Biology subjects

Jensen, M. K.

Publications and source records attributed to Jensen, M. K..

At least 19 recordsLinked to original sources

bioRxiv2026

Biocatalytic Production of Galantamine in Yeast through Cytochrome P450 Optimization

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. Tete-a-Tete 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 b 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 M 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.

synthetic biology↗

bioRxiv2026

Engineering Biosensors to Enhance Monoterpene Indole Alkaloid Production in Yeast

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 (EC50<10 M). X-ray crystallography and computational modeling provided insight into the MIA binding of the evolved biosensor variants. The RamR biosensing platform was functionalized in yeast and subsequently applied in a cost-effective semi-throughput screening campaign of a 188-gene overexpression library to identify high-performing cell factory designs for strictosidine, the common precursor for all MIAs. The fluorescent biosensor signal correlated with HPLC quantification (r2 = 0.932) allowing identification of single metabolic engineering hits which when combined yielded a maximum titer of >220 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.

synthetic biology↗

bioRxiv2024

A synthetic platform for multiplex screening of cell-cell communication between soil fungi

Fungi are essential members across ecosystems, yet phytopathogenic fungi pose an increasing risk to crop yields. Despite their ecologic importance, cell-cell communication in fungi is underexplored, partly due to the lack of high-throughput techniques. Here we developed a Yeast Mating Platform (YeMaP) to investigate the interaction between fungal G protein-coupled receptors (GPCRs) and pheromone peptides. We used YeMaP for high- throughput screening of 8,000 pheromone sequences and identified new peptides with improved agonism or antagonism action. We found that these peptides can be applied in a native fungal system such as the plant pathogen Fusarium oxysporum, to control hyphal chemotropism and reduce plant root penetration. Additionally, we utilized YeMaP in a one- pot assay to investigate how abiotic factors influence the communication of multiple pheromone-GPCR combinations and found that the cell-cell communication mediated by the GPCR Ste2 from F. oxysporum signalled robustly across different abiotic factors, while other fungal GPCR-pheromone interactions were more sensitive to changes. Taken together, YeMaP accelerates the identification of fungal GPCR-peptide interactions by enabling one- pot assays, and serves as model system for studying fungal cell-cell communication.

synthetic biology↗

bioRxiv2024

Metabolic engineering of yeast for de novo production of kratom monoterpene indole alkaloids

Monoterpene indole alkaloids (MIAs) from Mitragyna speciosa ("kratom"), such as mitragynine and speciogynine, are promising novel scaffolds for opioid receptor ligands for treatment of pain, addiction, and depression. While kratom leaves have been used for centuries in South-East Asia as stimulant and pain management substance, the biosynthetic pathway of these psychoactives have only recently been partially elucidated. Here, we demonstrate the de novo production of mitragynine and speciogynine in Saccharomyces cerevisiae through the reconstruction of a five-step synthetic pathway from common MIA precursor strictosidine comprising fungal tryptamine 4-monooxygenase to bypass an unknown kratom hydroxylase. Upon optimizing cultivation conditions, a titer of [~]290 {micro}g/L kratom MIAs from glucose was achieved. Untargeted metabolomics analysis of lead production strains led to the identification of numerous shunt products derived from the activity of strictosidine synthase (STR) and dihydrocorynantheine synthase (DCS), highlighting them as candidates for enzyme engineering to further improve kratom MIAs production in yeast. Finally, by feeding fluorinated tryptamine and expressing a human tailoring enzyme, we further demonstrate production of fluorinated and hydroxylated mitragynine derivatives with potential applications in drug discovery campaigns. Altogether, this study introduces a yeast cell factory platform for the biomanufacturing of complex natural and new-to-nature kratom MIAs derivatives with therapeutic potential.

synthetic biology↗

bioRxiv2024

Harnessing Chemical Space Neural Networks to Systematically Annotate GPCR ligands

Machine learning (ML) 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 (hGPCRs) in FDA-approved drugs, exhaustive in-distribution drug-target interaction (DTI) testing across all pairs of hGPCRs 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 (OOD) exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks (CSNN) that leverages network homophily and training-free graph neural networks (GNNs) with Labels as Features (LaF). We show that CSNNs ability to make accurate predictions strongly correlates with network homophily. Thus, LaFs strongly increase a ML models 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 DTIs, 539 compounds, 7 hGPCRs) to discover novel DTIs for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

bioinformatics↗

bioRxiv2023

Engineered yeast cells simulating CD19+ cancers to control CAR T cell activation

Chimeric antigen receptor (CAR) T cells have become an established immunotherapy and show promising results for the treatment of hematological cancers. However, modulation of surface levels of the targeted antigen in cancer cells affects the quality and safety of CAR T cell therapy. Here we present the Synthetic Cellular Advanced Signal Adapter (SCASA) system, based on successful engineering of yeast to simulate cancer cells with tunable surface-antigen densities, as a tool for controlled activation of CAR T cell responses and assessment of antigen density effects. Specifically, we demonstrate I) controllable antigen-densities of CD19 on yeast using G protein-coupled receptors (GPCRs), II) a customizable system allowing choice of signal input and modular pathway engineering for precise fine-tuning of the output, III) synthetic cell-cell communication with CAR T cells and the application of CD19-displaying yeast in the characterization of CAR designs, and IV) more efficient and robust activational control of clinically-derived CAR T cells in comparison to the NALM6 cancer cell line. Based on this yeast-based antigen-presenting cell system, we envision efficient assessment of how varying antigen densities in cancer cells affect CAR T cell responses and ultimately support development of safer and better quality of personalized cancer therapies.

synthetic biology↗

bioRxiv2023

Regulation of the Drosophila transcriptome by Pumilio and CCR4-NOT deadenylase

The sequence-specific RNA-binding protein Pumilio controls development of Drosophila; however, the network of mRNAs that it regulates remains incompletely characterized. In this study, we utilize knockdown and knockout approaches coupled with RNA-Seq to measure the impact of Pumilio on the transcriptome of Drosophila cells. We also used an improved RNA co-immunoprecipitation method to identify Pumilio bound mRNAs in Drosophila embryos. Integration of these datasets with the content of Pumilio binding motifs across the transcriptome revealed novel direct Pumilio target genes involved in neural, muscle, wing, and germ cell development, and cellular proliferation. These genes include components of Wnt, TGF-beta, MAPK/ERK, and Notch signaling pathways, DNA replication, and lipid metabolism. Additionally, we identified the mRNAs regulated by the CCR4-NOT deadenylase complex, a key factor in Pumilio-mediated repression, and observed concordant regulation of Pumilio:CCR4-NOT target mRNAs. Computational modeling revealed that Pumilio binding, binding site number, density, and sequence context are important determinants of regulation. Moreover, the content of optimal synonymous codons in target mRNAs exhibits a striking functional relationship to Pumilio and CCR4-NOT regulation, indicating that the inherent translation efficiency and stability of the mRNA modulates their response to these trans-acting regulatory factors. Together, the results of this work provide new insights into the Pumilio regulatory network and mechanisms, and the parameters that influence the efficacy of Pumilio-mediated regulation.

molecular biology↗

bioRxiv2023

Microbial cell factory optimisation using genome-wide host-pathway interaction screens

The ubiquity of genetic interactions in living cells challenges the concept of parts orthogonality, which is a cornerstone of synthetic biology. Parts, such as heterologously expressed genes, draw from shared pools of limited cellular resources and interactions between parts themselves and their host are inevitable. Instead of trying to eliminate or disregard these interactions, we propose to leverage them to promote desirable phenotypes. We recently described CRI-SPA, a method for high-throughput genome-wide gene delivery and screening of host:pathway interactions in Saccharomyces cerevisiae. In this study, we combine this method with biosensor-based high-throughput screening and high-density colony image analysis to identify lead engineering targets for optimising cis-cis-muconic acid (CCM) production in yeast cell factories. Using the biosensor screen, we phenotype >9,700 genotypes for their interaction with the heterologously expressed CCM biosynthesis pathway, including both gene knock-out and overexpression, and identify novel metabolic targets belonging to sulphur assimilation and methionine synthesis, as well as cellular redox homeostasis, positively impacting CCM biosynthesis by up to 280%. Our genome-wide exploration of host pathway interaction opens novel strategies for the metabolic engineering of yeast cell factories.

synthetic biology↗

bioRxiv2023

A Systems Level Explanation for Gompertzian Mortality Patterns is provided by the "Multiple and Inter-dependent Component Cause Model"

Understanding and facilitating healthy aging has become a major goal in medical research and it is becoming increasingly acknowledged that there is a need for understanding the aging phenotype as a whole rather than focusing on individual factors. Here, we provide a universal explanation for the emergence of Gompertzian mortality patterns using a systems approach to describe aging in complex organisms that consist of many inter-dependent subsystems. Our model relates to the Sufficient-Component Cause Model, widely used within the field of epidemiology, and we show that including inter-dependencies between subsystems and modeling the temporal evolution of subsystem failure results in Gompertizan mortality on the population level. Our model also provides temporal trajectories of mortality-risk for the individual. These results may give insight into understanding how biological age evolves stochastically within the individual, and how this in turn leads to a natural heterogeneity of biological age in a population.

systems biology↗

bioRxiv2023

Literate programming for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of complex biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of a metabolic pathway designs for production of a key precursor to medicinal alkaloids. The teemi platform is publicly available at PyPi and GitHub.

bioinformatics↗

bioRxiv2022

CRI-SPA, a mating based CRISPR-Cas9 assisted method for high-throughput genetic modification of yeast strain libraries

AbstractBiological functions are orchestrated by intricate networks of interacting genetic elements. Predicting the interaction landscape remains a challenge for systems biology and the identification of phenotypic maximas would be of great benefit to synthetic biology. Thus, new research tools allowing simple and rapid mapping of sequence to function are required to forward these research fields. Here, we describe CRI-SPA, a method allowing the transfer of a chromosomal genetic feature from a donor strain to arrayed strains in large libraries of Saccharomyces cerevisiae. CRI-SPA is based on mating, CRISPR-Cas9-induced gene conversion and Selective Ploidy Ablation and is executed within a week. We demonstrate the power of CRI-SPA by transferring four genes responsible for the production of betaxanthin, a yellow biosensor for the morphine precursor L-DOPA, into each strain of the yeast knock-out collection ({approx}4800 strains), providing a genome-wide overview of the genetic requirements for betaxanthin production. CRI-SPA is fast, highly reproducible, can be massively parallelized with automation and does not require selection for the transferred genetic feature.

systems biology↗

bioRxiv2022

Engineered cell differentiation and sexual reproduction in probiotic and mating yeasts

G protein-coupled receptors (GPCRs) enable cells to sense environmental cues and are indispensable for coordinating vital processes including quorum sensing, proliferation, and sexual reproduction. GPCRs comprise the largest class of cell surface receptors in eukaryotes, and for more than three decades the pheromone-induced mating pathway in bakers yeast Saccharomyces cerevisiae has served as a model for studying heterologous GPCRs (hGPCRs). Here we report transcriptome profiles following mating pathway activation in native and hGPCR-signaling yeast, and use a model-guided approach to correlate gene expression to morphological changes. From this we demonstrate mating between haploid cells armed with hGPCRs and endogenous biosynthesis of their cognate ligands. Furthermore, we devise a ligand-free screening strategy for hGPCR compatibility with the yeast mating pathway and enable hGPCR-signaling in the probiotic yeast Saccharomyces boulardii. Combined, our findings enable new means to study mating, hGPCR-signaling, and cell-cell communication in a model eukaryote and yeast probiotics. One-sentence summaryEnabling sensing in probiotic yeast and synthetic yeast mating with human and fungal G protein-coupled receptors.

cell biology↗

bioRxiv2021

HSP70-binding motifs function as protein quality control degrons

Protein quality control (PQC) degrons are short protein segments that target misfolded proteins for proteasomal degradation, and thus protect cells against the accumulation of potentially toxic non-native proteins. Studies have shown that PQC degrons are hydrophobic and rarely contain negatively charged residues, features which are shared with chaperone-binding regions. Here we explore the notion that chaperone-binding regions may function as PQC degrons. When directly tested, we found that a canonical Hsp70-binding motif (the APPY peptide) functioned as a dose-dependent PQC degron both in yeast and in human cells. In yeast, Hsp70, Hsp110, Fes1, and the E3 Ubr1 target the APPY degron. Screening revealed that the sequence space within the chaperone-binding region of APPY that is compatible with degron function is vast. We find that the number of exposed Hsp70-binding sites in the yeast proteome correlates with a reduced protein abundance and half-life. Our results suggest that when protein folding fails, chaperone-binding sites may operate as PQC degrons, and PQC-linked degradation therefore overlaps in specificity with chaperone binding. This sheds new light on how the PQC system has evolved to exploit the intrinsic capacity of chaperones to recognize misfolded proteins, thereby placing them at the nexus of protein folding and degradation. Significance StatementIt is broadly accepted that misfolded proteins are often rapidly degraded by the ubiquitin-proteasome system, but how cells specifically recognize this immensely diverse group of proteins is largely unknown. Here we show that upon uncoupling of protein folding from protein degradation, a canonical chaperone-binding motif doubles as a degradation signal (degron), and that within the context of a Hsp70-binding region, many sequences are compatible with degron function. We find that degradation is correlated with the number of Hsp70-binding sites within a protein, and that the number of exposed Hsp70-binding sites in the yeast proteome correlates with more rapid degradation.

biochemistry↗

bioRxiv2021

Inositol hexakisphosphate is a critical regulator of Integrator assembly and function

Integrator has critical roles in noncoding RNA 3'-end processing as well as transcription attenuation of selected mRNAs. IntS11 is the endonuclease for RNA cleavage, as a part of the IntS4-IntS9-IntS11 complex (Integrator cleavage module, ICM). Our structure of the Drosophila ICM, determined by cryo-electron microscopy at 2.74 [A] resolution, unexpectedly revealed the stable association of an inositol hexakisphosphate (IP6) molecule. The binding site is located in a highly electropositive pocket at an interface among all three subunits of ICM, 55 [A] away from the IntS11 active site and generally conserved in other ICMs. IP6 binding is also confirmed in human ICM. Mutations of residues in this binding site or disruption of IP6 biosynthesis significantly reduced Integrator assembly and activity in snRNA 3'-end processing. Our structural and functional studies reveal that Integrator is subject to intricate cellular control and IP6 is a critical regulator of Integrator assembly and function in Drosophila, humans, and likely other organisms.

molecular biology↗

bioRxiv2021

Serotonin GPCR-based biosensing modalities in yeast

Serotonin is a key neurotransmitter involved in numerous physiological processes and serves as an important precursor for manufacturing bioactive indoleamines and alkaloids used in the treatment of human pathologies. In humans, serotonin sensing and signaling can occur by 12 G protein-coupled receptors (GPCRs) coupled to G proteins. To systematically assess serotonin GPCR signaling, we characterized reporter gene expression of a 144-sized library encoding all 12 human serotonin GPCRs in combination with 12 different G proteins in yeast exposed to serotonin. For the 5-HT4 receptor, we observe 25- and 64-fold changes in EC50 values and dynamic reporter gene outputs, respectively. Furthermore, we show that optimal biosensing designs enable high-resolution sensing of serotonin produced in yeast, as well as provide a platform for characterization of 19 serotonin GPCR polymorphisms found in human populations. Taken together, our study highlights serotonin biosensing modalities of relevance to both biotechnological and human health applications. HighlightsO_LIHuman serotonin G protein-coupled receptors display promiscuous G coupling in yeast C_LIO_LIG-coupled serotonin receptors display up to 64-fold changes in reporter expression output C_LIO_LIDifferences in G protein evokes 25- and 2-fold difference in EC50 and sensitivity, respectively C_LIO_LISerotonin receptor 5-HT4 and human SNP variants display physiologically relevant EC50 values in yeast C_LIO_LI5-HT4 can be applied for high-resolution biosensing of serotonin produced from yeast C_LI

bioengineering↗

bioRxiv2021

FIND-IT: Ultrafast mining of genome diversity

Novel crop improvement methodologies, including the exploitation of natural genetic variation, are urgently required to feed our rapidly growing human population in the context of global climate change. Here we describe a Fast Identification of Nucleotide variants by DigITal PCR (FIND-IT) method for the rapid identification of pre-targeted genetic variants or rare alleles in large genomic populations. Libraries of 500,000 individuals can be screened and desired variants isolated within two weeks. FIND-IT is widely applicable for mining valuable diversity in any genomic population, including elite breeding and wild germplasm collections. The method provides single nucleotide resolution that has been validated by identifying and isolating knockout lines, non-synonymous codon changes and variants of miRNA and transcription factor binding sites in the agronomically important crop barley. In contrast to existing methods, FIND-IT does not require transformation, cloning or enzymatic steps, and is exempt from GMO regulations. Thus, FIND-IT can be applied immediately to elite crop cultivars and can be tailored to minimize or eliminate time-consuming backcrossing requirements.

genetics↗

bioRxiv2021

A synthetic RNA-mediated evolution system in yeast

Laboratory evolution is a powerful approach to search for genetic adaptations to new or improved phenotypes, yet either relies on labour-intensive human-guided iterative rounds of mutagenesis and selection, or prolonged adaptation regimes based on naturally evolving cell populations. Here we present CRISPR- and RNA-assisted in vivo directed evolution (CRAIDE) of genomic loci using evolving chimeric donor gRNAs continuously delivered from an error-prone T7 RNA polymerase, and directly introduced as RNA repair donors into genomic targets under either Cas9 or dCas9 guidance. We validate CRAIDE by evolving novel functional variants of an auxotrophic marker gene, and by conferring resistance to a toxic amino acid analogue in bakers yeast Saccharomyces cerevisiae with a mutation rate >3,000-fold higher compared to spontaneous native rate, thus enabling the first demonstrations of in vivo delivery and information transfer from long evolving RNA donor templates into genomic context without the use of in vitro supplied and pre-programmed repair donors.

bioengineering↗

bioRxiv2020

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection, and amplification. While powerful, systematic searches through large sequence-spaces is a labor-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generates enzyme variants using the recently developed orthogonal replication system, OrthoRep, and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a ratelimiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers >13-fold compared to reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study, is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

synthetic biology↗