Biology subjects

Jensen, M. K.

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

23 records · Page 2Linked to original sources

bioRxiv2020

Regulatory control circuits for stabilizing long-term anabolic product formation in yeast

Engineering living cells for production of chemicals, enzymes and therapeutics can burden cells due to use of limited native co-factor availability and/or expression burdens, totalling a fitness deficit compared to parental cells encoded through long evolutionary trajectories to maximise fitness. Ultimately, this discrepancy puts a selective pressure against fitness-burdened engineered cells under prolonged bioprocesses, and potentially leads to complete eradication of high-performing engineered cells at the population level. Here we present the mutation landscapes of fitness-burdened yeast cells engineered for vanillin-{beta}-glucoside production. Next, we design synthetic control circuits based on transcriptome analysis and biosensors responsive to vanillin-{beta}-glucoside pathway intermediates in order to stabilize vanillin-{beta}-glucoside production over [~]55 generations in sequential passage experiments. Furthermore, using biosensors with two different modes of action we identify control circuits linking vanillin-{beta}-glucoside pathway flux to various essential cellular functions, and demonstrate control circuits robustness and 92% higher vanillin-{beta}-glucoside production, including 5-fold increase in total vanillin-{beta}-glucoside pathway metabolite accumulation, in a fed-batch fermentation compared to vanillin-{beta}-glucoside producing cells without control circuits.

synthetic biology↗

bioRxiv2020

The folding and unfolding behavior of ribonuclease H on the ribosome

The health of a cell depends on accurate translation and proper protein folding; misfolding can lead to aggregation and disease. The first opportunity for a protein to fold occurs during translation, when the ribosome and surrounding environment can affect the energy landscape of the nascent chain. However, quantifying these environmental effects is challenging due to the ribosomal proteins and rRNA, which preclude most spectroscopic measurements of protein energetics. We have applied two gel-based approaches, pulse proteolysis and force-peptide arrest assays, to probe the folding and unfolding pathways of RNase H ribosome-stalled nascent chains. We find that ribosome-stalled RNase H has an increased unfolding rate compared to free RNase H, which completely accounts for observed changes in protein stability and indicates that the folding rate is unchanged. Using arrest peptide-based force-profile analysis, we assayed the force generated during the folding of RNase H on the ribosome. Surprisingly, we find that population of the RNase H folding intermediate is required to generate sufficient force to release the SecM stall and that readthrough of the stall sequence directly correlates with the stability of the folding intermediate. Together, these data imply that the folding pathway of RNase H is unchanged on the ribosome. Furthermore, our data indicate that the ribosome promotes unfolding while the nascent chain is proximal to the ribosome, which may limit the deleterious effects of misfolding and assist in folding fidelity.

biophysics↗

bioRxiv2019

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts.

bioengineering↗

bioRxiv2019

Evolution-guided engineering of small-molecule biosensors

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve and functionalize prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely bakers yeast Saccharomyces cerevisiae. From a single round of directed evolution of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis, cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion of function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a non-native prokaryote chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.

synthetic biology↗

bioRxiv2017

An orthogonal and pH-tunable sensor-selector for muconic acid biosynthesis in yeast

Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show the sensor-selector does not affect production, and find that tuning pH of the cultivation medium limits the rise of non-producing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produced more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.

synthetic biology↗