Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis.

Area of Science:

• Virology
• Immunology
• Genomics

Background:

• Vaccinia virus (VV) is a vaccine vector, with the Tiantan strain (VVTT) historically used in China for smallpox eradication.
• VVTT, despite its efficacy, is associated with significant adverse toxic effects.
• Recent monkeypox outbreaks highlight the importance of understanding orthopoxvirus safety and pathogenesis.

Purpose of the Study:

• To identify key genes and pathways involved in vaccinia virus Tiantan strain (VVTT) infection using transcriptomic analysis and machine learning.
• To elucidate the molecular mechanisms underlying VVTT infection and its toxic side effects.
• To provide a foundation for developing safer and more effective VVTT-based vaccines.

Main Methods:

• Transcriptomic analysis to profile gene expression during VVTT infection.
• Machine learning algorithms (Random Forest, MRMR, XGBoost, LASSOCV) for feature selection to identify critical genes.
• Quantitative PCR (qPCR) for validation of identified target genes.

Main Results:

• eXtreme Gradient Boosting (XGBoost) was the most effective machine learning algorithm, achieving an ROC curve of 0.98.
• Key pathways implicated in VVTT infection include spinocerebellar ataxia and the p53 signaling pathway.
• Three critical targets—ARC, JUNB, and EGR2—were identified and validated during VVTT infection.

Conclusions:

• The study elucidates the cellular infection mechanism of VVTT, enhancing understanding of smallpox vaccine pathogenesis.
• Identified genes and pathways provide crucial insights for developing improved VVTT-based vaccines with reduced toxicity.
• This research contributes to global health security by advancing knowledge of orthopoxvirus and pandemic preparedness.