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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要莱姆病是一种蜱传疾病，在美国的患病率显着增加，是一个紧迫的问题需要在早期阶段改进诊断，而此时的治疗是最有效的。早期疾病的表现是周围出现游走性红斑 (EM) 或“牛眼皮疹” 在蜱叮咬部位，20-30% 的患者不会出现 EM，进一步使诊断变得复杂化。患有 EM 但根据当前标准两级测试算法 (STTTA) 呈血清阴性的患者。拟议的研究解决了改进血清学测试以诊断早期莱姆病的需要这些患者，而该疾病对治疗最有反应。能够区分 STTTA 阳性急性莱姆病样本与地方性对照的新的抗原发现方法依赖于代表供体的整个结合空间。使用基于抗体结合谱的机器学习模型来分析循环抗体库包含 126,050 个肽的多样化、随机库，平均长度为 9 个氨基酸，这是一个稀疏的库然后使用所有可能的氨基酸组合的表示来识别病原体。具有高预测能力的表位被组合成具有诊断功效的表位。通过添加以下内容解决了 STTTA 血清阴性患者诊断早期莱姆病的需要预测对该患者群体具有特异性的抗原的补充证明的诊断功效。在之前的原理验证研究中确定的概念抗原组将使用扩大了 STTTA 血清阴性供体和流行病对照人群的莱姆病特异性。将使用一组相似的疾病（包括自身免疫性疾病和蜱传疾病）来确认疾病这项工作预计将产生数据，证明新型免疫分析方法的可行性。此外，目前的测试还无法对早期莱姆病患者进行诊断。演示抗原发现方法作为识别困难诊断抗原的一种手段病原体。