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

项目总结/摘要 真菌感染的及时诊断是指导有效治疗的迫切需要。侵袭性真菌 感染的流行率正在增加，每年在全世界造成数百万人死亡， 构成了越来越大的威胁这在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确定 病原体和报告其抗真菌药敏谱，侵袭性真菌感染的死亡率可超过 百分之四十这反过来又导致临床医生依赖于经验性和预防性使用抗真菌药，这可能是无效的， 导致不必要的毒性，并选择抵抗。迫切需要快速精确的诊断测定， 改善患者预后并指导我们有限的抗真菌药物库的有效部署。 为了满足这一紧迫的公共卫生需求， 关于“促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标签的快速真菌鉴定和抗真菌药敏试验。这种方法依赖 关于病原体诊断的新范例，最近在细菌中得到验证，并在简单的， 在NanoString平台上进行稳健、定量、多重荧光杂交分析。检测高度 丰富、保守的核糖体RNA（rRNA）序列使大范围、超敏感的病原体 识别.与此同时，量化抗菌剂暴露后的关键信使RNA水平， 表型抗菌药物敏感性测试（AST），依赖于死亡或生长的细胞- 在转录上与未被抑制的那些在几分钟内不同（Bhattacharyya等，Nature 医学，出版中）。因为AST的这种方法将基因表达作为早期表型变化来测量 在敏感菌株中，它不依赖于抗性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-抗微生物剂对。 该建议旨在首先计算设计并实验验证一组杂交 探针唯一识别来自48种临床显著真菌病原体的18 S和28 S rRNA， 共同导致人类绝大多数侵袭性真菌感染。初步数据显示， rRNA靶标足够丰富，可以在不扩增的情况下检测单个真菌细胞， 直接从临床样品中检测不到4小时。下一步，RNA-Seq将用于分析转录水平， 12种常见真菌病原体的变化，耐药性对这些病原体具有重要的临床后果， 对三种主要抗真菌药治疗的反应。抗真菌应答转录本， 将真菌分离株分类为敏感或抗性将通过适应机器学习算法来选择， 是为了这个目的而在细菌中开发的。最后，这两种方法将在模拟和真实的上进行试验 临床真菌样本初步数据表明，这些方法可以在不到4小时内识别真菌， 主要样本，并在真菌培养阳性的6小时内提供AST结果。

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.