Ultrasensitive digital quantification of cytokines and bacteria predicts septic shock outcomes
Ultrasensitive digital quantification of cytokines and bacteria predicts septic shock outcomes
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DOI：
10.1038/s41467-020-16124-9
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发表时间：
2020-05-25
影响因子：
16.6
通讯作者：
Tay, Savas
中科院分区：
文献类型：
--
作者：
Abasiyanik, M. Fatih;Wolfe, Krysta;Tay, Savas
病原体和宿主生物标志物的定量对于感染性疾病的诊断、监测和治疗至关重要。在这里，我们展示了灵敏和快速的定量细菌负荷和细胞因子从人类生物样本，以产生可行的假设。我们的数字分析测量IL-6和TNF-α蛋白，革兰氏阴性（GN）和革兰氏阳性（GP）细菌DNA，以及具有毫微微摩尔灵敏度的耐药基因bla（TEM）。我们使用我们的方法来表征哮喘患者的支气管肺泡灌洗液，并发现与健康受试者相比，GN细菌和IL-6水平升高。然后，我们分析了感染性休克患者的血浆，发现IL-6和bla（TEM）水平升高与死亡率相关，而IL-6水平降低与恢复相关。令人惊讶的是，较低的GN细菌水平与较高的死亡概率相关。将决策树分析应用于我们的测量，我们能够预测死亡率和败血性休克的恢复率，准确率超过90%。传染病的诊断、监测和靶向治疗需要超灵敏的生物标志物检测方法。在这里，作者开发了一种炎症标志物、细菌DNA和抗生素耐药基因的数字检测方法，并将其应用于哮喘患者，预测感染性休克的死亡率。
Quantification of pathogen and host biomarkers is essential for the diagnosis, monitoring, and treatment of infectious diseases. Here, we demonstrate sensitive and rapid quantification of bacterial load and cytokines from human biological samples to generate actionable hypotheses. Our digital assay measures IL-6 and TNF-alpha proteins, gram-negative (GN) and gram-positive (GP) bacterial DNA, and the antibiotic-resistance gene bla(TEM) with femtomolar sensitivity. We use our method to characterize bronchoalveolar lavage fluid from patients with asthma, and find elevated GN bacteria and IL-6 levels compared to healthy subjects. We then analyze plasma from patients with septic shock and find that increasing levels of IL-6 and bla(TEM) are associated with mortality, while decreasing IL-6 levels are associated with recovery. Surprisingly, lower GN bacteria levels are associated with higher probability of death. Applying decision-tree analysis to our measurements, we are able to predict mortality and rate of recovery from septic shock with over 90% accuracy. Ultrasensitive methods for detection of biomarkers for infectious disease are needed for diagnosing, monitoring and targeting treatment. Here the authors develop a digital assay for inflammatory markers, bacterial DNA and antibotic-resistance genes and apply it to characterise asthma patients and predict mortality from septic shock.