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， 伯氏疏螺旋体（Borrelia burgdorferi，Bb），每年都有更多的病例。至少10%的莱姆病患者 在标准抗生素治疗后出现持续症状。目前，还没有工具可以预测 莱姆病发病轨迹;研究进展的重大障碍。这项研究的一个主要目标 一项提案是确定一种生物标志物，可以准确预测哪些患者继续康复， 了解导致持续症状的宿主免疫反应和疾病病理机制。 虽然在历史上，本领域一直显著关注使用大量IgG和IgM抗体来测定抗体的浓度。 诊断莱姆病，相反，我们量化了所有不同的IgG亚型，IgE和伊加同种型抗体。我们 发现在抗生素治疗后完全康复的急性莱姆病患者的血浆中含有相反的 亚型和同种型的水平比发展持续症状的患者高。我们发现了一本小说 在继续康复的患者中，不同抗体类型的保护性免疫谱比率。我们 假设这种抗体比率是一种生物标志物，可以预测谁从莱姆病中康复， 抗生素治疗后会出现持续症状的人 为了进一步探索这一点，我们开发了一种新的基于Flow的免疫分析技术，我们称之为FLIP， 更好地分析与Bb结合的抗体的同种型和亚型。FLIP创新地使用Live Bb作为 沉淀出病原体特异性抗体的诱饵。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子 以前在很大程度上被忽视的IgG亚型和不同同种型（如伊加和IgE）的功能 在莱姆研究中。这一点很重要，因为我们在三分之一的人中发现了与Bb结合的IgE水平高得令人担忧。 有持续症状的人在小鼠中，这种IgE触发肥大细胞脱粒。这可能 表明对Bb或在其他细菌中也发现的Bb部分产生过敏型反应， 并指出用于过敏的治疗方案。我们建议进一步完善我们的新型预测免疫 生物标志物比率，并在多个患者队列（包括急性莱姆病患者）上测试我们的新FLIP技术， 有持续症状的患者和健康对照组。我们建议把我们目前的横截面 我们将这项研究转化为一项新的前瞻性研究，这样我们就可以测试我们的抗体比率是否真正具有预测性和准确性。 这项研究具有广泛的意义。为科学家和医生创造能力， 预测疾病轨迹可以通过专注于那些在 最大的风险是无法恢复。在未来，它可以为临床护理提供信息，并实现新的靶向治疗 通过帮助免疫系统匹配我们发现的保护性抗体比例来预防长期疾病， 更有效地应对伯氏疏螺旋体。