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

目前，美国每年估计有476,000例莱姆病病例，由 伯氏疏螺旋体(BB)和每年更多的病例。至少10%的莱姆病患者 在标准抗生素治疗后出现持续性症状。目前，还没有工具来预测 莱姆病的疾病轨迹；研究进展的一个重大障碍。这项研究的主要目标之一 建议确定一种生物标记物，准确预测哪些患者继续康复，并进一步发展 了解导致持续性症状的宿主免疫反应和疾病的病理机制。 虽然历史上该领域一直非常关注使用大量的免疫球蛋白和免疫球蛋白M抗体来 诊断莱姆病，相反，我们量化了所有不同的抗体IgG亚型、IgE和IgA亚型。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成分 亚型和同型水平高于出现持续性症状的患者。我们发现了一部小说 恢复者不同抗体类型的保护性免疫图谱比率。我们 假设这个抗体比率是一个生物标志物，可以预测谁能从莱姆病中康复 抗生素后和谁将继续有持续的症状。 为了进一步探索这一点，我们开发了一种新的基于流的免疫分析技术，我们称之为Flip to 更好地描述与BB结合的抗体的同种类型和亚型。Flip创新性地使用现场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的分析 导致持续症状的机制。这包括测试下游免疫效应器 免疫球蛋白亚型和不同同型如免疫球蛋白A和免疫球蛋白E的功能以前很大程度上被忽视 莱姆研究公司。这一点很重要，因为我们发现在三分之一的人中，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触发肥大细胞脱颗粒。这可能会 表明对BB或在其他细菌中也发现的部分BB产生过敏型反应， 并指出了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性免疫 生物标志物比率并在包括急性莱姆病患者在内的多个患者队列上测试我们的新翻转技术， 有持续性症状的患者和健康对照。我们提议将我们目前的横截面 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率是否真的具有预测性和准确性。 这一研究具有广泛的领域意义。为科学家和医生创造了 预测疾病轨迹可以通过将重点放在 无法恢复的风险最高。未来，它可以为临床护理提供信息，并使新的靶向疗法成为可能 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，以及 更有效地应对伯氏疏螺旋体。

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.