Coping with complexity in metabolic engineering

Shifting redox reaction equilibria on demand using an orthogonal redox cofactor

Nature's two redox cofactors, nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP+), are held at different reduction potentials, driving catabolism and anabolism in opposite directions. In biomanufacturing, there is a need to flexibly control redox reaction direction decoupled from catabolism and anabolism. We established nicotinamide mononucleotide (NMN+) as a noncanonical cofactor orthogonal to NAD(P)+. Here we present the development of Nox Ortho, a reduced NMN+ (NMNH)-specific oxidase, that completes the toolkit to modulate NMNH:NMN+ ratio together with an NMN+-specific glucose dehydrogenase (GDH Ortho). The design principle discovered from Nox Ortho engineering and modeling is facilely translated onto six different enzymes to create NMN(H)-orthogonal biocatalysts with a consistent ~103-106-fold cofactor specificity switch from NAD(P)+ to NMN+. We assemble these enzymes to produce stereo-pure 2,3-butanediol in cell-free systems and in Escherichia coli, enabled by NMN(H)'s distinct redox ratio firmly set by its designated driving forces, decoupled from both NAD(H) and NADP(H).

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design-build-test-learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

Optimizing microbial networks through metabolic bypasses

Metabolism has long been considered as a relatively stiff set of biochemical reactions. This somewhat outdated and dogmatic view has been challenged over the last years, as multiple studies exposed unprecedented plasticity of metabolism by exploring rational and evolutionary modifications within the metabolic network of cell factories. Of particular importance is the emergence of metabolic bypasses, which consist of enzymatic reaction(s) that support unnatural connections between metabolic nodes. Such novel topologies can be generated through the introduction of heterologous enzymes or by upregulating native enzymes (sometimes relying on promiscuous activities thereof). Altogether, the adoption of bypasses resulted in an expansion in the capacity of the host's metabolic network, which can be harnessed for bioproduction. In this review, we discuss modifications to the canonical architecture of central carbon metabolism derived from such bypasses towards six optimization purposes: stoichiometric gain, overcoming kinetic limitations, solving thermodynamic barriers, circumventing toxic intermediates, uncoupling product synthesis from biomass formation, and altering redox cofactor specificity. The metabolic costs associated with bypass-implementation are likewise discussed, including tailoring their design towards improving bioproduction.

Engineering formaldehyde dehydrogenase from Pseudomonas putida to favor nicotinamide cytosine dinucleotide

The enzyme formaldehyde dehydrogenase (FalDH) from Pseudomonas putida is of particular interest for biotechnological applications as it catalyses the oxidation of formaldehyde independent of glutathione. However, the consumption of a stoichiometric amount of nicotinamide adenine dinucleotide (NAD) can be challenging at the metabolic level as this may affect many other NAD-linked processes. A potential solution is to engineer FalDH to utilize non-natural cofactors. Here we devised FalDH variants to favor nicotinamide cytosine dinucleotide (NCD) by structure-guided modification of the binding pocket for the adenine moiety of NAD. Several mutants were obtained and the best one FalDH 9B2 had over 150-fold higher preference for NCD than NAD. Molecular docking analysis indicated that the cofactor binding pocket shrinked to better fit NCD, a smaller-sized cofactor. FalDH 9B2 together with other NCD-linked enzymes offer opportunities to assemble orthogonal pathways for biological conversion of C1 molecules.

Engineering a nicotinamide mononucleotide redox cofactor system for biocatalysis

Biological production of chemicals often requires the use of cellular cofactors, such as nicotinamide adenine dinucleotide phosphate (NADP+). These cofactors are expensive to use in vitro and difficult to control in vivo. We demonstrate the development of a noncanonical redox cofactor system based on nicotinamide mononucleotide (NMN+). The key enzyme in the system is a computationally designed glucose dehydrogenase with a 107-fold cofactor specificity switch toward NMN+ over NADP+ based on apparent enzymatic activity. We demonstrate that this system can be used to support diverse redox chemistries in vitro with high total turnover number (~39,000), to channel reducing power in Escherichia coli whole cells specifically from glucose to a pharmaceutical intermediate, levodione, and to sustain the high metabolic flux required for the central carbon metabolism to support growth. Overall, this work demonstrates efficient use of a noncanonical cofactor in biocatalysis and metabolic pathway design.

Bacterial microcompartments: catalysis-enhancing metabolic modules for next generation metabolic and biomedical engineering

Bacterial cells have long been thought to be simple cells with little spatial organization, but recent research has shown that they exhibit a remarkable degree of subcellular differentiation. Indeed, bacteria even have organelles such as magnetosomes for sensing magnetic fields or gas vesicles controlling cell buoyancy. A functionally diverse group of bacterial organelles are the bacterial microcompartments (BMCs) that fulfill specialized metabolic needs. Modification and reengineering of these BMCs enable innovative approaches for metabolic engineering and nanomedicine.

Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes

Essential metabolic reactions are shaping constituents of metabolic networks, enabling viable and distinct phenotypes across diverse life forms. Here we analyse and compare modelling predictions of essential metabolic functions with experimental data and thereby identify core metabolic pathways in prokaryotes. Simulations of 15 manually curated genome-scale metabolic models were integrated with 36 large-scale gene essentiality datasets encompassing a wide variety of species of bacteria and archaea. Conservation of metabolic genes was estimated by analysing 79 representative genomes from all the branches of the prokaryotic tree of life. We find that essentiality patterns reflect phylogenetic relations both for modelling and experimental data, which correlate highly at the pathway level. Genes that are essential for several species tend to be highly conserved as opposed to non-essential genes which may be conserved or not. The tRNA-charging module is highlighted as ancestral and with high centrality in the networks, followed closely by cofactor metabolism, pointing to an early information processing system supplied by organic cofactors. The results, which point to model improvements and also indicate faults in the experimental data, should be relevant to the study of centrality in metabolic networks and ancient metabolism but also to metabolic engineering with prokaryotes.

If we tried to list every known chemical reaction within an organism–human, plant or even bacteria–we would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular species–prokaryotes–to understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design.

Enhancing Light-Driven 1,3-Propanediol Production by Using Natural Compartmentalization of Differentiated Cells

Synthetic biology emerges as a powerful approach for unlocking the potential of cyanobacteria to produce various chemicals. However, the highly oxidative intracellular environment of cyanobacteria is incompatible to numerous introduced enzymes from anaerobes. In this study, we explore a strategy based on natural compartmentalization of cyanobacterial heterocysts to overcome the incompatibility. Hence, the oxygen-sensitive 1,3-propanediol (1,3-PDO) biosynthetic pathway was selected as a model and insulated in heterocysts to evaluate the proposed strategy. Thus, the genes from different sources for 1,3-PDO production were tandemly arrayed with promoter, resulting the assembled 1,3-PDO synthetic cassettes. Then the synthetic cassettes were integrated into the chromosome of Anabaena sp. strain PCC7120 by homologous recombination, respectively. The engineered strain P11 containing the genes from facultative anaerobe Klebsiella pneumoniae (cassette KP) accumulated 46.0 mg L^-1 of 1,3-PDO when heterocysts were present, which is approximately 1.7-fold higher than that of no heterocysts. As for the strains (P12, P13, and P14) containing the genes from strictly anaerobic bacterium Clostridium butyricum (cassette CB), the product 1,3-PDO could only be detected when heterocysts were present. These results indicate that insulation of the oxygen-sensitive 1,3-PDO pathway with heterocysts is an effective way to protect these enzymes in cyanobacteria. The strategy may have the potential of serving as a universal strategy to overcome the incompatibility of oxygen-sensitivity in synthetic biology.

Xenobiology: State-of-the-Art, Ethics, and Philosophy of New-to-Nature Organisms

The basic chemical constitution of all living organisms in the context of carbon-based chemistry consists of a limited number of small molecules and polymers. Until the twenty-first century, biology was mainly an analytical science and has now reached a point where it merges with engineering science, paving the way for synthetic biology. One of the objectives of synthetic biology is to try to change the chemical compositions of living cells, that is, to create an artificial biological diversity, which in turn fosters a new sub-field of synthetic biology, xenobiology. In particular, the genetic code in living systems is based on highly standardized chemistry composed of the same "letters" or nucleotides as informational polymers (DNA, RNA) and the 20 amino acids which serve as basic building blocks for proteins. The universality of the genetic code enables not only vertical gene transfer within the same species but also horizontal gene transfer across biological taxa, which require a high degree of standardization and interconnectivity. Although some minor alterations of the standard genetic code are found in nature (e.g., proteins containing non-conical amino acids exist in nature, and some organisms use alternated coding systems), all structurally deep chemistry changes within living systems are generally lethal, making the creation of artificial biological system an extremely difficult challenge.In this context, one of the great challenges for bioscience is the development of a strategy for expanding the standard basic chemical repertoire of living cells. Attempts to alter the meaning of the genetic information stored in DNA as an informational polymer by changing the chemistry of the polymer (i.e., xeno-nucleic acids) or by changes in the genetic code have already yielded successful results. In the future this should enable the partial or full redirection of the biological information flow to generate "new" version(s) of the genetic code derived from the "old" biological world.In addition to the scientific challenges, the attempt to increase biochemical diversity also raises important ethical and philosophical issues. Although promotors of this branch of synthetic biology highlight the many potential applications to come (e.g., novel tools for diagnostics and fighting infection diseases), such developments could also bring risks affecting social, political, and other structures of nearly all societies.

Engineering bacterial microcompartments with heterologous enzyme cargos

Bacterial microcompartments (BMCs) are intracellular proteinaceous organelles devoid of a lipid membrane that encapsulates enzymes of metabolic pathways. Salmonella enterica synthesizes propanediol-utilization BMCs containing enzymes involved in the degradation of 1,2-propanediol. BMCs can be designed to enclose heterologous proteins, paving the way to engineered catalytic microreactors. Here, we investigate broader applicability of this design principle by directing three different enzymes to the BMC. We demonstrate that β-galactosidase, esterase Est5, and cofactor-dependent glycerol dehydrogenase can be directed to the BMC and copurified with the microcompartment shell in a catalytically active form. We show that the BMC shell protects enzymes from pH-dependent but not from temperature stress. Moreover, we provide evidence that the heterologously expressed BMCs act as a moderately selective diffusion barrier for lipophilic small molecules.

On-chip analysis, indexing and screening for chemical producing bacteria in a microfluidic static droplet array

Economic production of chemicals from microbes necessitates development of high-producing strains and an efficient screening technology is crucial to maximize the effect of the most popular strain improvement method, the combinatorial approach. However, high-throughput screening has been limited for assessment of diverse intracellular metabolites at the single-cell level. Herein, we established a screening platform that couples a microfluidic static droplet array (SDA) and an artificial riboswitch to analyse intracellular metabolite concentration from single microbial cells. Using this system, we entrapped single Escherichia coli cells in SDA to measure intracellular l-tryptophan concentrations. It was validated that intracellular l-tryptophan concentration can be evaluated by the fluorescence from the riboswitch. Moreover, high-producing strains were successfully screened from a mutagenized library, exhibiting up to 145% productivity compared to its parental strain. This platform will be widely applicable to strain improvement for diverse metabolites by developing new artificial riboswitches.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Biotechnological potential of plant-associated endophytic fungi: hope versus hype

The potential of endophytes, particularly endophytic fungi, capable of demonstrating desirable functional traits worth exploitation using red biotechnology is well established. However, these discoveries have not yet translated into industrial bioprocesses for commercial production of biopharmaceuticals using fungal endophytes. Here, we define the current challenges in transforming curiosity driven discoveries into industrial scale endophyte biotechnology. The possible practical, feasible, and sustainable strategies that can lead to harnessing fungal endophyte-mediated pharmaceutical products are discussed.

Identification of malic enzyme mutants depending on 1,2,3-triazole moiety-containing nicotinamide adenine dinucleotide analogs

An activity screening between 1,2,3-triazole moiety-containing nicotinamide adenine dinucleotide (NAD) analogs and malic enzyme (ME) mutants identified some mutants capable of taking NAD analogs as the cofactor. One particular pair, ME-L310K/L404S and the analog B-8 had good catalytic efficiency and cofactor specificity. The new system gained about 1200-fold cofactor specificity shift from NAD toward B-8 in terms of oxidative decarboxylation of l-malate. Our results provided insightful information for the development of orthogonal redox system that is of particular important to precisely control engineered metabolic pathways.