Michael Krogh Jensen

Galantamine is a pharmaceutically relevant Amaryllidaceae alkaloid used for the treatment of Alzheimer's disease. Its structural complexity and low abundance in plants motivate the development of alternative manufacturing routes. Here, we established the first engineered yeast platform for the biocatalytic production of galantamine from 4OMe-norbelladine. Heterologous expression of the downstream pathway enzymes NtCYP96T6, NtNMT1, and NtAKR1 from Narcissus cv. Tête-à-Tête in Saccharomyces cerevisiae was complemented by optimization of cultivation temperature, carbon source, and medium pH enabling the first demonstration of galantamine production in yeast. Systematic screening of cytochrome P450 reductase and cytochrome b₅ partners to boost NtCYP96T6 activity led to the identification of a novel reductase mined from the Narcissus pseudonarcissus transcriptome, NpCPR, which supported the highest pathway flux and yielded 7.0 ± 0.6 mg/L galantamine, corresponding to a 7.6 % molar yield from 250 μM 4OMe-norbelladine and an approximately 173-fold improvement over the parental strain. We further exploited this yeast cell factory for the precursor-directed biosynthesis of 7F-galantamine, highlighting the potential of pathway enzyme promiscuity to access new-to-nature GAL analogues that may be challenging to produce through conventional chemical synthesis. Together, this work establishes a foundation for microbial galantamine production and biosynthetic diversification of its pharmaceutically relevant scaffold.

Monoterpene Indole Alkaloids (MIAs) are a diverse family of plant natural products with various medicinal applications. Although MIAs, such as vinblastine and reserpine, are clinically validated, sourcing of MIAs for clinical use or drug discovery from natural resources or via chemical synthesis is hampered due to their scarcity and chemical complexity. Refactoring MIA biosynthesis pathways in microbial cell factories could offer an alternative, more stable and potentially sustainable manufacturing route for alkaloid medicines and novel therapies. However, reaching commercially attractive titers, rates and yields remains challenging owing to the length and complexity of these metabolic pathways. One critical bottleneck is the low screening throughput and very high cost of the analytical methods used to quantify MIA for optimizing production. In this study, we evolved RamR, a promiscuous bacterial transcription factor to respond to five different MIAs, resulting in highly sensitive and selective sensor variants (EC50220 mg/L strictosidine, 3-fold higher than the parental reference strain. This study demonstrates the development of selective biosensors for MIAs and the cost-effective identification of novel metabolic engineering hits for optimizing MIA production in microbial cell factories.

Currently available random and untargeted DNA mutagenesis techniques are limited by both the number of consecutive nucleotides that can be mutated and by the type of accessible mutations. These methodologies also create multiple different mutated sites within each DNA sequence-of-interest, which significantly confounds any precise and high-throughput phenotype-to-genotype mapping. Here, we describe two unique and cell-independent DNA mutagenesis methods that enable either a single random and small-scale (1–30 nt) duplication, deletion, or insertion of an entire DNA motif (RADDIM), or nucleotide-constrained mutagenesis of random DNA regions spanning >8 consecutive nucleotides (NSM). By utilizing these mechanistically unique methods, we randomly duplicated and deleted cryptic regulatory DNA elements in two yeast promoters (pACT1 and pTEF1) to change their transcriptional expression. We randomly mutated the protein structure of an inactivated β-lactamase (TEM-1) to restore its enzymatic function by generating multiple, consecutive in-frame InDels. We also selectively mutated the AT-content and introduced TATA-box–like sequences and homopolymeric mutations, within random DNA regions. Collectively, RADDIM and NSM allow for an unprecedented level of bespoke DNA mutagenesis at random DNA locations, expanding the toolkit for genetic engineering, directed evolution, and the functional mapping of novel protein structures and cryptic regulatory DNA motifs.

The yeast Saccharomyces cerevisiae is widely employed in industrial biotechnology for chemical and pharmaceutical production. However, engineering yeast for high product titre remains challenging due to metabolic imbalances and competition for cellular resources. To address this, we developed an orthogonal quorum-sensing (QS) system based on N -acyl-homoserine lactones (AHLs) for cell density-dependent regulation in yeast. Using metabolic engineering, we established AHL production in yeast. Next, we improved AHL-biosensors via directed evolution and a novel growth-based screening strategy with amdS as a counter-selectable marker. We identified three LuxR variants with enhanced sensitivity and confirmed N86 to play an important role in their sensitivity to ligands, corroborating literature on the native system in bacteria. These sensitive LuxR variants were engineered for QS-controlled expression of a reporter gene, demonstrated by delayed autonomous expression of yeGFP. Additionally, we engineered LuxR to function as a repressor, achieving QS-dependent repression. The QS system was applied to enhance aloesone production, a plant-derived metabolite with cosmetic and pharmaceutical applications. The established system showed 51% increased production through QS-controlled repression of FAS1 . This work establishes a versatile QS-based regulatory platform to support dynamic pathway regulation for metabolic engineering in yeast.

Chimeric antigen receptor (CAR) T cells have become an established immunotherapy with promising results for the treatment of hematological malignancies. However, modulation of the targeted antigen’s surface level in cancer cells affects the quality and safety of CAR-T cell therapy. Here we present an engineered yeast-based antigen system for simulation of cancer cells with precise regulation of surface-antigen densities, providing a tool for controlled activation of CAR T cells and systematic assessment of antigen density effects. This S ynthetic C ellular A dvanced S ignal A dapter (SCASA) system uses G protein-coupled receptor signaling to control cancer antigen densities on the yeast surface and provides a customizable platform allowing selectable signal inputs and modular pathway engineering for precise output fine-tuning. In relation to CD19+ cancers, we demonstrate synthetic cellular communication between CD19-displaying yeast and human CAR T cells as well as applications in high-throughput characterization of different CAR designs. We show that yeast is an alternative to conventional technologies (e.g. microbeads) and can provide higher activation control of clinically derived CAR T cells in vitro, relative to cancer cells. In summary, we present a customizable yeast-based platform for high-throughput characterization of CAR-T cell functionality and show potential applications within therapeutic T cells in clinical settings.

The yeast Saccharomyces cerevisiae is widely employed in industrial biotechnology for chemical and pharmaceutical production. However, engineering yeast for high product titers remains challenging due to metabolic imbalances and competition for cellular resources. To address this, we developed an orthogonal quorum sensing (QS) system based on N -acyl-homoserine lactones (AHLs) for cell density-dependent regulation in yeast. Using metabolic engineering, we established AHL production in yeast. Next, we improved AHL-biosensors via directed evolution and a novel growth-based screening strategy with amdS as a counter-selectable marker. We identified LuxR variants with enhanced sensitivity, which were engineered for QS-controlled expression of a reporter gene. Additionally, we engineered LuxR to function as a repressor, achieving QS-dependent repression. The QS system was applied to enhance aloesone production, a plant-derived metabolite with cosmetic and pharmaceutical applications. The established system showed 51% increased production through QS-controlled repression of FAS1 . This work establishes a versatile QS-based regulatory platform to support dynamic pathway regulation for metabolic engineering in yeast.

Global food security is increasingly challenged by climate change and unsustainable agriculture, emphasizing the need for strategies to enhance crop productivity. Understanding the interplay between crop health and soil microbiomes is crucial. This study explores the link between crop health, observed via multi-spectral satellite imagery, and fungal soil microbiome taxonomy. We associate the normalized difference vegetation index with fungal microbiomes in wheat, barley, and maize using a two-step machine learning process. The first step adjusts normalized difference vegetation index values for abiotic confounders using a random forest model trained on Lucas 2018 topsoil and ERA5 climate datasets. The second step clusters operational taxonomy unit counts from fungal DNA, revealing significant differences in residual normalized difference vegetation index values. To identify potential bio-fertilizer candidates, we compare the average relative abundance of operational taxonomy unit clusters and construct sparse biological networks. Key findings are: (I) clusters with higher plant pathogenic genera have lower normalized difference vegetation index values; (II) clusters with higher influential scores for multiple beneficial genera have higher normalized difference vegetation index values; (III) lower abundance taxonomy (1-3%) seems to regulate microbial networks; (IV) the influence of beneficial vs. pathogenic taxonomy is relative to their abundance. The study links satellite imagery to fungal microbiomes, providing a baseline for exploring fungal bio-fertilizers.

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

High-throughput DNA transformation techniques are invaluable when generating high-diversity mutant libraries, a cornerstone of successful protein engineering. However, transformation efficiencies have a direct correlation with the probability of introducing multiple DNA molecules into each cell, although reliable library screenings require cells that contain a single unique genotype. Thus, transformation methods that yield a high multiplicity of transformations are unsuitable for high-diversity library screenings. Here, we describe an innovative yeast library transformation method that is both simple and highly efficient. Our dual heat-shock and electroporation approach (HEEL) creates high-quality DNA libraries by increasing the fraction of mono-transformed yeast cells from 20% to over 70% of all transformed cells, thus allowing for near-perfect phenotype-to-genotype associations. HEEL also allows more than 10 ⁷ yeast cells per reaction to be transformed with a circular plasmid molecule, which corresponds to an almost 100-fold improvement compared with current yeast transformation methods. To further refine our library screening approach, we integrated an automated yeast genotyping workflow with a dual-barcode design that employs both a single nucleotide polymorphism and a high-diversity region. This design allows for robust identification and quantification of unique genotypes within a heterogeneous population using standard Sanger sequencing. Our findings demonstrate that the longstanding trade-off between the size and quality of transformed yeast libraries can be overcome. By employing the HEEL method, large DNA libraries can be transformed into yeast with high-efficiency, while maintaining high library quality, essential for successful mutant screenings. This advancement holds significant promise for the fields of molecular biology and protein engineering. IMPORTANCE: With the recent expansion of artificial intelligence in the field of synthetic biology, there has never been a greater need for high-quality data and reliable measurements of phenotype-to-genotype relationships. However, one major obstacle to creating accurate computer-based models is the current abundance of low-quality phenotypic measurements originating from numerous high-throughput but low-resolution assays. Rather than increasing the quantity of measurements, new studies should aim to generate as accurate measurements as possible. The HEEL methodology presented here aims to address this issue by minimizing the problem of multi-plasmid uptake during high-throughput yeast DNA transformations, which leads to the creation of heterogeneous cellular genotypes. HEEL should enable highly accurate phenotype-to-genotype measurements going forward, which could be used to construct better computer-based models.

Exploring metabolic and protein engineering solutions, this article discusses breakthroughs in enhancing cytochrome P450 performance, paving the way for more sustainable and efficient bioprocesses in natural product manufacturing.