Ni(II)-binding affinity of CcNikZ-II and its homologs: the role of the HH-prong and variable loop revealed by structural and mutational studies

Extracytoplasmic Ni(II)-binding proteins (NiBPs) are molecular shuttles involved in cellular nickel uptake. Here, we determined the crystal structure of apo CcNikZ-II at 2.38 Å, which revealed a Ni(II)-binding site comprised of the double His (HH-)prong (His511, His512) and a short variable (v-)loop nearby (Thr59-Thr64, TEDKYT). Mutagenesis of the site identified Glu60 and His511 as critical for high affinity Ni(II)-binding. Phylogenetic analysis showed 15 protein clusters with two groups containing the HH-prong. Metal-binding assays with 11 purified NiBPs containing this feature yielded higher Ni(II)-binding affinities. Replacement of the wild type v-loop with those from other NiBPs improved the affinity by up to an order of magnitude. This work provides molecular insights into the determinants for Ni(II) affinity and paves way for NiBP engineering.

A Multifunctional Dehalobacter? Tandem Chloroform and Dichloromethane Degradation in a Mixed Microbial Culture

Chloroform (CF) and dichloromethane (DCM) contaminate groundwater sites around the world but can be cleaned up through bioremediation. Although several strains of Dehalobacter restrictus can reduce CF to DCM and multiple Peptococcaceae can ferment DCM, these processes cannot typically happen simultaneously due to CF sensitivity in the known DCM-degraders or electron donor competition. Here, we present a mixed microbial culture that can simultaneously metabolize CF and DCM and create an additional enrichment culture fed only DCM. Through genus-specific quantitative polymerase chain reaction, we find that Dehalobacter grows while either CF alone or DCM alone is converted, indicating its involvement in both metabolic steps. Additionally, the culture was maintained for over 1400 days without the addition of an exogenous electron donor, and through electron balance calculations, we show that DCM metabolism would produce sufficient reducing equivalents (likely hydrogen) for CF respiration. Together, these results suggest intraspecies electron transfer could occur to continually reduce CF in the culture. Minimizing the addition of electron donor reduces the cost of bioremediation, and "self-feeding" could prolong bioremediation activity long after donor addition ends. Overall, understanding this mechanism informs strategies for culture maintenance and scale-up and benefits contaminated sites where the culture is employed for remediation worldwide.

Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels

Biotechnology has revolutionized the development of sustainable energy sources by harnessing biomass as a feedstock for energy production. However, challenges such as recalcitrant feedstocks and inefficient metabolic pathways hinder the large-scale integration of renewable energy systems. Enzyme engineering has emerged as a powerful tool to address these challenges by enhancing enzyme activity, specificity, and stability. Generative machine learning (ML) models have shown great promise in accelerating protein design, allowing for the generation of novel protein sequences with desired properties by navigating vast spaces. This review paper aims to summarize the state of the art in generative models for protein design and how they can be applied to bioenergy applications, including the underlying architectures and training strategies. Additionally, it highlights the importance of high-quality datasets for training and evaluating generative models, organizes available datasets for generative protein design, and discusses the potential of applying generative models to strain design for bioenergy production.

Quantifying metal-binding specificity of CcNikZ-II from Clostridium carboxidivorans in the presence of competing metal ions

Many proteins bind transition metal ions as cofactors to carry out their biological functions. Despite binding affinities for divalent transition metal ions being predominantly dictated by the Irving-Williams series for wild-type proteins, in vivo metal ion binding specificity is ensured by intracellular mechanisms that regulate free metal ion concentrations. However, a growing area of biotechnology research considers the use of metal-binding proteins in vitro to purify specific metal ions from wastewater, where specificity is dictated by the protein's metal binding affinities. A goal of metalloprotein engineering is to modulate these affinities to improve a protein's specificity towards a particular metal; however, the quantitative relationship between the affinities and the equilibrium metal-bound protein fractions depends on the underlying binding mechanisms. Here we demonstrate a high-throughput intrinsic tryptophan fluorescence quenching method to validate binding models in multi-metal solutions for CcNikZ-II, a nickel-binding protein from Clostridium carboxidivorans. Using our validated models, we quantify the relationship between binding affinity and specificity in different classes of metal-binding models for CcNikZ-II. We further illustrate the potential relevance of data-informed models to predicting engineering targets for improved specificity.

Engineering Escherichia coli to produce aromatic chemicals from ethylene glycol

Microbial overproduction of aromatic chemicals has gained considerable industrial interest and various metabolic engineering approaches have been employed in recent years to address the associated challenges. So far, most studies have used sugars (mostly glucose) or glycerol as the primary carbon source. In this study, we used ethylene glycol (EG) as the main carbon substrate. EG could be obtained from the degradation of plastic and cellulosic wastes. As a proof of concept, Escherichia coli was engineered to transform EG into L-tyrosine, a valuable aromatic amino acid. Under the best fermentation condition, the strain produced 2 g/L L-tyrosine from 10 g/L EG, outperforming glucose (the most common sugar feedstock) in the same experimental conditions. To prove the concept that EG can be converted into different aromatic chemicals, E. coli was further engineered with a similar approach to synthesize other valuable aromatic chemicals, L-phenylalanine and p-coumaric acid. Finally, waste polyethylene terephthalate (PET) bottles were degraded using acid hydrolysis and the resulting monomer EG was transformed into L-tyrosine using the engineered E. coli, yielding a comparable titer to that obtained using commercial EG. The strains developed in this study should be valuable to the community for producing valuable aromatics from EG.

Genetic Engineering of Acidithiobacillus ferridurans Using CRISPR Systems To Mitigate Toxic Release in Biomining

Biomining processes utilize microorganisms, such as Acidithiobacillus, to extract valuable metals by producing sulfuric acid and ferric ions that dissolve sulfidic minerals. However, excessive production of these compounds can result in metal structure corrosion and groundwater contamination. Synthetic biology offers a promising solution to improve Acidithiobacillus strains for sustainable, eco-friendly, and cost-effective biomining, but genetic engineering of these slow-growing microorganisms is challenging with current inefficient and time-consuming methods. To address this, we established a CRISPR-dCas9 system for gene knockdown in A. ferridurans JAGS, successfully downregulating the transcriptional levels of two genes involved in sulfur oxidation. More importantly, we constructed an all-in-one CRISPR-Cas9 system for fast and efficient genome editing in A. ferridurans JAGS, achieving seamless gene deletion (HdrB3), promoter substitution (Prus to Ptac), and exogenous gene insertion (GFP). Additionally, we created a HdrB-Rus double-edited strain and performed biomining experiments to extract Ni from pyrrhotite tailings. The engineered strain demonstrated a similar Ni recovery rate to wild-type A. ferridurans JAGS but with significantly lower production of iron ions and sulfuric acid in leachate. These high-efficient CRISPR systems provide a powerful tool for studying gene functions and creating useful recombinants for synthetic biology-assisted biomining applications in the future.

Molecular mechanism of plasmid-borne resistance to sulfonamide antibiotics

The sulfonamides (sulfas) are the oldest class of antibacterial drugs and inhibit the bacterial dihydropteroate synthase (DHPS, encoded by folP), through chemical mimicry of its co-substrate p-aminobenzoic acid (pABA). Resistance to sulfa drugs is mediated either by mutations in folP or acquisition of sul genes, which code for sulfa-insensitive, divergent DHPS enzymes. While the molecular basis of resistance through folP mutations is well understood, the mechanisms mediating sul-based resistance have not been investigated in detail. Here, we determine crystal structures of the most common Sul enzyme types (Sul1, Sul2 and Sul3) in multiple ligand-bound states, revealing a substantial reorganization of their pABA-interaction region relative to the corresponding region of DHPS. We use biochemical and biophysical assays, mutational analysis, and in trans complementation of E. coli ΔfolP to show that a Phe-Gly sequence enables the Sul enzymes to discriminate against sulfas while retaining pABA binding and is necessary for broad resistance to sulfonamides. Experimental evolution of E. coli results in a strain harboring a sulfa-resistant DHPS variant that carries a Phe-Gly insertion in its active site, recapitulating this molecular mechanism. We also show that Sul enzymes possess increased active site conformational dynamics relative to DHPS, which could contribute to substrate discrimination. Our results reveal the molecular foundation for Sul-mediated drug resistance and facilitate the potential development of new sulfas less prone to resistance.

Engineered nickel bioaccumulation in Escherichia coli by NikABCDE transporter and metallothionein overexpression

Mine wastewater often contains dissolved metals at concentrations too low to be economically extracted by existing technologies, yet too high for environmental discharge. The most common treatment is chemical precipitation of the dissolved metals using limestone and subsequent disposal of the sludge in tailing impoundments. While it is a cost-effective solution to meet regulatory standards, it represents a lost opportunity. In this study, we engineered Escherichia coli to overexpress its native NikABCDE transporter and a heterologous metallothionein to capture nickel at concentrations in local effluent streams. We found the engineered strain had a 7-fold improvement in the bioaccumulation performance for nickel compared to controls, but also observed a drastic decrease in cell viability due to metabolic burden or inducer (IPTG) toxicity. Growth kinetic analysis revealed the IPTG concentrations used based on past studies lead to growth inhibition, thus delineating future avenues for optimization of the engineered strain and its growth conditions to perform in more complex environments.

A Universal Strategy to Promote Secretion of G+/G- Bacterial Extracellular Vesicles and Its Application in Host Innate Immune Responses

Both Gram-positive and Gram-negative bacteria release nanosized extracellular vesicles called membrane vesicles (MVs, 20-400 nm), which have great potential in various biomedical applications due to their abilities to deliver effector molecules and induce therapeutic responses. To fully utilize bacterial MVs for therapeutic purposes, regulated and enhanced production of MVs would be highly advantageous. In this study, we developed a universal method to enhance MV yields in both G+/G- bacteria through an autonomous controlled peptidoglycan hydrolase (PGase) expression system. A significant increase (9.37-fold) of MV concentration was observed in engineered E. coli Nissle 1917 compared to the wild-type. With the help of this autonomous system, for the first time we experimentally confirmed horizontal gene transfer and nutrient acquisition in a cocultured bacterial consortium. Furthermore, the engineered probiotic E. coli strains with high yield of MVs showed higher activation of the innate immune responses in human embryonic kidney 293T (HEK293T) and human colorectal carcinoma cells (HCT116), thereby demonstrating the great potential of engineering probiotics in immunology and further living therapeutics in humans.

Recent progress in microbial fuel cells using substrates from diverse sources

Increasing untreated environmental outputs from industry and the rising human population have increased the burden of wastewater and other waste streams on the environment. The most prevalent wastewater treatment methods include the activated sludge process, which requires aeration and is, therefore, energy and cost-intensive. The current trend towards a circular economy facilitates the recovery of waste materials as a resource. Along with the amount, the complexity of wastewater is increasing day by day. Therefore, wastewater treatment processes must be transformed into cost-effective and sustainable methods. Microbial fuel cells (MFCs) use electroactive microbes to extract chemical energy from waste organic molecules to generate electricity via waste treatment. This review focuses use of MFCs as an energy converter using wastewater from various sources. The different substrate sources that are evaluated include industrial, agricultural, domestic, and pharmaceutical types. The article also highlights the effect of operational parameters such as organic load, pH, current, and concentration on the MFC output. The article also covers MFC functioning with respect to the substrate, and the associated performance parameters, such as power generation and wastewater treatment matrices, are given. The review also illustrates the success stories of various MFC configurations. We emphasize the significant measures required to fill in the gaps related to the effect of substrate type on different MFC configurations, identification of microbes for use as biocatalysts, and development of biocathodes for the further improvement of the system. Finally, we shortlisted the best performing substrates based on the maximum current and power, Coulombic efficiency, and chemical oxygen demand removal upon the treatment of substrates in MFCs. This information will guide industries that wish to use MFC technology to treat generated effluent from various processes.

Genome-Scale Metabolic Modeling Reveals Sequential Dysregulation of Glutathione Metabolism in Livers from Patients with Alcoholic Hepatitis

Alcoholic hepatitis (AH) is the most severe form of alcoholic liver disease for which there is no efficacious treatment aiding most patients. AH manifests differently in individuals, with some patients showing debilitating symptoms more so than others. Previous studies showed significant metabolic dysregulation associated with AH. Therefore, we sought to analyze how the activity of metabolic pathways differed in the liver of patients with varying degrees of AH severity. We utilized a genome-scale metabolic modeling approach that allowed for integration of a generic human cellular metabolic model with specific RNA-seq data corresponding to healthy and multiple liver disease states to predict the metabolic fluxes within each disease state. Additionally, we performed a systems-level analysis of the transcriptomic data and predicted metabolic flux data to identify the regulatory and functional differences in liver metabolism with increasing severity of AH. Our results provide unique insights into the sequential dysregulation of the solute transport mechanisms underlying the glutathione metabolic pathway with increasing AH disease severity. We propose targeting of the solute transporters in the glutathione pathway to mimic the flux activity of the healthy liver state as a potential therapeutic intervention for AH.

Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling

Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family. We attribute this to low-grade substrate encoding methods and over-compressed sequence representations received by downstream predictive models. In this study, we propose a new substrate-encoding based on Extended Connectivity Fingerprints (ECFPs) and a convolutional-pooling of the sequence embeddings. Through testing on an activity profiling dataset of haloalkanoate dehalogenase superfamily that measures activities of 218 phosphatases against 168 substrates, we show substantial improvements in predictive performances of compound-protein interaction modeling. In addition, we also test the workflow on three other datasets from the halogenase, kinase and aminotransferase families and show that our pipeline achieves good performance on these datasets as well. We further demonstrate the utility of this downstream model architecture by showing that it achieves good performance with six different protein embeddings, including ESM-1b (Rives et al., 2021), TAPE (Rao et al., 2019), ProtBert, ProtAlbert, ProtT5, and ProtXLNet (Elnaggar et al., 2021). This study provides a new workflow for activity prediction on novel substrates that can be used to engineer new enzymes for sustainability applications.

KinMod database: a tool for investigating metabolic regulation

The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation.

Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz

Genetic engineering of extremely acidophilic Acidithiobacillus species for biomining: Progress and perspectives

With global demands for mineral resources increasing and ore grades decreasing, microorganisms have been increasingly deployed in biomining applications to recover valuable metals particularly from normally considered waste, such as low-grade ores and used consumer electronics. Acidithiobacillus are a genus of chemolithoautotrophic extreme acidophiles that are commonly found in mining process waters and acid mine drainage, which have been reported in several studies to aid in metal recovery from bioremediation of metal-contaminated sites. Compared to conventional mineral processing technologies, biomining is often cited as a more sustainable and environmentally friendly process, but long leaching cycles and low extraction efficiency are main disadvantages that have hampered its industrial applications. Genetic engineering is a powerful technology that can be used to enhance the performance of microorganisms, such as Acidithiobacillus species. In this review, we compile existing data on Acidithiobacillus species' physiological traits and genomic characteristics, progresses in developing genetic tools to engineer them: plasmids, shutter vectors, transformation methods, selection markers, promoters and reporter systems developed, and genome editing techniques. We further propose genetic engineering strategies for enhancing biomining efficiency of Acidithiobacillus species and provide our perspectives on their future applications.

Harnessing synthetic biology for sustainable biomining with Fe/S-oxidizing microbes

Biomining is a biotechnological approach where microorganisms are used to recover metals from ores and waste materials. While biomining applications are motivated by critical issues related to the climate crisis (e.g., habitat destruction due to mine effluent pollution, metal supply chains, increasing demands for cleantech-critical metals), its drawbacks hinder its widespread commercial applications: lengthy processing times, low recovery, and metal selectivity. Advances in synthetic biology provide an opportunity to engineer iron/sulfur-oxidizing microbes to address these limitations. In this forum, we review recent progress in synthetic biology-enhanced biomining with iron/sulfur-oxidizing microbes and delineate future research avenues.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

REVOLVER: A low-cost automated protein purifier based on parallel preparative gravity column workflows

Protein purification is a ubiquitous procedure in biochemistry and the life sciences, and represents a key step in the protein production pipeline. The need for scalable and parallel protein purification systems is driven by the demands for increasing the throughput of recombinant protein characterization. Therefore, automating the process to simultaneously handle multiple samples with minimal human intervention is highly desirable, yet there are only a handful of such systems that have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER takes a cell lysate sample and completes a full protein purification process with almost no human intervention and yields results indistinguishable from those obtained by an experienced biochemist when purifying a real-world protein sample. We further present and describe MULTI-VOLVER, a scalable version of the REVOLVER that allows for parallel purification of up to six samples and can be built for under $250 USD. Both systems can help accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering.

On the design principles of metabolic flux sensing

Metabolism is precisely coordinated, with the goal of balancing fluxes to maintain robust growth. However, coordinating fluxes requires information about rates, which can only be inferred through concentrations. While flux-sensitive metabolites have been reported, the design principles underlying such sensing have not been clearly elucidated. Here we use kinetic modeling to show that substrate concentrations of thermodynamically constrained reactions reflect upstream flux and therefore carry information about rates. Then we use untargeted multi-omic data from Escherichia coli and Saccharomyces cerevisiae to show that the concentrations of some metabolites in central carbon metabolism reflect fluxes as a result of thermodynamic constraints. We then establish, using 37 real concentration-flux relationships across both organisms, that in vivo ΔG∘≥-4 kJ/mol is the threshold above which substrates are likely to be sensitive to upstream flux(es).

Strategies for mitigating the effects of a pandemic on Indian medical tourism

India is a favorable destination for medical tourism because ofits quality healthcare services and affordable treatment costs. Itis a continuously growing industry across the globe. But wheneverany pandemic situation arises, it adversely affects the industry.Currently, COVID-19 has a severe negative impact onthe medical tourism sector. This paper discusses the effects of apandemic, critical issues, SWOT analysis, and components thatmitigate the adverse effects of pandemic diseases on medicaltourism in India. During this phase, it is crucial to encouragedomestic medical tourism for the sustainability of this sector. India’sheritage of ancient medicine promotion, along with modernmedicine can captivate the medical tourists. The destinationcountry requires meeting the expectations of tourists throughquality services. With India entering this unexpected downturnin this sector, destination country it must emphasize the slowand steady growth of medical tourism against the current andpotential future pandemics.

A gap-filling algorithm for prediction of metabolic interactions in microbial communities

The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.

Automation assisted anaerobic phenotyping for metabolic engineering

BACKGROUND

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste.

RESULTS

In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale.

CONCLUSIONS

Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.

SARS-CoV-2 receptor-binding mutations and antibody contact sites

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations can impact infectivity, viral load, and overall morbidity/mortality during infection. In this analysis, we look at the mutational landscape of the SARS-CoV-2 receptor-binding domain, a structure that is antigenic and allows for viral binding to the host. We develop a bioinformatics platform and analyze 104 193 Global Initiative on Sharing All Influenza Data sequences acquired on 15 October 2020, with a majority of sequences (96%) containing point mutations. We report high frequency mutations with improved binding affinity to ACE2 including S477N, N439K, V367F, and N501Y and address the potential impact of RBD mutations on antibody binding. The high frequency S477N mutation is present in 6.7% of all SARS-CoV-2 sequences, co-occurs with D614G, and is currently present in 14 countries. To address RBD-antibody interactions, we take a subset of human-derived antibodies and define their interacting residues using PDBsum. Our analysis shows that RBD mutations were found in approximately 9% of our dataset, with some mutations improving RBD-ACE2 interactions. We also show that antibody-mediated immunity against SARS-CoV-2 enlists broad coverage of the RBD, with multiple antibodies targeting a variety of RBD regions. These data suggest that it is unlikely for neutralization/RBD antibody binding to be significantly impacted, as a whole, in the presence of RBD point mutations that conserve the RBD structure.

Integration of a physiologically-based pharmacokinetic model with a whole-body, organ-resolved genome-scale model for characterization of ethanol and acetaldehyde metabolism

Ethanol is one of the most widely used recreational substances in the world and due to its ubiquitous use, ethanol abuse has been the cause of over 3.3 million deaths each year. In addition to its effects, ethanol's primary metabolite, acetaldehyde, is a carcinogen that can cause symptoms of facial flushing, headaches, and nausea. How strongly ethanol or acetaldehyde affects an individual depends highly on the genetic polymorphisms of certain genes. In particular, the genetic polymorphisms of mitochondrial aldehyde dehydrogenase, ALDH2, play a large role in the metabolism of acetaldehyde. Thus, it is important to characterize how genetic variations can lead to different exposures and responses to ethanol and acetaldehyde. While the pharmacokinetics of ethanol metabolism through alcohol dehydrogenase have been thoroughly explored in previous studies, in this paper, we combined a base physiologically-based pharmacokinetic (PBPK) model with a whole-body genome-scale model (WBM) to gain further insight into the effect of other less explored processes and genetic variations on ethanol metabolism. This combined model was fit to clinical data and used to show the effect of alcohol concentrations, organ damage, ALDH2 enzyme polymorphisms, and ALDH2-inhibiting drug disulfiram on ethanol and acetaldehyde exposure. Through estimating the reaction rates of auxiliary processes with dynamic Flux Balance Analysis, The PBPK-WBM was able to navigate around a lack of kinetic constants traditionally associated with PK modelling and demonstrate the compensatory effects of the body in response to decreased liver enzyme expression. Additionally, the model demonstrated that acetaldehyde exposure increased with higher dosages of disulfiram and decreased ALDH2 efficiency, and that moderate consumption rates of ethanol could lead to unexpected accumulations in acetaldehyde. This modelling framework combines the comprehensive steady-state analyses from genome-scale models with the dynamics of traditional PK models to create a highly personalized form of PBPK modelling that can push the boundaries of precision medicine.

Advancing Undergraduate Synthetic Biology Education: Insights from a Canadian iGEM Student Perspective

The last two decades have seen vigorous activity in synthetic biology research and ever-increasing applications of its technologies. However, pedagogical research pertaining to teaching synthetic biology is scarce, especially when compared to other science and engineering disciplines. Within Canada there are only three universities that offer synthetic biology programs; two of which are at the undergraduate level. Rather than take place in formal academic settings, many Canadian undergraduate students are introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition. Although the iGEM competition has had a transformative impact on synthetic biology training in other nations, the impact in Canada has been relatively modest. Consequently, the iGEM competition is still a major setting for synthetic biology education in Canada. To promote further development of synthetic biology education, we surveyed undergraduate students from the Canadian iGEM design teams of 2019. We extracted insights from these data using qualitative analysis to provide recommendations for best teaching practices in synthetic biology undergraduate education, which we describe through our proposed Framework for Transdisciplinary Synthetic Biology Education (FTSBE).

Macrophage immunomodulation through new polymers that recapitulate functional effects of itaconate as a power house of innate immunity

Itaconate (ITA) is an emerging powerhouse of innate immunity with therapeutic potential that is limited in its ability to be administered in a soluble form. We developed a library of polyester materials that incorporate ITA into polymer backbones resulting in materials with inherent immunoregulatory behavior. Harnessing hydrolytic degradation release from polyester backbones, ITA polymers resulted in the mechanism specific immunoregulatory properties on macrophage polarization in vitro. In a functional assay, the polymer-released ITA inhibited bacterial growth on acetate. Translation to an in vivo model of biomaterial associated inflammation, intraperitoneal injection of ITA polymers demonstrated a rapid resolution of inflammation in comparison to a control polymer silicone, demonstrating the value of sustained biomimetic presentation of ITA.

Engineering Photosynthetic Bioprocesses for Sustainable Chemical Production: A Review

Microbial production of chemicals using renewable feedstocks such as glucose has emerged as a green alternative to conventional chemical production processes that rely primarily on petroleum-based feedstocks. The carbon footprint of such processes can further be reduced by using engineered cells that harness solar energy to consume feedstocks traditionally considered to be wastes as their carbon sources. Photosynthetic bacteria utilize sophisticated photosystems to capture the energy from photons to generate reduction potential with such rapidity and abundance that cells often cannot use it fast enough and much of it is lost as heat and light. Engineering photosynthetic organisms could enable us to take advantage of this energy surplus by redirecting it toward the synthesis of commercially important products such as biofuels, bioplastics, commodity chemicals, and terpenoids. In this work, we review photosynthetic pathways in aerobic and anaerobic bacteria to better understand how these organisms have naturally evolved to harness solar energy. We also discuss more recent attempts at engineering both the photosystems and downstream reactions that transfer reducing power to improve target chemical production. Further, we discuss different methods for the optimization of photosynthetic bioprocess including the immobilization of cells and the optimization of light delivery. We anticipate this review will serve as an important resource for future efforts to engineer and harness photosynthetic bacteria for chemical production.

Opportunities and Challenges for Microbial Synthesis of Fatty Acid-Derived Chemicals (FACs)

Global warming and uneven distribution of fossil fuels worldwide concerns have spurred the development of alternative, renewable, sustainable, and environmentally friendly resources. From an engineering perspective, biosynthesis of fatty acid-derived chemicals (FACs) is an attractive and promising solution to produce chemicals from abundant renewable feedstocks and carbon dioxide in microbial chassis. However, several factors limit the viability of this process. This review first summarizes the types of FACs and their widely applications. Next, we take a deep look into the microbial platform to produce FACs, give an outlook for the platform development. Then we discuss the bottlenecks in metabolic pathways and supply possible solutions correspondingly. Finally, we highlight the most recent advances in the fast-growing model-based strain design for FACs biosynthesis.

Engineering Escherichia coli for the utilization of ethylene glycol

Background

A considerable challenge in the development of bioprocesses for producing chemicals and fuels has been the high cost of feedstocks relative to oil prices, making it difficult for these processes to compete with their conventional petrochemical counterparts. Hence, in the absence of high oil prices in the near future, there has been a shift in the industry to produce higher value compounds such as fragrances for cosmetics. Yet, there is still a need to address climate change and develop biotechnological approaches for producing large market, lower value chemicals and fuels.

Results

In this work, we study ethylene glycol (EG), a novel feedstock that we believe has promise to address this challenge. We engineer Escherichia coli (E. coli) to consume EG and examine glycolate production as a case study for chemical production. Using a combination of modeling and experimental studies, we identify oxygen concentration as an important metabolic valve in the assimilation and use of EG as a substrate. Two oxygen-based strategies are thus developed and tested in fed-batch bioreactors. Ultimately, the best glycolate production strategy employed a target respiratory quotient leading to the highest observed fermentation performance. With this strategy, a glycolate titer of 10.4 g/L was reached after 112 h of production time in a fed-batch bioreactor. Correspondingly, a yield of 0.8 g/g from EG and productivity of 0.1 g/L h were measured during the production stage. Our modeling and experimental results clearly suggest that oxygen concentration is an important factor in the assimilation and use of EG as a substrate. Finally, our use of metabolic modeling also sheds light on the intracellular distribution through central metabolism, implicating flux to 2-phosphoglycerate as the primary route for EG assimilation.

Conclusion

Overall, our work suggests that EG could provide a renewable starting material for commercial biosynthesis of fuels and chemicals that may achieve economic parity with petrochemical feedstocks while sequestering carbon dioxide.

Overcoming Challenges in Expressing Iron-Sulfur Enzymes in Yeast

Iron-sulfur clusters are metal cofactors that are present in all domains of life. Many enzymes that require these cofactors have biotechnological importance, because they can be used to uncover catabolic routes to new sugar substrates or can be a critical part of pathways to produce chemicals and biofuels. However, the expression of these iron-sulfur enzymes of bacterial origin in yeast at high levels is a significant bottleneck. Intermediates upstream of the enzyme accumulate, because the activity of these enzymes is either low or completely absent. In this review, we examine possible explanations for this limitation, discuss potential genetic interventions in the yeast host that can increase iron-sulfur enzyme activity, and suggest future directions for creating more efficient yeast hosts capable of high iron-sulfur enzyme expression.

A microplate screen to estimate metal-binding affinities of metalloproteins

Solute-binding proteins (SBPs) from ATP-binding cassette (ABC) transporters play crucial roles across all forms of life in transporting compounds against chemical gradients. Some SBPs have evolved to scavenge metal substrates from the environment with nanomolar and micromolar affinities (K_D). There exist well established techniques like isothermal titration calorimetry for thoroughly studying these metalloprotein interactions with metal ions, but they are low-throughput. For protein libraries comprised of many metalloprotein homologues and mutants, and for collections of buffer conditions and potential ligands, the throughput of these techniques is paramount. In this study, we describe an improved method termed the microITFQ-LTA and validated it using CjNikZ, a well-characterized nickel-specific SBP (Ni-BP) from Campylobacter jejuni. We then demonstrated how the microITFQ-LTA can be designed to screen through a small collection of buffers and ligands to elucidate the binding profile of a putative Ni-BP from Clostridium carboxidivorans that we call CcSBPII. Through this study, we showed CcSBPII can bind to various metal ions with K_D ranged over 3 orders of magnitude. In the presence of l-histidine, CcSBPII could bind to Ni²⁺ over 2000-fold more tightly, which was 11.6-fold tighter than CjNikZ given the same ligand.

Speeding up the core algorithm for the dual calculation of minimal cut sets in large metabolic networks

Background

The concept of minimal cut sets (MCS) has become an important mathematical framework for analyzing and (re)designing metabolic networks. However, the calculation of MCS in genome-scale metabolic models is a complex computational problem. The development of duality-based algorithms in the last years allowed the enumeration of thousands of MCS in genome-scale networks by solving mixed-integer linear problems (MILP). A recent advancement in this field was the introduction of the MCS² approach. In contrast to the Farkas-lemma-based dual system used in earlier studies, the MCS² approach employs a more condensed representation of the dual system based on the nullspace of the stoichiometric matrix, which, due to its reduced dimension, holds promise to further enhance MCS computations.

Results

In this work, we introduce several new variants and modifications of duality-based MCS algorithms and benchmark their effects on the overall performance. As one major result, we generalize the original MCS² approach (which was limited to blocking the operation of certain target reactions) to the most general case of MCS computations with arbitrary target and desired regions. Building upon these developments, we introduce a new MILP variant which allows maximal flexibility in the formulation of MCS problems and fully leverages the reduced size of the nullspace-based dual system. With a comprehensive set of benchmarks, we show that the MILP with the nullspace-based dual system outperforms the MILP with the Farkas-lemma-based dual system speeding up MCS computation with an averaged factor of approximately 2.5. We furthermore present several simplifications in the formulation of constraints, mainly related to binary variables, which further enhance the performance of MCS-related MILP. However, the benchmarks also reveal that some highly condensed formulations of constraints, especially on reversible reactions, may lead to worse behavior when compared to variants with a larger number of (more explicit) constraints and involved variables.

Conclusions

Our results further enhance the algorithmic toolbox for MCS calculations and are of general importance for theoretical developments as well as for practical applications of the MCS framework.

Metabolically engineered recombinant Saccharomyces cerevisiae for the production of 2-Deoxy-scyllo-inosose (2-DOI)

Saccharomyces cerevisiae is a versatile industrial host for chemical production and has been engineered to produce efficiently many valuable compounds. 2-Deoxy-scyllo-inosose (2-DOI) is an important precursor for the biosynthesis of 2-deoxystreptamine-containing aminoglycosides antibiotics and benzenoid metabolites. Bacterial and cyanobacterial strains have been metabolically engineered to generate 2-DOI; nevertheless, the production of 2-DOI using a yeast host has not been reported. Here, we have metabolically engineered a series of CEN.PK yeast strains to produce 2-DOI using a synthetically yeast codon-optimized btrC gene from Bacillus circulans. The expression of the 2-Deoxy-scyllo-inosose synthase (2-DOIS) gene was successfully achieved via an expression vector and through chromosomal integration at a high-expression locus. In addition, the production of 2-DOI was further investigated for the CEN.PK knockout strains of phosphoglucose isomerase (Δpgi1), D-glucose-6-phosphate dehydrogenase (Δzwf1) and a double mutant (Δpgi1, Δzwf1) in a medium consisting of 2% fructose and 0.05% glucose as a carbon source. We have found that all the recombinant strains are capable of producing 2-DOI and reducing it into scyllo-quercitol and (-)-vibo-quercitol. Comparatively, the high production of 2-DOI and its analogs was observed for the recombinant CEN.PK-btrC carrying the multicopy btrC-expression vector. GC/MS analysis of culture filtrates of this strain showed 11 times higher response in EIC for the m/z 479 (methyloxime-tetra-TMS derivative of 2-DOI) than the YP-btrC recombinant that has only a single copy of btrC expression cassette integrated into the genomic DNA of the CEN.PK strain. The knockout strains namely Δpgi1-btrC and Δpgi1Δzwf1-btrC, that are transformed with the btrC-expression plasmids, have inactive Pgi1 and produced only traces of the compounds. In contrast, Δzwf1-btrC recombinant which has intact pgi1 yielded relatively higher amount of the carbocyclic compounds. Additionally, ¹H-NMR analysis of samples showed slow consumption of fructose and no accumulation of 2-DOI and the quercitols in the culture broth of the recombinant CEN.PK-btrC suggesting that S. cerevisiae is capable of assimilating 2-DOI.

A scalable method for parameter identification in kinetic models of metabolism using steady-state data

MOTIVATION

In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models.

RESULTS

Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform.

AVAILABILITY AND IMPLEMENTATION

https://github.com/LMSE/ident.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dynamic Cell Programming with Quorum Sensing-Controlled CRISPRi Circuit

Synthetic biology is enabling rapid advances in the areas of biomanufacturing and live therapeutics. Dynamic circuits that can be used to regulate cellular resources and microbial community behavior represent a defining focus of synthetic biology, and have attracted tremendous interest. However, the existing dynamic circuits are mostly gene editing-dependent or cell lysis-based, which limits their broad and convenient application, and in some cases, such lysis-based circuits can suffer from genetic instability due to evolution. There is limited research in quorum sensing-assisted CRISPRi, which can function in a gene editing-independent manner. Here, we constructed a series of quorum sensing controlled CRISPRi systems (Q-CRISPRi), which can dynamically program bacteria by using customized sgRNA without introducing cell lysis. We successfully applied Q-CRISPRi circuits to dynamically program gene expression, population density, phenotype, physical property, and community composition of microbial consortia. The strategies reported here represent methods for dynamic cell programming and could be effective in programming industrially and medically important microorganisms to offer better control of their metabolism and behavior.

A novel C-terminal degron identified in bacterial aldehyde decarbonylases using directed evolution

BACKGROUND

Aldehyde decarbonylases (ADs), which convert acyl aldehydes into alkanes, supply promising solution for producing alkanes from renewable feedstock. However the instability of ADs impedes their further application. Therefore, the current study aimed to investigate the degradation mechanism of ADs and engineer it towards high stability.

RESULTS

Here, we describe the discovery of a degradation tag (degron) in the AD from marine cyanobacterium Prochlorococcus marinus using error-prone PCR-based directed evolution system. Bioinformatic analysis revealed that this C-terminal degron is common in bacterial ADs and identified a conserved C-terminal motif, RMSAYGLAAA, representing the AD degron (ADcon). Furthermore, we demonstrated that the ATP-dependent proteases ClpAP and Lon are involved in the degradation of AD-tagged proteins in E. coli, thereby limiting alkane production. Deletion or modification of the degron motif increased alkane production in vivo.

CONCLUSION

This work revealed the presence of a novel degron in bacterial ADs responsible for its instability. The in vivo experiments proved eliminating or modifying the degron could stabilize AD, thereby producing higher titers of alkanes.

Rational engineering of 2-deoxyribose-5-phosphate aldolases for the biosynthesis of (R)-1,3-butanediol

Carbon-carbon bond formation is one of the most important reactions in biocatalysis and organic chemistry. In nature, aldolases catalyze the reversible stereoselective aldol addition between two carbonyl compounds, making them attractive catalysts for the synthesis of various chemicals. In this work, we identified several 2-deoxyribose-5-phosphate aldolases (DERAs) having acetaldehyde condensation activity, which can be used for the biosynthesis of (R)-1,3-butanediol (1,3BDO) in combination with aldo-keto reductases (AKRs). Enzymatic screening of 20 purified DERAs revealed the presence of significant acetaldehyde condensation activity in 12 of the enzymes, with the highest activities in BH1352 from Bacillus halodurans, TM1559 from Thermotoga maritima, and DeoC from Escherichia coli The crystal structures of BH1352 and TM1559 at 1.40-2.50 Å resolution are the first full-length DERA structures revealing the presence of the C-terminal Tyr (Tyr²²⁴ in BH1352). The results from structure-based site-directed mutagenesis of BH1352 indicated a key role for the catalytic Lys¹⁵⁵ and other active-site residues in the 2-deoxyribose-5-phosphate cleavage and acetaldehyde condensation reactions. These experiments also revealed a 2.5-fold increase in acetaldehyde transformation to 1,3BDO (in combination with AKR) in the BH1352 F160Y and F160Y/M173I variants. The replacement of the WT BH1352 by the F160Y or F160Y/M173I variants in E. coli cells expressing the DERA + AKR pathway increased the production of 1,3BDO from glucose five and six times, respectively. Thus, our work provides detailed insights into the molecular mechanisms of substrate selectivity and activity of DERAs and identifies two DERA variants with enhanced activity for in vitro and in vivo 1,3BDO biosynthesis.

Impact framework: A python package for writing data analysis workflows to interpret microbial physiology

Microorganisms can be genetically engineered to solve a range of challenges in diverse including health, environmental protection and sustainability. The natural complexity of biological systems makes this an iterative cycle, perturbing metabolism and making stepwise progress toward a desired phenotype through four major stages: design, build, test, and data interpretation. This cycle has been accelerated by advances in molecular biology (e.g. robust DNA synthesis and assembly techniques), liquid handling automation and scale-down characterization platforms, generating large heterogeneous data sets. Here, we present an extensible Python package for scientists and engineers working with large biological data sets to interpret, model, and visualize data: the IMPACT (Integrated Microbial Physiology: Analysis, Characterization and Translation) framework. Impact aims to ease the development of Python-based data analysis workflows for a range of stakeholders in the bioengineering process, offering open-source tools for data analysis, physiology characterization and translation to visualization. Using this framework, biologists and engineers can opt for reproducible and extensible programmatic data analysis workflows, mediating a bottleneck limiting the throughput of microbial engineering. The Impact framework is available at https://github.com/lmse/impact.

AYbRAH: a curated ortholog database for yeasts and fungi spanning 600 million years of evolution

Budding yeasts inhabit a range of environments by exploiting various metabolic traits. The genetic bases for these traits are mostly unknown, preventing their addition or removal in a chassis organism for metabolic engineering. Insight into the evolution of orthologs, paralogs and xenologs in the yeast pan-genome can help bridge these genotypes; however, existing phylogenomic databases do not span diverse yeasts, and sometimes cannot distinguish between these homologs. To help understand the molecular evolution of these traits in yeasts, we created Analyzing Yeasts by Reconstructing Ancestry of Homologs (AYbRAH), an open-source database of predicted and manually curated ortholog groups for 33 diverse fungi and yeasts in Dikarya, spanning 600 million years of evolution. OrthoMCL and OrthoDB were used to cluster protein sequence into ortholog and homolog groups, respectively; MAFFT and PhyML reconstructed the phylogeny of all homolog groups. Ortholog assignments for enzymes and small metabolite transporters were compared to their phylogenetic reconstruction, and curated to resolve any discrepancies. Information on homolog and ortholog groups can be viewed in the AYbRAH web portal (https://lmse.github.io/aybrah/), including functional annotations, predictions for mitochondrial localization and transmembrane domains, literature references and phylogenetic reconstructions. Ortholog assignments in AYbRAH were compared to HOGENOM, KEGG Orthology, OMA, eggNOG and PANTHER. PANTHER and OMA had the most congruent ortholog groups with AYbRAH, while the other phylogenomic databases had greater amounts of under-clustering, over-clustering or no ortholog annotations for proteins. Future plans are discussed for AYbRAH, and recommendations are made for other research communities seeking to create curated ortholog databases.

An interspecies malate-pyruvate shuttle reconciles redox imbalance in an anaerobic microbial community

Microbes in ecosystems often develop coordinated metabolic interactions. Therefore, understanding metabolic interdependencies between microbes is critical to deciphering ecosystem function. In this study, we sought to deconstruct metabolic interdependencies in organohalide-respiring consortium ACT-3 containing Dehalobacter restrictus using a combination of metabolic modeling and experimental validation. D. restrictus possesses a complete set of genes for amino acid biosynthesis yet when grown in isolation requires amino acid supplementation. We reconciled this discrepancy using flux balance analysis considering cofactor availability, enzyme promiscuity, and shared protein expression patterns for several D. restrictus strains. Experimentally, ¹³C incorporation assays, growth assays, and metabolite analysis of D. restrictus strain PER-K23 cultures were performed to validate the model predictions. The model resolved that the amino acid dependency of D. restrictus resulted from restricted NADPH regeneration and predicted that malate supplementation would replenish intracellular NADPH. Interestingly, we observed unexpected export of pyruvate and glutamate in parallel to malate consumption in strain PER-K23 cultures. Further experimental analysis using the ACT-3 transfer cultures suggested the occurrence of an interspecies malate-pyruvate shuttle reconciling a redox imbalance, reminiscent of the mitochondrial malate shunt pathway in eukaryotic cells. Altogether, this study suggests that redox imbalance and metabolic complementarity are important driving forces for metabolite exchange in anaerobic microbial communities.

An Optimized Bistable Metabolic Switch To Decouple Phenotypic States during Anaerobic Fermentation

Metabolic engineers aim to genetically modify microorganisms to improve their ability to produce valuable compounds. Despite the prevalence of growth-coupled production processes, these strategies can significantly limit production rates. Instead, rates can be improved by decoupling and optimizing growth and production independently, and operating with a growth stage followed by a production stage. Here, we implement a bistable transcriptional controller to decouple and switch between these two states. We optimize the controller in anaerobic conditions, typical of industrial fermentations, to ensure stability and tight expression control, while improving switching dynamics. The stability of this controller can be maintained through a simulated seed train scale-up from 5 mL to 500 000 L, indicating industrial feasibility. Finally, we demonstrate a two-stage production process using our optimal construct to improve the instantaneous rate of lactate production by over 50%, motivating the use of these systems in broad metabolic engineering applications.