Rapid fungal identification and antifungal susceptibility testing through quantitative, multiplexed RNA detection

通过定量、多重 RNA 检测进行快速真菌鉴定和抗真菌药敏测试

• 批准号: 10661058
• 负责人: ROBY PAUL BHATTACHARYYA
• 金额: $ 64.98万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10661058
• 关键词: Address; Advanced Development; Antibiotics; Antifungal Agents; Antifungal Therapy; Antimicrobial susceptibility; Aspergillus; Azoles; Bacteria; Bacterial Infections; Bacterial RNA; Biological Assay; Biopsy Specimen; Blood; Bronchoalveolar Lavage; Candida; Candida auris; Cells; Cessation of life; Class Zygomycetes; Classification; Clinical; Clinical Management; Clinical Microbiology; Collaborations; Data; Detection; Diagnosis; Diagnostic; Diagnostic tests; Drug resistance; Frequencies; Funding Opportunities; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genetic; Genetic Transcription; Genotype; Goals; Growth; Hour; Human; Measures; Medical; Medicine; Messenger RNA; Methods; Modeling; Morbidity - disease rate; Multi-Drug Resistance; Mycoses; Nature; Patient-Focused Outcomes; Patients; Pattern; Performance; Phenotype; Phylogenetic Analysis; Polyenes; Positioning Attribute; Predisposition; Prevalence; Public Health; RNA; RNA Sequences; RNA, Ribosomal, 28S; Reporting; Resistance; Ribosomal RNA; Sampling; Severities; Skin; Specimen; Speed; Sputum; Swab; Symptoms; Taxonomy; Test Result; Testing; Time; Toxic effect; Transcript; Urine; Work; antimicrobial; candidate selection; clinically significant; computational pipelines; design; detection limit; detection platform; diagnostic assay; effective therapy; efflux pump; experience; fungus; improved; machine learning algorithm; microbial; mortality; nano-string; novel; novel strategies; overexpression; pathogen; pathogenic fungus; patient population; personalized diagnostics; prophylactic; random forest; rapid test; resistance mechanism; response; transcriptome sequencing; transcriptomics; treatment response

PROJECT SUMMARY / ABSTRACT Timely diagnostics for fungal infections are sorely needed to guide effective therapy. Invasive fungal infections are increasing in prevalence, causing millions of deaths each year worldwide, and drug resistance poses a rising threat. Due in large part to slow, outmoded diagnostics that require days of culture to identify the pathogen and report its antifungal susceptibility profile, mortality from invasive fungal infections can exceed 40%. This in turn leads clinicians to rely on empiric and prophylactic use of antifungals that may be ineffective, cause needless toxicity, and select for resistance. Rapid precision diagnostic assays are critically needed to improve patient outcomes and guide efficient deployment of our limited antifungal arsenal. To address this urgent public health need, in response to a specific funding opportunity announcement on “Advancing Development of Rapid Fungal Diagnostics” (PA-19-080), this proposal describes a strategy for rapid fungal identification and antifungal susceptibility testing based on RNA signatures. This approach relies on a novel paradigm for pathogen diagnostics, recently validated in bacteria and implemented on a simple, robust, quantitative, multiplexed fluorescent hybridization assay on the NanoString platform. Detection of highly abundant, conserved ribosomal RNA (rRNA) sequences enables broad-range, ultrasensitive pathogen identification. Meanwhile, quantifying key messenger RNA levels following antimicrobial exposure enables phenotypic antimicrobial susceptibility testing (AST), relying on the principle that cells that are dying or growth- arrested are transcriptionally distinct within minutes from those that are not (Bhattacharyya et al, Nature Medicine, in press). Because this approach to AST measures gene expression as an early phenotypic change in susceptible strains, it does not rely on foreknowledge of the genetic basis of resistance in order to classify susceptibility, and can thus be generalized to any pathogen-antimicrobial pair. This proposal aims to first computationally design and experimentally validate a set of hybridization probes to uniquely recognize the 18S and 28S rRNA from each of 48 clinically significant fungal pathogens that together cause the vast majority of invasive fungal infections in humans. Preliminary data show that these rRNA targets are abundant enough to detect a single fungal cell without amplification, enabling ultrasensitive detection in <4 hours directly from clinical samples. Next, RNA-Seq will be used to profile transcriptional changes in 12 common fungal pathogens for which resistance has important clinical consequences in response to treatment with the three major classes of antifungals. Antifungal-responsive transcripts that best classify fungal isolates as susceptible or resistant will be chosen by adapting machine learning algorithms that were developed for this purpose in bacteria. Finally, both approaches will be piloted on simulated and real clinical fungal samples. Preliminary data suggest that these approaches can identify fungi within <4 hours from a primary sample, and deliver AST results within <6 hours of a positive fungal culture.

项目摘要 /摘要 迫切需要及时的真菌感染诊断来指导有效的治疗。侵入性真菌 感染的患病率正在增加，每年在全球造成数百万人的死亡和抗药性 构成不断上升的威胁。在很大程度上是由于缓慢，过时的诊断，需要数天的文化才能识别 病原体并报告其抗真菌敏感性，侵入性真菌感染的死亡率可能超过 40％。这反过 快速精确诊断测定至关重要 改善患者预后，并指导有限的抗真菌武器库的有效部署。 为了应对这种紧急的公共卫生需求，以回应特定的资金机会公告 关于“推进快速真菌诊断的开发”（PA-19-080），该提案描述了一种策略 基于RNA特征的快速真菌鉴定和抗真菌敏感性测试。这种方法可以缓解 在一种新颖的病原体诊断范式上，最近在细菌中进行了验证，并以简单的， 纳米串平台上的稳健，定量，多重荧光杂交测定法。高度检测 丰富的核糖体RNA（RRNA）序列可实现宽范围，超敏感的病原体 鉴别。平均指定抗菌暴露后量化关键的使者RNA水平可以实现 表型抗菌敏感性测试（AST），依赖于以下原则：垂死或生长的细胞 被捕的在几分钟之内与未逮捕的不同（Bhattacharyya等人，自然 医学，印刷中）。因为这种AST方法将基因表达衡量作为早期表型变化 在敏感的菌株中，它不依赖于抗药性的遗传基础的预知来分类 敏感性，因此可以推广到任何病原体 - 抗微生物对。 该建议旨在首先在计算上设计并实验验证一组杂交 从48种具有临床意义的真菌病原体中的每一种中，都可以唯一地识别18s和28s rRNA的问题 共同引起人类绝大多数侵入性真菌感染。初步数据表明这些 rRNA靶标足够丰富，可以检测到单个真菌细胞而不放大，从而使超敏感 直接从临床样品中直接从<4小时检测。接下来，RNA-seq将用于介绍转录 抵抗具有重要临床后果的12种常见真菌病原体的变化 对三种主要类抗真菌剂的治疗的反应。抗真菌反应的成绩单最好 将真菌分离株分类为易感或抗性，将通过适应机器学习算法来选择 是为此目的开发的。最后，这两种方法都将在模拟和真实的 临床真菌样品。初步数据表明，这些方法可以在<4小时内识别真菌 一个主要样本，并在阳性真菌培养后<6小时内提供AST。

项目成果

A novel rRNA hybridization-based approach to rapid, accurate Candida identification directly from blood culture.

一种基于 rRNA 杂交的新型方法，可直接从血培养物中快速、准确地鉴定念珠菌。

• DOI: 10.1093/mmy/myac065
• 发表时间: 2022
• 期刊: Medical mycology
• 影响因子: 2.9
• 作者: Matzko,MichelleE;Sephton-Clark,PoppyCS;Young,EleanorL;Jhaveri,TulipA;Martinsen,MelanieA;Mojica,Evan;Boykin,Rich;Pierce,VirginiaM;Cuomo,ChristinaA;Bhattacharyya,RobyP
• 通讯作者: Bhattacharyya,RobyP