比较转录组学和机器学习分析确定了暴露于小反刍害虫病毒的山羊的关键基因和免疫失调。

IF 1.9 4区 医学 Q3 GENETICS & HEREDITY
Varsha Ramesh, Kuralayanapalya P Suresh, Shijili Mambully, Swati Rani, Archana V Patil, Jayashree Anand, S Yamini Sri, Vinayagamurthy Balamurugan
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引用次数: 0

摘要

尽管正在进行疫苗接种工作,小反刍兽疫仍在印度等流行地区构成重大挑战。虽然现有疫苗是有效的,但它们有一定的局限性,例如耐热性和无法区分感染动物和接种动物(DIVA)。了解在自然感染和疫苗诱导免疫过程中宿主-病原体相互作用的分子机制对于制定下一代控制策略至关重要。本研究对天然感染pprv的山羊(GSE132429, n = 16)和接种Sungri/96疫苗的山羊(GSE155504, n = 10)的外周血单个核细胞进行了转录组学比较分析,以解释保护性免疫的共同和不同的分子特征。差异基因表达在感染样本中鉴定出1,874个deg(238个上调,534个下调),在接种样本中鉴定出1,838个deg(286个上调,534个下调)。对比分析显示,两种情况下共有12个中心基因上调和11个中心基因下调,表明疫苗接种成功激活了与自然感染相似的保护性免疫途径,包括细胞因子-细胞因子受体相互作用、IL-17信号传导和rig样受体信号传导。重要的是,鉴定出了区分感染和疫苗接种的条件特异性基因:感染特异性基因如IL-6和IL1A表示病理性炎症,而疫苗特异性基因包括核糖体蛋白(RPS27A, RPS14, RPS29, RPS18),反映了受控制的免疫记忆形成。机器学习验证这些独特的中心基因获得了卓越的分类准确性(> 90%),证实了它们在DIVA应用中区分pprv感染动物和接种动物的强大生物标志物潜力。这些发现表明,目前的小反刍兽疫疫苗有效地模拟了自然感染的关键方面,同时保持了独特的保护特性,为开发增强型诊断工具、具有DIVA能力的下一代疫苗和有针对性的治疗干预提供了潜在目标,以减轻小反刍动物种群的小反刍兽疫负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative transcriptomic and machine learning analysis identifies key genes and immune dysregulation in goats exposed to peste des petits ruminants virus.

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.

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来源期刊
Virus Genes
Virus Genes 医学-病毒学
CiteScore
3.30
自引率
0.00%
发文量
76
审稿时长
3 months
期刊介绍: Viruses are convenient models for the elucidation of life processes. The study of viruses is again on the cutting edge of biological sciences: systems biology, genomics, proteomics, metagenomics, using the newest most powerful tools. Huge amounts of new details on virus interactions with the cell, other pathogens and the hosts – animal (including human), insect, fungal, plant, bacterial, and archaeal - and their role in infection and disease are forthcoming in perplexing details requiring analysis and comments. Virus Genes is dedicated to the publication of studies on the structure and function of viruses and their genes, the molecular and systems interactions with the host and all applications derived thereof, providing a forum for the analysis of data and discussion of its implications, and the development of new hypotheses.
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