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Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine

Jing Li1, JingXin Ren2, HuiPing Liao3

  • 1School of Computer Science, Baicheng Normal University, Baicheng, Jilin, China.

Frontiers in Microbiology
|April 3, 2023
PubMed
Summary

This study used machine learning to identify key genes like NRF2 that regulate the immune response to COVID-19 vaccines. Findings reveal gene expression patterns linked to vaccination timing and dose, aiding understanding of vaccine-induced immunity.

Keywords:
SARS-CoV-2blood transcriptomeimmune responsemachine learningvaccination

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Area of Science:

  • Immunology
  • Genomics
  • Computational Biology

Background:

  • COVID-19 remains a global health concern, with vaccination as a key mitigation strategy.
  • Understanding the immune response to SARS-CoV-2 vaccines is crucial for optimizing public health interventions.
  • Gene expression patterns correlate with vaccine efficacy and duration of immunity.

Purpose of the Study:

  • To identify specific genes that trigger and control the immune response to COVID-19 under various vaccination scenarios.
  • To analyze blood transcriptomes using machine learning to find key genetic markers of vaccine response.
  • To elucidate the molecular mechanisms underlying vaccine-induced antiviral immunity.

Main Methods:

  • Machine learning algorithms applied to blood transcriptome data from 161 individuals across six vaccination groups (ChAdOx1 and BNT162b2).
  • Utilized five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, PFI) to assess gene importance.
  • Employed incremental feature selection with four classification algorithms to identify essential genes and classification rules.

Main Results:

  • Identified five essential genes (NRF2, RPRD1B, NEU3, SMC5, TPX2) associated with immune response, linked to different vaccination schedules.
  • Developed classification rules that describe distinct vaccination scenarios based on gene expression.
  • Demonstrated the utility of machine learning in analyzing complex transcriptomic data for vaccine response.

Conclusions:

  • NRF2, RPRD1B, NEU3, SMC5, and TPX2 are critical genes influencing COVID-19 vaccine-induced immune responses.
  • Gene expression patterns provide insights into the molecular mechanisms of vaccine efficacy.
  • This research offers a data-driven approach to understanding and potentially enhancing vaccine strategies.