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: 10436213
• 负责人: ROBY PAUL BHATTACHARYYA
• 金额: $ 65.73万
• 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/10436213
• 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; base; 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.

项目概要/摘要 迫切需要及时诊断真菌感染以指导有效的治疗。侵袭性真菌 感染的流行率不断上升，每年导致全世界数百万人死亡，并且耐药性 构成日益严重的威胁。很大程度上是由于缓慢、过时的诊断方法需要数天的培养才能识别出 病原体并报告其抗真菌敏感性概况，侵袭性真菌感染的死亡率可能超过 40%。这反过来导致临床医生依赖于经验性和预防性使用可能无效的抗真菌药物， 造成不必要的毒性，并选择耐药性。迫切需要快速精确的诊断分析 改善患者治疗效果并指导有效部署我们有限的抗真菌武器库。 为了满足这一紧迫的公共卫生需求，响应特定的资助机会公告 关于“推进快速真菌诊断的发展”（PA-19-080），该提案描述了一项战略 基于 RNA 特征的快速真菌鉴定和抗真菌药敏测试。这种方法依赖于 一种新的病原体诊断范例，最近在细菌中得到验证，并在一个简单的、 NanoString 平台上的稳健、定量、多重荧光杂交测定。检测高度 丰富、保守的核糖体 RNA (rRNA) 序列可实现广泛、超敏感的病原体 鉴别。同时，量化暴露于抗菌药物后的关键信使 RNA 水平使得 表型抗菌药物敏感性测试（AST），依赖于正在死亡或生长的细胞的原理 被逮捕的人在几分钟内与那些没有被逮捕的人在转录上截然不同（Bhattacharyya 等人，《自然》 医学，正在出版）。因为这种 AST 方法将基因表达作为早期表型变化来测量 在易感菌株中，它不依赖于对耐药性遗传基础的预知来进行分类 敏感性，因此可以推广到任何病原体-抗生素对。 该提案旨在首先通过计算设计并通过实验验证一组杂交 探针独特地识别 48 种具有临床意义的真菌病原体中的每一种的 18S 和 28S rRNA， 它们共同导致人类绝大多数侵袭性真菌感染。初步数据显示，这些 rRNA 靶标足够丰富，无需扩增即可检测单个真菌细胞，从而实现超灵敏 直接从临床样本中检测不到 4 小时。接下来，RNA-Seq 将用于分析转录 12 种常见真菌病原体的变化，其耐药性具有重要的临床后果 对三大类抗真菌药物治疗的反应。最好的抗真菌反应转录本 将通过采用机器学习算法来选择将真菌分离株分类为易感或耐药 是为此目的在细菌中开发的。最后，这两种方法都将在模拟和真实环境中进行试点 临床真菌样本。初步数据表明，这些方法可以在 4 小时内识别出真菌 初级样品，并在真菌培养呈阳性后 6 小时内提供 AST 结果。