Bienick Matthew S, Young Katherine W, Klesmith Justin R, Detwiler Emily E, Tomek Kyle J, Whitehead Timothy A
Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, United States of America.
Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, United States of America; High School Honors Science/Mathematics/Engineering Program, Michigan State University, East Lansing, Michigan, United States of America.
PLoS One. 2014 Oct 6;9(10):e109105. doi: 10.1371/journal.pone.0109105. eCollection 2014.
In exponentially growing bacteria, expression of heterologous protein impedes cellular growth rates. Quantitative understanding of the relationship between expression and growth rate will advance our ability to forward engineer bacteria, important for metabolic engineering and synthetic biology applications. Recently, a work described a scaling model based on optimal allocation of ribosomes for protein translation. This model quantitatively predicts a linear relationship between microbial growth rate and heterologous protein expression with no free parameters. With the aim of validating this model, we have rigorously quantified the fitness cost of gene expression by using a library of synthetic constitutive promoters to drive expression of two separate proteins (eGFP and amiE) in E. coli in different strains and growth media. In all cases, we demonstrate that the fitness cost is consistent with the previous findings. We expand upon the previous theory by introducing a simple promoter activity model to quantitatively predict how basal promoter strength relates to growth rate and protein expression. We then estimate the amount of protein expression needed to support high flux through a heterologous metabolic pathway and predict the sizable fitness cost associated with enzyme production. This work has broad implications across applied biological sciences because it allows for prediction of the interplay between promoter strength, protein expression, and the resulting cost to microbial growth rates.
在指数生长的细菌中，异源蛋白的表达会阻碍细胞生长速率。对表达与生长速率之间关系的定量理解将提升我们对细菌进行正向工程改造的能力，这对于代谢工程和合成生物学应用至关重要。最近，一项研究描述了一种基于核糖体用于蛋白质翻译的最优分配的比例模型。该模型定量预测了微生物生长速率与异源蛋白表达之间的线性关系，且无自由参数。为了验证该模型，我们通过使用一组合成组成型启动子文库，在不同菌株和生长培养基中驱动大肠杆菌中两种不同蛋白质（增强绿色荧光蛋白和amiE）的表达，严格量化了基因表达的适应性成本。在所有情况下，我们都证明适应性成本与先前的研究结果一致。我们通过引入一个简单的启动子活性模型来扩展先前的理论，以定量预测基础启动子强度与生长速率和蛋白质表达之间的关系。然后，我们估计了支持通过异源代谢途径的高通量所需的蛋白质表达量，并预测了与酶产生相关的可观适应性成本。这项工作对应用生物学领域具有广泛的意义，因为它能够预测启动子强度、蛋白质表达以及对微生物生长速率产生的成本之间的相互作用。
