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.

项目总结/摘要 莱姆病是一种蜱传疾病，在美国的患病率明显增加， 需要在治疗最有效的早期阶段改进诊断。虽然经典的临床 早期疾病的表现是存在游走性红斑（EM），或“牛眼皮疹”， 蜱叮咬部位，20-30%的患者不存在EM。更复杂的诊断是一个比例， 存在EM，但在当前标准两层测试算法（STTTA）上为血清阴性的患者。 这项拟议中的研究解决了对改进血清学检测以诊断早期莱姆病的需求， 这些患者，而这种疾病是对治疗最敏感的。一个概念验证的抗原组 能够区分STTTA阳性的急性莱姆病样本与地方病对照样本， 新的抗原发现方法。这种方法依赖于表示供体的整个绑定空间， 循环抗体库，其使用基于抗体结合谱的机器学习模型与抗体结合， 126，050个肽的多样化的随机文库，平均长度为9个氨基酸，这是一个稀疏的 代表所有可能的氨基酸组合。然后使用所得模型来识别病原体 具有高预测能力的表位组合成具有诊断功效的组。在这里， 在STTTA血清阴性患者中诊断早期莱姆病的需要通过增加以下内容来解决： 预测为该患者群体特异性的抗原。补充的证据的诊断效力- 在先前的原理验证研究中确定的概念抗原组将使用 扩大STTTA血清阴性献血者队列和地方病控制。检测板对莱姆的专属性 疾病将通过一组类似疾病来确认，包括自身免疫性疾病和蜱传疾病。 疾病这项工作预计将产生数据，证明一种新的免疫测定的可行性， 诊断早期莱姆病患者目前错过了目前的测试。此外，它还将作为 证明抗原发现方法作为鉴定诊断抗原的手段， 病原体