Biology subjects

Millan-Oropeza, A.

Publications and source records attributed to Millan-Oropeza, A..

2 recordsLinked to original sources

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↗

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↗