Use of Adaptive Laboratory Evolution To Discover Key Mutations Enabling Rapid Growth of Escherichia coli K-12 MG1655 on Glucose Minimal Medium

Abstract

Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. Much of ALE's utility is derived from reproducibly obtained fitness increases. Identifying causal genetic changes and their combinatorial effects is challenging and time-consuming. Understanding how these genetic changes enable increased fitness can be difficult. A series of approaches that address these challenges was developed and demonstrated using Escherichia coli K-12 MG1655 on glucose minimal media at 37°C. By keeping E. coli in constant substrate excess and exponential growth, fitness increases up to 1.6-fold were obtained compared to the wild type. These increases are comparable to previously reported maximum growth rates in similar conditions but were obtained over a shorter time frame. Across the eight replicate ALE experiments performed, causal mutations were identified using three approaches: identifying mutations in the same gene/region across replicate experiments, sequencing strains before and after computationally determined fitness jumps, and allelic replacement coupled with targeted ALE of reconstructed strains. Three genetic regions were most often mutated: the global transcription gene rpoB, an 82-bp deletion between the metabolic pyrE gene and rph, and an IS element between the DNA structural gene hns and tdk. Model-derived classification of gene expression revealed a number of processes important for increased growth that were missed using a gene classification system alone. The methods described here represent a powerful combination of technologies to increase the speed and efficiency of ALE studies. The identified mutations can be examined as genetic parts for increasing growth rate in a desired strain and for understanding rapid growth phenotypes.A daptive laboratory evolution (ALE) is a growing field facilitated by whole-genome sequencing. The process of ALE involves the continuous culturing of an organism over multiple generations. During an ALE experiment, mutations arise, and those beneficial to the selection pressure are fixed over time in the population. Most ALE experiments analyze a perturbation from a reference state to another (e.g., environmental [1,2] or genetic [3]). After adaptation, understanding what genetic changes enabled an increase in fitness is often desirable (4). Generally there are two methods of evolving microorganisms: batch cultures and chemostats. Each method has its own advantages and disadvantages, in terms of maintenance, growth environment, and selection pressures (5). Applications of ALE are numerous and include those for biotechnological goals, such as improving tolerance to a given compound of interest (6-8), or more progressive uses such as improving electrical current consumption in an organism (9). In addition, there has been a significant focus on using ALE to understand antibiotic resistance to given compounds (i.e., drugs) in order to combat clinical resistance (10). A number of in-depth reviews on ALE have appeared ...

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

“…S. aureus is known to have a complicated gene regulatory structure that may inhibit its growth on minimal media, despite the presence of biosynthetic pathways for these metabolites (12,14). Longer-term growth or training in the laboratory for these strains may allow for eventual growth in the minimal medium defined here (35).…”

A Model for Designing Adaptive Laboratory Evolution Experiments

“…A spline was fit to the growth rate of each experiment, and significant increases in growth rates were identified as discussed previously (7). The resulting jumps in growth rates showed that the plateaus in growth occurred at specific values ( Fig.…”

“…Automation was utilized here and in previous studies (4,7,41). Manual processes are often hampered by researcher availability, whereas machines can measure and passage cells around the clock; for example, approximately 5 to 7 passages per day were performed in automated studies (4,7,41), compared to 1 or 2 passages per day in manual studies (14,15,18).…”

“…The selection pressure (i.e., exponential growth, biomass yield, stationary phase, or lag phase) is responsible for the outcome of the evolution study (4,(7)(8)(9)(10). For example, in a 24-h serially passaged batch culture ALE experiment with fast-growing bacteria, the culture is subjected to alternating environments of feast and famine.…”

“…This can be seen in a comparison of two studies that evolved wild-type E. coli K-12 MG1655 on M9 glucose minimal medium (7,18). One study (7) used a consistent passage size of 800 l from 25-ml batches on an automated platform.…”