Underground metabolism: network-level perspective and biotechnological potential

Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota

Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Optimal experimental design (OED) for the growth rate of microbial populations. Are they really more "optimal" than uniform designs?

Secondary growth models from predictive microbiology can describe how the growth rate of microbial populations varies with environmental conditions. Because these models are built based on time and resource consuming experiments, model-based Optimal Experimental Design (OED) can be of interest to reduce the experimental load. In this study, we identify optimal experimental designs for two common models (full Ratkowsky and Cardinal Parameters Model (CPM)) for a different number of experiments (10-30). Calculations are also done fixing one or more model parameters, observing that this decision strongly affects the layout of the OED. Using in silico experiments, we conclude that OEDs are more informative than conventional (equidistant) designs with the same number of experiments. However, OEDs cluster the experiments near the growth limits (Xmin and Xmax) resulting in impractical designs with aggregated experimental runs ~10 times longer than conventional designs. To mitigate this, we propose a novel optimality criterion (i.e., the objective function) that accounts for the aggregated time. The novel criterion provides a reduction in parameter uncertainty with respect to the conventional design, without an increase in the experimental load. These results underline that an OED is only based on information theory (Fisher information), so the results can be impractical when actual experimental limitations are considered. The study also emphasizes that most OED schemes identify where to measure, but do not give an indication on how many experiments should be made. In this sense, numerical simulations can estimate the parameter uncertainty that would be obtained for a particular experimental design (OED or not). These results and methodologies (available in Open Code) can guide the design of future experiments for the development of secondary growth models.

The hierarchy of sugar catabolization in Lactococcus cremoris

IMPORTANCE

The availability of nutrients to microorganisms varies considerably between different environments, and changes can occur rapidly. As a general rule, a fast growth rate-typically growth on glucose-is associated with the repression of other carbohydrate utilization genes, but it is not clear to what extent catabolite repression is exerted by other sugars. We investigated the hierarchy of sugar utilization after substrate transitions in Lactococcus cremoris. For this, we determined the proteome and carbohydrate utilization capacity after growth on different sugars. The results show that the preparedness of cells for the utilization of "slower" sugars is not strictly determined by the growth rate. The data point to individual proteins relevant for various sugar transitions and suggest that the evolutionary history of the organism might be responsible for deviations from a strictly growth rate-related sugar catabolization hierarchy.

UniKP: a unified framework for the prediction of enzyme kinetic parameters

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.

Multi-scale models of whole cells: progress and challenges

Whole-cell modeling is "the ultimate goal" of computational systems biology and "a grand challenge for 21st century" (Tomita, Trends in Biotechnology, 2001, 19(6), 205-10). These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of "system" models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

Deuterated water as a substrate-agnostic isotope tracer for investigating reversibility and thermodynamics of reactions in central carbon metabolism

Stable isotope tracers are a powerful tool for the quantitative analysis of microbial metabolism, enabling pathway elucidation, metabolic flux quantification, and assessment of reaction and pathway thermodynamics. 13C and 2H metabolic flux analysis commonly relies on isotopically labeled carbon substrates, such as glucose. However, the use of 2H-labeled nutrient substrates faces limitations due to their high cost and limited availability in comparison to 13C-tracers. Furthermore, isotope tracer studies in industrially relevant bacteria that metabolize complex substrates such as cellulose, hemicellulose, or lignocellulosic biomass, are challenging given the difficulty in obtaining these as isotopically labeled substrates. In this study, we examine the potential of deuterated water (2H2O) as an affordable, substrate-neutral isotope tracer for studying central carbon metabolism. We apply 2H2O labeling to investigate the reversibility of glycolytic reactions across three industrially relevant bacterial species -C. thermocellum, Z. mobilis, and E. coli-harboring distinct glycolytic pathways with unique thermodynamics. We demonstrate that 2H2O labeling recapitulates previous reversibility and thermodynamic findings obtained with established 13C and 2H labeled nutrient substrates. Furthermore, we exemplify the utility of this 2H2O labeling approach by applying it to high-substrate C. thermocellum fermentations -a setting in which the use of conventional tracers is impractical-thereby identifying the glycolytic enzyme phosphofructokinase as a major bottleneck during high-substrate fermentations and unveiling critical insights that will steer future engineering efforts to enhance ethanol production in this cellulolytic organism. This study demonstrates the utility of deuterated water as a substrate-agnostic isotope tracer for examining flux and reversibility of central carbon metabolic reactions, which yields biological insights comparable to those obtained using costly 2H-labeled nutrient substrates.

Enhancing the ethanol production by exploiting a novel metagenomic-derived bifunctional xylanase/β-glucosidase enzyme with improved β-glucosidase activity by a nanocellulose carrier

Some enzymes can catalyze more than one chemical conversion for which they are physiologically specialized. This secondary function, which is called underground, promiscuous, metabolism, or cross activity, is recognized as a valuable feature and has received much attention for developing new catalytic functions in industrial applications. In this study, a novel bifunctional xylanase/β-glucosidase metagenomic-derived enzyme, PersiBGLXyn1, with underground β-glucosidase activity was mined by in-silico screening. Then, the corresponding gene was cloned, expressed and purified. The PersiBGLXyn1 improved the degradation efficiency of organic solvent pretreated coffee residue waste (CRW), and subsequently the production of bioethanol during a separate enzymatic hydrolysis and fermentation (SHF) process. After characterization, the enzyme was immobilized on a nanocellulose (NC) carrier generated from sugar beet pulp (SBP), which remarkably improved the underground activity of the enzyme up to four-fold at 80°C and up to two-fold at pH 4.0 compared to the free one. The immobilized PersiBGLXyn1 demonstrated 12 to 13-fold rise in half-life at 70 and 80°C for its underground activity. The amount of reducing sugar produced from enzymatic saccharification of the CRW was also enhanced from 12.97 g/l to 19.69 g/l by immobilization of the enzyme. Bioethanol production was 29.31 g/l for free enzyme after 72 h fermentation, while the immobilized PersiBGLXyn1 showed 51.47 g/l production titre. Overall, this study presented a cost-effective in-silico metagenomic approach to identify novel bifunctional xylanase/β-glucosidase enzyme with underground β-glucosidase activity. It also demonstrated the improved efficacy of the underground activities of the bifunctional enzyme as a promising alternative for fermentable sugars production and subsequent value-added products.

Designing Microbial Cell Factories for the Production of Chemicals

The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.

Plant biosynthetic gene clusters in the context of metabolic evolution

Covering: up to 2022Plants produce a wide range of structurally and biosynthetically diverse natural products to interact with their environment. These specialised metabolites typically evolve in limited taxonomic groups presumably in response to specific selective pressures. With the increasing availability of sequencing data, it has become apparent that in many cases the genes encoding biosynthetic enzymes for specialised metabolic pathways are not randomly distributed on the genome. Instead they are physically linked in structures such as arrays, pairs and clusters. The exact function of these clusters is debated. In this review we take a broad view of gene arrangement in plant specialised metabolism, examining types of structures and variation. We discuss the evolution of biosynthetic gene clusters in the wider context of metabolism, populations and epigenetics. Finally, we synthesise our observations to propose a new hypothesis for biosynthetic gene cluster formation in plants.

Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods

The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions.

Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction

Enzyme turnover numbers (kcat) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured kcat data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput kcat prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture kcat changes for mutated enzymes and identify amino acid residues with a strong impact on kcat values. We applied this approach to predict genome-scale kcat values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted kcat values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale.

Evolutionary Origin and Functional Diversification of Aminotransferases

Aminotransferases (ATs) are pyridoxal 5′-phosphate–dependent enzymes that catalyze the transamination reactions between amino acid donor and keto acid acceptor substrates. Modern AT enzymes constitute ∼2% of all classified enzymatic activities, play central roles in nitrogen metabolism, and generate multitude of primary and secondary metabolites. ATs likely diverged into four distinct AT classes before the appearance of the last universal common ancestor and further expanded to a large and diverse enzyme family. Although the AT family underwent an extensive functional specialization, many AT enzymes retained considerable substrate promiscuity and multifunctionality because of their inherent mechanistic, structural, and functional constraints. This review summarizes the evolutionary history, diverse metabolic roles, reaction mechanisms, and structure–function relationships of the AT family enzymes, with a special emphasis on their substrate promiscuity and multifunctionality. Comprehensive characterization of AT substrate specificity is still needed to reveal their true metabolic functions in interconnecting various branches of the nitrogen metabolic network in different organisms.

Underground metabolism as a rich reservoir for pathway engineering

Motivation

Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level ‘underground’ enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering.

Results

Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development.

Availability and implementation

The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism.

Supplementary information

Supplementary data are available at Bioinformatics online.

Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx

Metabolic “dark matter” describes currently unknown metabolic processes, which form a blind spot in our general understanding of metabolism and slow down the development of biosynthetic cell factories and naturally derived pharmaceuticals. Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 489 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of biochemical pathways and evaluates the biochemical vicinity of molecule classes (https://lcsb-databases.epfl.ch/Atlas2).

“Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. Here the authors present ATLASx, a repository of known and predicted enzymatic reaction, connecting millions of compounds to help synthetic biologists and metabolic engineers to design and explore metabolic pathways.”

Escherichia coli Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological systems. Adaptive Laboratory Evolution (ALE) leverages nature’s problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study describes how novel strain designs, or genome sequences not yet observed in ALE experiments or published designs, can be extracted from aggregated ALE data and demonstrates this by designing, building, and testing three novel Escherichia coli strains with fitnesses comparable to ALE mutants. These designs were achieved through a meta-analysis of aggregated ALE mutations data (63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13 957 observed mutations), which additionally revealed global ALE mutation trends that inform on ALE-derived strain design principles. Such informative trends anticipate ALE-derived strain designs as largely gene-centric, as opposed to noncoding, and composed of a relatively small number of beneficial variants (approximately 6). These results demonstrate how strain design efforts can be enhanced by the meta-analysis of aggregated ALE data.

Underground metabolism facilitates the evolution of novel pathways for vitamin B6 biosynthesis

Abstract

The term vitamin B6 is a designation for the vitamers pyridoxal, pyridoxamine, pyridoxine and the respective phosphate esters pyridoxal-5′-phosphate (PLP), pyridoxamine-5′-phosphate and pyridoxine-5′-phosphate. Animals and humans are unable to synthesise vitamin B6. These organisms have to take up vitamin B6 with their diet. Therefore, vitamin B6 is of commercial interest as a food additive and for applications in the pharmaceutical industry. As yet, two naturally occurring routes for de novo synthesis of PLP are known. Both routes have been genetically engineered to obtain bacteria overproducing vitamin B6. Still, major genetic engineering efforts using the existing pathways are required for developing fermentation processes that could outcompete the chemical synthesis of vitamin B6. Recent suppressor screens using mutants of the Gram-negative and Gram-positive model bacteria Escherichia coli and Bacillus subtilis, respectively, carrying mutations in the native pathways or heterologous genes uncovered novel routes for PLP biosynthesis. These pathways consist of promiscuous enzymes and enzymes that are already involved in vitamin B6 biosynthesis. Thus, E. coli and B. subtilis contain multiple promiscuous enzymes causing a so-called underground metabolism allowing the bacteria to bypass disrupted vitamin B6 biosynthetic pathways. The suppressor screens also show the genomic plasticity of the bacteria to suppress a genetic lesion. We discuss the potential of the serendipitous pathways to serve as a starting point for the development of bacteria overproducing vitamin B6.

Key points

• Known vitamin B6 routes have been genetically engineered.

• Underground metabolism facilitates the emergence of novel vitamin B6 biosynthetic pathways.

• These pathways may be suitable to engineer bacteria overproducing vitamin B6.

L-Alanine Prototrophic Suppressors Emerge from L-Alanine Auxotroph through Stress-Induced Mutagenesis in Escherichia coli

In Escherichia coli, L-alanine is synthesized by three isozymes: YfbQ, YfdZ, and AvtA. When an E. coli L-alanine auxotrophic isogenic mutant lacking the three isozymes was grown on L-alanine-deficient minimal agar medium, L-alanine prototrophic mutants emerged considerably more frequently than by spontaneous mutation; the emergence frequency increased over time, and, in an L-alanine-supplemented minimal medium, correlated inversely with L-alanine concentration, indicating that the mutants were derived through stress-induced mutagenesis. Whole-genome analysis of 40 independent L-alanine prototrophic mutants identified 16 and 18 clones harboring point mutation(s) in pyruvate dehydrogenase complex and phosphotransacetylase-acetate kinase pathway, which respectively produce acetyl coenzyme A and acetate from pyruvate. When two point mutations identified in L-alanine prototrophic mutants, in pta (D656A) and aceE (G147D), were individually introduced into the original L-alanine auxotroph, the isogenic mutants exhibited almost identical growth recovery as the respective cognate mutants. Each original- and isogenic-clone pair carrying the pta or aceE mutation showed extremely low phosphotransacetylase or pyruvate dehydrogenase activity, respectively. Lastly, extracellularly-added pyruvate, which dose-dependently supported L-alanine auxotroph growth, relieved the L-alanine starvation stress, preventing the emergence of L-alanine prototrophic mutants. Thus, L-alanine starvation-provoked stress-induced mutagenesis in the L-alanine auxotroph could lead to intracellular pyruvate increase, which eventually induces L-alanine prototrophy.

Computational approaches to understanding nutrient metabolism and metabolic disorders

Computational methods are becoming more and more essential to elucidate biological systems. Many different approaches exist with pros and cons. This paper reviews the most useful technologies focusing on nutrient metabolism and metabolic disorders. Space limitation prevents from exploring the examples in details, but pointers to the relevant papers are reported.

RetroRules: a database of reaction rules for engineering biology

RetroRules is a database of reaction rules for metabolic engineering (https://retrorules.org). Reaction rules are generic descriptions of chemical reactions that can be used in retrosynthesis workflows in order to enumerate all possible biosynthetic routes connecting a target molecule to its precursors. The use of such rules is becoming increasingly important in the context of synthetic biology applied to de novo pathway discovery and in systems biology to discover underground metabolism due to enzyme promiscuity. Here, we provide for the first time a complete set containing >400 000 stereochemistry-aware reaction rules extracted from public databases and expressed in the community-standard SMARTS (SMIRKS) format, augmented by a rule representation at different levels of specificity (the atomic environment around the reaction center). Such numerous representations of reactions expand natural chemical diversity by predicting de novo reactions of promiscuous enzymes.

How enzyme promiscuity and horizontal gene transfer contribute to metabolic innovation

Promiscuity is the coincidental ability of an enzyme to catalyze its native reaction and additional reactions that are not biological functions in the same active site. Promiscuity plays a central role in enzyme evolution and is thus a useful property for protein and metabolic engineering. This review examines enzyme evolution holistically, beginning with evaluating biochemical support for four enzyme evolution models. As expected, there is strong biochemical support for the subfunctionalization and innovation-amplification-divergence models, in which promiscuity is a central feature. In many cases, however, enzyme evolution is more complex than the models indicate, suggesting much is yet to be learned about selective pressures on enzyme function. A complete understanding of enzyme evolution must also explain the ability of metabolic networks to integrate new enzyme activities. Hidden within metabolic networks are underground metabolic pathways constructed from promiscuous activities. We discuss efforts to determine the diversity and pervasiveness of underground metabolism. Remarkably, several studies have discovered that some metabolic defects can be repaired via multiple underground routes. In prokaryotes, metabolic innovation is driven by connecting enzymes acquired by horizontal gene transfer (HGT) into the metabolic network. Thus, we end the review by discussing how the combination of promiscuity and HGT contribute to evolution of metabolism in prokaryotes. Future studies investigating the contribution of promiscuity to enzyme and metabolic evolution will need to integrate deeper probes into the influence of evolution on protein biophysics, enzymology, and metabolism with more complex and realistic evolutionary models. ENZYMES: lactate dehydrogenase (EC 1.1.1.27), malate dehydrogenase (EC 1.1.1.37), OSBS (EC 4.2.1.113), HisA (EC 5.3.1.16), TrpF, PriA (EC 5.3.1.24), R-mandelonitrile lyase (EC 4.1.2.10), Maleylacetate reductase (EC 1.3.1.32).

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.

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

Background

Metabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous—i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.

Results

Our workflow utilizes PROXIMAL—a tool that uses reactant–product transformation patterns from the KEGG database—to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in Escherichia coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).

Conclusions

We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.

Electronic supplementary material

The online version of this article (10.1186/s12934-019-1156-3) contains supplementary material, which is available to authorized users.

Modular Engineering of Biomass Degradation Pathways

Production of fuels and chemicals from renewable lignocellulosic feedstocks is a promising alternative to petroleum-derived compounds. Due to the complexity of lignocellulosic feedstocks, microbial conversion of all potential substrates will require substantial metabolic engineering. Non-model microbes offer desirable physiological traits, but also increase the difficulty of heterologous pathway engineering and optimization. The development of modular design principles that allow metabolic pathways to be used in a variety of novel microbes with minimal strain-specific optimization will enable the rapid construction of microbes for commercial production of biofuels and bioproducts. In this review, we discuss variability of lignocellulosic feedstocks, pathways for catabolism of lignocellulose-derived compounds, challenges to heterologous engineering of catabolic pathways, and opportunities to apply modular pathway design. Implementation of these approaches will simplify the process of modifying non-model microbes to convert diverse lignocellulosic feedstocks.

Enzyme promiscuity shapes adaptation to novel growth substrates

Evidence suggests that novel enzyme functions evolved from low‐level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems‐level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non‐native substrates in Escherichia coli K‐12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non‐native substrates—D‐lyxose, D‐2‐deoxyribose, D‐arabinose, m‐tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high‐efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome‐scale model simulations of metabolism with enzyme promiscuity.

A bacterial checkpoint protein for ribosome assembly moonlights as an essential metabolite-proofreading enzyme

In eukaryotes, adventitious oxidation of erythrose-4-phosphate, an intermediate of the pentose phosphate pathway (PPP), generates 4-phosphoerythronate (4PE), which inhibits 6-phosphogluconate dehydrogenase. 4PE is detoxified by metabolite-proofreading phosphatases such as yeast Pho13. Here, we report that a similar function is carried out in Bacillus subtilis by CpgA, a checkpoint protein known to be important for ribosome assembly, cell morphology and resistance to cell wall-targeting antibiotics. We find that ΔcpgA cells are intoxicated by glucose or other carbon sources that feed into the PPP, and that CpgA has high phosphatase activity with 4PE. Inhibition of 6-phosphogluconate dehydrogenase (GndA) leads to intoxication by 6-phosphogluconate, a potent inhibitor of phosphoglucose isomerase (PGI). The coordinated shutdown of PPP and glycolysis leads to metabolic gridlock. Overexpression of GndA, PGI, or yeast Pho13 suppresses glucose intoxication of ΔcpgA cells, but not cold sensitivity, a phenotype associated with ribosome assembly defects. Our results suggest that CpgA is a multifunctional protein, with genetically separable roles in ribosome assembly and metabolite proofreading.

Adventitious oxidation of erythrose-4-phosphate generates 4-phosphoerythronate, which is detoxified by metabolite-proofreading phosphatases in eukaryotes. Here, Sachla & Helmann show that a similar function is carried out in Bacillus subtilis by a checkpoint protein involved in ribosome assembly.

Exploiting enzyme promiscuity to shape plant specialized metabolism

The amazing variability of plant metabolism and its rapid divergence during evolution pose fundamental questions as to the driving forces, mechanisms, and players in metabolic differentiation. This review examines concepts that help us understand adaptive pathway evolution, with a particular emphasis on plant specialized metabolism, previously often termed secondary metabolism. Following a general introduction to pathway and metabolite evolution, the focus is directed to enzyme promiscuity and its classification. Promiscuous enzymes (or substrates), 'silent' elements of the metabolome, and the 'underground metabolism' may be used and combined to evolve 'new' metabolic pathways. It appears that new pathways rarely appear from scratch, but instead emerge from 'floppy' enzymes and elements of a 'messy' metabolism, and in this way a range of metabolites is generated, some of which may provide benefits to the plant.

Reframing gene essentiality in terms of adaptive flexibility

BACKGROUND

Essentiality assays are important tools commonly utilized for the discovery of gene functions. Growth/no growth screens of single gene knockout strain collections are also often utilized to test the predictive power of genome-scale models. False positive predictions occur when computational analysis predicts a gene to be non-essential, however experimental screens deem the gene to be essential. One explanation for this inconsistency is that the model contains the wrong information, possibly an incorrectly annotated alternative pathway or isozyme reaction. Inconsistencies could also be attributed to experimental limitations, such as growth tests with arbitrary time cut-offs. The focus of this study was to resolve such inconsistencies to better understand isozyme activities and gene essentiality.

RESULTS

In this study, we explored the definition of conditional essentiality from a phenotypic and genomic perspective. Gene-deletion strains associated with false positive predictions of gene essentiality on defined minimal medium for Escherichia coli were targeted for extended growth tests followed by population sequencing and transcriptome analysis. Of the twenty false positive strains available and confirmed from the Keio single gene knock-out collection, 11 strains were shown to grow with longer incubation periods making these actual true positives. These strains grew reproducibly with a diverse range of growth phenotypes. The lag phase observed for these strains ranged from less than one day to more than 7 days. It was found that 9 out of 11 of the false positive strains that grew acquired mutations in at least one replicate experiment and the types of mutations ranged from SNPs and small indels associated with regulatory or metabolic elements to large regions of genome duplication. Comparison of the detected adaptive mutations, modeling predictions of alternate pathways and isozymes, and transcriptome analysis of KO strains suggested agreement for the observed growth phenotype for 6 out of the 9 cases where mutations were observed.

CONCLUSIONS

Longer-term growth experiments followed by whole genome sequencing and transcriptome analysis can provide a better understanding of conditional gene essentiality and mechanisms of adaptation to such perturbations. Compensatory mutations are largely reproducible mechanisms and are in agreement with genome-scale modeling predictions to loss of function gene deletion events.

Selecting the Best: Evolutionary Engineering of Chemical Production in Microbes

Microbial cell factories have proven to be an economical means of production for many bulk, specialty, and fine chemical products. However, we still lack both a holistic understanding of organism physiology and the ability to predictively tune enzyme activities in vivo, thus slowing down rational engineering of industrially relevant strains. An alternative concept to rational engineering is to use evolution as the driving force to select for desired changes, an approach often described as evolutionary engineering. In evolutionary engineering, in vivo selections for a desired phenotype are combined with either generation of spontaneous mutations or some form of targeted or random mutagenesis. Evolutionary engineering has been used to successfully engineer easily selectable phenotypes, such as utilization of a suboptimal nutrient source or tolerance to inhibitory substrates or products. In this review, we focus primarily on a more challenging problem-the use of evolutionary engineering for improving the production of chemicals in microbes directly. We describe recent developments in evolutionary engineering strategies, in general, and discuss, in detail, case studies where production of a chemical has been successfully achieved through evolutionary engineering by coupling production to cellular growth.

Artificial Gene Amplification in Escherichia coli Reveals Numerous Determinants for Resistance to Metal Toxicity

When organisms are subjected to environmental challenges, including growth inhibitors and toxins, evolution often selects for the duplication of endogenous genes, whose overexpression can provide a selective advantage. Such events occur both in natural environments and in clinical settings. Microbial cells-with their large populations and short generation times-frequently evolve resistance to a range of antimicrobials. While microbial resistance to antibiotic drugs is well documented, less attention has been given to the genetic elements responsible for resistance to metal toxicity. To assess which overexpressed genes can endow gram-negative bacteria with resistance to metal toxicity, we transformed a collection of plasmids overexpressing all E. coli open reading frames (ORFs) into naive cells, and selected for survival in toxic concentrations of six transition metals: Cd, Co, Cu, Ni, Ag, Zn. These selections identified 48 hits. In each of these hits, the overexpression of an endogenous E. coli gene provided a selective advantage in the presence of at least one of the toxic metals. Surprisingly, the majority of these cases (28/48) were not previously known to function in metal resistance or homeostasis. These findings highlight the diverse mechanisms that biological systems can deploy to adapt to environments containing toxic concentrations of metals.

Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries

With rapid improvements in next-generation sequencing technologies, our knowledge about metabolism of many organisms is rapidly increasing. However, gaps in metabolic networks exist due to incomplete knowledge (e.g., missing reactions, unknown pathways, unannotated and misannotated genes, promiscuous enzymes, and underground metabolic pathways). In this review, we discuss recent advances in gap-filling algorithms based on genome-scale metabolic models and the importance of both high-throughput experiments and detailed biochemical characterization, which work in concert with in silico methods, to allow a more accurate and comprehensive understanding of metabolism.