Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity

A new paradigm for the regulation of A40926B0 biosynthesis

Dalbavancin is a novel glycopeptide antibiotic with activity against a broad range of Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus (MRSA). A40926B0 is the direct precursor of dalbavancin, but the regulatory mechanisms underlying its biosynthesis are not well understood. Additionally, the presence of seven homologs leads to significant metabolic burden, affecting both production and purity of A40926B0. To further reveal the transcriptional regulatory mechanism of A40926B0 biosynthesis in N. gerenzanensis L70, this study employed multiple strategies to explore the regulatory network of its biosynthesis from both intracluster and extracluster aspects. WblA regulates gene expression within and outside the biosynthetic gene cluster (BGC), impacting multiple biosynthetic pathways, and Dbv3, a key regulator in the A40926B0 cluster, positively influences biosynthesis. Using a bottom-up (DNA to protein) regulator mining strategy with the key intra-cluster regulator Dbv3, it was determined that GlnR is also involved in the regulation of secondary metabolism, while BkdR regulates precursor supply. By constructing the combination of GlnR, BkdR and Dbv3 together with the WblA deletion, the regulatory network of A40926B0 was reconstructed, resulting in a 92 % improvement in purity of A40926B0. The objective of this study is to elucidate the regulatory mechanisms governing A40926B0 biosynthesis by constructing a comprehensive, multidimensional model of its regulatory network. The findings of this study serve to enhance our comprehension of A40926B0 biosynthesis and furnish insights into broader strategies for enhancing the production of other natural products and secondary metabolites in industrial microbiology.

Development of a Multienzyme Cascade for Salvianolic Acid A Synthesis from l-Tyrosine

Salvianolic acid A (SAA) has an important application value for preventing and treating cardiovascular diseases. In this study, we developed a novel multienzyme cascade system for the efficient biosynthesis of SAA, utilizing l-tyrosine (l-Tyr) as a cost-effective and stable starting material. The cascade system incorporated four enzymes: membrane-bound l-amino acid deaminase from Proteus vulgaris (Pvml-AAD), d-lactate dehydrogenase from Pediococcus acidilactici (Pad-LDH), 4-hydroxyphenylacetate 3-hydroxylase from Escherichia coli (EcHpaBC), and formate dehydrogenase from Mycobacterium vaccae N10 (MvFDH). All reaction steps in the cascade system were thermodynamically favorable. In addition, to avoid generating an unstable intermediate (3,4-dihydroxyphenyl-pyruvate, DHPPA), which was produced owing to the promiscuity of EcHpaBC and Pad-LDH, we performed the cascade system according to the reaction sequence of deamination, chiral reduction, and ortho-hydroxylation. Under optimized conditions, the developed cascade system yielded 81.67 mM SAA from an initial concentration of 100 mM l-Tyr, corresponding to a yield of 81.67%.

Pickaxe: a Python library for the prediction of novel metabolic reactions

BACKGROUND

Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.

RESULTS

Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset.

CONCLUSION

Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

MOTIVATION

While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes.

RESULTS

This article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways.

AVAILABILITY AND IMPLEMENTATION

A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction

MOTIVATION

Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Providing computational tools for the exploration of the enzyme-substrate interaction space can expedite experimentation and benefit applications such as constructing synthesis pathways for novel biomolecules, identifying products of metabolism on ingested compounds, and elucidating xenobiotic metabolism. Recommender systems (RS), which are currently unexplored for the enzyme-substrate interaction prediction problem, can be utilized to provide enzyme recommendations for substrates, and vice versa. The performance of Collaborative-Filtering (CF) RSs; however, hinges on the quality of embedding vectors of users and items (enzymes and substrates in our case). Importantly, enhancing CF embeddings with heterogeneous auxiliary data, specially relational data (e.g. hierarchical, pairwise or groupings), remains a challenge.

RESULTS

We propose an innovative general RS framework, termed Boost-RS that enhances RS performance by 'boosting' embedding vectors through auxiliary data. Specifically, Boost-RS is trained and dynamically tuned on multiple relevant auxiliary learning tasks Boost-RS utilizes contrastive learning tasks to exploit relational data. To show the efficacy of Boost-RS for the enzyme-substrate prediction interaction problem, we apply the Boost-RS framework to several baseline CF models. We show that each of our auxiliary tasks boosts learning of the embedding vectors, and that contrastive learning using Boost-RS outperforms attribute concatenation and multi-label learning. We also show that Boost-RS outperforms similarity-based models. Ablation studies and visualization of learned representations highlight the importance of using contrastive learning on some of the auxiliary data in boosting the embedding vectors.

AVAILABILITY AND IMPLEMENTATION

A Python implementation for Boost-RS is provided at https://github.com/HassounLab/Boost-RS. The enzyme-substrate interaction data is available from the KEGG database (https://www.genome.jp/kegg/).

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.

Approaches for completing metabolic networks through metabolite damage and repair discovery

Metabolites are prone to damage, either via enzymatic side reactions, which collectively form the underground metabolism, or via spontaneous chemical reactions. The resulting non-canonical metabolites that can be toxic, are mended by dedicated "metabolite repair enzymes." Deficiencies in the latter can cause severe disease in humans, whereas inclusion of repair enzymes in metabolically engineered systems can improve the production yield of value-added chemicals. The metabolite damage and repair loops are typically not yet included in metabolic reconstructions and it is likely that many remain to be discovered. Here, we review strategies and associated challenges for unveiling non-canonical metabolites and metabolite repair enzymes, including systematic approaches based on high-resolution mass spectrometry, metabolome-wide side-activity prediction, as well as high-throughput substrate and phenotypic screens.