The electrophysiology of biofilm development and drug resistance

项目摘要 生物膜本质上是耐药性的。虽然介导生物膜中抗性的信号通路被记录， 直到最近才发现电生理学在改变庆大霉素抗性中的重要性。然而，作用 电生理学在生物膜形成和成熟中的作用尚不清楚。此外，我们目前缺乏高吞吐量 筛选技术，用于评估针对临床生物膜的治疗策略的杀菌潜力。 在我的K99培训期间，我将研究机械刺激的钙波动如何改变c-di-GMP池 (Aim 1.1）和细菌群集（目标1.2）导致生物膜形成。同时，我将部署一个测定- 以高通量检测生物膜活力以筛选针对生物膜的抗生素佐剂（目的2.1）。在这个阶段，我 培训将侧重于生物膜的培养、非光学电生理数据的分析和机器学习的构建 在我的R 00阶段中应用的模型。 在我的K99阶段之后，我将使用光学和非光学方法研究成熟生物膜的电生理学。 技术，并将生物膜生命周期中抗生素耐受性的变化与电生理学的变化相关联（目的 1.3）。同时，我将建立和训练深度学习模型，以预测庆大霉素佐剂对生长缓慢的细胞的作用。 基于我在K99阶段收集的可行性数据（目标2.2）。 我的项目的产品将丰富我们对生物膜电生理学的理解， 它们的形成或促进它们的保留，这是益生菌所希望的。此外，这项工作将提供新的机器 学习寻找抗生素佐剂以对抗耐药临床生物膜的技术。

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.