ALE: A method for rapid improvements in biotech

The acronym ALE may lead one’s thoughts to beer, and as a matter of fact Adaptive Laboratory Evolution has something to do with fermentation (but not beer per se). ALE is about speeding up the timescales associated to evolution and pointing it towards a predefined goal. Used in fermentations in industrial biotech, the researchers behind a literature review in Metabolic Engineering find that ALE can optimise, amongst other things, production and fitness.

“In a single tube of growth you are sampling hundreds of millions of mutations, and as the cells grow, the bacteria are competing against each other and whichever one happens to have adaptive mutation will take over the population,” says first author Postdoc Troy E. Sandberg from the Department of Bioengineering at University of California, San Diego.

Five ALE approaches

The different uses of ALE can be roughly categorised into five general application areas: Growth rate optimisation, increasing tolerance, substrate utilisation, optimising product yield, and general discovery. But often, one ALE experiment ends up improving more than one property, because of general improved fitness and selection for multiple sought after traits.

“When you speed up evolution from what could naturally take thousands to potentially millions of years to occur to a few weeks or months, you often end up with a microbe that has adapted in many ways,” says Adam Feist, corresponding author of this review. Adam Feist is Research Scientist at the Department of Bioengineering, University of California, San Diego and Senior Researcher and Co-PI at The Novo Nordisk Foundation Center for Biosustainability at Technical University of Denmark, where he also serves as Group Leader of ALE Group.

Superb at optimising growth and fitness

The review clearly shows that ALE can increase growth rates, even in culturing environments where one might expect growth to already be close to optimal. For example, a study of a one month ALE experiment in glucose minimal medium resulted in growth rate improvements of up to 60% for the commonly used lab strain bacterium E. coli K-12 MG1655.

ALE can also fix fitness defects. In the review, the authors point out a study, in which an engineered M. extorquens strain with a foreign metabolic pathway had huge fitness defects. But after doing ALE, the evolved strain showed an approximate 150% growth rate improvement, and its morphological abnormalities had disappeared in the process. Further, the researchers got insight into the metabolic consequences resulting from introduction of the foreign pathway.

In another study, a genome-reduced E. coli grew 3-fold slower than the wild-type in glucose minimal media. After a more than 1000 generation ALE experiment, this severe fitness deficit was almost completely gone.

Increased tolerance

ALE has also proven efficient in increasing a production strains’ tolerance towards, amongst other things, high and low pH values, temperatures and unwanted metabolic by-products.

Compounds with desirable features that have potential biotechnological value can often not be efficiently produced in living systems due to toxicity. For example, many carboxylic acids used in the plastic industry induce membrane damage to the microbial cell factories. Furthermore, rationally engineering of greater membrane integrity has failed to improve carboxylic acid production.

A group from Iowa State University tried to improve tolerance towards the carboxylic acid called octanoic acid in E.coli using ALE. The resulting strain had improved tolerance not just to octanoic acid, but also several other carboxylic acids and butanol isomers, with concurrent 5 fold higher production titer.

The authors also highlight the importance of saving frozen stocks of cell culture during ALE experiments. These intermediate batches will provide valuable insight into the causality of the resulting strain, i.e. which mutations have led to the top performing strain. Intermediate cultures will of course not always have as potent of mutational effects and require screening to find the most causal mutations, but this will lead to important insight into the evolutionary pathways.

Lab automation drives ALE

Adam also points out that recent years’ advancements in laboratory automation and robotics has driven the fast development of efficient ALE methods. ALE experiments can in principle be done by hand, but it is hard and tedious, because one needs to take out samples, involving numerous pipetting jobs and dilutions around the clock at fixed time intervals.

“Lab automation and sequencing are now enabling ALE to be an efficient tool for industrial biology,” Adam Feist says.

The big data generated from sequencing both mid-way stocks and final optimised strains can then be used to model what happened during lab evolution in test tubes. Using these big piles of data smartly could lead to new discoveries and predictions that will help scientists who want to do ALE but also in their rational design strategies, Adam explains:

“Going forward, we want to focus on putting mutational information in organised formats for prospective use and use ALE derived information to engineer different aspects in the strains that we are interested in,” Adam Feist says.