Machine learning-based development of serologic test for acute Lyme disease diagnosis

基于机器学习的急性莱姆病诊断血清学检测的开发

• 批准号: 10259497
• 负责人: Laimonas Kelbauskas
• 金额: $ 30万
• 依托单位: BIOMORPH TECHNOLOGIES LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-04 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259497
• 关键词: Acute; Address; Algorithms; Amino Acids; Antibodies; Antibody Repertoire; Antigens; Area; Autoimmune; Autoimmune Diseases; Binding; Borrelia burgdorferi; Centers for Disease Control and Prevention (U.S.); Chemicals; Classification; Clinical; Computer Models; Custom; Data; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Differential Diagnosis; Disease; Early Diagnosis; Early Intervention; Ensure; Epitopes; Evaluation; Exanthema; Health; Human; Immunoassay; Immunology; Incidence; Laboratories; Length; Libraries; Lyme Disease; Machine Learning; Methods; Modeling; Molecular; Patient-Focused Outcomes; Patients; Peptides; Performance; Pilot Projects; Prevalence; Protein Engineering; Proteins; Proteome; Research; Risk; Sampling; Serology; Serology test; Serum; Services; Site; Specificity; Surface Antigens; Symptoms; Synthetic Antigens; Test Result; Testing; Therapeutic Intervention; Tick-Borne Diseases; United States; Work; antigen diagnostic; base; biomarker panel; classification algorithm; clinical diagnostics; cohort; computerized tools; cost; cross reactivity; density; design; diagnosis standard; diagnostic assay; disease diagnosis; effective therapy; erythema migrans; falls; improved; in silico; machine learning method; member; molecular diagnostics; novel; novel diagnostics; pathogen; patient population; response; seasonal influenza; synthetic peptide; technological innovation; tick bite; tick-borne

Project Summary/Abstract Lyme Disease is a tickborne illness with markedly increasing prevalence in the United States and an urgent need for improved diagnostics in its early stages, when treatment is most efficient. While classic clinical presentation of the early illness is the presence of erythema migrans (EM), or “bullseye rash”, surrounding the tick bite site, 20-30% of patients do not present with EM. Further complicating diagnosis is a proportion of patients who present with EM, but are seronegative on the current standard two-tiered test algorithm (STTTA). The proposed research addresses the need for improved serological tests to diagnose early Lyme Disease in these patients, while the disease is the most responsive to treatment. A proof-of-concept antigen panel capable of distinguishing STTTA-positive acute Lyme samples from endemic controls was identified using a novel antigen discovery approach. This approach relies on representing an entire binding space of a donor’s circulating antibody repertoire using machine learning models based on the antibody binding profile to a diverse, random library of 126,050 peptides with an average length of 9 amino acids, which is a sparse representation of all possible amino acid combinations. Resulting models are then used to identify pathogen epitopes with high predictive power that are combined into a panel with diagnostic efficacy. Here, the unmet need of diagnosing early Lyme disease in STTTA-seronegative patients is addressed by the addition of antigens predicted as specific to this patient population. Diagnostic efficacy of the supplemented proof-of- concept antigen panel, that was identified in a previous proof-of-principle study, will be tested using an expanded cohort of STTTA seronegative donors and endemic controls. Specificity of the panel for Lyme disease will be confirmed using a panel of look-a-like illnesses including autoimmune diseases and tickborne diseases. This work is expected to yield data demonstrating the feasibility of a novel immunoassay for the diagnosis of early stage Lyme Disease patients currently missed by present tests. Additionally, it will serve as a demonstration of the antigen discovery approach as a means to identify diagnostic antigens for difficult pathogens.

项目摘要/摘要 莱姆病是一种tick疾病，在美国的患病率明显增加，紧急 当治疗最有效时，需要在早期阶段进行改进的诊断。而经典的临床 早期疾病的介绍是围绕着偏偏偏偏偏偏转（EM）或“ bulseye rash” tick咬伤部位，20-30％的患者不患有EM。进一步复杂的诊断是 出现EM但在当前标准两层测试算法（STTTA）的患者。 拟议的研究涉及需要改进血清学检查以诊断早期莱姆病的必要性 这些患者，而这种疾病对治疗最有反应。概念验证抗原面板 能够使用A鉴定出能够区分stta阳性急性莱姆莱姆样品和内在对照。 新颖的抗原发现方法。这种方法依赖于代表捐助者的整个装订空间 使用机器学习模型基于抗体结合曲线的循环抗体库 潜水员，126,050肽的随机文库，平均长度为9个氨基酸，这很稀疏 所有可能的氨基酸组合的表示。然后将结果模型用于识别病原体 具有较高预测能力的表位结合到具有诊断效率的面板中。在这里，未满足 通过添加 抗原预测为该患者人群。补充证明的诊断效率 在先前的原则研究中确定的概念抗原面板将使用 扩展的STTTA血清染色供体和内粒对照组的队列。面板的特异性莱姆 疾病将使用一系列类似a的疾病来确认，包括自身免疫性疾病和tickborne 疾病。预计这项工作将产生数据，证明了新型免疫测定的可行性 目前测试目前遗漏的莱姆病患者的诊断。此外，它将用作 抗原发现方法的演示是确定困难的诊断抗原的一种手段 病原体。