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

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.

Grant Number: 5R01AI153405-05
NIH Institute/Center: NIH
Principal Investigator: ROBY BHATTACHARYYA