Model-driven discovery of underground metabolic functions in Escherichia coli

Development and applications of genome-scale metabolic network models

The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework

Enzyme commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab initio computational approaches were proposed to predict EC numbers for given input protein sequences. However, the prediction performance (accuracy, recall, and precision), usability, and efficiency of existing methods decreased seriously when dealing with recently discovered proteins, thus still having much room to be improved. Here, we report HDMLF, a hierarchical dual-core multitask learning framework for accurately predicting EC numbers based on novel deep learning techniques. HDMLF is composed of an embedding core and a learning core; the embedding core adopts the latest protein language model for protein sequence embedding, and the learning core conducts the EC number prediction. Specifically, HDMLF is designed on the basis of a gated recurrent unit framework to perform EC number prediction in the multi-objective hierarchy, multitasking manner. Additionally, we introduced an attention layer to optimize the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against 4 representative methods demonstrate that HDMLF stably delivers the highest performance, which improves accuracy and F1 score by 60% and 40% over the state of the art, respectively. An additional case study of tyrB predicted to compensate for the loss of aspartate aminotransferase aspC, as reported in a previous experimental study, shows that our model can also be used to uncover the enzyme promiscuity. Finally, we established a web platform, namely, ECRECer (https://ecrecer.biodesign.ac.cn), using an entirely could-based serverless architecture and provided an offline bundle to improve usability.

On the evolution of natural product biosynthesis

Natural products are the raw material for drug discovery programmes. Bioactive natural products are used extensively in medicine and agriculture and have found utility as antibiotics, immunosuppressives, anti-cancer drugs and anthelminthics. Remarkably, the natural role and what mechanisms drive evolution of these molecules is relatively poorly understood. The exponential increase in genome and chemical data in recent years, coupled with technical advances in bioinformatics and genetics have enabled progress to be made in understanding the evolution of biosynthetic gene clusters and the products of their enzymatic machinery. Here we discuss the diversity of natural products, incorporating the mechanisms that govern evolution of metabolic pathways and how this can be applied to biosynthetic gene clusters. We build on the nomenclature of natural products in terms of primary, integrated, secondary and specialised metabolism and place this within an ecology-evolutionary-developmental biology framework. This eco-evo-devo framework we believe will help to clarify the nature and use of the term specialised metabolites in the future.

Systems-Wide Dissection of Organic Acid Assimilation in Pseudomonas aeruginosa Reveals a Novel Path To Underground Metabolism

The human pathogen Pseudomonas aeruginosa (Pa) is one of the most frequent and severe causes of nosocomial infection. This organism is also a major cause of airway infections in people with cystic fibrosis (CF). Pa is known to have a remarkable metabolic plasticity, allowing it to thrive under diverse environmental conditions and ecological niches; yet, little is known about the central metabolic pathways that sustain its growth during infection or precisely how these pathways operate. In this work, we used a combination of 'omics approaches (transcriptomics, proteomics, metabolomics, and ¹³C-fluxomics) and reverse genetics to provide systems-level insight into how the infection-relevant organic acids succinate and propionate are metabolized by Pa. Moreover, through structural and kinetic analysis of the 2-methylcitrate synthase (2-MCS; PrpC) and its paralogue citrate (CIT) synthase (GltA), we show how these two crucial enzymatic steps are interconnected in Pa organic acid assimilation. We found that Pa can rapidly adapt to the loss of GltA function by acquiring mutations in a transcriptional repressor, which then derepresses prpC expression. Our findings provide a clear example of how "underground metabolism," facilitated by enzyme substrate promiscuity, "rewires" Pa metabolism, allowing it to overcome the loss of a crucial enzyme. This pathogen-specific knowledge is critical for the advancement of a model-driven framework to target bacterial central metabolism. IMPORTANCE Pseudomonas aeruginosa is an opportunistic human pathogen that, due to its unrivalled resistance to antibiotics, ubiquity in the built environment, and aggressiveness in infection scenarios, has acquired the somewhat dubious accolade of being designated a "critical priority pathogen" by the WHO. In this work, we uncover the pathways and mechanisms used by P. aeruginosa to grow on a substrate that is abundant at many infection sites: propionate. We found that if the organism is prevented from metabolizing propionate, the substrate turns from being a convenient nutrient source into a potent poison, preventing bacterial growth. We further show that one of the enzymes involved in these reactions, 2-methylcitrate synthase (PrpC), is promiscuous and can moonlight for another essential enzyme in the cell (citrate synthase). Indeed, mutations that abolish citrate synthase activity (which would normally prevent the cell from growing) can be readily overcome if the cell acquires additional mutations that increase the expression of PrpC. This is a nice example of the evolutionary utility of so-called "underground metabolism."

Functional mining of novel terpene synthases from metagenomes

BACKGROUND

Terpenes are one of the most diverse and abundant classes of natural biomolecules, collectively enabling a variety of therapeutic, energy, and cosmetic applications. Recent genomics investigations have predicted a large untapped reservoir of bacterial terpene synthases residing in the genomes of uncultivated organisms living in the soil, indicating a vast array of putative terpenoids waiting to be discovered.

RESULTS

We aimed to develop a high-throughput functional metagenomic screening system for identifying novel terpene synthases from bacterial metagenomes by relieving the toxicity of terpene biosynthesis precursors to the Escherichia coli host. The precursor toxicity was achieved using an inducible operon encoding the prenyl pyrophosphate synthetic pathway and supplementation of the mevalonate precursor. Host strain and screening procedures were finely optimized to minimize false positives arising from spontaneous mutations, which avoid the precursor toxicity. Our functional metagenomic screening of human fecal metagenomes yielded a novel β-farnesene synthase, which does not show amino acid sequence similarity to known β-farnesene synthases. Engineered S. cerevisiae expressing the screened β-farnesene synthase produced 120 mg/L β-farnesene from glucose (2.86 mg/g glucose) with a productivity of 0.721 g/L∙h.

CONCLUSIONS

A unique functional metagenomic screening procedure was established for screening terpene synthases from metagenomic libraries. This research proves the potential of functional metagenomics as a sequence-independent avenue for isolating targeted enzymes from uncultivated organisms in various environmental habitats.

Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation

Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.

Carbohydrate Metabolism in Bacteria: Alternative Specificities in ADP-Glucose Pyrophosphorylases Open Novel Metabolic Scenarios and Biotechnological Tools

We explored the ability of ADP-glucose pyrophosphorylase (ADP-Glc PPase) from different bacteria to use glucosamine (GlcN) metabolites as a substrate or allosteric effectors. The enzyme from the actinobacteria Kocuria rhizophila exhibited marked and distinctive sensitivity to allosteric activation by GlcN-6P when producing ADP-Glc from glucose-1-phosphate (Glc-1P) and ATP. This behavior is also seen in the enzyme from Rhodococcus spp., the only one known so far to portray this activation. GlcN-6P had a more modest effect on the enzyme from other Actinobacteria (Streptomyces coelicolor), Firmicutes (Ruminococcus albus), and Proteobacteria (Agrobacterium tumefaciens) groups. In addition, we studied the catalytic capacity of ADP-Glc PPases from the different sources using GlcN-1P as a substrate when assayed in the presence of their respective allosteric activators. In all cases, the catalytic efficiency of Glc-1P was 1-2 orders of magnitude higher than GlcN-1P, except for the unregulated heterotetrameric protein (GlgC/GgD) from Geobacillus stearothermophilus. The Glc-1P substrate preference is explained using a model of ADP-Glc PPase from A. tumefaciens based on the crystallographic structure of the enzyme from potato tuber. The substrate-binding domain localizes near the N-terminal of an α-helix, which has a partial positive charge, thus favoring the interaction with a hydroxyl rather than a charged primary amine group. Results support the scenario where the ability of ADP-Glc PPases to use GlcN-1P as an alternative occurred during evolution despite the enzyme being selected to use Glc-1P and ATP for α-glucans synthesis. As an associated consequence in such a process, certain bacteria could have improved their ability to metabolize GlcN. The work also provides insights in designing molecular tools for producing oligo and polysaccharides with amino moieties.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

Systems biology approach to functionally assess the Clostridioides difficile pangenome reveals genetic diversity with discriminatory power

SignificanceClostridioides difficile infections are the most common source of hospital-acquired infections and are responsible for an extensive burden on the health care system. Strains of the C. difficile species comprise diverse lineages and demonstrate genome variability, with advantageous trait acquisition driving the emergence of endemic lineages. Here, we present a systems biology analysis of C. difficile that evaluates strain-specific genotypes and phenotypes to investigate the overall diversity of the species. We develop a strain typing method based on similarity of accessory genomes to identify and contextualize genetic loci capable of discriminating between strain groups.

Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A

Staphylococcus epidermidis is a common commensal of collagen-rich regions of the body, such as the skin, but also represents a threat to patients with medical implants (joints and heart), and to preterm babies. Far less studied than Staphylococcus aureus, the mechanisms behind this increasingly recognised pathogenicity are yet to be fully understood. Improving our knowledge of the metabolic processes that allow S. epidermidis to colonise different body sites is key to defining its pathogenic potential. Thus, we have constructed a fully curated, genome-scale metabolic model for S. epidermidis RP62A, and investigated its metabolic properties with a focus on substrate auxotrophies and its utilisation for energy and biomass production. Our results show that, although glucose is available in the medium, only a small portion of it enters the glycolytic pathways, whils most is utilised for the production of biofilm, storage and the structural components of biomass. Amino acids, proline, valine, alanine, glutamate and arginine, are preferred sources of energy and biomass production. In contrast to previous studies, we have shown that this strain has no real substrate auxotrophies, although removal of proline from the media has the highest impact on the model and the experimental growth characteristics. Further study is needed to determine the significance of proline, an abundant amino acid in collagen, in S. epidermidis colonisation.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

SAR11 Cells Rely on Enzyme Multifunctionality To Metabolize a Range of Polyamine Compounds

In the ocean surface layer and cell culture, the polyamine transport protein PotD of SAR11 bacteria is often one of the most abundant proteins detected. Polyamines are organic cations at seawater pH produced by all living organisms and are thought to be an important component of dissolved organic matter (DOM) produced in planktonic ecosystems. We hypothesized that SAR11 cells uptake and metabolize multiple polyamines and use them as sources of carbon and nitrogen. Metabolic footprinting and fingerprinting were used to measure the uptake of five polyamine compounds (putrescine, cadaverine, agmatine, norspermidine, and spermidine) in two SAR11 strains that represent the majority of SAR11 cells in the surface ocean environment, “Candidatus Pelagibacter” strain HTCC7211 and “Candidatus Pelagibacter ubique” strain HTCC1062. Both strains took up all five polyamines and concentrated them to micromolar or millimolar intracellular concentrations. Both strains could use most of the polyamines to meet their nitrogen requirements, but polyamines did not fully substitute for their requirements of glycine (or related compounds) or pyruvate (or related compounds). Our data suggest that potABCD transports all five polyamines and that spermidine synthase, speE, is reversible, catalyzing the breakdown of spermidine and norspermidine, in addition to its usual biosynthetic role. These findings provide support for the hypothesis that enzyme multifunctionality enables streamlined cells in planktonic ecosystems to increase the range of DOM compounds they metabolize.

Cofactor Specificity of Glucose-6-Phosphate Dehydrogenase Isozymes in Pseudomonas putida Reveals a General Principle Underlying Glycolytic Strategies in Bacteria

Glucose-6-phosphate dehydrogenase (G6PDH) is widely distributed in nature and catalyzes the first committing step in the oxidative branch of the pentose phosphate (PP) pathway, feeding either the reductive PP or the Entner-Doudoroff pathway. Besides its role in central carbon metabolism, this dehydrogenase provides reduced cofactors, thereby affecting redox balance. Although G6PDH is typically considered to display specificity toward NADP⁺, some variants accept NAD⁺ similarly or even preferentially. Furthermore, the number of G6PDH isozymes encoded in bacterial genomes varies from none to more than four orthologues. On this background, we systematically analyzed the interplay of the three G6PDH isoforms of the soil bacterium Pseudomonas putida KT2440 from genomic, genetic, and biochemical perspectives. P. putida represents an ideal model to tackle this endeavor, as its genome harbors gene orthologues for most dehydrogenases in central carbon metabolism. We show that the three G6PDHs of strain KT2440 have different cofactor specificities and that the isoforms encoded by zwfA and zwfB carry most of the activity, acting as metabolic “gatekeepers” for carbon sources that enter at different nodes of the biochemical network. Moreover, we demonstrate how multiplication of G6PDH isoforms is a widespread strategy in bacteria, correlating with the presence of an incomplete Embden-Meyerhof-Parnas pathway. The abundance of G6PDH isoforms in these species goes hand in hand with low NADP⁺ affinity, at least in one isozyme. We propose that gene duplication and relaxation in cofactor specificity is an evolutionary strategy toward balancing the relative production of NADPH and NADH.

IMPORTANCE Protein families have likely arisen during evolution by gene duplication and divergence followed by neofunctionalization. While this phenomenon is well documented for catabolic activities (typical of environmental bacteria that colonize highly polluted niches), the coexistence of multiple isozymes in central carbon catabolism remains relatively unexplored. We have adopted the metabolically versatile soil bacterium Pseudomonas putida KT2440 as a model to interrogate the physiological and evolutionary significance of coexisting glucose-6-phosphate dehydrogenase (G6PDH) isozymes. Our results show that each of the three G6PDHs in this bacterium display distinct biochemical properties, especially at the level of cofactor preference, impacting bacterial physiology in a carbon source-dependent fashion. Furthermore, the presence of multiple G6PDHs differing in NAD⁺ or NADP⁺ specificity in bacterial species strongly correlates with their predominant metabolic lifestyle. Our findings support the notion that multiplication of genes encoding cofactor-dependent dehydrogenases is a general evolutionary strategy toward achieving redox balance according to the growth conditions.

Reconstructing organisms in silico: genome-scale models and their emerging applications

Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.

Metabolic reconstruction of Pseudomonas chlororaphis ATCC 9446 to understand its metabolic potential as a phenazine-1-carboxamide-producing strain

Pseudomonas chlororaphis is a plant-associated bacterium with reported antagonistic activity against different organisms and plant growth-promoting properties. P. chlororaphis possesses exciting biotechnological features shared with another Pseudomonas with a nonpathogenic phenotype. Part of the antagonistic role of P. chlororaphis is due to its production of a wide variety of phenazines. To expand the knowledge of the metabolic traits of this organism, we constructed the first experimentally validated genome-scale model of P. chlororaphis ATCC 9446, containing 1267 genes and 2289 reactions, and analyzed strategies to maximize its potential for the production of phenazine-1-carboxamide (PCN). The resulting model also describes the capability of P. chlororaphis to carry out the denitrification process and its ability to consume sucrose (Scr), trehalose, mannose, and galactose as carbon sources. Additionally, metabolic network analysis suggested fatty acids as the best carbon source for PCN production. Moreover, the optimization of PCN production was performed with glucose and glycerol. The optimal PCN production phenotype requires an increased carbon flux in TCA and glutamine synthesis. Our simulations highlight the intrinsic H₂O₂ flux associated with PCN production, which may generate cellular stress in an overproducing strain. These results suggest that an improved antioxidative strategy could lead to optimal performance of phenazine-producing strains of P. chlororaphis. KEY POINTS : • This is the first publication of a metabolic model for a strain of P. chlororaphis. • Genome-scale model is worthy tool to increase the knowledge of a non model organism. • Fluxes simulations indicate a possible effect of H₂O₂ on phenazines production. • P. chlororaphis can be a suitable model for a wide variety of compounds.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Understanding the metabolism of the tetralin degrader Sphingopyxis granuli strain TFA through genome-scale metabolic modelling

Sphingopyxis granuli strain TFA is an α-proteobacterium that belongs to the sphingomonads, a group of bacteria well-known for its degradative capabilities and oligotrophic metabolism. Strain TFA is the only bacterium in which the mineralisation of the aromatic pollutant tetralin has been completely characterized at biochemical, genetic, and regulatory levels and the first Sphingopyxis characterised as facultative anaerobe. Here we report additional metabolic features of this α-proteobacterium using metabolic modelling and the functional integration of genomic and transcriptomic data. The genome-scale metabolic model (GEM) of strain TFA, which has been manually curated, includes information on 743 genes, 1114 metabolites and 1397 reactions. This represents the largest metabolic model for a member of the Sphingomonadales order thus far. The predictive potential of this model was validated against experimentally calculated growth rates on different carbon sources and under different growth conditions, including both aerobic and anaerobic metabolisms. Moreover, new carbon and nitrogen sources were predicted and experimentally validated. The constructed metabolic model was used as a platform for the incorporation of transcriptomic data, generating a more robust and accurate model. In silico flux analysis under different metabolic scenarios highlighted the key role of the glyoxylate cycle in the central metabolism of strain TFA.

Directed evolution of Anabaena variabilis phenylalanine ammonia-lyase (PAL) identifies mutants with enhanced activities

There is broad interest in engineering phenylalanine ammonia-lyase (PAL) for its biocatalytic applications in industry and medicine. While site-specific mutagenesis has been employed to improve PAL stability or substrate specificity, combinatorial techniques are poorly explored. Here, we report development of a directed evolution technique to engineer PAL enzymes. Central to this approach is a high-throughput enrichment that couples E. coli growth to PAL activity. Starting with the PAL used in the formulation of pegvaliase for PKU therapy, we report previously unidentified mutations that increase turnover frequency almost twofold after only a single round of engineering.

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 y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function

Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the ‘y-ome’) which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with ‘y’ is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.

Current status and applications of genome-scale metabolic models

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

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.

Coupling S-adenosylmethionine-dependent methylation to growth: Design and uses

We present a selection design that couples S-adenosylmethionine–dependent methylation to growth. We demonstrate its use in improving the enzyme activities of not only N-type and O-type methyltransferases by 2-fold but also an acetyltransferase of another enzyme category when linked to a methylation pathway in Escherichia coli using adaptive laboratory evolution. We also demonstrate its application for drug discovery using a catechol O-methyltransferase and its inhibitors entacapone and tolcapone. Implementation of this design in Saccharomyces cerevisiae is also demonstrated.

Many important biological processes require methylation, e.g., DNA methylation and synthesis of flavoring compounds, neurotransmitters, and antibiotics. Most methylation reactions in cells are catalyzed by S-adenosylmethionine (SAM)–dependent methyltransferases (Mtases) using SAM as a methyl donor. Thus, SAM-dependent Mtases have become an important enzyme category of biotechnological interests and as healthcare targets. However, functional implementation and engineering of SAM-dependent Mtases remains difficult and is neither cost effective nor high throughput. Here, we are able to address these challenges by establishing a synthetic biology approach, which links Mtase activity to cell growth such that higher Mtase activity ultimately leads to faster cell growth. We show that better-performing variants of the examined Mtases can be readily obtained by growth selection after repetitive cell passages. We also demonstrate the usefulness of our approach for discovery of Mtase-specific drug candidates. We further show our approach is not only applicable in bacteria, exemplified by Escherichia coli, but also in eurkaryotic organisms such as budding yeast Saccharomyces cerevisiae.

Evolutionary engineering of Escherichia coli for improved anaerobic growth in minimal medium accelerated lactate production

Anaerobic fermentation is a favorable process for microbial production of bulk chemicals like ethanol and organic acids. Low productivity is the bottleneck of several anaerobic processes which has significant impact on the technique competitiveness of production strain. Improving growth rate of production strain can speed up the total production cycle and may finally increase productivity of anaerobic processes. In this work, evolutionary engineering of wild-type strain Escherichia coli W3110 was adopted to improve anaerobic growth in mineral medium. Significant increases in exponential growth rate and stationary cell density were achieved in evolved strain WE269, and a 96.5% increase in lactate productivity has also been observed in batch fermentation of this strain with M9 minimal medium. Then, an engineered strain for lactate production (BW100) was constructed by using WE269 as a platform and 98.3 g/L lactate (with an optical purity of D-lactate above 95%) was produced in a 5-L bioreactor after 48 h with a productivity of 2.05 g/(L·h). Finally, preliminary investigation demonstrated that mutation in sucD (sucD M245I) (encoding succinyl-CoA synthetase); ilvG (ilvG Δ1bp) (encoding acetolactate synthase 2 catalytic subunit), and rpoB (rpoB T1037P) (encoding RNA polymerase β subunit) significantly improved anaerobic growth of E. coli. Double-gene mutation in ilvG and sucD resumed most of the growth potential of evolved strain WE269. This work suggested that improving anaerobic growth of production host can increase productivity of organic acids like lactate, and specific mutation-enabled improved growth may also be applied to metabolic engineering for production of other bulk chemicals.

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.

Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

The social network of microorganisms - how auxotrophies shape complex communities

Microorganisms engage in complex interactions with other organisms and their environment. Recent studies have shown that these interactions are not limited to the exchange of electron donors. Most microorganisms are auxotrophs, thus relying on external nutrients for growth, including the exchange of amino acids and vitamins. Currently, we lack a deeper understanding of auxotrophies in microorganisms and how nutrient requirements differ between different strains and different environments. In this Opinion article, we describe how the study of auxotrophies and nutrient requirements among members of complex communities will enable new insights into community composition and assembly. Understanding this complex network over space and time is crucial for developing strategies to interrogate and shape microbial communities.

A Postgenomic Perspective on Molecular Cytogenetics

BACKGROUND

The postgenomic era is featured by massive data collection and analyses from various large scale-omics studies. Despite the promising capability of systems biology and bioinformatics to handle large data sets, data interpretation, especially the translation of -omics data into clinical implications, has been challenging.

DISCUSSION

In this perspective, some important conceptual and technological limitations of current systems biology are discussed in the context of the ultimate importance of the genome beyond the collection of all genes. Following a brief summary of the contributions of molecular cytogenetics/cytogenomics in the pre- and post-genomic eras, new challenges for postgenomic research are discussed. Such discussion leads to a call to search for a new conceptual framework and holistic methodologies.

CONCLUSION

Throughout this synthesis, the genome theory of somatic cell evolution is highlighted in contrast to gene theory, which ignores the karyotype-mediated higher level of genetic information. Since "system inheritance" is defined by the genome context (gene content and genomic topology) while "parts inheritance" is defined by genes/epigenes, molecular cytogenetics and cytogenomics (which directly study genome structure, function, alteration and evolution) will play important roles in this postgenomic era.

Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test

INTRODUCTION

Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies.

MATERIALS AND METHODS

The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow.

CONCLUSIONS

Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.

Genome-scale metabolic models as platforms for identification of novel genes as antimicrobial drug targets

The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.

Modeling the multi-scale mechanisms of macromolecular resource allocation

As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.

Improving the success and impact of the metabolic engineering design, build, test, learn cycle by addressing proteins of unknown function

Rational, predictive metabolic engineering of organisms requires an ability to associate biological activity to the corresponding gene(s). Despite extensive advances in the 20 years since the Escherichia coli genome was published, there are still gaps in our knowledge of protein function. The substantial amount of data that has been published, such as: omics-level characterization in a myriad of conditions; genome-scale libraries; and evolution and genome sequencing, provide means of identifying and prioritizing proteins for characterization. This review describes the scale of this knowledge gap, demonstrates the benefit of addressing the knowledge gap, and demonstrates the availability of interesting candidates for characterization.

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.

Sexual recombination and increased mutation rate expedite evolution of Escherichia coli in varied fitness landscapes

Sexual recombination and mutation rate are theorized to play different roles in adaptive evolution depending on the fitness landscape; however, direct experimental support is limited. Here we examine how these factors affect the rate of adaptation utilizing a “genderless” strain of Escherichia coli capable of continuous in situ sexual recombination. The results show that the populations with increased mutation rate, and capable of sexual recombination, outperform all the other populations. We further characterize two sexual and two asexual populations with increased mutation rate and observe maintenance of beneficial mutations in the sexual populations through mutational sweeps. Furthermore, we experimentally identify the molecular signature of a mating event within the sexual population that combines two beneficial mutations to generate a fitter progeny; this evidence suggests that the recombination event partially alleviates clonal interference. We present additional data suggesting that stochasticity plays an important role in the combinations of mutations observed.

Sexual recombination and mutation rate may play different roles in adaptive evolution depending on the fitness landscape. Here, Peabody et al. examine how the two factors affect the rate of adaptation of an E. coli strain capable of sexual recombination, under different conditions during experimental evolution.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

A Non-natural Protein Rescues Cells Deleted for a Key Enzyme in Central Metabolism

An important goal of synthetic biology is to create novel proteins that provide life-sustaining functions in living organisms. Recent attempts to produce novel proteins have focused largely on rational design involving significant computational efforts. In contrast, nature does not design sequences a priori. Instead, nature relies on Darwinian evolution to select biologically functional sequences from nondesigned sequence space. To mimic natural selection in the laboratory, we combed through libraries of novel sequences and selected proteins that rescue E. coli cells deleted for conditionally essential genes. One such gene, gltA, encodes citrate synthase, the enzyme responsible for metabolic entry into the citric acid cycle. The de novo protein SynGltA was isolated as a rescuer of ΔgltA. However, SynGltA is not an enzyme. Instead, SynGltA allows cells to recover from a defect in central carbon and energy metabolism by altering the regulation of an alternative metabolic pathway. Specifically, SynGltA dramatically enhances the expression of prpC, a gene encoding methylcitrate synthase in the propionate degradation pathway. This endogenous protein has promiscuous catalytic activity, which when overexpressed, compensates for the deletion of citrate synthase. While the molecular details responsible for this overexpression have not been elucidated, the results clearly demonstrate that non-natural proteins-unrelated to sequences in nature-can provide life-sustaining functions by altering gene regulation in natural organisms.