Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate

Design, construction and optimization of formaldehyde growth biosensors with broad application in biotechnology

Formaldehyde is a key metabolite in natural and synthetic one-carbon metabolism. To facilitate the engineering of formaldehyde-producing enzymes, the development of sensitive, user-friendly, and cost-effective detection methods is required. In this study, we engineered Escherichia coli to serve as a cellular biosensor capable of detecting a broad range of formaldehyde concentrations. Using both natural and promiscuous formaldehyde assimilation enzymes, we designed three distinct E. coli growth biosensor strains that depend on formaldehyde for cell growth. These strains were engineered to be auxotrophic for one or several essential metabolites that could be produced through formaldehyde assimilation. The respective assimilating enzyme was expressed from the genome to compensate the auxotrophy in the presence of formaldehyde. We first predicted the formaldehyde dependency of the biosensors by flux balance analysis and then analysed it experimentally. Subsequent to strain engineering, we enhanced the formaldehyde sensitivity of two biosensors either through adaptive laboratory evolution or modifications at metabolic branch points. The final set of biosensors demonstrated the ability to detect formaldehyde concentrations ranging approximately from 30 μM to 13 mM. We demonstrated the application of the biosensors by assaying the in vivo activity of different methanol dehydrogenases in the most sensitive strain. The fully genomic nature of the biosensors allows them to be deployed as "plug-and-play" devices for high-throughput screenings of extensive enzyme libraries. The formaldehyde growth biosensors developed in this study hold significant promise for advancing the field of enzyme engineering, thereby supporting the establishment of a sustainable one-carbon bioeconomy.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Production of glycerate from glucose using engineered Escherichiacoli

In this study, glycerate was produced from glucose using engineered Escherichia coli BW25113. Plasmid pSR3 carrying gpd1 and gpp2 encoding two isoforms of glycerol-3-phosphate dehydrogenase from Saccharomyces cerevisiae and plasmid pLB2 carrying aldO encoding alditol oxidase from Streptomyces violaceoruber were introduced into E. coli to enable the production of glycerate from glucose via glycerol. Disruptions of garK and glxK genes in the E. coli genome were performed to minimize the consumption of glycerate produced. As a result, E. coli carrying these plasmids could produce nearly three times higher concentration of glycerate (0.50 ± 0.01 g/L) from 10 g/L glucose compared to E. coli EG_2 (0.14 ± 0.02 g/L). In M9 medium, disruption of garK and glxK resulted in an impaired growth rate with low production of glycerate, while supplementation of 0.5 g/L casamino acids and 0.5 g/L manganese sulfate to the medium replenished the growth rate and elevated the glycerate titer. Further disruption of glpF, encoding a glycerol transporter, increased the glycerate production to 0.80 ± 0.00 g/L. MR2 medium improved the glycerate production titers and specific productivities of E. coli EG_4, EG_5, and EG_6. Upscale production of glycerate was carried out in a jar fermentor with MR2 medium using E. coli EG_6, resulting in an improvement in glycerate production up to 2.37 ± 0.46 g/L with specific productivity at 0.34 ± 0.11 g-glycerate/g-cells. These results indicate that E. coli is an appropriate host for glycerate production from glucose.

Activating Silent Glycolysis Bypasses in Escherichia coli

All living organisms share similar reactions within their central metabolism to provide precursors for all essential building blocks and reducing power. To identify whether alternative metabolic routes of glycolysis can operate in E. coli, we complementarily employed in silico design, rational engineering, and adaptive laboratory evolution. First, we used a genome-scale model and identified two potential pathways within the metabolic network of this organism replacing canonical Embden-Meyerhof-Parnas (EMP) glycolysis to convert phosphosugars into organic acids. One of these glycolytic routes proceeds via methylglyoxal and the other via serine biosynthesis and degradation. Then, we implemented both pathways in E. coli strains harboring defective EMP glycolysis. Surprisingly, the pathway via methylglyoxal seemed to immediately operate in a triosephosphate isomerase deletion strain cultivated on glycerol. By contrast, in a phosphoglycerate kinase deletion strain, the overexpression of methylglyoxal synthase was necessary to restore growth of the strain. Furthermore, we engineered the "serine shunt" which converts 3-phosphoglycerate via serine biosynthesis and degradation to pyruvate, bypassing an enolase deletion. Finally, to explore which of these alternatives would emerge by natural selection, we performed an adaptive laboratory evolution study using an enolase deletion strain. Our experiments suggest that the evolved mutants use the serine shunt. Our study reveals the flexible repurposing of metabolic pathways to create new metabolite links and rewire central metabolism.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Growth-coupled selection of synthetic modules to accelerate cell factory development

Synthetic biology has brought about a conceptual shift in our ability to redesign microbial metabolic networks. Combining metabolic pathway-modularization with growth-coupled selection schemes is a powerful tool that enables deep rewiring of the cell factories’ biochemistry for rational bioproduction.

Model reduction of genome-scale metabolic models as a basis for targeted kinetic models

Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic engineering. However, they lack the detail and time dimension that kinetic models with enzyme dynamics offer. Model reduction can be used to bridge the gap between the two methods and allow for the integration of kinetic models into the Design-Built-Test-Learn cycle. Here we show that these reduced size models can be representative of the dynamics of the original model and demonstrate the automated generation and parameterisation of such models. Using these minimal models of metabolism could allow for further exploration of dynamic responses in metabolic networks.

Methanol-dependent Escherichia coli strains with a complete ribulose monophosphate cycle

Methanol is a biotechnologically promising substitute for food and feed substrates since it can be produced renewably from electricity, water and CO2. Although progress has been made towards establishing Escherichia coli as a platform organism for methanol conversion via the energy efficient ribulose monophosphate (RuMP) cycle, engineering strains that rely solely on methanol as a carbon source remains challenging. Here, we apply flux balance analysis to comprehensively identify methanol-dependent strains with high potential for adaptive laboratory evolution. We further investigate two out of 1200 candidate strains, one with a deletion of fructose-1,6-bisphosphatase (fbp) and another with triosephosphate isomerase (tpiA) deleted. In contrast to previous reported methanol-dependent strains, both feature a complete RuMP cycle and incorporate methanol to a high degree, with up to 31 and 99% fractional incorporation into RuMP cycle metabolites. These strains represent ideal starting points for evolution towards a fully methylotrophic lifestyle.

Functional reconstitution of a bacterial CO2 concentrating mechanism in Escherichia coli

Many photosynthetic organisms employ a CO₂ concentrating mechanism (CCM) to increase the rate of CO₂ fixation via the Calvin cycle. CCMs catalyze ≈50% of global photosynthesis, yet it remains unclear which genes and proteins are required to produce this complex adaptation. We describe the construction of a functional CCM in a non-native host, achieved by expressing genes from an autotrophic bacterium in an Escherichia coli strain engineered to depend on rubisco carboxylation for growth. Expression of 20 CCM genes enabled E. coli to grow by fixing CO₂ from ambient air into biomass, with growth in ambient air depending on the components of the CCM. Bacterial CCMs are therefore genetically compact and readily transplanted, rationalizing their presence in diverse bacteria. Reconstitution enabled genetic experiments refining our understanding of the CCM, thereby laying the groundwork for deeper study and engineering of the cell biology supporting CO₂ assimilation in diverse organisms.

Microbes as Biosensors

The ability to detect disease early and deliver precision therapy would be transformative for the treatment of human illnesses. To achieve these goals, biosensors that can pinpoint when and where diseases emerge are needed. Rapid advances in synthetic biology are enabling us to exploit the information-processing abilities of living cells to diagnose disease and then treat it in a controlled fashion. For example, living sensors could be designed to precisely sense disease biomarkers, such as by-products of inflammation, and to respond by delivering targeted therapeutics in situ. Here, we provide an overview of ongoing efforts in microbial biosensor design, highlight translational opportunities, and discuss challenges for enabling sense-and-respond precision medicines.

An "energy-auxotroph" Escherichia coli provides an in vivo platform for assessing NADH regeneration systems

An efficient in vivo regeneration of the primary cellular resources NADH and ATP is vital for optimizing the production of value-added chemicals and enabling the activity of synthetic pathways. Currently, such regeneration routes are tested and characterized mainly in vitro before being introduced into the cell. However, in vitro measurements could be misleading as they do not reflect enzyme activity under physiological conditions. Here, we construct an in vivo platform to test and compare NADH regeneration systems. By deleting dihydrolipoyl dehydrogenase in Escherichia coli, we abolish the activity of pyruvate dehydrogenase and 2-ketoglutarate dehydrogenase. When cultivated on acetate, the resulting strain is auxotrophic to NADH and ATP: acetate can be assimilated via the glyoxylate shunt but cannot be oxidized to provide the cell with reducing power and energy. This strain can, therefore, serve to select for and test different NADH regeneration routes. We exemplify this by comparing several NAD-dependent formate dehydrogenases and methanol dehydrogenases. We identify the most efficient enzyme variants under in vivo conditions and pinpoint optimal feedstock concentrations that maximize NADH biosynthesis while avoiding cellular toxicity. Our strain thus provides a useful platform for comparing and optimizing enzymatic systems for cofactor regeneration under physiological conditions.

An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets

The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.