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 ...

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Background: PRMT7 is a poorly characterized member of the family of protein arginine methyltransferases (PRMTs) that catalyze the modification of a wide group of proteins. Results: A bacterially expressed fusion protein of human PRMT7 produces only -monomethylated arginine (-MMA) derivatives. Conclusion: PRMT7 is a type III PRMT distinct from type I and type II PRMTs characterized to date. Significance: -MMA may be a distinct post-translational modification.

Adaptive laboratory evolution (ALE) has emerged as a valuable method by which to investigate microbial adaptation to a desired environment. Here, we performed ALE to 42 °C of ten parallel populations of Escherichia coli K-12 MG1655 grown in glucose minimal media. Tightly controlled experimental conditions allowed selection based on exponential-phase growth rate, yielding strains that uniformly converged toward a similar phenotype along distinct genetic paths. Adapted strains possessed as few as 6 and as many as 55 mutations, and of the 144 genes that mutated in total, 14 arose independently across two or more strains. This mutational recurrence pointed to the key genetic targets underlying the evolved fitness increase. Genome engineering was used to introduce the novel ALE-acquired alleles in random combinations into the ancestral strain, and competition between these engineered strains reaffirmed the impact of the key mutations on the growth rate at 42 °C. Interestingly, most of the identified key gene targets differed significantly from those found in similar temperature adaptation studies, highlighting the sensitivity of genetic evolution to experimental conditions and ancestral genotype. Additionally, transcriptomic analysis of the ancestral and evolved strains revealed a general trend for restoration of the global expression state back toward preheat stressed levels. This restorative effect was previously documented following evolution to metabolic perturbations, and thus may represent a general feature of ALE experiments. The widespread evolved expression shifts were enabled by a comparatively scant number of regulatory mutations, providing a net fitness benefit but causing suboptimal expression levels for certain genes, such as those governing flagellar formation, which then became targets for additional ameliorating mutations. Overall, the results of this study provide insight into the adaptation process and yield lessons important for the future implementation of ALE as a tool for scientific research and engineering.

Catalysis using iron–sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response inEscherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron–sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron–sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype–phenotype relationships for stress responses on a genome-wide basis.