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<p>Peste des petits ruminants (PPR) continues to pose a significant challenge in endemic regions, such as India, despite ongoing vaccination efforts. Although the existing vaccines are effective, they have certain limitations, such as thermolability and the inability to distinguish between infected and vaccinated animals (DIVA). Understanding the molecular mechanisms governing host–pathogen interactions during natural infection versus vaccine-induced immunity is crucial for developing next-generation control strategies. This study conducted comparative transcriptomic analysis of peripheral blood mononuclear cells from naturally PPRV-infected goats (GSE132429, <i>n</i> = 16) and Sungri/96-vaccinated goats (GSE155504, <i>n</i> = 10) to decipher shared and distinct molecular signatures of protective immunity. Differential gene expression identified 1,874 DEGs in infected samples (238 up-regulated, 534 down-regulated) and 1,838 DEGs in vaccinated samples (286 up-regulated, 534 down-regulated). Comparative analysis revealed 12 up-regulated and 11 down-regulated hub genes shared between both conditions, demonstrating that vaccination successfully activates similar protective immune pathways as natural infection, including cytokine–cytokine receptor interaction, IL-17 signaling, and RIG-like receptor signaling. Importantly, condition-specific genes were identified that distinguish infection from vaccination: infection-specific genes like IL-6 and IL1A indicated pathological inflammation, while vaccination-specific genes included ribosomal proteins (RPS27A, RPS14, RPS29, RPS18), reflecting controlled immune memory formation. Machine learning validation of these unique hub genes achieved exceptional classification accuracy (&gt; 90%), confirming their robust biomarker potential for DIVA applications in distinguishing PPRV-infected animals from vaccinated animals. These findings establish that current PPR vaccines effectively mimic key aspects of natural infection while maintaining distinct protective characteristics, offering potential targets for developing enhanced diagnostic tools, next-generation vaccines with DIVA capabilities, and targeted therapeutic interventions to reduce PPR burden on small ruminant populations.</p>

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Comparative transcriptomic and machine learning analysis identifies key genes and immune dysregulation in goats exposed to peste des petits ruminants virus

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Varsha Ramesh,
  • Kuralayanapalya P. Suresh,
  • Shijili Mambully,
  • Swati Rani,
  • Archana V. Patil,
  • Jayashree Anand,
  • S. Yamini Sri,
  • Vinayagamurthy Balamurugan

摘要

Peste des petits ruminants (PPR) continues to pose a significant challenge in endemic regions, such as India, despite ongoing vaccination efforts. Although the existing vaccines are effective, they have certain limitations, such as thermolability and the inability to distinguish between infected and vaccinated animals (DIVA). Understanding the molecular mechanisms governing host–pathogen interactions during natural infection versus vaccine-induced immunity is crucial for developing next-generation control strategies. This study conducted comparative transcriptomic analysis of peripheral blood mononuclear cells from naturally PPRV-infected goats (GSE132429, n = 16) and Sungri/96-vaccinated goats (GSE155504, n = 10) to decipher shared and distinct molecular signatures of protective immunity. Differential gene expression identified 1,874 DEGs in infected samples (238 up-regulated, 534 down-regulated) and 1,838 DEGs in vaccinated samples (286 up-regulated, 534 down-regulated). Comparative analysis revealed 12 up-regulated and 11 down-regulated hub genes shared between both conditions, demonstrating that vaccination successfully activates similar protective immune pathways as natural infection, including cytokine–cytokine receptor interaction, IL-17 signaling, and RIG-like receptor signaling. Importantly, condition-specific genes were identified that distinguish infection from vaccination: infection-specific genes like IL-6 and IL1A indicated pathological inflammation, while vaccination-specific genes included ribosomal proteins (RPS27A, RPS14, RPS29, RPS18), reflecting controlled immune memory formation. Machine learning validation of these unique hub genes achieved exceptional classification accuracy (> 90%), confirming their robust biomarker potential for DIVA applications in distinguishing PPRV-infected animals from vaccinated animals. These findings establish that current PPR vaccines effectively mimic key aspects of natural infection while maintaining distinct protective characteristics, offering potential targets for developing enhanced diagnostic tools, next-generation vaccines with DIVA capabilities, and targeted therapeutic interventions to reduce PPR burden on small ruminant populations.