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疫苗接种免疫反应的预测特征及免疫设定点重塑的影响。

Predictive signatures of immune response to vaccination and implications of the immune setpoint remodeling.

作者信息

Ramos Irene

机构信息

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

出版信息

mSphere. 2025 Feb 25;10(2):e0050224. doi: 10.1128/msphere.00502-24. Epub 2025 Jan 24.

Abstract

In 2020, I featured two articles in the "mSphere of Influence" commentary series that had profound implications for the field of immunology and helped shape my research perspective. These articles were "Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses" by Tsang et al. (Cell 157:499-513, 2014, https://doi.org/10.1016/j.cell.2014.03.031) and "A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection" by Fourati et al. (Nat Commun 9:4418, 2018, https://doi.org/10.1038/s41467-018-06735-8). From these topics, the identification of signatures predictive of immune responses to vaccination has greatly advanced and pivoted our understanding of how the immune state at the time of vaccination predicts (and potentially determines) vaccination outcomes. While most of this work has been done using influenza vaccination as a model, pan-vaccine signatures have been also identified. The key implications are their potential use to predict who will respond to vaccinations and to inform strategies for fine-tuning the immune setpoint to enhance immune responses. In addition, investigations in this area led us to understand that immune perturbations, such as acute infections and vaccinations, can remodel the baseline immune state and alter immune responses to future exposures, expanding this exciting field of research. These processes are likely epigenetically encoded, and some examples have already been identified and are discussed in this minireview. Therefore, further research is essential to gain a deeper understanding of how immune exposures modify the epigenome and transcriptome, influence the immune setpoint in response to vaccination, and define its exposure-specific characteristics.

摘要

2020年,我在“mSphere of Influence”评论系列中发表了两篇文章,它们对免疫学领域有着深远影响,并帮助塑造了我的研究视角。这两篇文章分别是曾等人发表的《人类免疫变异的全球分析揭示疫苗接种后反应的基线预测指标》(《细胞》157:499 - 513,2014年,https://doi.org/10.1016/j.cell.2014.03.031)以及富拉蒂等人发表的《一项众包分析以从头识别预测病毒感染易感性的分子特征》(《自然通讯》9:4418,2018年,https://doi.org/10.1038/s41467-018-06735-8)。从这些主题中,预测疫苗接种免疫反应特征的识别极大地推进并转变了我们对于接种疫苗时的免疫状态如何预测(并可能决定)疫苗接种结果的理解。虽然这项工作大多以流感疫苗接种为模型进行,但也已经识别出了泛疫苗特征。其关键意义在于它们有可能用于预测谁会对疫苗接种产生反应,并为微调免疫设定点以增强免疫反应的策略提供依据。此外,该领域的研究让我们明白,诸如急性感染和疫苗接种等免疫扰动可以重塑基线免疫状态,并改变对未来暴露的免疫反应,从而拓展了这个令人兴奋的研究领域。这些过程可能是由表观遗传编码的,并且已经识别出了一些例子,并在这篇小型综述中进行了讨论。因此,进一步的研究对于更深入地理解免疫暴露如何修饰表观基因组和转录组、影响疫苗接种时的免疫设定点以及定义其暴露特异性特征至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9346/11852852/26ad654a57b6/msphere.00502-24.f001.jpg

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