Biology subjects

Sonnenschein, N.

Publications and source records attributed to Sonnenschein, N..

12 recordsLinked to original sources

bioRxiv2023

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Kinetic models of enzymes have a long history of use for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of bottom up parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard macroscopic kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and microscopic rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. The code and case studies are provided in the MASSef package built on top of the MASS Toolbox in Mathematica. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates. Author SummaryDetailed kinetic models of metabolism offer the promise of enabling new predictions of metabolic behavior and prospective design of metabolic function. However, parameterizing such models remains a substantial challenge. Historically, the kinetics of many enzymes have been measured using in vitro assays, but integrating this data into consistent large-scale models and filling gaps in available data has been a primary difficulty. Here, we provide an algorithmic approach to parameterize enzyme kinetic models using diverse enzyme kinetic data. The approach reconciles inconsistent data and addresses the issue of gaps in available data implicitly through sampling alternative parameter sets. We provide a number of case studies demonstrating the approach on different enzymes. This work empowers the use of the large amount of historical and machine learning-estimated enzyme data and will aid in the construction of biochemically-accurate models of metabolism.

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↗

bioRxiv2023

Geckopy 3.0: enzyme constraints, thermodynamics constraints and omics integration in python

GEnome-scale Metabolic (GEM) models are knowledge bases of the reactions and metabolites of a particular organism. These GEM models allow for the simulation of the metabolism - e.g. calculating growth and production yields - based on the stoichiometry, reaction directionality and uptake rates of the metabolic network. Over the years, several extensions have been added to take into account other actors in metabolism, going beyond pure stoichiometry. One such extension is enzyme-constraint models, which enable the integration of kinetic data and proteomics data into GEM models. Given its relatively recent formulation, there are still challenges in standardization and data reconciliation between the model and the experimental measurements. In this work, we present geckopy 3.0 (Genome-scale model Enzyme Constraints, using Kinetics and Omics in python), an actualization from scratch of the previous python implementation of the same name. This update tackles the aforementioned challenges, in an effort to reach maturity in enzyme-constraint modeling. With the new geckopy, proteins are typed in the SBML document, taking advantage of the SBML Groups extension, in compliance with community standards. Additionally, a suite of relaxation algorithms - in the form of linear and mixed-integer linear programming problems - has been added to facilitate reconciliation of raw proteomics data with the metabolic model. Several functionalities to integrate experimental data were implemented, including an interface layer with pytfa for the usage of thermodynamics and metabolomics constraints. Finally, the relaxation algorithms were benchmarked against public proteomics datasets in Escherichia coli for different conditions, revealing targets for improving the enzyme constrained model and/or the proteomics pipeline. IMPORTANCEThe metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model which is able to efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxations algorithms in a package called geckopy. Additionally, to ensure that enzyme-constraint models follow the community standards, a format for the proteins is postulated. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. We hope that the package and algorithms presented here will serve useful for the constraint-based modeling community.

systems biology↗

bioRxiv2022

Engineering of Pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model

Pseudomonas putida KT2440 is an attractive bacterial host for biotechnological production of valuable chemicals from renewable lignocellulosic feedstocks as it can valorize lignin-derived aromatics or cellulosic glucose. P. putida EM42, a genome-reduced variant of P. putida KT2440 endowed with advantageous physiological properties, was recently engineered for growth on cellobiose, a major cellooligosaccharide product of enzymatic cellulose hydrolysis. Co-utilization of cellobiose with glucose was achieved in a mutant lacking periplasmic glucose dehydrogenase Gcd (PP_1444). However, the cause of the observed co-utilization was not understood and the {Delta}gcd strain suffered from a significant growth defect. In this study, we aimed to investigate the basis of the simultaneous uptake of the two sugars and accelerate the growth of P. putida EM42 {Delta}gcd mutant for the bioproduction of valuable compounds from glucose and cellobiose. We show that the gcd deletion abolished the inhibition of the exogenous {beta}-glucosidase BglC from Thermobifida fusca by the intermediates of the periplasmic glucose oxidation pathway. The additional deletion of the hexR gene, which encodes a repressor of the upper glycolysis genes, failed to restore the rapid growth on glucose. The reduced growth rate of the {Delta}gcd mutant was partially compensated by the implantation of heterologous glucose (Glf from Zymomonas mobilis) and cellobiose (LacY from Escherichia coli) transporters. Remarkably, this intervention resulted in the accumulation of pyruvate in aerobic P. putida cultures. We demonstrated that the excess of this key metabolic intermediate can be redirected to the enhanced biosynthesis of ethanol and lactate. The overproduction of pyruvate was then unveiled by an upgraded genome-scale metabolic model constrained with proteomic and kinetic data. The model pointed to the saturation of glucose catabolism enzymes due to unregulated substrate uptake and it predicted improved bioproduction of pyruvate-derived chemicals by the engineered strain. This work sheds light on the co-metabolism of cellulosic sugars in an attractive biotechnological host and introduces a novel strategy for pyruvate overproduction in bacterial cultures under aerobic conditions. HighlightsO_LICo-utilization of glucose and cellobiose achieved in P. putida EM42 {Delta}gcd mutant. C_LIO_LIGrowth defect of the mutant compensated by implanting exogenous sugar transporters. C_LIO_LIEnhanced influx of carbon caused aerobic overproduction of pyruvate and acetate. C_LIO_LICarbon from excess pyruvate streamed into ethanol or L-lactate. C_LIO_LIPyruvate overproduction unveiled by a mathematical model of P. putida metabolism. C_LI

bioengineering↗

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↗

bioRxiv2021

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here we developed a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions (16304 reactions in total). The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data revealed that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation - known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providing a framework to understand metabolic adaptation in eukaryal cells.

systems biology↗

bioRxiv2021

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into GEMs was first enabled by the GECKO method, allowing the study of phenotypes constrained by protein limitations. Here, we upgraded the GECKO toolbox in order to enhance models with enzyme and proteomics constraints for any organism with an available GEM reconstruction. With this, enzyme-constrained models (ecModels) for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus were generated, aiming to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions revealed that upregulation and high saturation of enzymes in amino acid metabolism were found to be common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO was further developed by the implementation of an automated framework for continuous and version-controlled update of ecModels, which was validated by producing additional high-quality ecModels for Escherichia coli and Homo sapiens. These efforts aim to facilitate the utilization of ecModels in basic science, metabolic engineering and synthetic biology purposes.

systems biology↗

bioRxiv2021

Genome-scale metabolic modelling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism.

Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.

microbiology↗

bioRxiv2021

Emergence of phenotypically distinct subpopulations is a factor in adaptation of recombinant Saccharomyces cerevisiae under glucose-limited conditions

Cells cultured in a nutrient-limited environment can undergo adaptation, which confers improved fitness under long-term energy limitation. We have previously shown how a recombinant S. cerevisiae strain, producing a heterologous insulin product, under glucose-limited conditions adapts over time at the average population level. In this paper, we investigated this adaptation at the single-cell level by application of FACS and showed that three apparent phenotypes underlie the adaptive response observed at the bulk level: (1) cells that drastically reduced insulin production (23 %), (2) cells with reduced enzymatic capacity in central carbon metabolism (46 %), (3) cells that exhibited pseudohyphal growth (31 %). We speculate that the phenotypic heterogeneity is a result of different mechanisms to increase fitness. Cells with reduced insulin productivity have increased fitness by reducing the burden of the heterologous insulin production and the populations with reduced enzymatic capacity of the central carbon metabolism and pseudohyphal growth have increased fitness towards the glucose-limited conditions. The results highlight the importance of considering population heterogeneity when studying adaptation and evolution.

bioengineering↗

bioRxiv2019

Adaptive laboratory evolution reveals general and specific chemical tolerance mechanisms and enhances biochemical production

Tolerance to high product concentrations is a major barrier to achieving economically viable processes for bio-based chemical production. Chemical tolerance mechanisms are often unknown, thus their rational design is not achievable. To reveal unknown tolerance mechanisms we used an automated platform to evolve Escherichia coli to grow in previously toxic concentrations of 11 chemicals that have applications as polymer precursors, chemical intermediates, or biofuels. Re-sequencing of isolates from 88 independently evolved populations, reconstruction of mutations, and cross-compound tolerance profiling was employed to uncover general and specific tolerance mechanisms. We found that: 1) the broad tolerance of strains towards chemicals varied significantly depending on the chemical stress condition under which the strain was evolved; 2) the strains that acquired high levels of NaCl tolerance also became broadly tolerant to most chemicals; 3) genetic tolerance mechanisms included alterations in regulatory, cell wall, transcriptional and translational functions, as well as more chemical-specific mechanisms related to transport and metabolism; 4) using pre-tolerized starting strains can significantly enhance subsequent production of chemicals when a production pathway is inserted; and 5) only a subset of the evolved isolates showed improved production indicating that this approach is especially useful when a large number of independently evolved isolates are screened for production. We provide a comprehensive genotype-phenotype map based on identified mutations and growth phenotypes for 224 chemical tolerant strains.

microbiology↗

bioRxiv2018

A genome-scale metabolic model for Methylococcus capsulatus predicts reduced efficiency uphill electron transfer to pMMO.

BackgroundGenome-scale metabolic models allow researchers to calculate yields, to predict consumption and production rates, and to study the effect of genetic modifications in silico, without running resource-intensive experiments. While these models have become an invaluable tool for optimizing industrial production hosts like E. coli and S. cerevisiae, few such models exist for one-carbon (C1) metabolizers.\n\nResultsHere we present a genome-scale metabolic model for Methylococcus capsulatus, a well-studied obligate methanotroph, which has been used as a production strain of single cell protein (SCP). The model was manually curated, and spans a total of 877 metabolites connected via 898 reactions. The inclusion of 730 genes and comprehensive annotations, make this model not only a useful tool for modeling metabolic physiology, but also a centralized knowledge base for M. capsulatus. With it, we determined that oxidation of methane by the particulate methane monooxygenase is most likely driven through uphill electron transfer operating at reduced efficiency as this scenario matches best with experimental data from literature.\n\nConclusionsThe metabolic model will serve the ongoing fundamental research of C1 metabolism, and pave the way for rational strain design strategies towards improved SCP production processes in M. capsulatus.

systems biology↗

bioRxiv2017

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genomescale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.

bioengineering↗