Design and Characterization of a Generalist Biosensor for Indole Derivatives.

Transcription factor (TF)-based biosensors are useful synthetic biology tools for applications in a variety of areas of biotechnology. A major challenge of biosensor circuits is the limited repertoire of identified and well-characterized TFs for applications of interest, in addition to the challenge of optimizing selected biosensors. In this work, we implement the IclR family repressor TF TtgV from Pseudomonas putida DOT-T1E as an indole-derivative biosensor in Escherichia coli. We optimize the genetic circuit utilizing different components, providing insights into biosensor design and expanding on previous studies investigating this TF. We discover novel physiologically relevant ligands of TtgV, such as skatole. The broad specificity of TtgV makes it a useful target for directed evolution and protein engineering toward desired specificity. TtgV, as an indole-derivative biosensor, is a promising genetic component for the detection of compounds with biological activities relevant to health and the gut microbiome.

From mec cassette to rdhA: a key Dehalobacter genomic neighborhood in a chloroform and dichloromethane-transforming microbial consortium.

Chloroform (CF) and dichloromethane (DCM) are groundwater contaminants of concern due to their high toxicity and inhibition of important biogeochemical processes such as methanogenesis. Anaerobic biotransformation of CF and DCM has been well documented but typically independently of one another. CF is the electron acceptor for certain organohalide-respiring bacteria that use reductive dehalogenases (RDases) to dechlorinate CF to DCM. In contrast, known DCM degraders use DCM as their electron donor, which is oxidized using a series of methyltransferases and associated proteins encoded by the mec cassette to facilitate the entry of DCM to the Wood-Ljungdahl pathway. The SC05 culture is an enrichment culture sold commercially for bioaugmentation, which transforms CF via DCM to CO2. This culture has the unique ability to dechlorinate CF to DCM using electron equivalents provided by the oxidation of DCM to CO2. Here, we use metagenomic and metaproteomic analyses to identify the functional genes involved in each of these transformations. Though 91 metagenome-assembled genomes were assembled, the genes for an RDase-named acdA-and a complete mec cassette were found to be encoded on a single contig belonging to Dehalobacter. AcdA and critical Mec proteins were also highly expressed by the culture. Heterologously expressed AcdA dechlorinated CF and other chloroalkanes but had 100-fold lower activity on DCM. Overall, the high expression of Mec proteins and the activity of AcdA suggest a Dehalobacter capable of dechlorination of CF to DCM and subsequent mineralization of DCM using the mec cassette. IMPORTANCE: Chloroform (CF) and dichloromethane (DCM) are regulated groundwater contaminants. A cost-effective approach to remove these pollutants from contaminated groundwater is to employ microbes that transform CF and DCM as part of their metabolism, thus depleting the contamination as the microbes continue to grow. In this work, we investigate bioaugmentation culture SC05, a mixed microbial consortium that effectively and simultaneously degrades both CF and DCM coupled to the growth of Dehalobacter. We identified the functional genes responsible for the transformation of CF and DCM in SC05. These genetic biomarkers provide a means to monitor the remediation process in the field.

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors.

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.

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

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

DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities.

There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework. Our method explores the large parametric space of genetic circuits in synthetic microbial communities to identify regions of evenness and stability. Due to the high computational costs of exhaustive sampling, we utilize adaptive sampling and surrogate modeling to reduce the number of simulations required to map the vast space. Our framework predicts engineering targets and computes their operating ranges to maximize the probability of the engineered community to have high evenness and stability. We demonstrate our approach by simulating five cocultures and one three-strain culture with different social interactions (cooperation, competition, and predation) employing quorum-sensing-based genetic circuits. In addition to guiding circuit tuning, our pipeline gives an opportunity for a detailed analysis of pockets of evenness and stability for the circuit under investigation, which can further help dissect the relationship between the two properties. DyMMM-LEAPS is easily customizable and can be expanded to a larger community with more complex interactions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Systems engineering of Escherichia coli for n-butane production.

Rising concerns about climate change and sustainable energy have attracted efforts towards developing environmentally friendly alternatives to fossil fuels. Biosynthesis of n-butane, a highly desirable petro-chemical, fuel additive and diluent in the oil industry, remains a challenge. In this work, we first engineered enzymes Tes, Car and AD in the termination module to improve the selectivity of n-butane biosynthesis, and ancestral reconstruction and a synthetic RBS significantly improved the AD abundance. Next, we did ribosome binding site (RBS) calculation to identify potential metabolic bottlenecks, and then mitigated the bottleneck with RBS engineering and precursor propionyl-CoA addition. Furthermore, we employed a model-assisted strain design and a nonrepetitive extra-long sgRNA arrays (ELSAs) and quorum sensing assisted CRISPRi to facilitate a dynamic two-stage fermentation. Through systems engineering, n-butane production was increased by 168-fold from 0.04 to 6.74 mg/L. Finally, the maximum n-butane production from acetate was predicted using parsimonious flux balance analysis (pFBA), and we achieved n-butane production from acetate produced by electrocatalytic CO reduction. Our findings pave the way for selectively producing n-butane from renewable carbon source.

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.

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.