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.

项目概要/摘要 莱姆病 (LD) 是一种蜱传疾病，在美国的患病率显着增加。 超过 80% 的 LD 病例可以使用现有的抗生素治疗方法得到有效治疗，10-15% 的患者 治疗后出现长期后遗症，表现为疲劳、认知障碍、关节疼痛等 症状，称为治疗后莱姆病综合症 (PTLDS)，导致患者经历 生活质量大幅下降，给患者和医疗保健造成显着的经济负担 由于缺乏经过批准的分子诊断测试，许多医生不予理睬这一概念。 PTLDS 完全导致患者的治疗选择很少或不明确，但目前尚未获得批准。 PTLDS 的治疗方法是存在的，新的证据表明早期治疗的临床结果明显更好 对许多慢性疾病和自身免疫性疾病进行靶向治疗干预的早期风险评估。 开发 PTLDS 提供了一个机会之窗，提醒患者和医生预测 长期症状结果并调整基于症状的治疗。 迫切需要识别免疫生物标志物来预测患者早期发生 PTLDS 的风险 在疾病的急性期，有可能通过以下方式显着改善临床结果： 所提出的方法从全面的干预中获取疾病特异性抗原信息。 该方法的新颖之处在于代表了患者循环抗体库的结合谱。 供体循环抗体库的整个结合空间，而不是简单地关注先验已知的 该方法依赖于使用针对抗体结合进行训练的机器学习模型。 对包含 126,050 个独特肽（平均长度为 9-10 个氨基酸）的多样化随机库进行分析 然后使用所有可能的组合 9 聚体序列的稀疏表示。 识别具有高预测能力的疾病相关病原体表位，可以将其组合成潜在的 用于 PTLDS 风险评估的小组重新获得了伯克氏杆菌抗原和/或自身抗体。 人类蛋白质组参与其中，并且有一组可以用作生物标志物来预测进展 疾病早期的 PTLDS 随时间变化的抗体反应将在一组纵向收集中进行分析。 患者从确诊的急性 LD 进展到治疗直至疾病结果的样本。 所采用的方法将使抗体反应的广度成为可能，包括潜在的反应/交叉反应 对人类蛋白质的反应性以及抗体反应的异质性进行检查 蛋白质抗原性的计算机预测将通过基于溶液的方法得到证实。 拟议的工作预计将鉴定一组伯克氏杆菌和潜在的人类自身免疫。 与 PTLDS 进展相关的抗原有望成为 PTLDS 的基础。 未来的诊断、治疗或产生可以在疾病模型中进行测试的假设。