Mutations that improve efficiency of a weak-link enzyme are rare compared to adaptive mutations elsewhere in the genome

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

Thermostable bacterial L-asparaginase for polyacrylamide inhibition and in silico mutational analysis

The L-asparaginase (ASPN) enzyme has received recognition in various applications including acrylamide degradation in the food industry. The synthesis and application of thermostable ASPN enzymes is required for its use in the food sector, where thermostable enzymes can withstand high temperatures. To achieve this goal, the bacterium Bacillus subtilis was isolated from the hot springs of Tapovan for screening the production of thermostable ASPN enzyme. Thus, ASPN with a maximal specific enzymatic activity of 0.896 U/mg and a molecular weight of 66 kDa was produced from the isolated bacteria. The kinetic study of the enzyme yielded a Km value of 1.579 mM and a Vmax of 5.009 µM/min with thermostability up to 100 min at 75 °C. This may have had a positive indication for employing the enzyme to stop polyacrylamide from being produced. The current study has also been extended to investigate the interaction of native and mutated ASPN enzymes with acrylamide. This concluded that the M10 (with 10 mutations) has the highest protein and thermal stability compared to the wild-type ASPN protein sequence. Therefore, in comparison to a normal ASPN and all other mutant ASPNs, M10 is the most favorable mutation. This research has also demonstrated the usage of ASPN in food industrial applications.

Synonymous edits in the Escherichia coli genome have substantial and condition-dependent effects on fitness

CRISPR-Cas-based genome editing is widely used in bacteria at scales ranging from construction of individual mutants to massively parallel libraries. This procedure relies on guide RNA-directed cleavage of the genome followed by repair with a template that introduces a desired mutation along with synonymous "immunizing" mutations to prevent re-cleavage of the genome after editing. Because the immunizing mutations do not change the protein sequence, they are often assumed to be neutral. However, synonymous mutations can change mRNA structures in ways that alter levels of the encoded proteins. We have tested the assumption that immunizing mutations are neutral by constructing a library of over 50,000 edits that consist of only synonymous mutations in Escherichia coli. Thousands of edits had substantial effects on fitness during growth of E. coli on acetate, a poor carbon source that is toxic at high concentrations. The percentage of high-impact edits varied considerably between genes and at different positions within genes. We reconstructed clones with high-impact edits and found that 69% indeed had significant effects on growth in acetate. Interestingly, fewer edits affected fitness during growth in glucose, a preferred carbon source, suggesting that changes in protein expression caused by synonymous mutations may be most important when an organism encounters challenging conditions. Finally, we showed that synonymous edits can have widespread effects; a synonymous edit at the 5' end of ptsI altered expression of hundreds of genes. Our results suggest that the synonymous immunizing edits introduced during CRISPR-Cas-based genome editing should not be assumed to be innocuous.

The low mutational flexibility of the EPSP synthase in Bacillus subtilis is due to a higher demand for shikimate pathway intermediates

Glyphosate (GS) inhibits the 5-enolpyruvyl-shikimate-3-phosphate (EPSP) synthase that is required for aromatic amino acid, folate and quinone biosynthesis in Bacillus subtilis and Escherichia coli. The inhibition of the EPSP synthase by GS depletes the cell of these metabolites, resulting in cell death. Here, we show that like the laboratory B. subtilis strains also environmental and undomesticated isolates adapt to GS by reducing herbicide uptake. Although B. subtilis possesses a GS-insensitive EPSP synthase, the enzyme is strongly inhibited by GS in the native environment. Moreover, the B. subtilis EPSP synthase mutant was only viable in rich medium containing menaquinone, indicating that the bacteria require a catalytically efficient EPSP synthase under nutrient-poor conditions. The dependency of B. subtilis on the EPSP synthase probably limits its evolvability. In contrast, E. coli rapidly acquires GS resistance by target modification. However, the evolution of a GS-resistant EPSP synthase under non-selective growth conditions indicates that GS resistance causes fitness costs. Therefore, in both model organisms, the proper function of the EPSP synthase is critical for the cellular viability. This study also revealed that the uptake systems for folate precursors, phenylalanine and tyrosine need to be identified and characterized in B. subtilis.

Recruitment of a Middling Promiscuous Enzyme Drives Adaptive Metabolic Evolution in Escherichia coli

A key step in metabolic pathway evolution is the recruitment of promiscuous enzymes to perform new functions. Despite the recognition that promiscuity is widespread in biology, factors dictating the preferential recruitment of one promiscuous enzyme over other candidates are unknown. Escherichia coli contains four sugar kinases that are candidates for recruitment when the native glucokinase machinery is deleted-allokinase (AlsK), manno(fructo)kinase (Mak), N-acetylmannosamine kinase (NanK), and N-acetylglucosamine kinase (NagK). The catalytic efficiencies of these enzymes are 103- to 105-fold lower than native glucokinases, ranging from 2,400 M-1 s-1 for the most active candidate, NagK, to 15 M-1 s-1 for the least active candidate, AlsK. To investigate the relationship between catalytic activities of promiscuous enzymes and their recruitment, we performed adaptive evolution of a glucokinase-deficient E. coli strain to restore glycolytic metabolism. We observed preferential recruitment of NanK via a trajectory involving early mutations that facilitate glucose uptake and amplify nanK transcription, followed by nonsynonymous substitutions in NanK that enhance the enzyme's promiscuous glucokinase activity. These substitutions reduced the native activity of NanK and reduced organismal fitness during growth on an N-acetylated carbon source, indicating that enzyme recruitment comes at a cost for growth on other substrates. Notably, the two most active candidates, NagK and Mak, were not recruited, suggesting that catalytic activity alone does not dictate evolutionary outcomes. The results highlight our lack of knowledge regarding biological drivers of enzyme recruitment and emphasize the need for a systems-wide approach to identify factors facilitating or constraining this important adaptive process.

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood-free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright-Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10-4.7 to 10-4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods-barcode lineage tracking and pairwise fitness assays-which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

Evolutionary repair reveals an unexpected role of the tRNA modification m1G37 in aminoacylation

The tRNA modification m¹G37, introduced by the tRNA methyltransferase TrmD, is thought to be essential for growth in bacteria because it suppresses translational frameshift errors at proline codons. However, because bacteria can tolerate high levels of mistranslation, it is unclear why loss of m¹G37 is not tolerated. Here, we addressed this question through experimental evolution of trmD mutant strains of Escherichia coli. Surprisingly, trmD mutant strains were viable even if the m¹G37 modification was completely abolished, and showed rapid recovery of growth rate, mainly via duplication or mutation of the proline-tRNA ligase gene proS. Growth assays and in vitro aminoacylation assays showed that G37-unmodified tRNA^Pro is aminoacylated less efficiently than m¹G37-modified tRNA^Pro, and that growth of trmD mutant strains can be largely restored by single mutations in proS that restore aminoacylation of G37-unmodified tRNA^Pro. These results show that inefficient aminoacylation of tRNA^Pro is the main reason for growth defects observed in trmD mutant strains and that proS may act as a gatekeeper of translational accuracy, preventing the use of error-prone unmodified tRNA^Pro in translation. Our work shows the utility of experimental evolution for uncovering the hidden functions of essential genes and has implications for the development of antibiotics targeting TrmD.

Amplicon remodeling and genomic mutations drive population dynamics after segmental amplification

New enzymes often evolve by duplication and divergence of genes encoding enzymes with promiscuous activities that have become important in the face of environmental opportunities or challenges. Amplifications that increase the copy number of the gene under selection commonly amplify many surrounding genes. Extra copies of these coamplified genes must be removed, either during or after evolution of a new enzyme. Here we report that amplicon remodeling can begin even before mutations occur in the gene under selection. Amplicon remodeling and mutations elsewhere in the genome that indirectly increase fitness result in complex population dynamics, leading to emergence of clones that have improved fitness by different mechanisms. In this work, one of the two most successful clones had undergone two episodes of amplicon remodeling, leaving only four coamplified genes surrounding the gene under selection. Amplicon remodeling in the other clone resulted in removal of 111 genes from the genome, an acceptable solution under these selection conditions, but one that would certainly impair fitness under other environmental conditions.

Setting the stage for evolution of a new enzyme

The evolution of novel enzymes has fueled the diversification of life on earth for billions of years. Insights into events that set the stage for the evolution of a new enzyme can be obtained from ancestral reconstruction and laboratory evolution. Ancestral reconstruction can reveal the emergence of a promiscuous activity in a pre-existing protein and the impact of subsequent mutations that enhance a new activity. Laboratory evolution provides a more holistic view by revealing mutations elsewhere in the genome that indirectly enhance the level of a newly important enzymatic activity. This review will highlight recent studies that probe the early stages of the evolution of a new enzyme from these complementary points of view.

Evolution of new enzymes by gene duplication and divergence

Thousands of new metabolic and regulatory enzymes have evolved by gene duplication and divergence since the dawn of life. New enzyme activities often originate from promiscuous secondary activities that have become important for fitness due to a change in the environment or a mutation. Mutations that make a promiscuous activity physiologically relevant can occur in the gene encoding the promiscuous enzyme itself, but can also occur elsewhere, resulting in increased expression of the enzyme or decreased competition between the native and novel substrates for the active site. If a newly useful activity is inefficient, gene duplication/amplification will set the stage for divergence of a new enzyme. Even a few mutations can increase the efficiency of a new activity by orders of magnitude. As efficiency increases, amplified gene arrays will shrink to provide two alleles, one encoding the original enzyme and one encoding the new enzyme. Ultimately, genomic rearrangements eliminate co-amplified genes and move newly evolved paralogs to a distant region of the genome.

Evolutionary Repair Experiments as a Window to the Molecular Diversity of Life

Comparative genomics reveals an unexpected diversity in the molecular mechanisms underlying conserved cellular functions, such as DNA replication and cytokinesis. However, the genetic bases and evolutionary processes underlying this 'molecular diversity' remain to be explained. Here, we review a tool to generate alternative mechanisms for conserved cellular functions and test hypotheses concerning the generation of molecular diversity - evolutionary repair experiments, in which laboratory microbial populations adapt in response to a genetic perturbation. We summarize the insights gained from evolutionary repair experiments, the spectrum and dynamics of compensatory mutations, and the alternative molecular mechanisms used to repair perturbed cellular functions. We relate these experiments to the modifications of conserved functions that have occurred outside the laboratory. We end by proposing strategies to improve evolutionary repair experiments as a tool to explore the molecular diversity of life.

Evolutionary repair: Changes in multiple functional modules allow meiotic cohesin to support mitosis

The role of proteins often changes during evolution, but we do not know how cells adapt when a protein is asked to participate in a different biological function. We forced the budding yeast, Saccharomyces cerevisiae, to use the meiosis-specific kleisin, recombination 8 (Rec8), during the mitotic cell cycle, instead of its paralog, Scc1. This perturbation impairs sister chromosome linkage, advances the timing of genome replication, and reduces reproductive fitness by 45%. We evolved 15 parallel populations for 1,750 generations, substantially increasing their fitness, and analyzed the genotypes and phenotypes of the evolved cells. Only one population contained a mutation in Rec8, but many populations had mutations in the transcriptional mediator complex, cohesin-related genes, and cell cycle regulators that induce S phase. These mutations improve sister chromosome cohesion and delay genome replication in Rec8-expressing cells. We conclude that changes in known and novel partners allow cells to use an existing protein to participate in new biological functions.

CRISPR/Cas9 recombineering-mediated deep mutational scanning of essential genes in Escherichia coli

Deep mutational scanning can provide significant insights into the function of essential genes in bacteria. Here, we developed a high-throughput method for mutating essential genes of Escherichia coli in their native genetic context. We used Cas9-mediated recombineering to introduce a library of mutations, created by error-prone PCR, within a gene fragment on the genome using a single gRNA pre-validated for high efficiency. Tracking mutation frequency through deep sequencing revealed biases in the position and the number of the introduced mutations. We overcame these biases by increasing the homology arm length and blocking mismatch repair to achieve a mutation efficiency of 85% for non-essential genes and 55% for essential genes. These experiments also improved our understanding of poorly characterized recombineering process using dsDNA donors with single nucleotide changes. Finally, we applied our technology to target rpoB, the beta subunit of RNA polymerase, to study resistance against rifampicin. In a single experiment, we validate multiple biochemical and clinical observations made in the previous decades and provide insights into resistance compensation with the study of double mutants.