PNAS：细菌适应环境的秘诀

　　

生物通报道：面对外部环境的改变，细菌能够快速适应并保持高速生长，这其中的机制是分子微生物学所面临的主要挑战之一。分析这一机制可以帮助人们理解，细菌是如何对抗生素产生抗性的。

近年来，抗生素滥用现象使耐药菌日渐增多，这已经成为了一个全球性的公共健康问题。在这样的背景下，研究细菌对环境改变的适应性尤为重要。日前，Uppsala大学的研究人员提出了，细菌巧妙调节自身基因表达，快速适应环境改变的模型。文章发表在美国国家科学院院刊PNAS杂志上。

为了在不同环境中实现快速生长，细菌需要调整自己的酶水平，以便有效利用现有环境中的营养源。细菌自身的蛋白生产，必须能够有效适应生活环境的快速变化（例如更换培养基）。这项研究中的模型向人们展示了，细菌根据生活环境的改变，调整自身蛋白水平的方式，同时指出了细菌适应性的极限。

自二十世纪中期以来人们开始了解到，细菌的生长不仅取决于周围环境的构成，也取决于环境的突然改变。在稳定环境内持续高速生长的细菌，需要具备一定的生理特性，而环境改变又要求细菌的生理机能可以快速适应。现在，Uppsala大学提出的新模型，体现了上述适应所需的最短时间。

“细菌为何能够快速适应环境的改变，我们尝试用模型回答这一具体问题。我们的模型体现了细菌蛋白质组在发生遗传学改变时，所采取的最佳策略。这一模型在相关领域有着广阔的应用前景，目前该模型正在接受进一步的检验，” Uppsala大学的Måns Ehrenberg教授说。

研究人员指出，在该模型的帮助下，人们可以对细菌的关键生理机能进行预测和检验。此外，这一模型将细菌的生理机能与群体遗传学联系起来，允许研究者在系统生物学水平上，分析细菌的生长与演化。

 

（生物通编辑：叶予）

生物通推荐原文摘要：

Optimal control of gene expression for fast proteome adaptation to environmental change

Bacterial populations growing in a changing world must adjust their proteome composition in response to alterations in the environment. Rapid proteome responses to growth medium changes are expected to increase the average growth rate and fitness value of these populations. Little is known about the dynamics of proteome change, e.g., whether bacteria use optimal strategies of gene expression for rapid proteome adjustments and if there are lower bounds to the time of proteome adaptation in response to growth medium changes. To begin answering these types of questions, we modeled growing bacteria as stoichiometrically coupled networks of metabolic pathways. These are balanced during steady-state growth in a constant environment but are initially unbalanced after rapid medium shifts due to a shortage of enzymes required at higher concentrations in the new environment. We identified an optimal strategy for rapid proteome adjustment in the absence of protein degradation and found a lower bound to the time of proteome adaptation after medium shifts. This minimal time is determined by the ratio between the Kullback–Leibler distance from the pre- to the postshift proteome and the postshift steady-state growth rate. The dynamics of optimally controlled proteome adaptation has a simple analytical solution. We used detailed numerical modeling to demonstrate that realistic bacterial control systems can emulate this optimal strategy for rapid proteome adaptation. Our results may provide a conceptual link between the physiology and population genetics of growing bacteria.