Biology subjects

Domenzain, I.

Publications and source records attributed to Domenzain, I..

7 recordsLinked to original sources

bioRxiv2024

Model-Driven Elucidation of Lactose and Galactose Metabolism via Oxidoreductive Pathway in Sungouiella intermedia for Cell Factory Applications

Converting industrial side streams into value-added chemicals using microbial cell factories is of increasing interest, as such processes offer solutions to reduce waste and production costs. However, developing new, efficient cell factories for precision fermentation remains challenging due to limited knowledge about their metabolic capabilities. Here, we investigate the lactose and galactose metabolism of the non-conventional yeast Sungouiella intermedia (formerly Candida intermedia), using knowledge-matching of high-quality genome-scale metabolic model (GEM) with extensive experimental analysis and determine its potential as a future cell factory on lactose-rich industrial side-streams. We show that this yeast possesses the conserved Leloir pathway as well as an oxidoreductive galactose catabolic route. Contextualization of RNAseq data into Sint-GEM highlights the regulatory mechanisms on the oxidoreductive pathway and how this pathway can enable adaptation to diverse environments. Model simulations, together with experimental data from continuous and batch bioreactors, indicate that S. intermedia uses upstream enzymes of the oxidoreductive pathway, in a condition-dependent manner, and produce the sugar alcohol galactitol as a carbon overflow metabolite, coupled to redox co-factor balancing during both lactose and galactose growth. Furthermore, the new metabolic insights facilitated the development of an improved bioprocess design, where an engineered S. intermedia strain could achieve galactitol yields of >90% of the theoretical maximum at improved production rates using the industrial side-stream cheese whey permeate as feedstock. Additional strain engineering resulted in galactitol-to-tagatose conversion, proving the versatility of the future production host. Overall, this work sheds new light on the intrinsic interplay between parallel metabolic pathways that shape the lactose and galactose catabolism in S. intermedia. It also demonstrates how a GEM combined with experimental analysis can work in synergy to fast-forward metabolic characterization and development of new, non-conventional yeast cell factories. HighlightsO_LIAn oxidoreductive pathway functions in concert with the Leloir pathway for galactose catabolism. C_LIO_LIGEM predicts that galactitol secretion enables efficient carbon overflow metabolism and maintains redox balance. C_LIO_LIKnowledge-matching of GEM with experimental results highlights cell factory potential. C_LIO_LIHigh galactitol yields and proof-of-concept tagatose production using whey permeate as feedstock. C_LI

bioengineering↗

bioRxiv2023

Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast

Development of efficient cell factories that can compete with traditional chemical production processes is complex and generally driven by case-specific strategies, based on the product and microbial host of interest. Despite major advancements in the field of metabolic modelling in recent years, prediction of genetic modifications for increased production remains challenging. Here we present a computational pipeline that leverages the concept of protein limitations in metabolism for prediction of optimal combinations of gene engineering targets for enhanced chemical bioproduction. We used our pipeline for prediction of engineering targets for 102 different chemicals using Saccharomyces cerevisiae as a host. Furthermore, we identified sets of gene targets predicted for groups of multiple chemicals, suggesting the possibility of rational model-driven design of platform strains for diversified chemical production. One sentence summaryNovel strain design algorithm ecFactory on top of enzyme-constrained models provides unprecedented chances for rational strain design and development.

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↗

bioRxiv2020

A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and its malfunction has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer and neurological disorders. Therefore, unravelling the role of nutrients as signaling molecules and metabolites as well as their interconnectivity may provide a deeper understanding of how these conditions occur. Both signalling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism, we developed a hybrid model, combining Boolean model, describing the signalling layer and the enzyme constraint model accounting for metabolism using a regulatory network as a link. The model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. We show that using this methodology one can investigat intrinsically different systems, such as signaling and metabolism, in the same model and gain insight into how the interplay between them can have non-trivial effects by showing a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions. In addition, the model showed that during fermentation, enzyme utilization is the more important factor governing the protein allocation, while in low glucose conditions robustness and control is prioritized. Author summaryElucidating the complex relationship between nutrient-induced signaling and metabolism represents a key in understanding the onset of many different human diseases like obesity, type 3 diabetes, cancer and many neurological disorders. In this work we proposed a hybrid modeling approach, combining Boolean representation of singaling pathways, like Snf11, TORC1 and PKA with the enzyme constrained model of metabolism linking them via the regulatory network. This allowed us to improve individual model predictions and elucidate how single components in the dynamic signaling layer affect the steady-state metabolism. The model has been tested under respiration and fermentation, reveling novel connections and further reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. Finally, we show a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions.

systems biology↗

bioRxiv2020

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for determining genome-wide absolute protein abundances. A known caveat of this approach is the poor technical reproducibility, i.e. how consistent the estimations are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We found that a simple normalization and rescaling approach performs as accurately yet more precisely than methods that rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results subsequently serve as a benchmark for assessing MS data quality for protein quantification, whilst also underscoring current limitations in this approach.

bioinformatics↗

bioRxiv2019

Young Genes are More Responsive to Environmental Stress than Ancient Genes in Budding Yeasts

The Saccharomycotina subphylum (budding yeasts) spans more than 400 million years of evolution and includes species that thrive in many of Earths harsh environments. Characterizing species that grow in harsh conditions could enable the design of more robust yeast strains for biotechnology. However, tolerance to stressful conditions is a multifactorial response, which is difficult to understand since many of the genes involved are as yet uncharacterized. In this work, three divergent yeast species were grown under multiple stressful conditions to identify stress-induced genes. For each condition, duplicated and non-conserved genes were significantly enriched for stress responsiveness compared to single-copy conserved genes. To understand this further, we developed a sorting method that considers evolutionary origin and duplication timing to assign an evolutionary age to each gene. Subsequent analysis of the sets of genes that changed expression revealed a relationship between stress-induced genes and the youngest gene set, regardless of the species or stress in question. These young genes are rarely essential for growth and evolve rapidly, which may facilitate their functionalization for stress tolerance and may explain their stress-induced expression. These findings show that systems-level analyses that consider gene age can expedite the identification of stress tolerance genes.

systems biology↗

bioRxiv2018

RAVEN 2.0: a versatile platform for metabolic network reconstruction and a case study on Streptomyces coelicolor

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community.\n\nAuthor summaryCellular metabolism is a large and complex network. Hence, investigations of metabolic networks are aided by in silico modelling and simulations. Metabolic networks can be derived from whole-genome sequences, through identifying what enzymes are present and connecting these to formalized chemical reactions. To facilitate the reconstruction of genome-scale models of metabolism (GEMs), we have developed RAVEN 2.0. This versatile toolbox can reconstruct GEMs fast, through either metabolic pathway databases KEGG and MetaCyc, or from homology with an existing GEM. We demonstrate RAVENs functionality through generation of a metabolic model of Streptomyces coelicolor, an antibiotic-producing bacterium. Comparison of this de novo generated GEM with a previously manually curated model demonstrates that RAVEN captures most of the previous model, and we subsequently reconstructed an updated model of S. coelicolor: Sco4. Following, we used Sco4 to predict promising targets for genetic engineering, which can be used to increase antibiotic production.

systems biology↗