A Model for Designing Adaptive Laboratory Evolution Experiments

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures

The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance. Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution in vitro to that of single sequence AMPs, using the ESKAPE pathogen Pseudomonas aeruginosa (P. aeruginosa) as a model gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to form biofilms. P. aeruginosa was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined. Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved in vitro, providing an alternative to classic antibiotics worth pursuing.

Improving 5-(hydroxymethyl)furfural (HMF) tolerance of Pseudomonas taiwanensis VLB120 by automated adaptive laboratory evolution (ALE)

The aldehyde 5-(hydroxymethyl)furfural (HMF) is of great importance for a circular bioeconomy. It is a renewable platform chemical that can be converted into a range of useful compounds to replace petroleum-based products such as the green plastic monomer 2,5-furandicarboxylic acid (FDCA). However, it also exhibits microbial toxicity for example hindering the efficient biotechnological valorization of lignocellulosic hydrolysates. Thus, there is an urgent need for tolerance-improved organisms applicable to whole-cell biocatalysis. Here, we engineer an oxidation-deficient derivative of the naturally robust and emerging biotechnological workhorse P. taiwanensis VLB120 by robotics-assisted adaptive laboratory evolution (ALE). The deletion of HMF-oxidizing enzymes enabled for the first time evolution under constant selection pressure by the aldehyde, yielding strains with consistently improved growth characteristics in presence of the toxicant. Genome sequencing of evolved clones revealed loss-of function mutations in the LysR-type transcriptional regulator-encoding mexT preventing expression of the associated efflux pump mexEF-oprN. This knowledge allowed reverse engineering of strains with enhanced aldehyde tolerance, even in a background of active or overexpressed HMF oxidation machinery, demonstrating a synergistic effect of two distinct tolerance mechanisms.

Signatures of selection in Mulinia lateralis underpinning its rapid adaptation to laboratory conditions

The dwarf surf clam, Mulinia lateralis, is considered as a model species for bivalves because of its rapid growth and short generation time. Recently, successful breeding of this species for multiple generations in our laboratory revealed its acquisition of adaptive advantages during artificial breeding. In this study, 310 individuals from five different generations were genotyped with 22,196 single nucleotide polymorphisms (SNPs) with the aim of uncovering the genetic basis of their adaptation to laboratory conditions. Results revealed that M. lateralis consistently maintained high genetic diversity across generations, characterized by high observed heterozygosity (H o: 0.2733-0.2934) and low levels of inbreeding (F is: -0.0244-0.0261). Population analysis indicated low levels of genetic differentiation among generations of M. lateralis during artificial breeding (F st <0.05). In total, 316 genomic regions exhibited divergent selection, with 168 regions under positive selection. Furthermore, 227 candidate genes were identified in the positive selection regions, which have functions including growth, stress resistance, and reproduction. Notably, certain selection signatures with significantly higher F st value were detected in genes associated with male reproduction, such as GAL3ST1, IFT88, and TSSK2, which were significantly upregulated during artificial breeding. This suggests a potential role of sperm-associated genes in the rapid evolutionary response of M. lateralis to selection in laboratory conditions. Overall, our findings highlight the phenotypic and genetic changes, as well as selection signatures, in M. lateralis during artificial breeding. This contributes to understanding their adaptation to laboratory conditions and underscores the potential for using this species to explore the adaptive evolution of bivalves.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to 'peak shift' to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

Adaptive laboratory evolution of Salmonella enterica in acid stress

Introduction

Adaptive laboratory evolution (ALE) studies play a crucial role in understanding the adaptation and evolution of different bacterial species. In this study, we have investigated the adaptation and evolution of Salmonella enterica serovar Enteritidis to acetic acid using ALE.

Materials and methods

Acetic acid concentrations below the minimum inhibitory concentration (sub-MIC) were used. Four evolutionary lineages (EL), namely, EL1, EL2, EL3, and EL4, of S. Enteritidis were developed, each demonstrating varying levels of resistance to acetic acid.

Results

The acetic acid MIC of EL1 remained constant at 27 mM throughout 70 days, while the MIC of EL2, EL3, and EL4 increased throughout the 70 days. EL4 was adapted to the highest concentration of acetic acid (30 mM) and demonstrated the highest increase in its MIC against acetic acid throughout the study, reaching an MIC of 35 mM on day 70. The growth rates of the evolved lineages increased over time and were dependent on the concentration of acetic acid used during the evolutionary process. EL4 had the greatest increase in growth rate, reaching 0.33 (h-1) after 70 days in the presence of 30 mM acetic acid as compared to EL1, which had a growth rate of 0.2 (h-1) after 70 days with no exposure to acetic acid. Long-term exposure to acetic acid led to an increased MIC of human antibiotics such as ciprofloxacin and meropenem against the S. enterica evolutionary lineages. The MIC of ciprofloxacin for EL1 stayed constant at 0.016 throughout the 70 days while that of EL4 increased to 0.047. Bacterial whole genome sequencing revealed single-nucleotide polymorphisms in the ELs in various genes known to be involved in S. enterica virulence, pathogenesis, and stress response including phoP, phoQ, and fhuA. We also observed genome deletions in some of the ELs as compared to the wild-type S. Enteritidis which may have contributed to the bacterial acid adaptation.

Discussion

This study highlights the potential for bacterial adaptation and evolution under environmental stress and underscores the importance of understanding the development of cross resistance to antibiotics in S. enterica populations. This study serves to enhance our understanding of the pathogenicity and survival strategies of S. enterica under acetic acid stress.

From Saccharomyces cerevisiae to Ethanol: Unlocking the Power of Evolutionary Engineering in Metabolic Engineering Applications

Increased human population and the rapid decline of fossil fuels resulted in a global tendency to look for alternative fuel sources. Environmental concerns about fossil fuel combustion led to a sharp move towards renewable and environmentally friendly biofuels. Ethanol has been the primary fossil fuel alternative due to its low carbon emission rates, high octane content and comparatively facile microbial production processes. In parallel to the increased use of bioethanol in various fields such as transportation, heating and power generation, improvements in ethanol production processes turned out to be a global hot topic. Ethanol is by far the leading yeast output amongst a broad spectrum of bio-based industries. Thus, as a well-known platform microorganism and native ethanol producer, baker's yeast Saccharomyces cerevisiae has been the primary subject of interest for both academic and industrial perspectives in terms of enhanced ethanol production processes. Metabolic engineering strategies have been primarily adopted for direct manipulation of genes of interest responsible in mainstreams of ethanol metabolism. To overcome limitations of rational metabolic engineering, an alternative bottom-up strategy called inverse metabolic engineering has been widely used. In this context, evolutionary engineering, also known as adaptive laboratory evolution (ALE), which is based on random mutagenesis and systematic selection, is a powerful strategy to improve bioethanol production of S. cerevisiae. In this review, we focus on key examples of metabolic and evolutionary engineering for improved first- and second-generation S. cerevisiae bioethanol production processes. We delve into the current state of the field and show that metabolic and evolutionary engineering strategies are intertwined and many metabolically engineered strains for bioethanol production can be further improved by powerful evolutionary engineering strategies. We also discuss potential future directions that involve recent advancements in directed genome evolution, including CRISPR-Cas9 technology.

Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.

Laboratory evolution reveals general and specific tolerance mechanisms for commodity chemicals

Although strain tolerance to high product concentrations is a barrier to the economically viable biomanufacturing of industrial chemicals, chemical tolerance mechanisms are often unknown. To reveal tolerance mechanisms, an automated platform was utilized to evolve Escherichia coli to grow optimally in the presence of 11 industrial chemicals (1,2-propanediol, 2,3-butanediol, glutarate, adipate, putrescine, hexamethylenediamine, butanol, isobutyrate, coumarate, octanoate, hexanoate), reaching tolerance at concentrations 60%-400% higher than initial toxic levels. Sequencing genomes of 223 isolates from 89 populations, reverse engineering, and cross-compound tolerance profiling were employed to uncover tolerance mechanisms. We show that: 1) cells are tolerized via frequent mutation of membrane transporters or cell wall-associated proteins (e.g., ProV, KgtP, SapB, NagA, NagC, MreB), transcription and translation machineries (e.g., RpoA, RpoB, RpoC, RpsA, RpsG, NusA, Rho), stress signaling proteins (e.g., RelA, SspA, SpoT, YobF), and for certain chemicals, regulators and enzymes in metabolism (e.g., MetJ, NadR, GudD, PurT); 2) osmotic stress plays a significant role in tolerance when chemical concentrations exceed a general threshold and mutated genes frequently overlap with those enabling chemical tolerance in membrane transporters and cell wall-associated proteins; 3) tolerization to a specific chemical generally improves tolerance to structurally similar compounds whereas a tradeoff can occur on dissimilar chemicals, and 4) using pre-tolerized starting isolates can hugely enhance the subsequent production of chemicals when a production pathway is inserted in many, but not all, evolved tolerized host strains, underpinning the need for evolving multiple parallel populations. Taken as a whole, this study provides a comprehensive genotype-phenotype map based on identified mutations and growth phenotypes for 223 chemical tolerant isolates.

Contributions of Adaptive Laboratory Evolution towards the Enhancement of the Biotechnological Potential of Non-Conventional Yeast Species

Changes in biological properties over several generations, induced by controlling short-term evolutionary processes in the laboratory through selective pressure, and whole-genome re-sequencing, help determine the genetic basis of microorganism's adaptive laboratory evolution (ALE). Due to the versatility of this technique and the imminent urgency for alternatives to petroleum-based strategies, ALE has been actively conducted for several yeasts, primarily using the conventional species Saccharomyces cerevisiae, but also non-conventional yeasts. As a hot topic at the moment since genetically modified organisms are a debatable subject and a global consensus on their employment has not yet been attained, a panoply of new studies employing ALE approaches have emerged and many different applications have been exploited in this context. In the present review, we gathered, for the first time, relevant studies showing the ALE of non-conventional yeast species towards their biotechnological improvement, cataloging them according to the aim of the study, and comparing them considering the species used, the outcome of the experiment, and the employed methodology. This review sheds light on the applicability of ALE as a powerful tool to enhance species features and improve their performance in biotechnology, with emphasis on the non-conventional yeast species, as an alternative or in combination with genome editing approaches.

Accelerated Adaptive Laboratory Evolution by Automated Repeated Batch Processes in Parallelized Bioreactors

Adaptive laboratory evolution (ALE) is a valuable complementary tool for modern strain development. Insights from ALE experiments enable the improvement of microbial cell factories regarding the growth rate and substrate utilization, among others. Most ALE experiments are conducted by serial passaging, a method that involves large amounts of repetitive manual labor and comes with inherent experimental design flaws. The acquisition of meaningful and reliable process data is a burdensome task and is often undervalued and neglected, but also unfeasible in shake flask experiments due to technical limitations. Some of these limitations are alleviated by emerging automated ALE methods on the μL and mL scale. A novel approach to conducting ALE experiments is described that is faster and more efficient than previously used methods. The conventional shake flask approach was translated to a parallelized, L scale stirred-tank bioreactor system that runs controlled, automated, repeated batch processes. The method was validated with a growth optimization experiment of E. coli K-12 MG1655 grown with glycerol minimal media as a benchmark. Off-gas analysis enables the continuous estimation of the biomass concentration and growth rate using a black-box model based on first principles (soft sensor). The proposed method led to the same stable growth rates of E. coli with the non-native carbon source glycerol 9.4 times faster than the traditional manual approach with serial passaging in uncontrolled shake flasks and 3.6 times faster than an automated approach on the mL scale. Furthermore, it is shown that the cumulative number of cell divisions (CCD) alone is not a suitable timescale for measuring and comparing evolutionary progress.

New Papiliotrema laurentii UFV-1 strains with improved acetic acid tolerance selected by adaptive laboratory evolution

The production of yeast oil from lignocellulosic biomasses is impaired by inhibitors formed during the pretreatment step, mainly acetic acid. Herein, we applied Adaptive Laboratory Evolution (ALE) to select three Acetic acid Tolerant Strains (ATS) of P. laurentii UFV-1. Different phenotypes emerged alongside evolution. The ATS II presented trade-offs in the absence of acetic acid, suggesting that it displays a specialized phenotype of tolerance to growth on organic acids. On the other hand, ATS I and ATS III presented phenotypes associated with the behavior of generalists. ATS I was considered the most promising evolved strain as it displayed the oleaginous phenotype in all conditions tested. Thus, we applied whole-genome sequencing to detect the mutations that emerged in this strain during the ALE. We found alterations in genes encoding proteins involved in different cellular functions, including multidrug resistance (MDR) transporters, energy metabolism, detoxification, coenzyme recycling, and cell envelope remodeling. To evaluate acetic acid stress responses, both parental and ATS I strains were cultivated in chemostat mode in the absence and presence of acetic acid. In contrast to ATS I, the parental strain presented alterations in the cell envelope and cell size under acetic acid stress conditions. Furthermore, the parental strain and the ATS I presented differences regarding acetic acid assimilation. Contrary to the parental strain, the ATS I displayed an increase in unsaturated fatty acid content irrespective of acetic acid stress, which might be related to improved tolerance to acetic acid. Altogether, these results provided insights into the mechanisms involved with the acetic acid tolerance displayed by ATS I and the responses of P. laurentii to this stressful condition.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.

Improvement of the catalytic performance of glycerol kinase from Bacillus subtilis by chromosomal site-directed mutagenesis

Glycerol kinase is the key enzyme in glycerol metabolism, and its catalytic efficiency has an important effect on glycerol utilization. Based on an analysis of the glycerol utilization pathway and regulation mechanism in B. subtilis, we conducted site-directed mutagenesis of the key glycerol kinase gene (glpK) on the chromosome to improve the glycerol utilization efficiency of Bacillus subtilis. Recombinant wild-type Bacillus subtilis glycerol kinase (BsuGlpK^WT) and two mutants (BsuGlpK^M270I and BsuGlpK^S71V) were successfully overexpressed in Escherichia coli BL21(DE3) and purified by Ni-IDA metal chelate chromatography. The specific activity of the BsuGlpK^M270I mutant (62.6 U/mg) was significantly higher (296.2%) than that of wild-type BsuGlpK^WT (15.8 U/mg). By contrast, the mutant BsuGlpK^S71V (4.89 U/mg) exhibited lower (69.1%) activity than BsuGlpK^WT, which suggested that variant S71V exhibited reduced catalytic efficiency for the substrate. Furthermore, the mutant strain B. subtilis M270I was constructed using a markerless delivery system, and exhibited a higher specific growth rate (improved by 11.3%, from 0.453 ± 0.012 to 0.511 ± 0.017 h^-1) and higher maximal biomass (cell dry weight increased by 16%, from 0.577 ± 0.033 to 0.721 ± 0.015 g/L) than the parental strain with a shortened lag phase (2 ~ 4 h shorter) in M9 minimal medium with glycerol. These results indicate that the mutated glpK resulted in improved glycerol utilization, which has broad application prospects.

Adaptive laboratory evolution triggers pathogen-dependent broad-spectrum antimicrobial potency in Streptomyces

BACKGROUND

In the present study, adaptive laboratory evolution was used to stimulate antibiotic production in a Streptomyces strain JB140 (wild-type) exhibiting very little antimicrobial activity against bacterial pathogens. The seven different competition experiments utilized three serial passages (3 cycles of adaptation-selection of 15 days each) in which Streptomyces strain (wild-type) was challenged repeatedly to one (bi-culture) or two (tri-culture) or three (quadri-culture) target pathogens. The study demonstrates a simple laboratory model to study the adaptive potential of evolved phenotypes and genotypes in Streptomyces to induce antibiotic production.

RESULTS

Competition experiments resulted in the evolution of the wild-type Streptomyces strain JB140 into the seven unique mutant phenotypes that acquired the ability to constitutively exhibit increased antimicrobial activity against three bacterial pathogens Salmonella Typhi (NCIM 2051), Staphylococcus aureus (NCIM 2079), and Proteus vulgaris (NCIM 2027). The mutant phenotypes not only effectively inhibited the growth of the tested pathogens but were also observed to exhibit improved antimicrobial responses against one clinical multidrug-resistant (MDR) uropathogenic Escherichia coli (UPEC 1021) isolate. In contrast to the adaptively evolved mutants, only a weak antimicrobial activity was detected in the wild-type parental strain. To get molecular evidence of evolution, RAPD profiles of the wild-type Streptomyces and its evolved mutants were compared which revealed significant polymorphism among them.

CONCLUSION

The competition-based adaptive laboratory evolution method can constitute a platform for evolutionary engineering to select improved phenotypes (mutants) with increased antibacterial profiles against targeted pathogens.

An automated DIY framework for experimental evolution of Pseudomonas putida

Adaptive laboratory evolution (ALE) is a general and effective strategy for optimizing the design of engineered genetic circuits and upgrading metabolic phenotypes. However, the specific characteristics of each microorganism typically ask for exclusive conditions that need to be adjusted to the biological chassis at stake. In this work, we have adopted a do-it-yourself (DIY) approach to implement a flexible and automated framework for performing ALE experiments with the environmental bacterium and metabolic engineering platform Pseudomonas putida. The setup includes a dual-chamber semi-continuous log-phase bioreactor design combined with an anti-biofilm layout to manage specific traits of this bacterium in long-term cultivation experiments. As a way of validation, the prototype was instrumental for selecting fast-growing variants of a P. putida strain engineered to metabolize D-xylose as sole carbon and energy source after running an automated 42 days protocol of iterative regrowth. Several genomic changes were identified in the evolved population that pinpointed the role of RNA polymerase in controlling overall physiological conditions during metabolism of the new carbon source.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Adaptive Laboratory Evolution of Microalgae: A Review of the Regulation of Growth, Stress Resistance, Metabolic Processes, and Biodegradation of Pollutants

Adaptive laboratory evolution (ALE) experiments are a serviceable method for the industrial utilization of the microalgae, which can improve the phenotype, performance, and stability of microalgae to obtain strains containing beneficial mutations. In this article, we reviewed the research into the microalgae ALE test and assessed the improvement of microalgae growth, tolerance, metabolism, and substrate utilization by ALE. In addition, the principles of ALE and the key factors of experimental design, as well as the issues and drawbacks of the microalgae ALE method were discussed. In general, improving the efficiency of ALE and verifying the stability of ALE resulting strains are the primary problems that need to be solved in future research, making it a promising method for the application of microalgae biotechnology.

Compensatory evolution of Pseudomonas aeruginosa's slow growth phenotype suggests mechanisms of adaptation in cystic fibrosis

Long-term infection of the airways of cystic fibrosis patients with Pseudomonas aeruginosa is often accompanied by a reduction in bacterial growth rate. This reduction has been hypothesised to increase within-patient fitness and overall persistence of the pathogen. Here, we apply adaptive laboratory evolution to revert the slow growth phenotype of P. aeruginosa clinical strains back to a high growth rate. We identify several evolutionary trajectories and mechanisms leading to fast growth caused by transcriptional and mutational changes, which depend on the stage of adaptation of the strain. Return to high growth rate increases antibiotic susceptibility, which is only partially dependent on reversion of mutations or changes in the transcriptional profile of genes known to be linked to antibiotic resistance. We propose that similar mechanisms and evolutionary trajectories, in reverse direction, may be involved in pathogen adaptation and the establishment of chronic infections in the antibiotic-treated airways of cystic fibrosis patients.

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.

Design of novel enzyme biocatalysts for industrial bioprocess: Harnessing the power of protein engineering, high throughput screening and synthetic biology

Biocatalysts have wider applications in various industries. Biocatalysts are generating bigger attention among researchers due to their unique catalytic properties like activity, specificity and stability. However the industrial use of many enzymes is hindered by low catalytic efficiency and stability during industrial processes. Properties of enzymes can be altered by protein engineering. Protein engineers are increasingly study the structure-function characteristics, engineering attributes, design of computational tools for enzyme engineering, and functional screening processes to improve the design and applications of enzymes. The potent and innovative techniques of enzyme engineering deliver outstanding opportunities for tailoring industrially important enzymes for the versatile production of biochemicals. An overview of the current trends in enzyme engineering is explored with important representative examples.

Elucidating aromatic acid tolerance at low pH in Saccharomyces cerevisiae using adaptive laboratory evolution

Toxicity from the external presence or internal production of compounds can reduce the growth and viability of microbial cell factories and compromise productivity. Aromatic compounds are generally toxic for microorganisms, which makes their production in microbial hosts challenging. Here we use adaptive laboratory evolution to generate Saccharomyces cerevisiae mutants tolerant to two aromatic acids, coumaric acid and ferulic acid. The evolution experiments were performed at low pH (3.5) to reproduce conditions typical of industrial processes. Mutant strains tolerant to levels of aromatic acids near the solubility limit were then analyzed by whole genome sequencing, which revealed prevalent point mutations in a transcriptional activator (Aro80) that is responsible for regulating the use of aromatic amino acids as the nitrogen source. Among the genes regulated by Aro80, ESBP6 was found to be responsible for increasing tolerance to aromatic acids by exporting them out of the cell. Further examination of the native function of Esbp6 revealed that this transporter can excrete fusel acids (byproducts of aromatic amino acid catabolism) and this role is shared with at least one additional transporter native to S. cerevisiae (Pdr12). Besides conferring tolerance to aromatic acids, ESBP6 overexpression was also shown to significantly improve the secretion in coumaric acid production strains. Overall, we showed that regulating the activity of transporters is a major mechanism to improve tolerance to aromatic acids. These findings can be used to modulate the intracellular concentration of aromatic compounds to optimize the excretion of such products while keeping precursor molecules inside the cell.

Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity

BACKGROUND

Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures.

RESULTS

Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine.

CONCLUSIONS

The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.

Current Status and Applications of Adaptive Laboratory Evolution in Industrial Microorganisms

Adaptive laboratory evolution (ALE) is an evolutionary engineering approach in artificial conditions that improves organisms through the imitation of natural evolution. Due to the development of multi-level omics technologies in recent decades, ALE can be performed for various purposes at the laboratory level. This review delineates the basics of the experimental design of ALE based on several ALE studies of industrial microbial strains and updates current strategies combined with progressed metabolic engineering, in silico modeling and automation to maximize the evolution efficiency. Moreover, the review sheds light on the applicability of ALE as a strain development approach that complies with non-recombinant preferences in various food industries. Overall, recent progress in the utilization of ALE for strain development leading to successful industrialization is discussed.

Harnessing the Power of Mutagenesis and Adaptive Laboratory Evolution for High Lipid Production by Oleaginous Microalgae and Yeasts

Oleaginous microalgae and yeasts represent promising candidates for large-scale production of lipids, which can be utilized for production of drop-in biofuels, nutraceuticals, pigments, and cosmetics. However, low lipid productivity and costly downstream processing continue to hamper the commercial deployment of oleaginous microorganisms. Strain improvement can play an essential role in the development of such industrial microorganisms by increasing lipid production and hence reducing production costs. The main means of strain improvement are random mutagenesis, adaptive laboratory evolution (ALE), and rational genetic engineering. Among these, random mutagenesis and ALE are straight forward, low-cost, and do not require thorough knowledge of the microorganism’s genetic composition. This paper reviews available mutagenesis and ALE techniques and screening methods to effectively select for oleaginous microalgae and yeasts with enhanced lipid yield and understand the alterations caused to metabolic pathways, which could subsequently serve as the basis for further targeted genetic engineering.

ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation

Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover causal mutations that confer desired phenotypic functions. ALE not only represents a controllable experimental approach to systematically discover genotype-phenotype relationships, but also allows for the revelation of the series of genetic alterations required to acquire the new phenotype. Numerous ALE studies have been published, providing a strong impetus for developing databases to warehouse experimental evolution information and make it retrievable for large-scale analysis. Here, the first step towards establishing this resource is presented: ALEdb (http://aledb.org). This initial release contains over 11 000 mutations that have been discovered from eleven ALE publications. ALEdb (i) is a web-based platform that comprehensively reports on ALE acquired mutations and their conditions, (ii) reports key mutations using previously established trends, (iii) enables a search-driven workflow to enhance user mutation functional analysis through mutation cross-reference, (iv) allows exporting of mutation query results for custom analysis, (v) includes a bibliome describing the databased experiment publications and (vi) contains experimental evolution mutations from multiple model organisms. Thus, ALEdb is an informative platform which will become increasingly revealing as the number of reported ALE experiments and identified mutations continue to expand.

Genetic Determinants Enabling Medium-Dependent Adaptation to Nafcillin in Methicillin-Resistant Staphylococcus aureus

Antimicrobial susceptibility testing standards driving clinical decision-making have centered around the use of cation-adjusted Mueller-Hinton broth (CA-MHB) as the medium with the notion of supporting bacterial growth, without consideration of recapitulating the in vivo environment. However, it is increasingly recognized that various medium conditions have tremendous influence on antimicrobial activity, which in turn may have major implications on the ability of in vitro susceptibility assays to predict antibiotic activity in vivo. To elucidate differential growth optimization and antibiotic resistance mechanisms, adaptive laboratory evolution was performed in the presence or absence of the antibiotic nafcillin with methicillin-resistant Staphylococcus aureus (MRSA) TCH1516 in either (i) CA-MHB, a traditional bacteriological nutritionally rich medium, or (ii) Roswell Park Memorial Institute (RPMI), a medium more reflective of the in vivo host environment. Medium adaptation analysis showed an increase in growth rate in RPMI, but not CA-MHB, with mutations in apt, adenine phosphoribosyltransferase, and the manganese transporter subunit, mntA, occurring reproducibly in parallel replicate evolutions. The medium-adapted strains showed no virulence attenuation. Continuous exposure of medium-adapted strains to increasing concentrations of nafcillin led to medium-specific evolutionary strategies. Key reproducibly occurring mutations were specific for nafcillin adaptation in each medium type and did not confer resistance in the other medium environment. Only the vraRST operon, a regulator of membrane- and cell wall-related genes, showed mutations in both CA-MHB- and RPMI-evolved strains. Collectively, these results demonstrate the medium-specific genetic adaptive responses of MRSA and establish adaptive laboratory evolution as a platform to study clinically relevant resistance mechanisms.IMPORTANCE The ability of pathogens such as Staphylococcus aureus to evolve resistance to antibiotics used in the treatment of infections has been an important concern in the last decades. Resistant acquisition usually translates into treatment failure and puts patients at risk of unfavorable outcomes. Furthermore, the laboratory testing of antibiotic resistance does not account for the different environment the bacteria experiences within the human body, leading to results that do not translate into the clinic. In this study, we forced methicillin-resistant S. aureus to develop nafcillin resistance in two different environments, a laboratory environment and a physiologically more relevant environment. This allowed us to identify genetic changes that led to nafcillin resistance under both conditions. We concluded that not only does the environment dictate the evolutionary strategy of S. aureus to nafcillin but also that the evolutionary strategy is specific to that given environment.

Adaptive Laboratory Evolution of Ale and Lager Yeasts for Improved Brewing Efficiency and Beer Quality

Yeasts directly impact the efficiency of brewery fermentations as well as the character of the beers produced. In recent years, there has been renewed interest in yeast selection and development inspired by the demand to utilize resources more efficiently and the need to differentiate beers in a competitive market. Reviewed here are the different, non-genetically modified (GM) approaches that have been considered, including bioprospecting, hybridization, and adaptive laboratory evolution (ALE). Particular emphasis is placed on the latter, which represents an extension of the processes that have led to the domestication of strains already used in commercial breweries. ALE can be used to accentuate the positive traits of brewing yeast as well as temper some of the traits that are less desirable from a modern brewer's perspective. This method has the added advantage of being non-GM and therefore suitable for food and beverage production.

High-Throughput Screening Technology in Industrial Biotechnology

Based on the development of automatic devices and rapid assay methods, various high-throughput screening (HTS) strategies have been established for improving the performance of industrial microorganisms. We discuss the most significant factors that can improve HTS efficiency, including the construction of screening libraries with high diversity and the use of new detection methods to expand the search range and highlight target compounds. We also summarize applications of HTS for enhancing the performance of industrial microorganisms. Current challenges and potential improvements to HTS in industrial biotechnology are discussed in the context of rapid developments in synthetic biology, nanotechnology, and artificial intelligence. Rational integration will be an important driving force for constructing more efficient industrial microorganisms with wider applications in biotechnology.

Comparison of Three Xylose Pathways in Pseudomonas putida KT2440 for the Synthesis of Valuable Products

Pseudomonas putida KT2440 is a well-established chassis in industrial biotechnology. To increase the substrate spectrum, we implemented three alternative xylose utilization pathways, namely the Isomerase, Weimberg, and Dahms pathways. The synthetic operons contain genes from Escherichia coli and Pseudomonas taiwanensis. For isolating the Dahms pathway in P. putida KT2440 two genes (PP_2836 and PP_4283), encoding an endogenous enzyme of the Weimberg pathway and a regulator for glycolaldehyde degradation, were deleted. Before and after adaptive laboratory evolution, these strains were characterized in terms of growth and synthesis of mono-rhamnolipids and pyocyanin. The engineered strain using the Weimberg pathway reached the highest maximal growth rate of 0.30 h-1. After adaptive laboratory evolution the lag phase was reduced significantly. The highest titers of 720 mg L-1 mono-rhamnolipids and 30 mg L-1 pyocyanin were reached by the evolved strain using the Weimberg or an engineered strain using the Isomerase pathway, respectively. The different stoichiometries of the three xylose utilization pathways may allow engineering of tailored chassis for valuable bioproduct synthesis.

Evolutionary Engineering of Cyanobacteria to Enhance the Production of α-Farnesene from CO2

Photosynthetic cyanobacteria can fix CO₂ and utilize it as the sole carbon source for cell growth and production of biochemicals. Here, we metabolically engineered Synechococcus elongatus PCC 7942 for an enhanced production of α-farnesene by optimizing the ribosome-binding site (RBS) of the codon-optimized farnesene synthase gene. The production of α-farnesene was found to be enhanced in strains with a low translation initiation rate, resulting in α-farnesene production (0.57 mg/(L day)). Using the RBS variants and random mutations, we performed fluorescence-based analysis of cells grown in 96-well culture plates to screen the α-farnesene-producing strains but could not improve the titers of the RBS-optimized strains. However, evolutionary engineering of the RBS-optimized strains resulted in a twofold increase in α-farnesene production (1.2 mg/(L day)) compared to the previous study. Therefore, combining metabolic and evolutionary engineering might be helpful for enhancing the cellular fitness of cyanobacteria for the production of target chemicals.

Critical Assessment of Streptomyces spp. Able to Control Toxigenic Fusaria in Cereals: A Literature and Patent Review

Mycotoxins produced by Fusarium species on cereals represent a major concern for food safety worldwide. Fusarium toxins that are currently under regulation for their content in food include trichothecenes, fumonisins, and zearalenone. Biological control of Fusarium spp. has been widely explored with the aim of limiting disease occurrence, but few efforts have focused so far on limiting toxin accumulation in grains. The bacterial genus Streptomyces is responsible for the production of numerous drug molecules and represents a huge resource for the discovery of new molecules. Streptomyces spp. are also efficient plant colonizers and able to employ different mechanisms of control against toxigenic fungi on cereals. This review describes the outcomes of research using Streptomyces strains and/or their derived molecules to limit toxin production and/or contamination of Fusarium species in cereals. Both the scientific and patent literature were analyzed, starting from the year 2000, and we highlight promising results as well as the current pitfalls and limitations of this approach.

Common principles and best practices for engineering microbiomes

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.

The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology

Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.

Generation of an E. coli platform strain for improved sucrose utilization using adaptive laboratory evolution

Background

Sucrose is an attractive industrial carbon source due to its abundance and the fact that it can be cheaply generated from sources such as sugarcane. However, only a few characterized Escherichia coli strains are able to metabolize sucrose, and those that can are typically slow growing or pathogenic strains.

Methods

To generate a platform strain capable of efficiently utilizing sucrose with a high growth rate, adaptive laboratory evolution (ALE) was utilized to evolve engineered E. coli K-12 MG1655 strains containing the sucrose utilizing csc genes (cscB, cscK, cscA) alongside the native sucrose consuming E. coli W.

Results

Evolved K-12 clones displayed an increase in growth and sucrose uptake rates of 1.72- and 1.40-fold on sugarcane juice as compared to the original engineered strains, respectively, while E. coli W clones showed a 1.4-fold increase in sucrose uptake rate without a significant increase in growth rate. Whole genome sequencing of evolved clones and populations revealed that two genetic regions were frequently mutated in the K-12 strains; the global transcription regulatory genes rpoB and rpoC, and the metabolic region related to a pyrimidine biosynthetic deficiency in K-12 attributed to pyrE expression. These two mutated regions have been characterized to confer a similar benefit when glucose is the main carbon source, and reverse engineering revealed the same causal advantages on M9 sucrose. Additionally, the most prevalent mutation found in the evolved E. coli W lineages was the inactivation of the cscR gene, the transcriptional repression of sucrose uptake genes.

Conclusion

The generated K-12 and W platform strains, and the specific sets of mutations that enable their phenotypes, are available as valuable tools for sucrose-based industrial bioproduction in the facile E. coli chassis.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1165-2) contains supplementary material, which is available to authorized users.

Vibrio sp. dhg as a platform for the biorefinery of brown macroalgae

Although brown macroalgae holds potential as an alternative feedstock, its utilization by conventional microbial platforms has been limited due to the inability to metabolize one of the principal sugars, alginate. Here, we isolate Vibrio sp. dhg, a fast-growing bacterium that can efficiently assimilate alginate. Based on systematic characterization of the genomic information of Vibrio sp. dhg, we establish a genetic toolbox for its engineering. We also demonstrate its ability to rapidly produce ethanol, 2,3-butanediol, and lycopene from brown macroalgae sugar mixture with high productivities and yields. Collectively, Vibrio sp. dhg can be used as a platform for the efficient conversion of brown macroalgae sugars into diverse value-added biochemicals.

OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs

Biological production of chemicals is an attractive alternative to petrochemical-based production, due to advantages in environmental impact and the spectrum of feasible targets. However, engineering microbial strains to overproduce a compound of interest can be a long, costly and painstaking process. If production can be coupled to cell growth it is possible to use adaptive laboratory evolution to increase the production rate. Strategies for coupling production to growth, however, are often not trivial to find. Here we present OptCouple, a constraint-based modeling algorithm to simultaneously identify combinations of gene knockouts, insertions and medium supplements that lead to growth-coupled production of a target compound. We validated the algorithm by showing that it can find novel strategies that are growth-coupled in silico for a compound that has not been coupled to growth previously, as well as reproduce known growth-coupled strain designs for two different target compounds. Furthermore, we used OptCouple to construct an alternative design with potential for higher production. We provide an efficient and easy-to-use implementation of the OptCouple algorithm in the cameo Python package for computational strain design.

Engineering Robust Production Microbes for Large-Scale Cultivation

Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.

How adaptive evolution reshapes metabolism to improve fitness: recent advances and future outlook

Adaptive laboratory evolution (ALE) has emerged as a powerful tool in basic microbial research and strain development. In the context of metabolic science and engineering, it has been applied to study gene knockout responses, expand substrate ranges, improve tolerance to process conditions, and to improve productivity via designed growth coupling. In recent years, advancements in ALE methods and systems biology measurement technologies, particularly genome sequencing and ¹³C metabolic flux analysis (¹³C-MFA), have enabled detailed study of the mechanisms and dynamics of evolving metabolism. In this review, we discuss a range of studies that have applied flux analysis to adaptively evolved strains, as well as modeling frameworks developed to predict and interpret evolved fluxes. These efforts link mutations to fitness-enhanced phenotypes, identify bottlenecks and approaches to resolve them, and address systems concepts such as optimality.

Rewiring metabolic network by chemical modulator based laboratory evolution doubles lipid production in Crypthecodinium cohnii

Dietary omega-3 long-chain polyunsaturated fatty acids docosahexaenoic acid (DHA, C22:6) can be synthesized in microalgae Crypthecodinium cohnii; however, its productivity is still low. Here, we established a new protocol termed as "chemical modulator based adaptive laboratory evolution" (CM-ALE) to enhance lipid and DHA productivity in C. cohnii. First, ACCase inhibitor sethoxydim based CM-ALE was applied to redirect carbon equivalents from starch to lipid. Second, CM-ALE using growth modulator sesamol as selection pressure was conducted to relive negative effects of sesamol on lipid biosynthesis in C. cohnii, which allows enhancement of biomass productivity by 30% without decreasing lipid content when sesamol was added. After two-step CM-ALE, the lipid and DHA productivity in C. cohnii was respectively doubled to a level of 0.046 g/L/h and 0.025 g/L/h in culture with addition of 1 mM sesamol, demonstrating that this two-step CM-ALE could be a valuable approach to maximize the properties of microalgae.