Unravelling Formaldehyde Metabolism in Bacteria: Road towards Synthetic Methylotrophy

Formaldehyde metabolism is prevalent in all organisms, where the accumulation of formaldehyde can be prevented through the activity of dissimilation pathways. Furthermore, formaldehyde assimilatory pathways play a fundamental role in many methylotrophs, which are microorganisms able to build biomass and obtain energy from single- and multicarbon compounds with no carbon–carbon bonds. Here, we describe how formaldehyde is formed in the environment, the mechanisms of its toxicity to the cells, and the cell’s strategies to circumvent it. While their importance is unquestionable for cell survival in formaldehyde rich environments, we present examples of how the modification of native formaldehyde dissimilation pathways in nonmethylotrophic bacteria can be applied to redirect carbon flux toward heterologous, synthetic formaldehyde assimilation pathways introduced into their metabolism. Attempts to engineer methylotrophy into nonmethylotrophic hosts have gained interest in the past decade, with only limited successes leading to the creation of autonomous synthetic methylotrophy. Here, we discuss how native formaldehyde assimilation pathways can additionally be employed as a premise to achieving synthetic methylotrophy. Lastly, we discuss how emerging knowledge on regulation of formaldehyde metabolism can contribute to creating synthetic regulatory circuits applied in metabolic engineering strategies.

Methanol Dehydrogenases as a Key Biocatalysts for Synthetic Methylotrophy

One-carbon (C1) chemicals are potential building blocks for cheap and sustainable re-sources such as methane, methanol, formaldehyde, formate, carbon monoxide, and more. These resources have the potential to be made into raw materials for various products used in our daily life or precursors for pharmaceuticals through biological and chemical processes. Among the soluble C1 substrates, methanol is regarded as a biorenewable platform feedstock because nearly all bioresources can be converted into methanol through syngas. Synthetic methylotrophy can be exploited to produce fuels and chemicals using methanol as a feedstock that integrates natural or artificial methanol assimilation pathways in platform microorganisms. In the methanol utilization in methylotrophy, methanol dehydrogenase (Mdh) is a primary enzyme that converts methanol to formaldehyde. The discovery of new Mdhs and engineering of present Mdhs have been attempted to develop synthetic methylotrophic bacteria. In this review, we describe Mdhs, including in terms of their enzyme properties and engineering for desired activity. In addition, we specifically focus on the application of various Mdhs for synthetic methylotrophy.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Mixing and matching methylotrophic enzymes to design a novel methanol utilization pathway in E. coli

One-carbon (C1) compounds, such as methanol, have recently gained attention as alternative low-cost and non-food feedstocks for microbial bioprocesses. Considerable research efforts are thus currently focused on the generation of synthetic methylotrophs by transferring methanol assimilation pathways into established bacterial production hosts. In this study, we used an iterative combination of dry and wet approaches to design, implement and optimize this metabolic trait in the most common chassis, E. coli. Through in silico modelling, we designed a new route that "mixed and matched" two methylotrophic enzymes: a bacterial methanol dehydrogenase (Mdh) and a dihydroxyacetone synthase (Das) from yeast. To identify the best combination of enzymes to introduce into E. coli, we built a library of 266 pathway variants containing different combinations of Mdh and Das homologues and screened it using high-throughput ¹³C-labeling experiments. The highest level of incorporation of methanol into central metabolism intermediates (e.g. 22% into the PEP), was obtained using a variant composed of a Mdh from A. gerneri and a codon-optimized version of P. angusta Das. Finally, the activity of this new synthetic pathway was further improved by engineering strategic metabolic targets identified using omics and modelling approaches. The final synthetic strain had 1.5 to 5.9 times higher methanol assimilation in intracellular metabolites and proteinogenic amino acids than the starting strain did. Broadening the repertoire of methanol assimilation pathways is one step further toward synthetic methylotrophy in E. coli.

Data-driven rational biosynthesis design: from molecules to cell factories

A proliferation of chemical, reaction and enzyme databases, new computational methods and software tools for data-driven rational biosynthesis design have emerged in recent years. With the coming of the era of big data, particularly in the bio-medical field, data-driven rational biosynthesis design could potentially be useful to construct target-oriented chassis organisms. Engineering the complicated metabolic systems of chassis organisms to biosynthesize target molecules from inexpensive biomass is the main goal of cell factory design. The process of data-driven cell factory design could be divided into several parts: (1) target molecule selection; (2) metabolic reaction and pathway design; (3) prediction of novel enzymes based on protein domain and structure transformation of biosynthetic reactions; (4) construction of large-scale DNA for metabolic pathways; and (5) DNA assembly methods and visualization tools. The construction of a one-stop cell factory system could achieve automated design from the molecule level to the chassis level. In this article, we outline data-driven rational biosynthesis design steps and provide an overview of related tools in individual steps.

Enumerating all possible biosynthetic pathways in metabolic networks

Exhaustive identification of all possible alternate pathways that exist in metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities. We developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular size between a given set of source and target molecules. Our algorithm employs a guided breadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways of a specified size producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from identifying amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying large graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein. MetQuest is available as a Python package, and the source codes can be found at https://github.com/RamanLab/metquest.

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

A review of parameters and heuristics for guiding metabolic pathfinding

Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.

METABOLIC RECONSTRUCTION FOR DEVELOPING A TISSUE-SPECIFIC MODEL FOR CATTLE GLYCOLYSIS USING COBRA

This study aims at developing a tissue-specific model for glycolysis in bovine mammary gland epithelial cells by incorporating gene expression data into metabolic reactions. Two types of data sets were embedded in the COnstraint-Based Reconstruction Analysis (COBRA) toolbox: metabolic reactions that overlay bovine genetic information into human glycolysis data from a public database and gene expression data of cattle acquired from lab experiments. As a result, we successfully generated a tissue-specific model of bovine glycolysis in bovine mammary gland epithelial cells, providing information on expression of metabolic pathways and gene annotations still required for curation.

Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle

Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.

Designing overall stoichiometric conversions and intervening metabolic reactions

Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C₂₊ metabolites with higher carbon efficiency.

White biotechnology: State of the art strategies for the development of biocatalysts for biorefining

White biotechnology is a term that is now often used to describe the implementation of biotechnology in the industrial sphere. Biocatalysts (enzymes and microorganisms) are the key tools of white biotechnology, which is considered to be one of the key technological drivers for the growing bioeconomy. Biocatalysts are already present in sectors such as the chemical and agro-food industries, and are used to manufacture products as diverse as antibiotics, paper pulp, bread or advanced polymers. This review proposes an original and global overview of highly complementary fields of biotechnology at both enzyme and microorganism level. A certain number of state of the art approaches that are now being used to improve the industrial fitness of biocatalysts particularly focused on the biorefinery sector are presented. The first part deals with the technologies that underpin the development of industrial biocatalysts, notably the discovery of new enzymes and enzyme improvement using directed evolution techniques. The second part describes the toolbox available by the cell engineer to shape the metabolism of microorganisms. And finally the last part focuses on the 'omic' technologies that are vital for understanding and guide microbial engineering toward more efficient microbial biocatalysts. Altogether, these techniques and strategies will undoubtedly help to achieve the challenging task of developing consolidated bioprocessing (i.e. CBP) readily available for industrial purpose.