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

基本信息

批准号：
10183157
负责人：
ROBY PAUL BHATTACHARYYA
金额：
$ 65.93万
依托单位：
BROAD INSTITUTE, INC.
依托单位国家：
美国
项目类别：
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/10183157
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 Immunoglobulin Variable Region 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

项目摘要

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侵袭性真菌治疗。感染的流行率不断上升，每年导致全世界数百万人死亡，并且耐药性造成的威胁日益严重，这在很大程度上是由于缓慢、过时的诊断方法需要数天的时间来识别。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%，这反过来导致依赖经验性和预防性使用抗真菌药物可能无效，引起不必要的毒性，并迫切需要快速精确的诊断测定。改善患者治疗效果并有效指导我们有限的抗真菌武器库的部署。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。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、多重荧光杂交检测。丰富、保守的核糖体 RNA (rRNA) 序列可实现广泛、超敏感的病原体同时，量化暴露于抗菌药物后的关键信使 RNA 水平可以实现这一点。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 et al, Nature 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，初步数据表明，这些共同导致了人类绝大多数的侵袭性真菌感染。 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。将通过采用机器学习算法来选择将真菌分离株分类为敏感或耐药最后，这两种方法都将在模拟和真实环境中进行试验。初步数据表明，这些方法可以在 4 小时内识别真菌。初级样品，并在真菌培养呈阳性后 6 小时内提供 AST 结果。