Engineering Multiplexed Synthetic Breath Biomarkers as Diagnostic Probes.

Shih-Ting Wang, Melodi Anahtar, Daniel M Kim, Tahoura S Samad, Claire M Zhang, Sahil Patel, Henry Ko, Chayanon Ngambenjawong, Cathy S Wang, Jesse D Kirkpatrick, Vardhman Kumar, Heather E Fleming, Sangeeta N Bhatia
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Abstract

Breath biopsy is emerging as a rapid and non-invasive diagnostic tool that links exhaled chemical signatures with specific medical conditions. Despite its potential, clinical translation remains limited by the challenge of reliably detecting endogenous, disease-specific biomarkers in breath. Synthetic biomarkers represent an emerging paradigm for precision diagnostics such that they amplify activity-based biochemical signals associated with disease fingerprints. However, their adaptation to breath biopsy has been constrained by the limited availability of orthogonal volatile reporters that are detectable in exhaled breath. Here, we engineer multiplexed breath biomarkers that couple aberrant protease activities to exogenous volatile reporters. We designed novel intramolecular reactions that leverage protease-mediated aminolysis, enabling the sensing of a broad spectrum of proteases, and that each release a unique reporter in breath. This approach was validated in a mouse model of influenza to establish baseline sensitivity and specificity in a controlled inflammatory setting, and subsequently applied to diagnose lung cancer using an autochthonous Alk -mutant model. We show that combining multiplexed reporter signals with machine learning algorithms enables tumor progression tracking, treatment response monitoring, and detection of relapse after 30 minutes. Our multiplexed breath biopsy platform highlights a promising avenue for rapid, point-of-care diagnostics across diverse disease states.

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工程多路合成呼吸生物标志物作为诊断探针。
呼吸活检正在成为一种快速、无创的诊断工具,可将呼出的化学特征与特定疾病联系起来。尽管呼吸活检具有很大的潜力,但由于难以可靠地检测呼吸中的内源性疾病特异性生物标记物,其临床应用仍然受到限制。合成生物标记物代表了精准诊断的一种新兴模式,它们能放大与疾病指纹相关的基于活性的生化信号。然而,由于可在呼出气体中检测到的正交挥发性报告物有限,它们在呼气活检中的应用一直受到限制。在这里,我们设计了将异常蛋白酶活性与外源挥发性报告物结合起来的多重呼气生物标记物。我们设计了新颖的分子内反应,利用蛋白酶介导的氨解作用,实现了对多种蛋白酶的感应,每种蛋白酶都能在呼气中释放出独特的报告物。这种方法在流感小鼠模型中进行了验证,以确定在受控炎症环境中的基线灵敏度和特异性,随后利用自体烷基突变体模型应用于肺癌诊断。我们的研究表明,将多重报告信号与机器学习算法相结合,可在 30 分钟后跟踪肿瘤进展、监测治疗反应和检测复发。我们的多路复用呼气活检平台为不同疾病状态下的快速床旁诊断提供了一条前景广阔的途径。
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