Teses e Dissertações
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Item Assessment of metabolic regulation and stress response in oleaginous yeasts by systems biology approaches(Universidade Federal de Viçosa, 2024-01-30) Almeida, Eduardo Luís Menezes de; Silveira, Wendel Batista da; http://lattes.cnpq.br/3157480507007265The current environmental crises promoted by the extensive use of oil have highlighted the necessity of alternative and sustainable production chains. Lipids and fatty acids from oleaginous yeasts produced from lignocellulosic hydrolysates are promising sources for oleochemicals. Hence, selecting and developing robust yeast strains that can grow and produce lipids in lignocellulosic hydrolysates is pivotal to broaden their applicability and viability. Lipomyces starkeyi is an oleaginous yeast capable of growing and producing lipids using a diverse range of carbon sources. Its growth and lipid production have been demonstrated in lignocellulosic biomasses. Papiliotrema laurentii can also assimilate sugars derived from agricultural wastes, such as glucose and xylose from lignocellulosic biomasses, and convert them into high lipid amounts. However, wild P. laurentii strains are highly sensitive to acetic acid, one of the primary inhibitors in lignocellulosic hydrolysates. The understanding of multifactorial stress responses and yeast physiology can be facilitated by the application of holistic approaches, including biological networks and high-throughput data. Therefore, we hypothesized that the use of systems biology approaches, including genome-scale metabolic models (GEMs) and transcriptomics, as well as their integration, can support us to understand the metabolism and stress responses of oleaginous yeasts and identify suitable targets for metabolic engineering. Herein, we propose the first GEM of L. starkeyi, lista-GEM. We reconstructed lista-GEM using two high-quality oleaginous yeast models as templates and curated it to reflect the metabolism of L. starkeyi. The simulated phenotypes and predicted flux distributions were in good accordance with experimental data. Then, we predicted targets to improve lipid production in glucose, xylose, and glycerol. Enzymes related to lipid synthesis in the endoplasmic reticulum, such as stearoyl-CoA desaturase, fatty-acyl-CoA synthase, diacylglycerol acyltransferase, and glycerol-3-phosphate acyltransferase, were the main targets to improve lipid production. Glycolytic genes were also 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. Hence, lista-GEM provides multiple metabolic engineeringtargets to improve lipid production by L. starkeyi using carbon sources from agricultural and industrial wastes. Furthermore, we combined transcriptome and genome-scale metabolic modeling to deepen our understanding regarding the targets of acetic acid stress, as well as the adaptive responses in P. laurentii. Acetic acid stress promoted global expression changes and most repressed genes were related to transcriptional and translational processes. Under stress, the sensitive strain induced DNA mismatch repair mechanisms and meiosis, while the tolerant strain negatively regulated autophagy and the cell cycle. The tolerant strain induced processes responsible for increasing the intracellular pH (e.g., arginase, ornithine metabolism, urea cycle), detoxification of toxic compounds (e.g., glutathione metabolism), and proton efflux. The tolerant strain also presented a remarkable NAD(P)H pool in the metabolic modeling analysis, which might support the reducing power required by tolerance mechanisms. Otherwise, the sensitive strain induced genes related to cell wall biogenesis and cobalamin synthesis. Overall, the genes and pathways described herein as tolerant-related can be useful in future metabolic engineering strategies to improve the tolerance of P. laurentii to weak acids, boosting its application in lignocellulosic-based biorefineries. Keywords: Metabolic modeling; Transcriptomics; Non-Saccharomyces.