History dependence of chemosensing strategy in Escherichia coli

描述（由申请人提供）：为了生存，生物体必须不断适应不断变化的条件。虽然其中一些反应代表了相对简单的稳态机制，但其他反应则更为复杂，反映了联想学习甚至预测能力。这种复杂的反应不仅仅是多细胞生物的领域-细菌也进化出了精细的策略来处理其复杂的，不断变化的环境，例如昼夜节律和温度/氧气关联。虽然这些例子主要反映了代谢适应，但最近有证据表明，细菌的感觉系统也会被细胞的生长环境重塑。特别是，大肠杆菌的趋化网络在蛋白质水平和比例上经历了约10倍的变化，以响应营养丰富度，温度和细胞密度。大量的检测可用于该系统和存在的经过良好测试的定量模型的操作，使其成为一个理想的目标，探索历史依赖感测的原则。为了进行这种探索，我们将种植E。大肠杆菌细胞在广泛的生理相关条件下，包括营养类型和丰度，温度，pH值，O2水平，渗透压，细胞密度，和多种化学信号的存在。然后，我们将在三个水平上表征趋化网络：蛋白质丰度，对刺激的信号响应（通过荧光共振能量转移）和趋化行为（通过跟踪在微流体梯度中游泳的单细胞）。我们将利用成熟的趋化性信号模型来解释我们的实验结果，并开发一个工作模型，用于研究生长条件如何重塑化学感受器。潜在的历史依赖性调节的分子机制，无论是已知的和新发现的，其特征在于通过测定mRNA和蛋白质水平/稳定性，并通过利用各种荧光报告。最后，我们将扩展现有的趋化信号模型，以确定趋化性能如何依赖于网络组成，预测蛋白质水平和受体协同性之间的最佳比例关系，并测试这些预测与微进化实验。

DESCRIPTION (provided by applicant): To survive, organisms must continually adapt to changing conditions. While some of these responses represent relatively simple mechanisms of homeostasis, others are more complex, reflecting associative learning and even predictive ability. Such sophisticated responses are not solely the domain of multicellular organisms - bacteria have also evolved refined strategies to deal with their complex, changing environments, e.g. circadian rhythms and temperature/oxygen association. While such examples primarily reflect metabolic adaptation, there is recent evidence that bacterial sensory systems are also reshaped by the cell's growth environment. In particular, the chemotaxis network of Escherichia coli undergoes ~10-fold changes in protein levels and ratios in response to nutrient abundance, temperature, and cell density. The large number of assays available for this system and the existence of well-tested quantitative models of its operation make it an ideal target to explore the principles of history-dependent sensing. To carry out this exploration, we will grow E. coli cells under a wide range of physiologically relevant conditions, including nutrient type and abundance, temperature, pH, O2 levels, osmolarity, cell density, and the presence of multiple chemical signals. We will then characterize the chemotactic network at three levels: protein abundances, signaling response to stimulation (via fluorescence resonance energy transfer), and chemotactic behavior (via tracking of single cells swimming in microfluidic gradients). We will exploit the well-established model for chemotactic signaling to interpret our experimental results, and to develop a working model for how growth conditions reshape the chemosensory apparatus. The molecular mechanisms underlying history-dependent regulation, both known and newly discovered, will be characterized by assaying mRNA and protein levels/stability and by exploiting a variety of fluorescent reporters. Finally, we will extend the existing model for chemotactic signaling to determine how chemotactic performance depends on network composition, predict optimal scaling relations between protein levels and receptor cooperativity, and test these predictions with microevolution experiments.