Universal Digital High Resolution Melt for the detection of pulmonary mold infections.
Universal Digital High Resolution Melt for the detection of pulmonary mold infections.
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用于检测肺部霉菌感染的通用数字高分辨率熔解液。
DOI：
10.1101/2023.11.09.566457
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Fraley,StephanieI
中科院分区：
文献类型：
--
作者：
Goshia,Tyler;Aralar,April;Wiederhold,Nathan;Jenks,JeffreyD;Mehta,SanjayR;Sinha,Mridu;Karmakar,Aprajita;Sharma,Ankit;Shrivastava,Rachit;Sun,Haoxiang;White,PLewis;Hoenigl,Martin;Fraley,StephanieI
侵袭性霉菌感染（IMIs）与高发病率相关，特别是在免疫功能低下的患者中，死亡率在40%至80%之间。早期开始适当的抗真菌治疗可以大大改善预后，但早期诊断仍然很难建立，往往需要多学科团队评估临床和放射学结果加上支持性真菌学结果。通用数字高分辨率解链（U-dHRM）分析可以实现IMI的快速和稳健诊断。为U-dHRM开发了一种通用真菌测定法，并用于生成19种临床相关真菌病原体的熔解曲线特征数据库。训练机器学习算法（ML）以自动分类这些病原体曲线并检测新的熔解曲线。对来自疑似IMI患者的73份临床支气管肺泡灌洗样本进行了性能评估。通过微量移液U-dHRM反应和桑格测序扩增子鉴定新曲线。U-dHRM实现了97%的整体真菌微生物鉴定准确度和约4小时的周转时间。U-dHRM在30个被归类为IMI的样品中检测到73%的致病性霉菌（曲霉属、毛霉目、长孢霉属和镰刀菌属），包括混合感染。通过要求在样品中检测到的致病性霉菌曲线的数量>8和样品体积为1 mL来优化特异性，这导致在21名无IMI的高危患者中具有100%的特异性。U-dHRM显示出作为标准真菌学测试的单独或组合诊断方法的前景。U-dHRM的速度，同时确定和量化临床相关的霉菌病原体在多微生物样本的能力，并检测新兴的机会致病菌可能有助于治疗决策，改善患者outcome.IMPORTANCEImprovements侵入性霉菌感染的诊断是迫切需要的。这项工作提出了一种新的分子检测方法，解决了技术和工作流程的挑战，提供快速的病原体检测，鉴定和定量，可以为治疗提供信息，以改善患者的预后。
Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus,Mucorales,Lomentospora, andFusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be>8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM’s speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes.IMPORTANCEImprovements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.
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作者：
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