A modular and synthetic biosynthesis platform for de novo production of diverse halogenated tryptophan-derived molecules.

Halogen-containing molecules are ubiquitous in modern society and present unique chemical possibilities. As a whole, de novo fermentation and synthetic pathway construction for these molecules remain relatively underexplored and could unlock molecules with exciting new applications in industries ranging from textiles to agrochemicals to pharmaceuticals. Here, we report a mix-and-match co-culture platform to de novo generate a large array of halogenated tryptophan derivatives in Escherichia coli from glucose. First, we engineer E. coli to produce between 300 and 700 mg/L of six different halogenated tryptophan precursors. Second, we harness the native promiscuity of multiple downstream enzymes to access unexplored regions of metabolism. Finally, through modular co-culture fermentations, we demonstrate a plug-and-play bioproduction platform, culminating in the generation of 26 distinct halogenated molecules produced de novo including precursors to prodrugs 4-chloro- and 4-bromo-kynurenine and new-to-nature halogenated beta carbolines.

Fluorescence-Based Screens for Engineering Enzymes Linked to Halogenated Tryptophan.

Directed evolution is often limited by the throughput of accurate screening methods. Here we demonstrate the feasibility of utilizing a singular transcription factor (TF)-system that can be refactored in two ways (both as an activator and repressor). Specifically, we showcase the use of previously evolved 5-halo- or 6-halo-tryptophan-specific TF biosensors suitable for the detection of a halogenated tryptophan molecule in vivo. We subsequently validate the biosensor's utility for two halogenase-specific halo-tryptophan accumulation screens. First, we isolated 5-tryptophan-halogenase, XsHal, from a mixed pool of halogenases with 100% efficiency. Thereafter, we generated a targeted library of the catalytic residue of 6-tryptophan halogenase, Th-Hal, and isolated functioning halogenases with 100% efficiency. Lastly, we refactor the TF circuit to respond to the depletion of halogenated tryptophan and prototype a high-throughput biosensor-directed evolution scheme to screen for downstream enzyme variants capable of promiscuously converting halogenated tryptophan. Altogether, this work takes a significant step toward the rapid and higher throughput screening of halogenases and halo-tryptophan converting enzymes to further reinforce efforts to enable high-level bioproduction of halogenated chemicals.

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme.

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer's medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC50 = 20 µM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4'-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

Snowprint: a predictive tool for genetic biosensor discovery.

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .

GroovDB: A Database of Ligand-Inducible Transcription Factors.

Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multilayer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data have been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides potential insights into alternative ligands and systems biology.

Using fungible biosensors to evolve improved alkaloid biosyntheses.

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors; using RamR as a starting point, we evolve highly specific (>100-fold preference) and sensitive (half-maximum effective concentration (EC50) < 30 µM) biosensors for the alkaloids tetrahydropapaverine, papaverine, glaucine, rotundine and noscapine. High-resolution structures reveal multiple evolutionary avenues for the malleable effector-binding site and the creation of new pockets for different chemical moieties. These sensors further enabled the evolution of a streamlined pathway for tetrahydropapaverine, a precursor to four modern pharmaceuticals, collapsing multiple methylation steps into a single evolved enzyme. Our methods for evolving biosensors enable the rapid engineering of pathways for therapeutic alkaloids.

Evolving a Generalist Biosensor for Bicyclic Monoterpenes.

Prokaryotic transcription factors can be repurposed as analytical and synthetic tools for precise chemical measurement and regulation. Monoterpenes encompass a broad chemical family that are commercially valuable as flavors, cosmetics, and fragrances, but have proven difficult to measure, especially in cells. Herein, we develop genetically encoded, generalist monoterpene biosensors by using directed evolution to expand the effector specificity of the camphor-responsive TetR-family regulator CamR from Pseudomonas putida. Using a novel negative selection coupled with a high-throughput positive screen (Seamless Enrichment of Ligand-Inducible Sensors, SELIS), we evolve CamR biosensors that can recognize four distinct monoterpenes: borneol, fenchol, eucalyptol, and camphene. Different evolutionary trajectories surprisingly yielded common mutations, emphasizing the utility of CamR as a platform for creating generalist biosensors. Systematic promoter optimization driving the reporter increased the system's signal-to-noise ratio to 150-fold. These sensors can serve as a starting point for the high-throughput screening and dynamic regulation of bicyclic monoterpene production strains.

Sorting for secreted molecule production using a biosensor-in-microdroplet approach.

Sorting large libraries of cells for improved small molecule secretion is throughput limited. Here, we combine producer/secretor cell libraries with whole-cell biosensors using a microfluidic-based screening workflow. This approach enables a mix-and-match capability using off-the-shelf biosensors through either coencapsulation or pico-injection. We demonstrate the cell type and library agnostic nature of this workflow by utilizing single-guide RNA, transposon, and ethyl-methyl sulfonate mutagenesis libraries across three distinct microbes (Escherichia coli, Saccharomyces cerevisiae, and Yarrowia lipolytica), biosensors from two organisms (E. coli and S. cerevisiae), and three products (triacetic acid lactone, naringenin, and L-DOPA) to identify targets improving production/secretion.

Directed Evolution of an Improved Aminoacyl-tRNA Synthetase for Incorporation of L-3,4-Dihydroxyphenylalanine (L-DOPA).

The catechol group of 3,4-dihydroxyphenylalanine (L-DOPA) derived from L-tyrosine oxidation is a key post-translational modification (PTM) in many protein biomaterials and has potential as a bioorthogonal handle for precision protein conjugation applications such as antibody-drug conjugates. Despite this potential, indiscriminate enzymatic modification of exposed tyrosine residues or complete replacement of tyrosine using auxotrophic hosts remains the preferred method of introducing the catechol moiety into proteins, which precludes many protein engineering applications. We have developed new orthogonal translation machinery to site-specifically incorporate L-DOPA into recombinant proteins and a new fluorescent biosensor to selectively monitor L-DOPA incorporation in vivo. We show simultaneous biosynthesis and incorporation of L-DOPA and apply this translation machinery to engineer a novel metalloprotein containing a DOPA-Fe chromophore.

FABP5 coordinates lipid signaling that promotes prostate cancer metastasis.

Prostate cancer (PCa) is defined by dysregulated lipid signaling and is characterized by upregulation of lipid metabolism-related genes including fatty acid binding protein 5 (FABP5), fatty acid synthase (FASN), and monoacylglycerol lipase (MAGL). FASN and MAGL are enzymes that generate cellular fatty acid pools while FABP5 is an intracellular chaperone that delivers fatty acids to nuclear receptors to enhance PCa metastasis. Since FABP5, FASN, and MAGL have been independently implicated in PCa progression, we hypothesized that FABP5 represents a central mechanism linking cytosolic lipid metabolism to pro-metastatic nuclear receptor signaling. Here, we show that the abilities of FASN and MAGL to promote nuclear receptor activation and PCa metastasis are critically dependent upon co-expression of FABP5 in vitro and in vivo. Our findings position FABP5 as a key driver of lipid-mediated metastasis and suggest that disruption of lipid signaling via FABP5 inhibition may constitute a new avenue to treat metastatic PCa.

Improving ionic liquid tolerance in Saccharomyces cerevisiae through heterologous expression and directed evolution of an ILT1 homolog from Yarrowia lipolytica.

Ionic liquids show promise for deconstruction of lignocellulosic biomass prior to fermentation. Yet, imidazolium ionic liquids (IILs) can be toxic to microbes even at concentrations present after recovery. Here, we show that dominant overexpression of an Ilt1p homolog (encoded by YlILT1/YALI0C04884) from the IIL-tolerant yeast Yarrowia lipolytica confers an improvement in 1-ethyl-3-methylimidazolium acetate tolerance in Saccharomyces cerevisiae compared to the endogenous Ilt1p (ScILT1/YDR090C). We subsequently enhance tolerance in S. cerevisiae through directed evolution of YlILT1 using growth-based selection, leading to identification of mutants that grow in up to 3.5% v/v ionic liquid. Lastly, we demonstrate that strains expressing YlILT1 variants demonstrate improved growth rate and ethanol production in the presence of residual IIL. This shows that dominant overexpression of a heterologous protein (wild type or evolved) from an IIL-tolerant yeast can increase tolerance in S. cerevisiae at concentrations relevant to bioethanol production from IIL-treated biomass.

Synthetic evolution.

The combination of modern biotechnologies such as DNA synthesis, λ red recombineering, CRISPR-based editing and next-generation high-throughput sequencing increasingly enables precise manipulation of genes and genomes. Beyond rational design, these technologies also enable the targeted, and potentially continuous, introduction of multiple mutations. While this might seem to be merely a return to natural selection, the ability to target evolution greatly reduces fitness burdens and focuses mutation and selection on those genes and traits that best contribute to a desired phenotype, ultimately throwing evolution into fast forward.

Custom selenoprotein production enabled by laboratory evolution of recoded bacterial strains.

Incorporation of the rare amino acid selenocysteine to form diselenide bonds can improve stability and function of synthetic peptide therapeutics. However, application of this approach to recombinant proteins has been hampered by heterogeneous incorporation, low selenoprotein yields, and poor fitness of bacterial producer strains. We report the evolution of recoded Escherichia coli strains with improved fitness that are superior hosts for recombinant selenoprotein production. We apply an engineered ß-lactamase containing an essential diselenide bond to enforce selenocysteine dependence during continuous evolution of recoded E. coli strains. Evolved strains maintain an expanded genetic code indefinitely. We engineer a fluorescent reporter to quantify selenocysteine incorporation in vivo and show complete decoding of UAG codons as selenocysteine. Replacement of native, labile disulfide bonds in antibody fragments with diselenide bonds vastly improves resistance to reducing conditions. Highly seleno-competent bacterial strains enable industrial-scale selenoprotein expression and unique diselenide architecture, advancing our ability to customize the selenoproteome.

Lipocalin-Type Prostaglandin D Synthase Is a Novel Phytocannabinoid-Binding Protein.

Lipocalin-type prostaglandin D synthase (L-PGDS; EC:5.3.99.2) is an enzyme with dual functional roles as a prostaglandin D2 -synthesizing enzyme and as an extracellular transporter for diverse lipophilic compounds in the cerebrospinal fluid (CSF). Transport of hydrophobic endocannabinoids is mediated by serum albumin in the blood and intracellularly by the fatty acid binding proteins, but no analogous transport mechanism has yet been described in CSF. L-PGDS has been reported to promiscuously bind a wide variety of lipophilic ligands and is among the most abundant proteins found in the CSF. Here, we examine the binding of several classes of endogenous and synthetic ligands to L-PGDS. Endocannabinoids exhibited low affinity toward L-PGDS, while cannabinoid metabolites and synthetic cannabinoids displayed higher affinities for L-PGDS. These results indicate that L-PGDS is unlikely to function as a carrier for endocannabinoids in the CSF, but it may bind and transport a subset of cannabinoids.

Continuous directed evolution for strain and protein engineering.

Continuous directed evolution is the 'while loop' of synthetic biology, autonomous cycles of mutation, selection, and self-replication that can lead to the rapid development of industrially relevant organisms, pathways, or molecules. Although this engineering strategy requires particular mutagenesis methods and well-defined selections, recent advances have facilitated its implementation. Control over selection pressure has been augmented by novel cost-effective continuous culturing devices with open source designs. New in vivo targeted mutagenesis methods have enabled continuous directed protein evolution in various organisms. Furthermore, advances in automation have enabled rational, semi-continuous directed evolution strategies that may yield fewer artefacts or parasites. Overall, continuous directed evolution is persistently demonstrating its capacity to rapidly generate biotechnologically valuable strains and proteins.