Discovery of early immunologic biomarkers for risk of PTLDS through machine learning-assisted broad temporal profiling of humoral immune response

通过机器学习辅助的体液免疫反应的广泛时间分析发现 PTLDS 风险的早期免疫生物标志物

• 批准号: 10738144
• 负责人: NEAL Walter WOODBURY
• 金额: $ 64.99万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-07 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10738144
• 关键词: Acute; Acute Disease; Affect; Aftercare; Algorithms; Amino Acids; Antibiotic Therapy; Antibodies; Antibody Repertoire; Antibody Response; Antigens; Arthralgia; Autoimmune; Autoimmune Diseases; Autoimmune Responses; Binding; Biological; Biological Assay; Biological Markers; Borrelia; Borrelia burgdorferi; Centers for Disease Control and Prevention (U.S.); Chronic Disease; Clinical; Clinical Treatment; Data; Development; Diagnosis; Diagnostic; Disease; Disease Outcome; Disease Progression; Disease model; Early Intervention; Epitopes; Evolution; Fatigue; Financial Hardship; Future; Generations; Goals; Health; Healthcare Systems; Heterogeneity; Human; Immune response; Immunoassay; Immunologic Markers; Immunologics; Impaired cognition; Incidence; Knowledge; Length; Libraries; Link; Longitudinal cohort; Lyme Disease; Machine Learning; Minor; Molecular; Molecular Diagnostic Testing; Outcome; Outcome Study; Pathology; Patients; Pattern; Peptides; Performance; Phase; Physicians; Post Treatment Lyme Disease Syndrome; Predisposition; Prevalence; Proteins; Proteome; Quality of life; Research; Risk; Risk Assessment; Sampling; Serum; Symptoms; System; Targeted Research; Testing; Therapeutic; Tick-Borne Diseases; Time; Training; Treatment outcome; United States; Validation; Work; biomarker identification; biomarker panel; candidate identification; candidate marker; cohort; combinatorial; cross reactivity; diagnostic tool; early detection biomarkers; experience; high risk; immunogenic; improved; in silico; interest; long-term sequelae; machine learning model; machine learning prediction; molecular diagnostics; novel; pathogen; patient stratification; prediction algorithm; predictive marker; predictive modeling; predictive panel; profiles in patients; progression risk; prospective; protein biomarkers; response; risk prediction; symptom management; symptom treatment; targeted treatment

Project Summary/Abstract Lyme Disease (LD) is a tickborne illness with markedly increasing prevalence in the United States. While more than 80% of the LD cases can be effectively treated using established antibiotic treatments, 10-15% of patients develop post-treatment, long-term sequalae manifested as fatigue, cognitive impairment, joint pain, and other symptoms and termed Post Treatment Lyme Disease Syndrome (PTLDS) causing patients to experience substantial loss in quality of life and resulting in a marked financial burden on both patient and health care system. The absence of approved molecular diagnostic tests leads many physicians to dismiss the notion of PTLDS entirely, leaving patients with few or poorly defined treatment options. While currently no approved treatment for PTLDS exists, emerging evidence suggests substantially better clinical outcomes from early intervention with targeted therapies in a number of chronic and autoimmune disorders. Early risk assessment of developing PTLDS offers a window of opportunity by alerting both the patient and the physician to anticipate a long-term symptomatic result and adjust symptom-based treatment. The proposed study focuses on the urgent need to identify immunologic biomarkers for predicting the risk of a patient developing PTLDS early during the acute phase of the disease and has the potential to markedly improve clinical outcomes through early intervention. The proposed approach derives disease-specific antigen information from a comprehensive binding profile of the patient’s circulating antibody repertoire. The novelty of the approach is in representing an entire binding space of a donor’s circulating antibody repertoire, instead of simply focusing on a priori known antigenic targets. The approach relies on using machine learning models trained on the antibody binding profile to a diverse, random library of 126,050 unique peptides with an average length of 9-10 amino acids as a sparse representation of all possible combinatorial 9-mer sequences. Predictive models are then used to identify disease-associated pathogen epitopes with high predictive power that can be combined into a potential panel for PTLDS risk assessment. It is hypothesized that B. burg. antigens and/or self-antibodies from the human proteome are involved and that there is a set that can be used as biomarkers to predict progression to PTLDS early in the disease. The antibody response over time will be profiled in a set of longitudinally collected patient samples as they progress from confirmed acute LD, through treatment to disease outcome. This approach applied will enable the breadth of the antibody response, including a potential response/cross- reactivity to human proteins to be examined along with the heterogeneity of antibody responses across a cohort of patients. In silico predictions of protein antigenicity will be confirmed using solution-based immunoassays. The proposed work is expected to identify a set of B. burg. and potentially human autoimmune antigens that are associated with progression to PTLDS. Such knowledge is expected to serve as the basis for future diagnostics, therapeutics or in the generation of hypothesis that can be tested in disease models.

项目摘要/摘要 莱姆病（LD）是一种滴答疾病，在美国的患病率显着增加。而更多 超过80％的LD病例可以使用已建立的抗生素治疗有效治疗，其中10-15％的患者 发展后，长期的序列症表现为疲劳，认知障碍，关节痛和其他 症状并称为治疗后莱姆病综合征（PTLD），导致患者经历 生活质量的巨大损失，并导致患者和医疗保健造成明显的财务燃烧 系统。缺乏批准的分子诊断测试导致许多医生驳回 PTLD完全使患者的治疗选择很少或定义较差。虽然目前尚未批准 存在PTLD的治疗，新兴的证据表明，早期的临床结局取得了明显的更好 在许多慢性和自身免疫性疾病中对靶向疗法进行干预。早期风险评估 开发PTLD可以通过提醒患者和身体的预测来提供机会窗口 长期症状结果并调整基于症状的治疗。拟议的研究重点是 迫切需要识别免疫生物标志物，以预测患者早期发展PTLD的风险 在疾病的急性阶段，有可能通过 早期干预。提出的方法从综合的 患者循环抗体库的结合曲线。该方法的新颖性在于代表 捐赠者循环抗体库的整个结合空间，而不是简单地专注于先验的已知 抗原靶标。该方法依赖于使用接受抗体结合的机器学习模型 剖面向潜水员，随机库，分别为126,050个独特的辣椒，平均长度为9-10个氨基酸作为一个 所有可能的组合9-Mer序列的稀疏表示。然后将预测模型用于 鉴定具有较高预测能力的疾病相关病原体表位，可以合并为潜力 PTLD风险评估的小组。假设B. Burg。抗原和/或自我抗体 人类蛋白质组涉及，并且有一个可以用作生物标志物预测发展的生物标志物 PTLD在疾病早期。随着时间的推移，抗体响应将在一组纵向收集 患者样本从确认的急性LD进行，再到治疗疾病结局。这 采用的方法将使抗体反应的广度，包括潜在的响应/交叉 对人蛋白的反应性以及跨A的抗体反应的异质性 患者队列。在用于蛋白质抗原性的计算机预测中，将使用基于溶液的 免疫测定。拟议的工作有望确定一组B. burg。并潜在的人类自身免疫性 与PTLD进展相关的抗原。这种知识有望作为 未来可以在疾病模型中测试的假设的诊断，治疗或产生。