Biology subjects

Silveira, W.

Publications and source records attributed to Silveira, W..

3 recordsLinked to original sources

bioRxiv2024

Multi-omics data and model integration reveal the main mechanisms associated with respiro-fermentative metabolism and ethanol stress responses in Kluyveromyces marxianus

Kluyveromyces marxianus is a yeast capable of fermenting sugars into ethanol and growing at high temperatures (>37{o}C). However, it is less tolerant to ethanol than Saccharomyces cerevisiae, which limits its application in second-generation ethanol production. Since the mechanisms of ethanol stress response are still poorly described, especially compared to S. cerevisiae, we used an integrative multi-omics approach, combining transcriptomics, coexpression networks, gene regulation, and genome-scale metabolic modelling to gain insights about these mechanisms. Through metabolic modelling, we predicted the occurrence of a respiro-fermentative metabolism and its onset as the dilution rate increased. From gene coexpression networks, we detected that the protein quality control system is a main mechanism involved in the ethanol stress response. Further, we identified key regulators in the ethanol stress response, such as HAP3, MET4, and SNF2, and assessed how disturbances in their gene expression affect cellular metabolism. We also found that amino acid metabolism, membrane lipid metabolism, and ergosterol exhibit increased metabolic flux under the explored conditions. These findings provide useful cues to develop and implement genetic and metabolic engineering strategies to enhance ethanol tolerance.

systems biology↗

bioRxiv2023

lista-GEM: the genome-scale metabolic reconstruction of Lipomyces starkeyi

Oleaginous yeasts cultivation in low-cost substrates is an alternative for more sustainable production of lipids and oleochemicals. Lipomyces starkeyi accumulates high amounts of lipids from different carbon sources, such as glycerol, and glucose and xylose (lignocellulosic sugars). Systems metabolic engineering approaches can further enhance its capabilities for lipid production, but no genome-scale metabolic networks have been reconstructed and curated for L. starkeyi. Herein, we propose lista-GEM, the first genome-scale metabolic model of L. starkeyi. We reconstructed the model using two high-quality models of oleaginous yeasts as templates and further curated the model to reflect the metabolism of L. starkeyi. We simulated phenotypes and predicted flux distributions in good accordance with experimental data. We also predicted targets to improve lipid production in glucose, xylose, and glycerol. The phase plane analysis indicated that the carbon availability affected lipid production more than oxygen availability. We found that the maximum lipid production in glucose and xylose required more oxygen than glycerol. Enzymes related to lipid synthesis in the endoplasmic reticulum were the main targets to improve lipid production: stearoyl-CoA desaturase, fatty-acyl-CoA synthase, diacylglycerol acyltransferase, and glycerol-3-phosphate acyltransferase. The glycolytic genes encoding pyruvate kinase, enolase, phosphoglycerate mutase, glyceraldehyde-3-phosphate dehydrogenase, and phosphoglycerate kinase were predicted as targets for overexpression. Pyruvate decarboxylase, acetaldehyde dehydrogenase, acetyl-CoA synthetase, adenylate kinase, inorganic diphosphatase, and triose-phosphate isomerase were predicted only when glycerol was the carbon source. Therefore, we demonstrated that lista-GEM provides multiple metabolic engineering targets to improve lipid production by L. starkeyi using carbon sources from agricultural and industrial wastes. HighlightsO_LILipomyces starkeyi can accumulate high amounts of lipids from carbon sources found in agricultural and industrial wastes. C_LIO_LIWe reconstructed lista-GEM, the first genome-scale metabolic model of L. starkeyi. C_LIO_LISimulated phenotypes were in line with experimental results of L. starkeyi. C_LIO_LIWe identified key gene targets for improving lipid production using metabolic engineering. C_LI

bioinformatics↗

bioRxiv2023

Accurate prediction of in vivo protein abundances by coupling constraint-based modeling and machine learning

Quantification of how different environmental cues affect protein allocation can provide important insights for understanding cell physiology. While absolute quantification of proteins can be obtained by resource-intensive mass-spectrometry-based technologies, prediction of protein abundances offers another way to obtain insights into protein allocation. Here we present CAMEL, a framework that couples constraint-based modelling with machine learning to predict protein abundance for any environmental condition. This is achieved by building machine learning models that leverage static features, derived from protein sequences, and condition-dependent features predicted from protein-constrained metabolic models. Our findings demonstrate that CAMEL results in excellent prediction of protein allocation in E. coli (average Pearson correlation of at least 0.9), and moderate performance in S. cerevisiae (average Pearson correlation of at least 0.5). Therefore, CAMEL outperformed contending approaches without using molecular read-outs from unseen conditions and provides a valuable tool for using protein allocation in biotechnological applications.

bioinformatics↗