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

项目摘要/摘要 对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 感染的流行率在增加，每年在全球范围内造成数百万人死亡，而且耐药性 构成了一个日益严重的威胁。在很大程度上是由于缓慢、过时的诊断需要数天的培养才能确定 病原体和报告其抗真菌药敏情况，侵袭性真菌感染的死亡率可超过 40%。这进而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，并选择抗药性。迫切需要快速准确的诊断分析来 改善患者预后并指导我们有限的抗真菌药物的有效部署。 为了解决这一紧迫的公共卫生需求，回应一项具体的资金机会公告 关于“推进真菌快速诊断技术的发展”(PA-19-080)，本提案描述了一项战略， 基于RNA签名的真菌快速鉴定和抗真菌药敏试验。这种方法依赖于 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、 在纳米串平台上进行可靠的、定量的、多重荧光杂交分析。检测到的高度 丰富、保守的核糖体RNA(RRNA)序列使范围广泛、超敏感的病原体 身份证明。同时，在抗菌药物暴露后量化关键信使RNA水平能够 表型抗菌素敏感性测试(AST)，依赖于正在死亡或生长的细胞- 被逮捕的人在几分钟内与其他人在转录上是不同的(Bhattacharyya等人，自然 医学，在印中)。因为AST的这种方法将基因表达作为一种早期表型变化来测量 在敏感品系中，它不依赖于对耐药性遗传基础的预先了解来进行分类 敏感性，因此可以推广到任何病原体-抗菌剂对。 这个建议的目的是首先通过计算设计和实验验证一组杂交 从48种临床显著的真菌病原体中唯一识别18S和28S rRNA的探针 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靶标丰富，不需要扩增就能检测到单个真菌细胞，从而实现了超灵敏 可在4小时内直接从临床样本中检测。接下来，RNA-Seq将被用来描述转录 12种常见真菌病原菌耐药性的变化及其临床意义 对三大类抗真菌药物的治疗反应。最好的抗真菌反应转录本 将真菌菌株分类为敏感或抗性将通过采用以下机器学习算法来选择 是为了在细菌中达到这一目的而开发的。最后，这两种方法都将在模拟和真实情况下进行试验 临床真菌样本。初步数据表明，这些方法可以在4小时内识别真菌。 一份初级样本，并在阳性真菌培养的&lt；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.