Biology subjects

Moreno-Paz, S.

Publications and source records attributed to Moreno-Paz, S..

5 recordsLinked to original sources

bioRxiv2024

Combinatorial engineering reveals shikimate pathway bottlenecks in para-aminobenzoic acid production in Pseudomonas putida

Combinatorial approaches in metabolic engineering enable the optimization of multigene pathways, thereby improving product titers. However, the optimization of complex metabolic pathways is hindered by their multiple interactions. Testing all possible combinations of suitable genetic parts is often prevented by the large number of possible variants. A valuable alternative to this is to use statistical design of experiments and linear modeling to collect important information for optimization without testing every possible combination. The shikimate pathway is an example of a complex metabolic pathway involved in the production of aromatic compounds, which are prevalent in industry. In this study, we explore the impact of the modulation of the expression levels of all the genes in the shikimate and para-aminobenzoic acid (pABA) biosynthesis pathways for pABA production (a widely used industrial intermediate) in Pseudomonas putida. We used this approach to select 14 representative strains from a total of 512 possible combinations. We obtained a range of product titers from 2 to 186.2 mg/l. This information was used to guide a second round of strain construction to further increase the production to 232.1 mg/l. Using this strategy, we demonstrate that aroB expression, encoding 3-dehydroquinate synthase, is a significant limiting factor in the production of pABA.

synthetic biology↗

bioRxiv2024

Model-guided metabolic engineering of curcuminoid Production in Pseudomonas putida

Production of value-added, plant-derived compounds in microbes increasingly attracts commercially interest in food and pharmaceutical industries. However, plant metabolic pathways are complex, require a robust balance of enzymes, cofactors, ATP and other metabolites, and often result in low production when transplanted to bacteria. This is exemplified by the biosynthesis of curcuminoids from the Curcuma longa plant. Here, we combine dynamic pathway modeling, systematic testing of isoenzymes, and the optimization of gene expression levels and substrate concentrations for the biosynthesis of curcuminoids in Pseudomonas putida, leading to unprecedented conversion rates of caffeic acid and tyrosine to curcumin. The development of kinetic ensemble models guided the design of production strains, emphasizing the necessity of high relative expression of c3h, curs2 and dcs and, the low relative expression of tal, comt, ccoaomt, and 4cl4. This optimization resulted in a strain that achieved a 10.8 {+/-}1.8% of the maximum theoretical yield of curcumin from tyrosine. This represents a 4.1-fold increase in production efficiency and the highest yield reported to date, demonstrating the potential of P. putida as a promising platform for curcuminoid production. Our findings highlight the effectiveness of our strategy not only in the advances in the production of curcuminoids but also in setting a framework for the biosynthesis of other complex compounds.

synthetic biology↗

bioRxiv2023

Machine learning-guided optimization of p-coumaric acid production in yeast

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a bio-based economy. To use them effectively, appropriate connections between each phase of the cycle are crucial. Using p-coumaric acid production in Saccharomyces cerevisiea as case study, we propose the use of one-pot library generation, random screening, targeted sequencing and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of ML models strongly enable pathway optimization, and propose feature importance and SHAP values as a guide to expand the design space of original libraries. This approach led to a 68% increased production of p-coumaric acid within two DBTL cycles.

bioengineering↗

bioRxiv2023

CFSA: Comparative Flux Sampling Analysis as a guide for strain design

Genome-scale metabolic models of microbial metabolism have extensively been used to guide the design of microbial cell factories, still, many of the available strain design algorithms often fail to produce a reduced list of targets for improved performance that can be implemented and validated in a step-wise manner. We present Comparative Flux Sampling Analysis (CFSA), a strain design method based on the extensive comparison of complete metabolic spaces corresponding to maximal or near-maximal growth and production phenotypes. The comparison is complemented by statistical analysis to identify reactions with altered flux that are suggested as targets for genetic interventions including up-regulations, down-regulations and gene deletions. We applied CFSA to the production of lipids by Cutaneotrichosporon oleaginosus and naringenin by Saccharomyces cerevisiae identifying engineering targets in agreement with previous studies as well as new interventions. CFSA is an easy-to-use, robust method that suggests potential metabolic engineering targets for growth-uncoupled production that can be applied to the design of microbial cell factories.

bioengineering↗

bioRxiv2021

Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors

Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bio-process design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyze flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models

bioengineering↗