A model of strongly biased chemotaxis reveals the trade-offs of different bacterial migration strategies.
A model of strongly biased chemotaxis reveals the trade-offs of different bacterial migration strategies.
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DOI：
10.1093/imammb/dqz007
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
R. Bearon;W. M. Durham
中科院分区：
文献类型：
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作者：
R. Bearon;W. M. Durham
许多细菌主动地将它们的运动偏向于更有利的营养环境。在液体中，细胞通过旋转螺旋状的鞭毛来游动，但在表面附着的生物膜中，细胞则使用称为皮利的钩状附属物来拉动自己沿着前进。在这两种运动形式中，细胞选择性地在相对直的“运行”和急剧的重新定向之间交替，以产生偏向的随机游走的化学引诱物梯度。然而，最近的实验表明，游泳和生物膜细胞采用从根本上不同的策略来产生趋化性：游泳细胞通常抑制重定向时，向上移动的趋化梯度，而生物膜细胞增加重定向时，向下移动的趋化梯度。造成这种差异的原因尚不清楚。在这里，我们开发了一个数学框架来理解这些不同的趋化策略如何影响细胞在群体水平上的分布。目前的连续模型通常假设在重定向率弱的偏见，并不能区分这两种策略，所以我们推导出一个模型，解决了如何漂移和扩散组件依赖于潜在的趋化策略的强趋化性。然后，我们测试预测从我们的连续体模型对个人为基础的模拟，并确定进一步的改进，使我们的连续体模型解决边界效应。我们的分析表明，游泳细胞采用的策略产生了更大的趋化漂移，但生物膜细胞使用的策略使它们能够更紧密地聚集在化学引诱物最丰富的地方。这种新的建模框架提供了新的定量见解，以了解游泳和生物膜细胞所经历的不同化学景观如何选择不同的趋化性产生方式。
Many bacteria actively bias their motility towards more favourable nutrient environments. In liquid, cells rotate their corkscrew-shaped flagella to swim, but in surface attached biofilms cells instead use grappling hook-like appendages called pili to pull themselves along. In both forms of motility, cells selectively alternate between relatively straight 'runs' and sharp reorientations to generate biased random walks up chemoattractant gradients. However, recent experiments suggest that swimming and biofilm cells employ fundamentally different strategies to generate chemotaxis: swimming cells typically suppress reorientations when moving up a chemoattractant gradient, whereas biofilm cells increase reorientations when moving down a chemoattractant gradient. The reason for this difference remains unknown. Here we develop a mathematical framework to understand how these different chemotactic strategies affect the distribution of cells at the population level. Current continuum models typically assume a weak bias in the reorientation rate and are not able to distinguish between these two strategies, so we derive a model for strong chemotaxis that resolves how both the drift and diffusive components depend on the underlying chemotactic strategy. We then test predictions from our continuum model against individual-based simulations and identify further refinements that allow our continuum model to resolve boundary effects. Our analyses reveal that the strategy employed by swimming cells yields a larger chemotactic drift, but the strategy used by biofilm cells allows them to more tightly aggregate where the chemoattractant is most abundant. This new modelling framework provides new quantitative insights into how the different chemical landscapes experienced by swimming and biofilm cells might select for divergent ways of generating chemotaxis.