Biology subjects

Sanchez, B. J.

Publications and source records attributed to Sanchez, B. J..

8 recordsLinked to original sources

bioRxiv2023

Yeast9: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This have increased the quality and scope of this model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or normal conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through the central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference in nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine-learning models. Based on those findings we anticipate that Yeast9 will empower systems biology studies of yeast metabolism.

systems 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

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

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↗

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

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts.

bioengineering↗

bioRxiv2018

SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework

A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can predict accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr.

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↗