Systems Vaccinology Approaches to Define and Predict Immunity in Response to Nontyphoidal Salmonella Conjugate Vaccines

定义和预测非伤寒沙门氏菌结合疫苗免疫反应的系统疫苗学方法

• 批准号: 10559619
• 负责人: Scott M. Baliban
• 金额: $ 12.92万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559619
• 关键词: Adult; Africa South of the Sahara; Animal Model; Antibiotics; Antibodies; Antibody Response; Antibody-mediated protection; Area; Award; Bacterial Infections; Bacterial Model; Bioinformatics; Biological Markers; Chemicals; Child; Childhood; Communicable Diseases; Complement; Computational Biology; Computer Analysis; Computer Models; Conjugate Vaccines; Data; Data Set; Development; Disease; Doctor of Philosophy; Early identification; Elements; Ensure; Flagellin; Future; Gene Expression; Genetic Transcription; Glycoconjugates; Goals; Gram-Negative Bacteria; Human; Human Volunteers; Immune Sera; Immune response; Immunity; Immunization; Immunology; Infant; Infection; Investigation; Knowledge; Link; Machine Learning; Macrophage; Measures; Mentored Research Scientist Development Award; Mentors; Metabolic; Modeling; Molecular; Molecular Profiling; Morbidity - disease rate; Multiomic Data; Mus; Nature; O Antigens; Pathogenicity; Pathway interactions; Phenotype; Polysaccharides; Positioning Attribute; Process; Program Development; Proteins; Public Health; Research; Research Personnel; Resistance; Salmonella Vaccines; Salmonella infections; Salmonella typhimurium; Serology; Serology test; Serum; Surface; System; Systemic infection; Target Populations; Training; Vaccinated; Vaccination; Vaccines; Virulent; antimicrobial; career; career development; clinical development; cohort; complex data; data integration; design; efficacy trial; immunogenic; in vivo; infancy; insight; machine learning algorithm; metabolomics; monomer; mortality; mouse model; non-typhoidal Salmonella; novel; predictive modeling; predictive signature; rational design; research clinical testing; response; seroconversion; skills; success; transcriptomics; vaccination outcome; vaccine candidate; vaccine development; vaccine immunogenicity; vaccine response; vaccine-induced antibodies; vaccine-induced immunity; vaccinology

PROJECT SUMMARY This proposal is for a Mentored Research Scientist Development Award (K01) for Scott Baliban, Ph.D. Training: My long-term career goal is to become an independent investigator in systems vaccinology, focusing on defining the elements that support protective immune responses to pediatric bacterial infections and using this knowledge to predict infection and vaccination outcomes. My current research expertise involves developing mouse models of bacterial infectious disease and exploring functional and protective aspects of vaccine-induced antibody responses. This application presents a five-year career development program meant to expand my vaccinology toolkit with new areas of expertise in bioinformatics and computational biology. Specifically, I will receive training in analyzing rich and complex data sets using multi-omics data integration and machine learning. My mentoring and advising team are experts in all areas of my proposed research, and I have designed a rigorous training plan that will ensure my success throughout the award. Research: The global rise in pediatric infections caused by invasive nontyphoidal Salmonella (iNTS) serovars Typhimurium and Enteritidis has created an urgent public health crisis. We have developed novel glycoconjugate vaccines consisting of the iNTS surface polysaccharide (core-O-polysaccharide [COPS]) linked to the flagellar monomer protein (FliC). COPS:FliC conjugates are immunogenic and protective in animal models; however, less is known about the mechanisms that support successful immunization as well as the in vivo effector function of protective vaccine-induced antibodies. My preliminary data demonstrate that infant mice respond sub-optimally to COPS:FliC immunization as compared to adult vaccine recipients and that COPS:FliC-induced antibodies are sufficient for robust protection against lethal iNTS challenge. In Aim 1, using S. Enteritidis COPS:FliC as an exemplar conjugate vaccine, I will build a predictive model of vaccine responsiveness based on both gene expression and metabolite perturbations after vaccination. In Aim 2, I will decipher the in vivo functionality of human anti-COPS:FliC antibodies using the infant mouse model of fatal iNTS challenge. Outlook: This study will identify vaccine-induced molecular pathways that correlate with COPS:FliC vaccination outcomes. It will also establish the in vivo importance of specific antibody effector functions for protection against in infant mice. These findings will support an R01 application where I will derive more accurate predictive models of COPS:FliC response quality by assessing the temporal dynamics of metabolomic and transcriptomic responses to vaccination in mice. This approach will be extended to S. Typhimurium COPS:FliC conjugates, and ultimately the predictive models will be verified in vaccinated human infants. I will also investigate mechanistic antibody correlates of protection in the infant mouse model with the goal of developing serological assays to measure anti-microbial functions. At the completion of the K01, I will be uniquely positioned for an independent career where I will apply systems vaccinology approaches to develop novel conceptual frameworks for infant immunology and the vaccination process.

项目摘要 该提案是为Scott Baliban博士颁发的指导研究科学家发展奖（K 01）。培训内容： 我的长期职业目标是成为系统疫苗学的独立研究者，专注于定义 支持对儿科细菌感染的保护性免疫反应的元素，并利用这些知识 来预测感染和疫苗接种的结果。我目前的研究专长包括开发小鼠模型 探讨疫苗诱导抗体的功能和保护作用 应答这个应用程序提出了一个五年的职业发展计划，旨在扩大我的疫苗学 在生物信息学和计算生物学的新领域的专业知识的工具包。具体来说，我将接受培训 使用多组学数据集成和机器学习分析丰富而复杂的数据集。我的指导 和顾问团队都是我所建议研究的各个领域的专家，并且我设计了严格的培训 这将确保我在整个奖项中取得成功。研究：全球儿科感染的上升导致 由侵入性非伤寒沙门氏菌（iNTS）血清型鼠伤寒和肠杆菌已造成紧急公众 健康危机。我们已经开发了由iNTS表面多糖组成的新型糖缀合物疫苗 （核心-O-多糖[COPS]）连接到鞭毛单体蛋白（FliC）。COPS：FliC缀合物是 在动物模型中具有免疫原性和保护性;然而，对支持免疫原性和保护性的机制知之甚少。 成功免疫以及保护性疫苗诱导的抗体的体内效应子功能。我 初步数据表明，与对照组相比， COPS：FliC诱导的抗体足以对成人疫苗接种者提供强大的保护， 致命iNTS挑战。在目标1中，使用S.肠炎COPS：FliC作为示例性结合疫苗，我将构建一个 基于基因表达和代谢物扰动的疫苗反应性预测模型， 预防针在目标2中，我将使用婴儿模型破译人抗COPS：FliC抗体的体内功能性。 致死性iNTS攻击的小鼠模型。展望：这项研究将确定疫苗诱导的分子途径， 与COPS：FliC疫苗接种结果相关。这也将确立特异性抗体在体内的重要性 在幼年小鼠中用于保护的效应器功能。这些发现将支持R 01应用程序， 通过评估COPS的时间动态，推导出更准确的COPS预测模型：FliC响应质量 在小鼠中对疫苗接种的代谢组学和转录组学应答。这种方法将被推广到S。 鼠伤寒COPS：FliC偶联物，最终预测模型将在接种疫苗的人中得到验证 婴儿。我还将研究在婴儿小鼠模型中与保护相关的机械抗体， 目的是开发血清学测定来测量抗微生物功能。在K 01完成后，我将 独特的定位为一个独立的职业生涯，我将应用系统疫苗学的方法来开发 婴儿免疫学和疫苗接种过程的新概念框架。