2015 — 2018 |
Bhattacharyya, Roby Paul |
K08Activity Code Description: To provide the opportunity for promising medical scientists with demonstrated aptitude to develop into independent investigators, or for faculty members to pursue research aspects of categorical areas applicable to the awarding unit, and aid in filling the academic faculty gap in these shortage areas within health profession's institutions of the country. |
Bioinformatic and Functional Analysis of Antibiotic-Responsive Small Non-Coding Rnas in Bacterial Pathogens @ Massachusetts General Hospital
? DESCRIPTION (provided by applicant): Antibiotic resistance is one of the most critical medical challenges of our time. New paradigms for understanding antibiotic action are desperately needed to direct new therapeutic strategies. Under the guidance of mentors at Massachusetts General Hospital and the Broad Institute of MIT and Harvard, the candidate has found dozens of antibiotic-responsive small non-coding RNA molecules (sRNAs) in drug- resistant bacterial pathogens that were not previously known to participate in the transcriptional response to antibiotics. This proposal seeks to further investigate this intriguing finding by systematically identifying antibiotic-responsive sRNAs using bioinformatic analysis of transcriptomic data that the candidate has already generated via RNA-Seq in the critical ESKAPE pathogens (Enterococcus, Staphylococcus aureus, Klebsiella and E. coli, Acinetobacter, Pseudomonas, Enterobacter). These pathogens were chosen for study because of their propensity to cause serious disease and to acquire drug resistance; they are recognized by the NIH, WHO, CDC, and Infectious Disease Society of America as high-priority threats. Candidate sRNAs will be analyzed for sequence conservation, covariation of expression with annotated genes, and predicted targets of regulation. The highest priority antibiotic-responsive sRNAs will be deleted or overexpressed, and the effects of these perturbations on the antibiotic response will be assessed through measures of antibiotic activity, basal and induced transcriptional profiling, and assays to identify binding partners and epistatic genes. Preliminary work has already identified one sRNA whose deletion accelerates antibiotic-mediated killing. The outcome of these studies will be to understand the role of these sRNAs in regulating transcriptional networks in response to antibiotic exposure, with the goal of identifying new therapeutic targets and strategies. This K08 Mentored Clinical Scientist Research Career Development Award proposal seeks to train the candidate to effectively explore this promising finding over a four-year period in preparation for an independent research career. The candidate's clinical training is in Infectious Disease, with prior doctoral training in molecular biology and biochemistry. During the course of this career development award, he will complete didactic and hands-on training in bioinformatic methodologies for analyzing genomic-scale datasets through coursework and experimentation, as well as training from an advisory committee of established senior scientists with collective expertise in genomic methodologies, network analysis, and bacteriology.
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0.907 |
2020 — 2021 |
Bhattacharyya, Roby Paul |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
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.
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0.903 |