Toward Synthetic Biology Strategies for Adipic Acid Production: An in Silico Tool for Combined Thermodynamics and Stoichiometric Analysis of Metabolic Networks

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

The EcoCyc Database (2023)

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Implementation of Synthetic Pathways to Foster Microbe-Based Production of Non-Naturally Occurring Carboxylic Acids and Derivatives

Microbially produced carboxylic acids (CAs) are considered key players in the implementation of more sustainable industrial processes due to their potential to replace a set of oil-derived commodity chemicals. Most CAs are intermediates of microbial central carbon metabolism, and therefore, a biochemical production pathway is described and can be transferred to a host of choice to enable/improve production at an industrial scale. However, for some CAs, the implementation of this approach is difficult, either because they do not occur naturally (as is the case for levulinic acid) or because the described production pathway cannot be easily ported (as it is the case for adipic, muconic or glucaric acids). Synthetic biology has been reshaping the range of molecules that can be produced by microbial cells by setting new-to-nature pathways that leverage on enzyme arrangements not observed in vivo, often in association with the use of substrates that are not enzymes’ natural ones. In this review, we provide an overview of how the establishment of synthetic pathways, assisted by computational tools for metabolic retrobiosynthesis, has been applied to the field of CA production. The translation of these efforts in bridging the gap between the synthesis of CAs and of their more interesting derivatives, often themselves non-naturally occurring molecules, is also reviewed using as case studies the production of methacrylic, methylmethacrylic and poly-lactic acids.

Mining and engineering exporters for titer improvement of macrolide biopesticides in Streptomyces

Exporter engineering is a promising strategy to construct high-yield Streptomyces for natural product pharmaceuticals in industrial biotechnology. However, available exporters are scarce, due to the limited knowledge of bacterial transporters. Here, we built a workflow for exporter mining and devised a tunable plug-and-play exporter (TuPPE) module to improve the production of macrolide biopesticides in Streptomyces. Combining genome analyses and experimental confirmations, we found three ATP-binding cassette transporters that contribute to milbemycin production in Streptomyces bingchenggensis. We then optimized the expression level of target exporters for milbemycin titer optimization by designing a TuPPE module with replaceable promoters and ribosome binding sites. Finally, broader applications of the TuPPE module were implemented in industrial S. bingchenggensis BC04, Streptomyces avermitilis NEAU12 and Streptomyces cyaneogriseus NMWT1, which led to optimal titer improvement of milbemycin A3/A4, avermectin B_1a and nemadectin α by 24.2%, 53.0% and 41.0%, respectively. Our work provides useful exporters and a convenient TuPPE module for titer improvement of macrolide biopesticides in Streptomyces. More importantly, the feasible exporter mining workflow developed here might shed light on widespread applications of exporter engineering in Streptomyces to boost the production of other secondary metabolites.

Biosynthesis of adipic acid in metabolically engineered Saccharomyces cerevisiae

Adipic Acid (AA) is a valued platform chemical compound, which can be used as a precursor of nylon-6,6. Due to the generation of an enormous amount of nitric oxide metabolites and the growing depletion of oil resources as a result of AA production from a mixture of cyclohexanol and cyclohexanone, the microbial methods for synthesizing AA have attracted significant attention. Of the several AA-producing pathways, the reverse adipate degradation pathway in Thermobifida fusca (Tfu RADP) is reported to be the most efficient, which has been confirmed in Escherichia coli. In this study, the heterologous Tfu RADP was constructed for producing AA in S. cerevisiae by co-expressing genes of Tfu_0875, Tfu_2399, Tfu_0067, Tfu_1647, Tfu_2576, and Tfu_2576. The AA titer combined with biomass, cofactors and other by-products was all determined after fermentation. During batch fermentation in a shake flask, the maximum AA titer was 3.83 mg/L, while the titer increased to 10.09 mg/L during fed-batch fermentation in a 5-L bioreactor after fermentation modification.

Thermodynamic Favorability of End Products of Anaerobic Glucose Metabolism

The eQuilibrator component contribution method allows calculation of the overall Gibbs energy changes for conversion of glucose to a wide range of final products in the absence of other oxidants. Values are presented for all possible combinations of products with up to three carbons and selected others. The most negative Gibbs energy change is for the formation of graphite and water (-499 kJ mol-1) followed by CH4 and CO2 (-430 kJ mol-1), the observed final products of anaerobic digestion. Other favored products (with various combinations having Gibbs energy changes between -300 and -367 kJ mol-1) are short-chain alkanes, fatty acids, dicarboxylic acids, and even hexane and benzene. The most familiar products, lactate and ethanol + CO2, are less favored (Gibbs energy changes of -206 and -265 kJ mol-1 respectively). The values presented offer an interesting perspective on observed metabolism and its evolutionary origins as well as on cells engineered for biotechnological purposes.

Approaches to Computational Strain Design in the Multiomics Era

Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.

The EcoCyc Database

EcoCyc is a bioinformatics database available at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on E. coli gene essentiality and on nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed via EcoCyc.org. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Advances in bio-based production of dicarboxylic acids longer than C4

Growing concerns of environmental pollution and fossil resource shortage are major driving forces for bio-based production of chemicals traditionally from petrochemical industry. Dicarboxylic acids (DCAs) are important platform chemicals with large market and wide applications, and here the recent advances in bio-based production of straight-chain DCAs longer than C4 from biological approaches, especially by synthetic biology, are reviewed. A couple of pathways were recently designed and demonstrated for producing DCAs, even those ranging from C5 to C15, by employing respective starting units, extending units, and appropriate enzymes. Furthermore, in order to achieve higher production of DCAs, enormous efforts were made in engineering microbial hosts that harbored the biosynthetic pathways and in improving properties of biocatalytic elements to enhance metabolic fluxes toward target DCAs. Here we summarize and discuss the current advantages and limitations of related pathways, and also provide perspectives on synthetic pathway design and optimization for hyper-production of DCAs.

Knowledge-Based Neuroendocrine Immunomodulation (NIM) Molecular Network Construction and Its Application

Growing evidence shows that the neuroendocrine immunomodulation (NIM) network plays an important role in maintaining and modulating body function and the homeostasis of the internal environment. The disequilibrium of NIM in the body is closely associated with many diseases. In the present study, we first collected a core dataset of NIM signaling molecules based on our knowledge and obtained 611 NIM signaling molecules. Then, we built a NIM molecular network based on the MetaCore database and analyzed the signaling transduction characteristics of the core network. We found that the endocrine system played a pivotal role in the bridge between the nervous and immune systems and the signaling transduction between the three systems was not homogeneous. Finally, employing the forest algorithm, we identified the molecular hub playing an important role in the pathogenesis of rheumatoid arthritis (RA) and Alzheimer’s disease (AD), based on the NIM molecular network constructed by us. The results showed that GSK3B, SMARCA4, PSMD7, HNF4A, PGR, RXRA, and ESRRA might be the key molecules for RA, while RARA, STAT3, STAT1, and PSMD14 might be the key molecules for AD. The molecular hub may be a potentially druggable target for these two complex diseases based on the literature. This study suggests that the NIM molecular network in this paper combined with the forest algorithm might provide a useful tool for predicting drug targets and understanding the pathogenesis of diseases. Therefore, the NIM molecular network and the corresponding online tool will not only enhance research on complex diseases and system biology, but also promote the communication of valuable clinical experience between modern medicine and Traditional Chinese Medicine (TCM).

Metabolic Engineering of the Shikimate Pathway for Production of Aromatics and Derived Compounds-Present and Future Strain Construction Strategies

The aromatic nature of shikimate pathway intermediates gives rise to a wealth of potential bio-replacements for commonly fossil fuel-derived aromatics, as well as naturally produced secondary metabolites. Through metabolic engineering, the abundance of certain intermediates may be increased, while draining flux from other branches off the pathway. Often targets for genetic engineering lie beyond the shikimate pathway, altering flux deep in central metabolism. This has been extensively used to develop microbial production systems for a variety of compounds valuable in chemical industry, including aromatic and non-aromatic acids like muconic acid, para-hydroxybenzoic acid, and para-coumaric acid, as well as aminobenzoic acids and aromatic α-amino acids. Further, many natural products and secondary metabolites that are valuable in food- and pharma-industry are formed outgoing from shikimate pathway intermediates. (Re)construction of such routes has been shown by de novo production of resveratrol, reticuline, opioids, and vanillin. In this review, strain construction strategies are compared across organisms and put into perspective with requirements by industry for commercial viability. Focus is put on enhancing flux to and through shikimate pathway, and engineering strategies are assessed in order to provide a guideline for future optimizations.

Biocatalytic production of adipic acid from glucose using engineered Saccharomyces cerevisiae

Adipic acid is an important industrial chemical used in the synthesis of nylon-6,6. The commercial synthesis of adipic acid uses petroleum-derived benzene and releases significant quantities of greenhouse gases. Biocatalytic production of adipic acid from renewable feedstocks could potentially reduce the environmental damage and eliminate the need for fossil fuel precursors. Recently, we have demonstrated the first enzymatic hydrogenation of muconic acid to adipic acid using microbial enoate reductases (ERs) - complex iron-sulfur and flavin containing enzymes. In this work, we successfully expressed the Bacillus coagulans ER in a Saccharomyces cerevisiae strain producing muconic acid and developed a three-stage fermentation process enabling the synthesis of adipic acid from glucose. The ability to express active ERs and significant acid tolerance of S. cerevisiae highlight the applicability of the developed yeast strain for the biocatalytic production of adipic acid from renewable feedstocks.

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An enzyme capable of reducing α pi bonds in carboxylic acids has been expressed in S. cerevisiae.

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The first yeast strain capable of complete adipic acid biosynthesis has been developed.

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A three-stage fermentation strategy has been proposed to convert glucose to adipic acid.