Automation, Optimization, and Characterization of Adaptive Laboratory Evolution
- Author(s): LaCroix, Ryan Alan
- Advisor(s): Palsson, Bernhard O
- et al.
Adaptive laboratory evolution (ALE) has emerged as an effective tool for scientific discovery and addressing biotechnological needs. A typical ALE experiment requires significant attention over the course of the experiment and can last up to months. When designing such experiments much consideration is given to the logistics of maintaining the experiment. Due to the difficult logistics, the consistency and throughput are often reduced. Overcoming these shortcoming is now possible with an automated platform. The automated platform was designed and built giving consideration to alleviating the design constraint that the time-sensitive processes impose on the experiment.
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 37ºC. By keeping E. coli in constant substrate-excess and exponential growth, fitness increases up to 1.6-fold were obtained over wild-type. These increases are comparable to previously-reported maximum growth rates in similar conditions but obtained over a shorter time frame. Across the 8 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 82bp 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 put forth 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.
The evolution process is increasingly being leveraged in laboratory settings for industrial and basic science applications. Despite an increasing deployment, there are no standardized procedures available for designing and performing adaptive laboratory evolution (ALE) experiments. Thus, there is a need to optimize the experimental design, specifically for determining termination criteria and for balancing outcomes with available resources (i.e., lab supplies, personnel, and time). To design and better understand ALE experiments, a simulator, ALEsim, was developed, validated, and applied to optimize ALE experimentation. The effects of various passage sizes were experimentally determined and subsequently evaluated with ALEsim to explain differences in experimental outcomes. Further, a beneficial mutation rate of 10-6.9-10-8.4 mutations per cell division was derived. A retrospective analysis of ALE experiments revealed that passage sizes typically employed in batch culture ALE experiments led to inefficient production and fixation of beneficial mutations. ALEsim and the results herein will aid in the design of ALE experiments to fit the exact needs of the project while taking into account the tradeoff in resources required, and lower the barrier of entry to this experimental technique.
With successful completion of an automated ALE platform and multiple applied cases, there became a need to expand the ALE protocol for variations of ALE. Specifically to accommodate adaptation to environments that cannot initially sustain growth. Two algorithms were developed to implement two variations of ALE, pathway activation of latent enzymes (PALE ALE) and tolerization (TALE). The purpose of the PALE ALE protocol was to adapt and organism to growth using a substrate is it natively is unable to utilize. An algorithm was developed to put significant selection pressure on the population to adapt all while maximizing the amount of genetic diversity being created. The purpose of the TALE module is to adapt an organism to an increasing amount of stress (e.g. temperature, physical, chemical, etc…). The algorithm specifically targeted putting enough stress on the culture as reasonable but also ensuring that the culture is still able to grow. This is critical since if growth is arrested the genetic diversity in the culture drops off significantly. These two algorithms were successfully implemented into the automated ALE platform.