Department of Biomedical Engineering, the Engineering Faculty, Tel Aviv University, Tel-Aviv, Israel.
The Sagol School of Neuroscience, Tel Aviv University, 69978, Tel-Aviv, Israel.
Sci Rep. 2020 Dec 3;10(1):21202. doi: 10.1038/s41598-020-78260-y.
mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.
mRNA 翻译是一个基本的细胞过程，消耗了细胞内的大部分能量；因此，它受到广泛的进化选择以进行优化，其效率可以影响宿主的生长速度。我们描述了一种通用的方法，通过基于全面的计算模型引入同义突变来提高任何生物体的生长速度（适应性）。这些算法引入了沉默突变，这些突变可以通过减少翻译过程中核糖体的交通堵塞，从而改善核糖体在细胞中的分配。结果，细胞中的资源可用性发生变化，导致生长速度提高。我们通过在酿酒酵母上进行实验来证明该方法的实现：我们表明，通过在两个经过计算选择的基因中引入几个突变，突变体的滴度增加了。我们的方法可以用于提高任何生物体的生长速度，前提是存在用于推断模型的数据，并且具有相关的基因组工程工具；因此，它在生物技术、医学和农业中预计将非常有用。
