Systems-level physiological basis of selection and epistasis in adaptation

描述(由申请人提供)：是否有可能预测生物网络中的选择和上位性相互作用的可能性？在这里，我们建议通过实验和计算方法的整合来定量地解决适应的生理基础。我们的模型系统是一个中心的、基本的和高度相互关联的代谢途径被禁用的系统，取而代之的是一个外来的、无关的途径。这一工程系统的独特优势是，这种替代特别导致适合度、生长速度和代谢流量减少3倍，产量降低2.5倍，中央代谢中枢内的流量重新分配30倍。由于这种改变直接导致次优性能，我们假设这将在实验进化期间将选择集中在这个子系统上，从而适应将在很大程度上通过被替换的路径和/或与其生理上相互作用的路径中的突变进行。此外，我们认为，对这个代谢子系统及其与代谢网络的连接进行越来越广泛和验证的数学模型，将使我们能够对改变单个系统组件的活性以及酶之间的上位性相互作用所产生的适合性影响进行可检验的预测。我们的初步结果既支持我们的模型的预测，也支持适应将打击这个中央代谢中枢的断言。我们的具体目标是1。)探索利用代谢模型进行选择的可能性，并通过调节酶的表达水平直接测试预测。进化祖先品系的复制种群，并在整个适应过程中检查表型和遗传变化。测试上位性在观察到或合成的自适应轨迹中的作用。该项目的结果将是一个新的模型系统和概念框架，以应用全面的系统生物学方法来理解适应中选择和上位性的生理基础。它还代表着通过水平基因转移解决引入新遗传物质后发生的适应问题的机会。我们预计，将选择和上位性纳入量化框架将对公共健康产生影响，从病原体的适应、代谢性疾病的建模，到对具有突变的癌基因和肿瘤抑制基因的癌细胞群体的“适应性”命运的预测。

DESCRIPTION (provided by applicant): Is it possible to predict both the potential for selection and epistatic interactions across a biological network? Here we propose to quantitatively address the physiological basis of adaptation through the integration of experimental and computational approaches. Our model system is one in which the central, essential and highly interconnected metabolic pathway of Methylobacterium has been disabled and replaced with a foreign, unrelated pathway. The unique advantage of this engineered system is that this replacement specifically results in a 3-fold reduction in fitness, growth rate and metabolic flux, as well as 2.5-fold lower yield and a 30-fold redistribution of flux within the central metabolic hub. Because this alteration directly causes sub-optimal performance, we hypothesize that this will focus selection upon this subsystem during experimental evolution such that adaptation will largely proceed through mutations in the substituted pathway and/or those that it physiologically interacts with. Furthermore, we suggest that increasingly extended and verified mathematical models of this metabolic subsystem and its connections to the metabolic network will allow us to make testable predictions of the fitness effects of altering the activity of individual system components, as well as epistatic interactions between enzymes. Our preliminary results support both our model's predictions and the assertion that adaptation will strike this central metabolic hub. Our specific aims are to 1.) explore the potential for selection with metabolic models and directly test predictions by modulating expression levels of enzymes, 2.) evolve replicate populations of the ancestral strain and examine phenotypic and genetic changes throughout the course of adaptation and 3.) test the role of epistasis in the adaptive trajectories observed or synthesized. The result of this project will be a novel model system and conceptual framework to apply a comprehensive, systems biology approach to understanding the physiological basis of selection and epistasis in adaptation. It also represents the opportunity to address adaptation occurring after introduction of new genetic material via horizontal gene transfer. We anticipate that placing selection and epistasis into a quantitative framework will have public health impacts ranging from the adaptation of pathogens, the modeling of metabolic diseases, to prognostic predictions of the 'adaptive' fate of a population of cancer cells with mutated oncogenes and tumor suppressors.