie Liang, Yang Pan ... Fanfan Zheng
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    Background:

    The associations of age at diagnosis of breast cancer with incident myocardial infarction (MI) and heart failure (HF) remain unexamined. Addressing this problem could promote understanding of the cardiovascular impact of breast cancer.

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    Data were obtained from the UK Biobank. Information on the diagnosis of breast cancer, MI, and HF was collected at baseline and follow-ups (median = 12.8 years). The propensity score matching method and Cox proportional hazards models were employed.

    Results:

    A total of 251,277 female participants (mean age: 56.8 ± 8.0 years), of whom 16,241 had breast cancer, were included. Among breast cancer participants, younger age at diagnosis (per 10-year decrease) was significantly associated with elevated risks of MI (hazard ratio [HR] = 1.36, 95% confidence interval [CI] 1.19–1.56, p<0.001) and HF (HR = 1.31, 95% CI 1.18–1.46, p<0.001). After propensity score matching, breast cancer patients with younger diagnosis age had significantly higher risks of MI and HF than controls without breast cancer.

    Conclusions:

    Younger age at diagnosis of breast cancer was associated with higher risks of incident MI and HF, underscoring the necessity to pay additional attention to the cardiovascular health of breast cancer patients diagnosed at younger age to conduct timely interventions to attenuate the subsequent risks of incident cardiovascular diseases.

    Funding:

    This study was supported by grants from the National Natural Science Foundation of China (82373665 and 81974490), the Nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (2021-RC330-001), and the 2022 China Medical Board-open competition research grant (22-466).

    1. Epidemiology and Global Health
    2. Genetics and Genomics
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    Background:

    Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here, we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations.

    Methods:

    We first examined the association between individual CpGs and cigarette smoking during pregnancy, and smoking exposure in two White European birth cohorts (n=744). Leveraging established CpGs for maternal smoking, we constructed a cord blood epigenetic score of maternal smoking that was validated in one of the European-origin cohorts (n=347). This score was then tested for association with smoking status, secondary smoking exposure during pregnancy, and health outcomes in offspring measured after birth in an independent White European (n=397) and a South Asian birth cohort (n=504).

    Results:

    Several previously reported genes for maternal smoking were supported, with the strongest and most consistent association signal from the GFI1 gene (6 CpGs with p<5 × 10-5). The epigenetic maternal smoking score was strongly associated with smoking status during pregnancy (OR = 1.09 [1.07, 1.10], p=5.5 × 10-33) and more hours of self-reported smoking exposure per week (1.93 [1.27, 2.58], p=7.8 × 10-9) in White Europeans. However, it was not associated with self-reported exposure (p>0.05) among South Asians, likely due to a lack of smoking in this group. The same score was consistently associated with a smaller birth size (–0.37±0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (–0.043±0.013 kg, p=0.0011) in the combined cohorts.

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    Funding:

    This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332.