Immune Responses to Influenza Vaccination

为了深入评估人类免疫反应，我帮助发起和协调了NIH人类免疫学中心的一项初步研究，该研究侧重于流感疫苗接种的免疫反应。本研究旨在帮助建立正常人类变异（人类免疫组）的数据库，并了解免疫状态的变异如何影响免疫应答和疾病（Tsang，Schwartzberg et al，Cell 2014）。 作为这些研究的后续行动，John Tsang博士的实验室进行了CITESeq技术，以揭示有助于疫苗反应性的基线状态（或设定点）的特定细胞贡献（Kotliarov et al Nat Med 2020）。引人注目的是，这些相同的签名可以预测某些类型的狼疮的严重程度。为了进一步了解对免疫的反应，CHI进行了一项后续研究，比较了对无佐剂和有佐剂的H5 N1流感疫苗的反应。为了补充这项工作，我们增加了新的检测方法，包括用于血清分析物深入分析的Somologics和CITE-Seq。 在过去的一年里，我们继续分析这些数据以供发表。值得注意的是，对佐剂的反应与对疫苗效力有贡献的基线状态的发现平行，提供了对疫苗接种的免疫反应性的共同主题，这可能有助于为新疫苗的评估提供信息，包括SARS-CoV 2的疫苗（Apps等人，修订版; Mule等人，修订版，medRxiv. 2023 PMID：37090674）。

In order to evaluate human immune responses in depth, I helped initiate and coordinate one of the initial studies from the NIH Center for Human Immunology, which focused on immune responses to vaccination for influenza. This study was designed to help build a data base of normal human variation (the human immunome) and to understand how variation in immune states contributes to immune responses and disease (Tsang, Schwartzberg et al, Cell 2014). As a follow-up to these studies, Dr. John Tsang's laboratory performed CITESeq technology to uncover specific cellular contributions to baseline states (or set points) that contribute to responsiveness to vaccines (Kotliarov et al Nat Med 2020). Strkingly, these same signatures can predict severity of certain types of Lupus. To further understand responses to immunization, the CHI conducted a follow-up study comparing responses to both an unadjuvanted and an adjuvanted vaccine against Influenza H5N1. To complement this work, we have added new assays, including Somologics for indepth analyses of serum analytes and CITE-Seq. In the last year, we have continued analysis of these data for publication. Notably, responses to adjuvants parallel findings on baseline status that contribute to vaccine efficacy, providing common themes on immune responsiveness to vaccination that may help inform evaluation of new vaccines including those for SARS-CoV2 (Apps et al, in revision; Mule et al, In Revision, medRxiv. 2023 PMID: 37090674).