Biology subjects

Tyo, K. E. J.

Publications and source records attributed to Tyo, K. E. J..

8 recordsLinked to original sources

bioRxiv2023

Aromatic natural products synthesis from aromatic lignin monomers using Acinetobacter baylyi ADP1

Achieving sustainable chemical synthesis and a circular economy will require process innovation to minimize or recover existing waste streams. Valorization of lignin biomass has the ability to advance this goal. While lignin has proved a recalcitrant feedstock for upgrading, biological approaches can leverage native microbial metabolism to simplify complex and heterogeneous feedstocks to tractable starting points for biochemical upgrading. Recently, we demonstrated that one microbe with lignin relevant metabolism, Acinetobacter baylyi ADP1, is both highly engineerable and capable of undergoing rapid design-build-test-learn cycles, making it an ideal candidate for these applications. Here, we utilize these genetic traits and ADP1s native {beta}-ketoadipate metabolism to convert mock alkali pretreated liquor lignin (APL) to two valuable natural products, vanillin-glucoside and resveratrol. En route, we create strains with up to 22 genetic modifications, including up to 8 heterologously expressed enzymes. Our approach takes advantage of preexisting aromatic species in APL (vanillate, ferulate, and p-coumarate) to create shortened biochemical routes to end products. Together, this work demonstrates ADP1s potential as a platform for upgrading lignin waste streams and highlights the potential for biosynthetic methods to maximize the existing chemical potential of lignin aromatic monomers.

synthetic biology↗

bioRxiv2023

Engineering Ca2+-dependent DNA polymerase activity

Advancements in synthetic biology have provided new opportunities in biosensing with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurement, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record biosensor state over time. However, slow signal transduction steps, due to the timescales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol {delta} replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol {delta} subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in developing DNAP-based biosensors.

synthetic biology↗

bioRxiv2022

Dynamic Kinetic Models Capture Cell-Free Metabolism for Improved Butanol Production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a novel combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

synthetic biology↗

bioRxiv2020

Distinguishing Critical, Beneficial, Neutral and Harmful Mutations Uncovered in the Directed Evolution of a Yeast Membrane Receptor

We present a reversion analysis of mutations introduced during the directed evolution of the yeast G-protein coupled receptor (GPCR) Ste2p to detect a peptide biomarker of chronic kidney disease. Two mutated receptors are analyzed in this study. Mutations acquired during directed evolution were reverted one at a time to the wild-type residue to assess the mutations contribution to receptor function. Mutations in the first and fifth transmembrane regions, the second intracellular loop and a truncation were found to be crucial for sensitive detection of the peptide biomarker. Some mutations acquired during directed evolution were found to be neutral to or harmful for biomarker detection. Mutations were also assessed for their contributions to increasing basal activity of the evolved receptors. A similar set of crucial mutations were found in the two receptors, implying a similar mechanism detection. The mutations are reasoned to appear to give the ability to detect a smaller sized peptide, affect interaction with the G-protein and allow for prolonged signaling after stimulation. These data should provide guidance for further engineering of Ste2p and other GPCRs.

synthetic biology↗

bioRxiv2019

Development of a genetic toolset for the highly engineerable and metabolically versatile Acinetobacter baylyi ADP1

One primary objective of synthetic biology is to improve the sustainability of chemical manufacturing. Biological systems can utilize a variety of carbon sources, including waste streams that pose challenges to traditional chemical processing such as lignin biomass, providing opportunity for remediation and valorization of these materials. Success, however, depends on identifying microorganisms that are both metabolically versatile and engineerable. This has been a historic hindrance. Here, we leverage the facile genetics of the metabolically versatile bacterium Acinetobacter baylyi ADP1 to create easy and rapid molecular cloning workflows, a promoter library, ribosomal binding site (RBS) variants, and an unprecedented number of bacterial chromosomal protein expression sites and variants. Moreover, we demonstrate the utility of these tools by examining ADP1s catabolic repression regulation, creating a strain with improved potential for lignin bioprocessing. Taken together, this work establishes ADP1 as an ideal host for a variety of sustainability and synthetic biology applications.

synthetic biology↗

bioRxiv2019

Recording temporal data onto DNA with minutes resolution

Recording biological signals can be difficult in three-dimensional matrices, such as tissue. We present a DNA polymerase-based strategy that records temporal biosignals locally onto DNA to be read out later, which could obviate the need to extract information from tissue on the fly. We use a template-independent DNA polymerase, terminal deoxynucleotidyl transferase (TdT) that probabilistically adds dNTPs to single-stranded DNA (ssDNA) substrates without a template. We show that in vitro, the dNTP-incorporation preference of TdT changes with the presence of Co2+, Ca2+, Zn2+ and temperature. Extracting the signal profile over time is possible by examining the dNTP incorporation preference along the length of synthesized ssDNA strands like a molecular ticker tape. We call this TdT-based untemplated recording of temporal local environmental signals (TURTLES). We show that we can determine the time of Co2+ addition to within two minutes over a 60-minute period. Further, TURTLES has the capability to record multiple fluctuations. We can estimate the rise and fall of an input Co2+ pulse to within three minutes. TURTLES has at least 200-fold better temporal resolution than all previous DNA-based recording techniques.

synthetic biology↗

bioRxiv2019

Dynamic control of pathway expression with riboregulated switchable feedback promoters

Dynamic pathway regulation has emerged as a promising strategy in metabolic engineering for improved system productivity and yield, and continues to grow in sophistication. Bacterial stress-response promoters allow dynamic gene regulation using the hosts natural transcriptional networks, but lack the flexibility to control the expression timing and overall magnitude of pathway genes. Here, we report a strategy that uses RNA transcriptional regulators to introduce another layer of control over the output of natural stress-response promoters. This new class of gene expression cassette, called a riboregulated switchable feedback promoter (rSFP), can be modularly activated using a variety of mechanisms, from manual induction to quorum sensing. We develop and apply rSFPs to regulate a toxic cytochrome P450 enzyme in the context of a Taxol precursor biosynthesis pathway and show this leads to 2.4x fold higher titers than from the best reported strain. We envision that rSFPs will become a valuable tool for flexible and dynamic control of gene expression in metabolic engineering, protein and biologic production, and many other applications.

synthetic biology↗

bioRxiv2018

Bayesian inference of metabolic kinetics from genome-scale multiomics data

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, integrating these data sources to generate predictions of steady-state metabolism typically requires a kinetic description of the enzymatic reactions that occur within a cell. Parameterizing these kinetic models from biological data can be computationally difficult, especially as the amount of data increases. Robust methods must also be able to quantify the uncertainty in model parameters as a function of the available data, which can be particularly computationally intensive. The field of Bayesian inference offers a wide range of methods for estimating distributions in parameter uncertainty. However, these techniques are poorly suited to kinetic metabolic modeling due to the complex kinetic rate laws typically employed and the resulting dynamic system that must be solved. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and variational inference methods. We demonstrate that detailed information on the posterior distribution of kinetic model parameters can be obtained efficiently at a variety of different problem scales, including large-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and developing new, efficient strain designs.

systems biology↗