Unlocking serology’s secrets: harnessing novel immune biomarkers to predict Lyme disease progression and recovery

揭开血清学的秘密：利用新型免疫生物标志物来预测莱姆病的进展和恢复

• 批准号: 10737313
• 负责人: Michal Caspi Tal
• 金额: $ 63.6万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737313
• 关键词: Acute; Aftercare; Antibiotic Therapy; Antibiotics; Antibodies; Bacteria; Binding; Biological Markers; Borrelia burgdorferi; Cell Degranulation; Clinical; Clinical Trials; Complement Activation; Complex; Computer Analysis; Cross-Sectional Studies; Data; Development; Diagnosis; Disease; Disease Progression; Disease susceptibility; Dose; Engineering; Exhibits; Flow Cytometry; Funding; Future; Goals; Heterogeneity; Human; Hypersensitivity; IgE; IgG1; IgG2; IgG3; IgG4; Immune; Immune System Diseases; Immune response; Immune system; Immunoglobulin A; Immunoglobulin G; Immunoglobulin M; Immunologic Markers; Immunologic Tests; Individual; Infection; Inflammatory; Institutional Review Boards; Label; Lyme Disease; Measures; Methods; Mouse Strains; Mus; Participant; Pathologic; Pathology; Patients; Persons; Phagocytosis; Physicians; Plasma; Prevalence; Prospective Studies; Proteins; Recovery; Research; Research Proposals; Sampling; Scientist; Serology; Severity of illness; Techniques; Technology; Testing; Therapeutic; United States; Validation; Vector-transmitted infectious disease; antibody engineering; clinical care; cohort; cost efficient; experience; high risk; immunoreaction; innovation; mast cell; new therapeutic target; novel; novel therapeutics; pathogen; persistent symptom; predictive marker; prevent; prospective; ratiometric; research study; response; sample collection; standard of care; tool

There are currently an estimated 476,000 cases of Lyme disease annually in the United States, caused by the bacteria Borrelia burgdorferi (Bb) and more cases every year. At least 10% of people with Lyme disease experience persistent symptoms after standard antibiotic treatment. Currently, there are no tools to predict Lyme disease illness trajectories; a significant barrier to research progress. A primary goal of this research proposal is to identify a biomarker that accurately predicts which patients go on to recover, and to advance understanding of host immune responses and disease pathomechanisms contributing to persistent symptoms. While there historically has been a significant focus in the field on using bulk IgG and IgM antibodies to diagnose Lyme, instead we quantified all the different IgG subtypes, IgE, and IgA isotypes of antibodies. We found that the plasma of acute Lyme patients who went on to fully recover after antibiotics contained opposite levels of subtypes and isotypes than patients who developed persistent symptoms. We identified a novel protective immune profiling ratio of the different antibody types in patients who went on to recover. We hypothesize that this antibody ratio is a biomarker that can predict who recovers from Lyme disease post-antibiotics and who will go on to have persistent symptoms. To further explore this, we developed a new FLow-based Immune Profiling technology that we call FLIP to better profile the isotypes and subtypes of antibodies that bind to Bb. The FLIP innovatively uses live Bb as bait to precipitate out pathogen specific antibodies. Next, we propose to conduct deep analysis into immune mechanisms contributing to persistent symptoms. This includes testing the downstream immune effector functions of IgG subtypes and different isotypes like IgA and IgE that have previously been largely overlooked in Lyme research. This is important because we found concerningly high levels of IgE that binds to Bb in a third of people experiencing persistent symptoms. In mice this IgE triggers mast cell degranulation. This could indicate the development of an allergy type response to Bb or parts of Bb that are also found in other bacteria, and point to treatment options used for allergies. We propose to further refine our novel predictive immune biomarker ratio and test our new FLIP technology on multiple patient cohorts including acute Lyme patients, patients with persistent symptoms, and healthy controls. We are proposing to turn our current cross-sectional study into a new prospective study so that we can test if our antibody ratio is truly predictive and accurate. There is immense field-wide significance for this research. Creating the ability for scientists and physicians to predict illness trajectories can enable smaller and more cost-efficient clinical trials by focusing on those at the highest risk of not recovering. In the future, it could inform clinical care and enable new targeted therapeutics that prevent long term illness by helping immune systems match the protective antibody ratio we found, and more effectively respond to Borrelia burgdorferi.

目前，美国每年估计有476,000例莱姆病病例，由 细菌Borrelia burgdorferi（BB）和每年更多的病例。至少10％的莱姆病患者 标准抗生素治疗后经历持续的症状。目前，没有工具可以预测 莱姆病疾病轨迹；研究进度的重大障碍。这项研究的主要目标 建议是确定一个生物标志物，该生物标志物准确地预测了哪些患者可以康复并进步 了解宿主免疫反应和疾病的病理机制，导致持续的症状。 虽然历史上一直存在着重要的重点，而是使用散装IgG和IgM抗体 诊断莱姆，我们量化了所有不同不同的IgG亚型，IgE和IgA抗体的IgA同种型。我们 发现抗生素含有相反的急性莱姆患者的血浆 比出现持续症状的患者的亚型和同种型水平。我们确定了一本小说 在继续康复的患者中，不同抗体类型的保护性免疫分析率。我们 假设该抗体比是一种生物标志物，可以预测谁从莱姆病中恢复过来 抗生素后，谁将继续出现持续的症状。 为了进一步探讨这一点，我们开发了一种新的基于流动的免疫分析技术 更好地介绍了与BB结合的抗体的同种型和亚型。 Flip创新地使用Live BB作为 诱饵以沉淀出病原体特异性抗体。接下来，我们建议对免疫进行深入分析 导致持续症状的机制。这包括测试下游免疫效应器 IgG亚型的功能以及以前在很大程度上被忽略的IgA和IgE（例如IgA和IgE）的功能 在莱姆研究中。这很重要，因为我们发现有关在第三个中与BB结合的高水平的IgE 经历持续症状的人。在小鼠中，这种IgE触发了肥大细胞的脱粒。可以 表明在其他细菌中也发现了对BB或BB部分的过敏型反应的发展， 并指向用于过敏的治疗选择。我们建议进一步完善我们的新型预测性免疫 生物标志物比并测试我们在包括急性莱姆患者（包括急性莱姆患者）的多个患者同类群体上测试我们的新翻转技术， 持续症状和健康对照的患者。我们提议转动当前的横截面 研究一项新的前瞻性研究，以便我们可以测试我们的抗体比是否真正预测和准确。 这项研究具有巨大的范围范围意义。创造了科学家和医生的能力 预测疾病轨迹可以通过专注于较小，更具成本效率的临床试验来实现 不恢复的最高风险。将来，它可以为临床护理提供信息，并启用新的目标治疗方法 通过帮助免疫系统与我们发现的保护性抗体比相匹配，这可以防止长期疾病，并且 更有效地回应Borrelia Burgdorferi。