Biology subjects

Benito-Vaquerizo, S.

Publications and source records attributed to Benito-Vaquerizo, S..

7 recordsLinked to original sources

bioRxiv2025

GEMsembler: cross-tool structural comparison and ensemble modeling improve metabolic model performance

Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance. Here, we introduce GEMsembler, a Python package designed to compare cross-tool GEMs, track the origin of model features, and build ensemble models containing any subset of the input models. GEMsembler provides comprehensive analysis functionality, including identification and visualisation of biosynthesis pathways, growth assessment and an agreement-based curation workflow. GEMsembler-curated ensemble models built from four Lactiplantibacillus plantarum and Escherichia coli automatically reconstructed models outperform the gold-standard models in auxotrophy and gene essentiality predictions. Optimising Gene-Protein-Reaction (GPR) combinations from ensemble models improves gene essentiality predictions, even in the manually curated gold-standard models. GEMsembler explains model performance by highlighting relevant metabolic pathways and GPRs alternatives, informing experiments to resolve model uncertainty. Thus, GEMsembler facilitates building more accurate and biologically informed metabolic models for systems biology applications.

systems biology↗

bioRxiv2024

System-level characterization of engineered and evolved formatotrophic E. coli strains

One-carbon compounds, such as formate, are promising and sustainable feedstocks for microbial bioproduction of fuels and chemicals. Growth of Escherichia coli on formate was recently achieved by introducing the reductive glycine pathway (rGlyP) into its genome, which is theoretically the most energy-efficient aerobic formate assimilation pathway. While adaptive laboratory evolution was used to enhance the growth rate and biomass yield significantly, still the best performing formatotrophic E. coli strain did not approach the theoretical optimal biomass yield of the rGlyP. In this study, we investigated these previously engineered formatotrophic E. coli strains to find out why the biomass yield was sub-optimal and how it may be improved. Through a combination of metabolic modelling, genomic and proteomic analysis, we identified several potential metabolic bottlenecks and future targets for optimization. This study also reveals further insights in the evolutionary mutations and related changes in proteome allocation that supported the already substantially improved growth of formatotrophic E. coli strain. This systems-level analysis provides key insights to realize high-yield, fast growing formatotrophic strains for future bioproduction.

synthetic biology↗

bioRxiv2023

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

bioRxiv2022

Engineered synthetic one-carbon fixation exceeds yield of the Calvin Cycle

One-carbon (C1) feedstocks derived from CO2 and renewable electricity, such as formate, are promising substrates for sustainable production of chemicals, food and fuels. Energetically more efficient, engineered C1-fixation pathways were proposed to increase biomass yields above their natural counterparts, but have so far not been shown to achieve this. Here, we replace the native energy-inefficient Calvin-Benson-Bassham (CBB) cycle in Cupriavidus necator by genomic integration of the synthetic reductive glycine pathway for growth on formate. Our final engineered strain reaches a higher biomass yield than the CBB-cycle-utilizing wild type, showing for the first time that efficiencies found in natural metabolism can be exceeded via a synthetic pathway. This yield increase demonstrates the potential of synthetic metabolism and is an important step towards realizing truly sustainable, economically feasible bio-based production.

synthetic biology↗

bioRxiv2022

Model-driven approach for the production of butyrate from CO2/H2 by a novel co-culture of C. autoethanogenum and C. beijerinckii.

One-carbon (C1) compounds are promising feedstocks for sustainable production of commodity chemicals. CO2 is a particularly advantageous C1-feedstock since it is an unwanted industrial off-gas that can be converted to valuable products while reducing its atmospheric levels. Acetogens are known microorganisms that can grow on CO2/H2 and syngas converting these substrates into ethanol and acetate. Co-cultivation of acetogens with microbes that can further process such products can expand the variety of products to, for example, medium chain fatty acids (MCFA) and longer chain alcohols. Solventogens are microorganisms known to produce MCFA and alcohols via the acetone, butanol and ethanol (ABE) fermentation in which acetate is a key metabolite. Thus, co-cultivation of a solventogen and acetogen in a consortium provides a potential platform to produce valuable chemicals from CO2. In this study, metabolic modelling was implemented to design a new co-culture of an acetogen and a solventogen to produce butyrate from CO2/H2 mixtures. The model-driven approach suggested the ability of the studied solventogenic species to grow on lactate/glycerol with acetate as co-substrate. This ability was confirmed experimentally by cultivation of Clostridium beijerinckii on these substrates in serum bottles and subsequently in pH-controlled bioreactors. Community modelling also suggested that a novel microbial consortium consisting of the acetogen Clostridium autoethanogenum, and the solventogen C. beijerinckii would be feasible and stable. On the basis of this prediction, a co-culture was experimentally established. C. autoethanogenum grew on CO2/H2 producing acetate and traces of ethanol. Acetate was in turn, consumed by C. beijerinckii together with lactate, producing butyrate. These results show that community modelling of metabolism is a valuable tool to guide the design of microbial consortia for the tailored production of chemicals from renewable resources.

microbiology↗

bioRxiv2022

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum

BackgroundMicrobial production of propionate from diluted streams of ethanol (e.g., deriving from syngas fermentation) is a sustainable alternative to the petrochemical production route. Yet, few ethanol-fermenting propionigenic bacteria are known, and understanding of their metabolism is limited. Anaerotignum neopropionicum is a propionate-producing bacterium that uses the acrylate pathway to ferment ethanol and CO2 to propionate and acetate. In this work, we used computational and experimental methods to study the metabolism of A. neopropionicum and, in particular, the pathway for conversion of ethanol into propionate. ResultsOur work describes iANEO_SB607, the first genome-scale metabolic model (GEM) of A. neopropionicum. The model was built combining the use of automatic tools with an extensive manual curation process, and it was validated with experimental data from this and published studies. The model predicted growth of A. neopropionicum on ethanol, lactate, sugars and amino acids, matching observed phenotypes. In addition, the model was used to implement a dynamic flux balance analysis (dFBA) approach that accurately predicted the fermentation profile of A. neopropionicum during batch growth on ethanol. A systematic analysis of the metabolism of A. neopropionicum combined with model simulations shed light into the mechanism of ethanol fermentation via the acrylate pathway, and revealed the presence of the electron-transferring complexes NADH-dependent reduced ferredoxin:NADP+ oxidoreductase (Nfn) and acryloyl-CoA reductase-EtfAB, identified for the first time in this bacterium. ConclusionsThe realisation of the GEM iANEO_SB607 is a stepping stone towards the understanding of the metabolism of the propionate-producer A. neopropionicum. With it, we have gained insight into the functioning of the acrylate pathway and energetic aspects of the cell, with focus on the fermentation of ethanol. Overall, this study provides a basis to further exploit the potential of propionigenic bacteria as microbial cell factories.

systems biology↗

bioRxiv2020

Modeling a co-culture of Clostridium autoethanogenum and Clostridium kluyveri to increase syngas conversion to medium-chain fatty-acids

Microbial fermentation of synthesis gas (syngas) is becoming more attractive for sustainable production of commodity chemicals. To date, syngas fermentation focuses mainly on the use of Clostridium species for the production of small organic molecules such as ethanol and acetate. The cocultivation of syngas-fermenting microorganisms with chain-elongating bacteria can expand the range of possible products, allowing, for instance, the production of medium-chain fatty acids (MCFA) and alcohols from syngas. To explore these possibilities, we report herein a genome-scale, constraint-based metabolic model to describe growth of a co-culture of Clostridium autoethanogenum and Clostridium kluyveri on syngas for the production of valuable compounds. Community flux balance analysis was used to gain insight into the metabolism of the two strains and their interactions, and to reveal potential strategies enabling production of butyrate and hexanoate. The model suggests that addition of succinate is one strategy to optimize the production of medium-chain fatty-acids from syngas with this co-culture. According to the predictions, addition of succinate increases the pool of crotonyl-CoA and the ethanol/acetate uptake ratio in C. kluyveri, resulting in the flux of up to 60% of electrons into hexanoate. Other potential way to optimize butyrate and hexanoate is to increase ethanol production by C. autoethanogenum. Deletion of either formate transport, acetaldehyde dehydrogenase or formate dehydrogenase (ferredoxin) from the metabolic model of C. autoethanogenum leads to a (potential) increase in ethanol production up to 150%, which is clearly very attractive.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗