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The electrophysiology of biofilm development and drug resistance

生物膜发育和耐药性的电生理学

基本信息

批准号：
10740493
负责人：
Christian T Meyer
金额：
$ 13.87万
依托单位：
UNIVERSITY OF COLORADO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-06 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10740493
关键词：
Address Adjuvant Aminoglycoside resistance Aminoglycosides Antibiotics Bacteria Basic Science Biological Assay Biotechnology COVID-19 Calcium Cell Survival Cells Cessation of life Chemical Structure Clinic Clinical Combined Antibiotics Complex DNA Sequence Alteration DNA biosynthesis Data Development Dimensions Drug Combinations Drug Synergism Drug resistance Electrophysiology (science)Gene Combinations Gentamicins Growth Hospitals Infection Intervention Life Cycle Stages Machine Learning Measurement Measures Mechanical Stimulation Mechanics Mediating Metabolic Methods Microbial Biofilms Morphology Optics Outcome Pharmaceutical Preparations Phase Physiological Physiology Positioning Attribute Postdoctoral Fellow Predictive Value Probiotics Refractory Resistance Role Signal Pathway Speed Suspensions Techniques Technology Testing Therapeutic Training Urinary tract infection Work antibiotic resistant infections antibiotic tolerance bactericide cell motility combat combinatorial commune deep learning deep learning model density drug resistant pathogen high throughput screening in silico innovation insight invention knockout gene machine learning model minimal inhibitory concentration multi-electrode arrays new technology non-genetic novel drug combination novel therapeutic intervention pre-clinical preempt screening sensor soft tissue synergism technology development treatment strategy

项目摘要

Project Summary Biofilms are intrinsically drug-resistant. While the signaling pathways mediating resistance in biofilms are documented, only recently has the importance of electrophysiology in modifying gentamicin resistance come to light. However, the role of electrophysiology in biofilm formation and maturation is unknown. Furthermore, we currently lack high throughput screening techniques for assessing the bactericidal potential of treatment strategies against clinical biofilms. During my K99 training, I will investigate how mechanically-stimulated calcium fluctuations modify the c-di-GMP pools (Aim 1.1) and bacterial swarming (Aim 1.2) leading to biofilm formation. Concomitantly, I will deploy an assay for measur- ing biofilm viability in high throughput to screen for antibiotic adjuvants against biofilms (Aim 2.1). During this phase, my training will focus on the culture of biofilms, analysis of non-optical electrophysiology data, and building machine learning models subsequently applied during my R00 phase. Following my K99 phase, I will investigate the electrophysiology of mature biofilms using both optical and non-optical techniques and correlate changes in antibiotic tolerance across the biofilm life-cycle with changes in electrophysiology (Aim 1.3). Concomitantly, I will build and train deep learning models to predict gentamicin adjuvants against slow-growing cells based on viability data collected in my K99 phase (Aim 2.2). The product of my project will enrich our understanding of biofilm electrophysiology and enable methods for mitigating their formation or promoting their retention as is desirable for probiotics. Additionally, this work will deliver new machine learning technologies for finding antibiotic adjuvants to combat drug-resistant clinical biofilms.

项目总结