Collaborative Research: Elucidating the Diversity of Bacterial Flagellation and Motility Through Mechanics

合作研究：通过力学阐明细菌鞭毛和运动的多样性

基本信息

批准号：
2027410
负责人：
Jeffrey Guasto
金额：
$ 31.25万
依托单位：
Tufts University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027410&HistoricalAwards=false
关键词：
Collaborative Research Elucidating Diversity Bacterial

项目摘要

The ability of bacteria to swim crucial to survival. Swimming is made possible by rotating thin filaments called flagella. There are many different ways that flagella are arranged on bacteria, and many different movement patterns, all of which enable swimming. Despite the success of bacteria swimming, few of these mechanisms are well understood. The objective of this project is to determine how the arrangements and flexible mechanics of flagella combine to produce differences in bacterial motility. The results of this project will have numerous potential societal impacts, since motility enables bacteria to spread through water supplies, and to establish microbiomes or infections in humans. This work will have important effects at multiple levels. At the cellular level, better understanding bacterial motility will help us understand cell resource uptake. At the ecosystem scale, motility affects the food web. The results of this work could lead to new approaches to mediate bacterial infection, further our understanding of bacterial evolution, and inspire biomimetic microrobots with enhanced functionality. In addition, the award will support outreach programs that teach the public about bacterial locomotion and its importance to society through health and ecology.Bacterial flagellar motility has largely been relegated to two idealized paradigms for uni- and multiflagellated cells, respectively. However, mounting evidence suggests that bacteria exhibit a plethora of flagellar arrangements and associated motility patterns that do not fit into the existing paradigms. It is not understood how flagellar motility patterns emerge from variations in arrangements and the flexible mechanics of flagella. This work will develop a mechanistic understanding of the vast understudied diversity of flagellar arrangements and motility patterns by linking them to the geometry and mechanics of flagella. This biomechanical understanding has been hindered by a lack of quantitative imaging of flagella, limited ability to experimentally perturb flagellar properties, and overly simplified models. We will overcome these challenges by using unique imaging capabilities, which enable data-driven and experimentally validated numerical modeling and hypothesis testing. Experimentally, this work incorporates advanced high-speed video microscopy techniques to capture the time-resolved kinematics of 10-nanometer diameter flagella, pushing the envelope of existing quantification methods for fluid-structure interactions. Numerically, this work advances the state-of-the-art in modeling elastohydrodynamics of actuated filaments. This work will establish a unique data set comprising the mechanical properties of flagella and the kinematics of bacterial motility from diverse species. It will use modeling to determine the physical mechanisms leading to observed motility patterns, and combine physical and numerical experiments to elucidate and classify the originsThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

细菌游泳的能力对于生存至关重要。游泳是通过旋转称为鞭毛的细丝来实现的。细菌上鞭毛的排列方式有很多种，运动模式也有很多种，所有这些都使得细菌能够游泳。尽管细菌游泳取得了成功，但这些机制很少被充分了解。该项目的目标是确定鞭毛的排列和柔性机制如何结合起来产生细菌运动的差异。该项目的结果将产生许多潜在的社会影响，因为细菌的运动性使细菌能够通过供水系统传播，并在人类中建立微生物组或感染。这项工作将在多个层面产生重要影响。在细胞水平上，更好地了解细菌运动将有助于我们了解细胞资源的吸收。在生态系统规模上，运动性影响食物网。这项工作的结果可能会带来介导细菌感染的新方法，进一步加深我们对细菌进化的理解，并激发具有增强功能的仿生微型机器人。此外，该奖项还将支持推广计划，向公众传授细菌运动及其通过健康和生态学对社会的重要性。细菌鞭毛运动在很大程度上被归为单鞭毛细胞和多鞭毛细胞的两种理想化范例。然而，越来越多的证据表明，细菌表现出过多的鞭毛排列和相关的运动模式，这些模式不符合现有的范式。目前尚不清楚鞭毛的运动模式是如何从鞭毛的排列变化和灵活机制中产生的。这项工作将通过将鞭毛排列和运动模式与鞭毛的几何和力学联系起来，对鞭毛排列和运动模式的巨大多样性进行机械理解。由于缺乏鞭毛定量成像、实验扰乱鞭毛特性的能力有限以及模型过于简化，这种生物力学理解受到阻碍。我们将通过使用独特的成像功能来克服这些挑战，从而实现数据驱动和经过实验验证的数值建模和假设检验。在实验上，这项工作采用了先进的高速视频显微镜技术来捕获 10 纳米直径鞭毛的时间分辨运动学，突破了现有流体-结构相互作用量化方法的极限。从数值上讲，这项工作推进了驱动细丝弹性流体动力学建模的最先进水平。这项工作将建立一个独特的数据集，包括鞭毛的机械特性和来自不同物种的细菌运动的运动学。它将使用建模来确定导致观察到的运动模式的物理机制，并结合物理和数值实验来阐明和分类起源。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。