Table 1_Serologic biomarker discovery for differentiating Lyme disease from diseases with similar clinical symptoms using broad profiling of antibody binding.docx

IntroductionLyme disease (LD) is a tick-borne disease that is a substantial public health burden with estimated about 0.5 million new cases per year in the US and increasing incidence. Differentiating Lyme disease, especially in its early stages, from other febrile illnesses with similar clinical symptoms (look-alike diseases) represents a significant challenge due to the lack of diagnostic tools. Current diagnostic tools based on serology were not specifically developed for differential diagnosis and show limited sensitivity in early LD resulting in high false negative rates.
MethodsThe work presented here focuses on a broad profiling of the humoral immune response in terms of circulating antibody repertoire in patients diagnosed with LD and a number of diseases with similar clinical symptoms. A combination of antibody binding to a library of linear, diverse peptides and machine learning methods revealed a panel of biomarker proteins from the proteome of the Borrelia burgdorferi bacterium (LD causing pathogen) that can be used to differentiate between LD and other diseases.
ResultsA subset of the biomarkers was independently validated and demonstrated to show robust differentiating power. Importantly, the discovered biomarkers distinguish between LD patients that previously tested negative with the current test standard (false negatives) and the look-alike diseases.
DiscussionThese findings are important in that the discovered biomarkers can be utilized for differential diagnosis of LD. Furthermore, because the discovery approach is agnostic, the results suggest that it can also be used for biomarker discovery of other diseases.

引言：莱姆病（Lyme disease, LD）是一种蜱传疾病，在美国每年约有50万新增病例，公共卫生负担沉重且发病率持续攀升。由于缺乏专用诊断工具，将莱姆病（尤其是早期阶段）与其他临床症状相似的发热性疾病（亦称仿似疾病）进行鉴别诊断，是一项极具挑战性的工作。当前基于血清学（serology）的诊断工具并非专为鉴别诊断开发，且在早期莱姆病中灵敏度有限，导致假阴性率居高不下。
方法：本研究聚焦于对确诊莱姆病患者及多种临床症状相似疾病患者的体液免疫应答展开广谱分析，具体围绕循环抗体库进行。研究通过将抗体与线性多样化肽库结合，并结合机器学习方法，从伯氏疏螺旋体（Borrelia burgdorferi，莱姆病致病病原体）的蛋白质组中筛选出一组生物标志物（biomarker）蛋白，可用于区分莱姆病与其他疾病。
结果：其中部分生物标志物已完成独立验证，展现出可靠的鉴别能力。尤为重要的是，所发现的生物标志物能够区分经当前检测标准判定为假阴性的莱姆病患者与仿似疾病患者。
讨论：本研究发现具有重要意义，所筛选出的生物标志物可用于莱姆病的鉴别诊断。此外，由于本研究的发现方法具有普适性，该思路亦可应用于其他疾病的生物标志物筛选。