[en] Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 μmol·L(-1) impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.
Disciplines :
Biotechnology
Author, co-author :
Minden, Steven ; Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart
Aniolek, Maria; Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart,
Sarkizi Shams Hajian, Christopher ; Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart
Teleki, Attila; Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart,
Zerrer, Tobias; Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart,
Delvigne, Frank ; Université de Liège - ULiège > Département GxABT > Microbial technologies ; Microbial Processes and Interactions (MiPI), TERRA Research and Teaching Centre
van Gulik, Walter; Department of Biotechnology, Delft University of Technology, van der Maasweg 6,
Deshmukh, Amit; Royal DSM, 2613 AX Delft, The Netherlands.
Noorman, Henk; Royal DSM, 2613 AX Delft, The Netherlands. ; Department of Biotechnology, Delft University of Technology, 2628 CD Delft, The
Takors, Ralf; Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart,
Language :
English
Title :
Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli.
Publication date :
18 March 2022
Journal title :
Metabolites
eISSN :
2218-1989
Publisher :
MDPI AG, Basel, Ch
Volume :
12
Issue :
3
Peer reviewed :
Peer Reviewed verified by ORBi
Funding number :
FKZ 031B0629/Federal Ministry of Education and Research/; 722361/ERA CoBioTech/
Mengal, P.; Wubbolts, M.; Zika, E.; Ruiz, A.; Brigitta, D.; Pieniadz, A.; Black, S. Bio-based Industries Joint Undertaking: The catalyst for sustainable bio-based economic growth in Europe. New Biotechnol. 2018, 40, 31–39. [CrossRef]
Singh, A.; Christensen, T.; Panoutsou, C. Policy review for biomass value chains in the European bioeconomy. Glob. Transit. 2021, 3, 13–42. [CrossRef]
Straathof, A.J.; Wahl, S.A.; Benjamin, K.R.; Takors, R.; Wierckx, N.; Noorman, H.J. Grand Research Challenges for Sustainable Industrial Biotechnology. Trends Biotechnol. 2019, 37, 1042–1050. [CrossRef] [PubMed]
What Is Horizon 2020? Available online: https://ec.europa.eu/programmes/horizon2020/en/what-horizon-2020 (accessed on 15 January 2022).
Takors, R. Scale-up of microbial processes: Impacts, tools and open questions. J. Biotechnol. 2012, 160, 3–9. [CrossRef]
Noorman, H. An industrial perspective on bioreactor scale-down: What we can learn from combined large-scale bioprocess and model fluid studies. Biotechnol. J. 2011, 6, 934–943. [CrossRef] [PubMed]
De Lorenzo, V.; Couto, J. The important versus the exciting: Reining contradictions in contemporary biotechnology. Microb. Biotechnol. 2018, 12, 32–34. [CrossRef]
Wehrs, M.; Tanjore, D.; Eng, T.; Lievense, J.; Pray, T.R.; Mukhopadhyay, A. Engineering robust production microbes for large-scale cultivation. Trends Microbiol. 2019, 27, 524–537. [CrossRef] [PubMed]
Schügerl, K. Comparison of different bioreactor performances. Bioprocess Biosyst. Eng. 1993, 9, 215–223. [CrossRef]
Vrábel, P.; van der Lans, R.G.; Luyben, K.C.; Boon, L.; Nienow, A.W. Mixing in large-scale vessels stirred with multiple radial or radial and axial up-pumping impellers: Modelling and measurements. Chem. Eng. Sci. 2000, 55, 5881–5896. [CrossRef]
Takors, R. Editorial: How can we ensure the successful transfer from lab-to large-scale production? Eng. Life Sci. 2016, 16, 587. [CrossRef]
Crater, J.S.; Lievense, J.C. Scale-up of industrial microbial processes. FEMS Microbiol. Lett. 2018, 365, 1–5. [CrossRef]
Hill, P.; Benjamin, K.; Bhattacharjee, B.; Garcia, F.; Leng, J.; Liu, C.-L.; Murarka, A.; Pitera, D.; Rodriguez Porcel, E.M.; da Silva, I.; et al. Clean manufacturing powered by biology: How Amyris has deployed technology and aims to do it better. J. Ind. Microbiol. Biotechnol. 2020, 47, 965–975. [CrossRef]
Florez, S.L. Accelerating Fermentation Process Development at Ginkgo Using Sartorius’ Ambr250 Platform. In Proceedings of the BIO World Congress on Industrial Biotechnology, Des Moines, IA, USA, 8–11 July 2019; Available online: https://www.bio.org/sites/default/files/legacy/bioorg/docs/FlorezGinkgo.pdf.
Ögmundarson, Ó.; Sukumara, S.; Herrgård, M.J.; Fantke, P. Combining Environmental and Economic Performance for Bioprocess Optimization. Trends Biotechnol. 2020, 38, 1203–1214. [CrossRef] [PubMed]
Neubauer, P.; Junne, S. Scale-down simulators for metabolic analysis of large-scale bioprocesses. Curr. Opin. Biotechnol. 2010, 21, 114–121. [CrossRef] [PubMed]
Täuber, S.; Golze, C.; Ho, P.; Lieres, E.V. DMSCC: A Microfluidic Platform for Microbial Single-Cell Cultivation under Dynamic Environmental Medium Conditions. bioRxiv 2020. [CrossRef] [PubMed]
Delvigne, F.; Takors, R.; Mudde, R.; Van Gulik, W.; Noorman, H. Bioprocess scale-up/down as integrative enabling technology: From fluid mechanics to systems biology and beyond. Microb. Biotechnol. 2017, 10, 1267–1274. [CrossRef] [PubMed]
Papagianni, M. Methodologies for Scale-down of Microbial Bioprocesses. J. Microb. Biochem. Technol. 2015, s5, 1–7. [CrossRef]
Lara, A.R.; Galindo, E.; Ramírez, O.T.; Palomares, L.A. Living with Heterogeneities in Bioreactors. Mol. Biotechnol. 2006, 34, 355–381. [CrossRef]
Haringa, C.; Mudde, R.F.; Noorman, H.J. From industrial fermentor to CFD-guided downscaling: What have we learned? Biochem. Eng. J. 2018, 140, 57–71. [CrossRef]
George, S.; Larsson, G.; Olsson, K.; Enfors, S.-O. Comparison of the Baker’s yeast process performance in laboratory and production scale. Bioprocess Biosyst. Eng. 1998, 18, 135–142. [CrossRef]
Bylund, F.; Collet, E.; Enfors, S.-O.; Larsson, G. Substrate gradient formation in the large-scale bioreactor lowers cell yield and increases by-product formation. Bioprocess Biosyst. Eng. 1998, 18, 171–180. [CrossRef]
de Jonge, L.P.; Buijs, N.A.A.; Pierick, A.T.; Deshmukh, A.; Zhao, Z.; Kiel, J.A.K.W.; Heijnen, J.J.; van Gulik, W.M. Scale-down of penicillin production in Penicillium chrysogenum. Biotechnol. J. 2011, 6, 944–958. [CrossRef] [PubMed]
Pham, T.-H.; Mauvais, G.; Vergoignan, C.; de Coninck, J.; Dumont, F.; Lherminier, J.; Cachon, R.; Feron, G. Gaseous environments modify physiology in the brewing yeast Saccharomyces cerevisiae during batch alcoholic fermentation. J. Appl. Microbiol. 2008, 105, 858–874. [CrossRef] [PubMed]
Kresnowati, M.T.A.P.; Van Winden, W.A.; Almering, M.J.H.; Pierick, A.T.; Ras, C.; A Knijnenburg, T.; Daran-Lapujade, P.; Pronk, J.T.; Heijnen, J.J.; Daran, J.-M. When transcriptome meets metabolome: Fast cellular responses of yeast to sudden relief of glucose limitation. Mol. Syst. Biol. 2006, 2, 49. [CrossRef]
Suarez-Mendez, C.A.; Ras, C.; Wahl, S.A. Metabolic adjustment upon repetitive substrate perturbations using dynamic 13C-tracing in yeast. Microb. Cell Factories 2017, 16, 161. [CrossRef]
Löffler, M.; Simen, J.D.; Jäger, G.; Schäferhoff, K.; Freund, A.; Takors, R. Engineering E. coli for large-scale production-Strategies considering ATP expenses and transcriptional responses. Metab. Eng. 2016, 38, 73–85. [CrossRef]
Zieringer, J.; Wild, M.; Takors, R. Data-driven in silico prediction of regulation heterogeneity and ATP demands of Escherichia coli in large-scale bioreactors. Biotechnol. Bioeng. 2021, 118, 265–278. [CrossRef]
Wright, N.R.; Wulff, T.; Palmqvist, E.A.; Jørgensen, T.R.; Workman, C.T.; Sonnenschein, N.; Rønnest, N.P.; Herrgård, M.J. Fluctuations in glucose availability prevent global proteome changes and physiological transition during prolonged chemostat cultivations of Saccharomyces cerevisiae. Biotechnol. Bioeng. 2020, 117, 2074–2088. [CrossRef]
Nieß, A.; Löffler, M.; Simen, J.D.; Takors, R. Repetitive Short-Term Stimuli Imposed in Poor Mixing Zones Induce Long-Term Adaptation of E. coli Cultures in Large-Scale Bioreactors: Experimental Evidence and Mathematical Model. Front. Microbiol. 2017, 8, 1195. [CrossRef]
Anane, E.; Sawatzki, A.; Neubauer, P.; Cruz-Bournazou, M.N. Modelling concentration gradients in fed-batch cultivations of E. coli-towards the flexible design of scale-down experiments. J. Chem. Technol. Biotechnol. 2019, 94, 516–526. [CrossRef]
Delvigne, F.; Goffin, P. Microbial heterogeneity affects bioprocess robustness: Dynamic single-cell analysis contributes to understanding of microbial populations. Biotechnol. J. 2013, 9, 61–72. [CrossRef] [PubMed]
Heins, A.-L.; Weuster-Botz, D. Population heterogeneity in microbial bioprocesses: Origin, analysis, mechanisms, and future perspectives. Bioprocess Biosyst. Eng. 2018, 41, 889–916. [CrossRef] [PubMed]
Nadal-Rey, G.; McClure, D.D.; Kavanagh, J.M.; Cassells, B.; Cornelissen, S.; Fletcher, D.F.; Gernaey, K.V. Development of dynamic compartment models for industrial aerobic fed-batch fermentation processes. Chem. Eng. J. 2021, 420, 130402. [CrossRef]
Lapin, A.; Müller, D.; Reuss, M. Dynamic Behavior of Microbial Populations in Stirred Bioreactors Simulated with Euler−Lagrange Methods: Traveling along the Lifelines of Single Cells. Ind. Eng. Chem. Res. 2004, 43, 4647–4656. [CrossRef]
Haringa, C.; Tang, W.; Deshmukh, A.T.; Xia, J.; Reuss, M.; Heijnen, J.J.; Mudde, R.F.; Noorman, H.J. Euler-Lagrange computational fluid dynamics for (bio) reactor scale down: An analysis of organism lifelines. Eng. Life Sci. 2016, 16, 652–663. [CrossRef]
Haringa, C.; Deshmukh, A.T.; Mudde, R.F.; Noorman, H.J. Euler-Lagrange analysis towards representative down-scaling of a 22 m 3 aerobic S. cerevisiae fermentation. Chem. Eng. Sci. 2017, 170, 653–669. [CrossRef]
Sarkizi Shams Hajian, C.; Haringa, C.; Noorman, H.; Takors, R. Predicting By-Product Gradients of Baker’s Yeast Production at Industrial Scale: A Practical Simulation Approach. Processes 2020, 8, 1554. [CrossRef]
Kuschel, M.; Takors, R. Simulated oxygen and glucose gradients as a prerequisite for predicting industrial scale performance a priori. Biotechnol. Bioeng. 2020, 117, 2760–2770. [CrossRef]
Ziegler, M.; Zieringer, J.; Döring, C.-L.; Paul, L.; Schaal, C.; Takors, R. Engineering of a robust Escherichia coli chassis and exploitation for large-scale production processes. Metab. Eng. 2021, 67, 75–87. [CrossRef]
Venturini Copetti, M. Yeasts and molds in fermented food production: An ancient bioprocess. Curr. Opin. Food Sci. 2019, 25, 57–61. [CrossRef]
Nielsen, J. Yeast systems biology: Model organism and cell factory. Biotechnol. J. 2019, 14, 1800421. [CrossRef] [PubMed]
Larsson, G.; Törnkvist, M.; Ståhl Wernersson, E.; Trägårdh, C.; Noorman, H.J.; Enfors, S.O. Substrate gradients in bioreactors: Origin and consequences. Bioprocess Biosyst. Eng. 1996, 14, 281–289. [CrossRef]
Noorman, H.; Hjertager, B.H.; Morud, K.; Targardh, C.; Enfors, S.-O.; Larsson, G.; Tornkvist, M. Measurement and Computational Fluid Dynamics Simulations of Saccharomyces cerevisiae Production in a 30 m3 Stirred Tank Reactor. Int. Symp. Bioreact. Perform. 1993, 150, 243–263.
Rizzi, M.; Baltes, M.; Theobald, U.; Reuss, M. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnol. Bioeng. 1997, 55, 592–608. [CrossRef]
Mashego, M.R.; van Gulik, W.M.; Vinke, J.L.; Visser, D.; Heijnen, J.J. In vivo kinetics with rapid perturbation experiments in Saccharomyces cerevisiae using a second-generation BioScope. Metab. Eng. 2006, 8, 370–383. [CrossRef]
Lao-Martil, D.; Verhagen, K.J.A.; Schmitz, J.P.J.; Teusink, B.; Wahl, S.A.; van Riel, N.A.W. Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities. Metabolites 2022, 12, 74. [CrossRef]
Theobald, U.M.; Mailinger, W.; Baltes, M.; Rizzi, M.; Reuss, M. In Vivo Analysis of Metabolic Dynamics in Saccharomyces cerevisiae: I. Experimental Observations. Biotechnol. Bioeng. 1997, 55, 305–316. [CrossRef]
Suarez-Mendez, C.; Sousa, A.; Heijnen, J.J.; Wahl, A. Fast “Feast/Famine” Cycles for Studying Microbial Physiology Under Dynamic Conditions: A Case Study with Saccharomyces cerevisiae. Metabolites 2014, 4, 347–372. [CrossRef]
Diderich, J.A.; Schepper, M.; van Hoek, P.; Luttik, M.A.H.; van Dijken, J.P.; Pronk, J.T.; Klaassen, P.; Boelens, H.F.M.; de Mattos, M.J.T.; van Dam, K.; et al. Glucose Uptake Kinetics and Transcription of HXTGenes in Chemostat Cultures of Saccharomyces cerevisiae. J. Biol. Chem. 1999, 274, 15350–15359. [CrossRef]
Marc, J.; Feria-Gervasio, D.; Mouret, J.-R.; Guillouet, S.E. Impact of oleic acid as co-substrate of glucose on “short” and “long-term” Crabtree effect in Saccharomyces cerevisiae. Microb. Cell Factories 2013, 12, 83. [CrossRef]
Vos, T.; Hakkaart, X.D.V.; de Hulster, E.A.F.; Van Maris, A.J.A.; Pronk, J.T.; Daran-Lapujade, P. Maintenance-energy requirements and robustness of Saccharomyces cerevisiae at aerobic near-zero specific growth rates. Microb. Cell Factories 2016, 15, 111. [CrossRef] [PubMed]
Eigenstetter, G.; Takors, R. Dynamic modeling reveals a three-step response of Saccharomyces cerevisiae to high CO2 levels accompanied by increasing ATP demands. FEMS Yeast Res. 2017, 17, fox008. [CrossRef] [PubMed]
Suarez-Mendez, C.; Hanemaaijer, M.; Pierick, A.T.; Wolters, J.C.; Heijnen, J.; Wahl, S. Interaction of storage carbohydrates and other cyclic fluxes with central metabolism: A quantitative approach by non-stationary 13 C metabolic flux analysis. Metab. Eng. Commun. 2016, 3, 52–63. [CrossRef] [PubMed]
Roubos, J.A.; Krabben, P.; Luiten, R.G.M.; Verbruggen, H.B.; Heijnen, J.J. A Quantitative Approach to Characterizing Cell Lysis Caused by Mechanical Agitation of Streptomyces clavuligerus. Biotechnol. Prog. 2001, 17, 336–347. [CrossRef]
Canelas, A.B.; Ras, C.; ten Pierick, A.; van Dam, J.C.; Heijnen, J.J.; van Gulik, W.M. Leakage-free rapid quenching technique for yeast metabolomics. Metabolomics 2008, 4, 226–239. [CrossRef]
Noubhani, A.; Bunoust, O.; Rigoulet, M.; Thevelein, J. Reconstitution of ethanolic fermentation in permeabilized spheroplasts of wild-type and trehalose-6-phosphate synthase mutants of the yeast Saccharomyces cerevisiae. JBIC J. Biol. Inorg. Chem. 2000, 267, 4566–4576. [CrossRef]
Blazquez, M.; Lagunas, R.; Gancedo, C.; Gancedo, J.M. Trehalose-6-phosphate, a new regulator of yeast glycolysis that inhibits hexokinases. FEBS Lett. 1993, 329, 51–54. [CrossRef]
Paalman, J.W.G.; Verwaal, R.; Slofstra, S.H.; Verkleij, A.J.; Boonstra, J.; Verrips, C. Trehalose and glycogen accumulation is related to the duration of the G1 phase of Saccharomyces cerevisiae. FEMS Yeast Res. 2003, 3, 261–268. [CrossRef]
Francois, J.; Walther, T.; Parrou, J.L. Genetics and Regulation of Glycogen and Trehalose Metabolism in Saccharomyces cerevisiae. Microb. Stress Toler. Biofuels 2012, 22, 29–56. [CrossRef]
Canelas, A.B.; Harrison, N.; Fazio, A.; Zhang, J.; Pitkänen, J.-P.; Brink, J.V.D.; Bakker, B.; Bogner, L.; Bouwman, J.; Castrillo, J.I.; et al. Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains. Nat. Commun. 2010, 1, 145–148. [CrossRef]
Yi, D.-G.; Huh, W.-K. UDP-glucose pyrophosphorylase Ugp1 is involved in oxidative stress response and long-term survival during stationary phase in Saccharomyces cerevisiae. Biochem. Biophys. Res. Commun. 2015, 467, 657–663. [CrossRef]
Bárcena, M.; Radermacher, M.; Bär, J.; Kopperschläger, G.; Ruiz, T. The structure of the ATP-bound state of S. cerevisiae phosphofructokinase determined by cryo-electron microscopy. J. Struct. Biol. 2007, 159, 135–143. [CrossRef]
Teusink, B.; Passarge, J.; Reijenga, C.A.; Esgalhado, M.; van der Weijden, C.C.; Schepper, M.; Walsh, M.C.; Bakker, B.M.; Van Dam, K.; Westerhoff, H.V.; et al. Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. JBIC J. Biol. Inorg. Chem. 2000, 267, 5313–5329. [CrossRef]
van den Brink, J. Dynamic Response of Saccharomyces cerevisiae to Fermentative Growth Conditions. Ph.D. Thesis, Technische Universiteit Delft, Delft, The Netherlands, 9 February 2009.
Jurica, M.S.; Mesecar, A.; Heath, P.J.; Shi, W.; Nowak, T.; Stoddard, B.L. The allosteric regulation of pyruvate kinase by fructose-1, 6-bisphosphate. Structure 1998, 6, 195–210. [CrossRef]
Gombert, A.K.; dos Santos, M.M.; Christensen, B.; Nielsen, J. Network Identification and Flux Quantification in the Central Metabolism of Saccharomyces cerevisiae under Different Conditions of Glucose Repression. J. Bacteriol. 2001, 183, 1441–1451. [CrossRef]
Zhang, J.; Pierick, A.T.; Van Rossum, H.M.; Seifar, R.M.; Ras, C.; Daran, J.-M.; Heijnen, J.J.; Wahl, S.A. Determination of the Cytosolic NADPH/NADP Ratio in Saccharomyces cerevisiae using Shikimate Dehydrogenase as Sensor Reaction. Sci. Rep. 2015, 5, 12846. [CrossRef]
Vemuri, G.N.; Eiteman, M.A.; McEwen, J.E.; Olsson, L.; Nielsen, J. Increasing NADH oxidation reduces overflow metabolism in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA 2007, 104, 2402–2407. [CrossRef]
Visser, D.; van Zuylen, G.A.; van Dam, J.C.; Eman, M.R.; Pröll, A.; Ras, C.; Wu, L.; van Gulik, W.M.; Heijnen, J.J. Analysis of in vivo kinetics of glycolysis in aerobic Saccharomyces cerevisiae by application of glucose and ethanol pulses. Biotechnol. Bioeng. 2004, 88, 157–167. [CrossRef]
Ball, W.J.; Atkinson, D.E. Adenylate energy charge in Saccharomyces cerevisiae during starvation. J. Bacteriol. 1975, 121, 975–982. [CrossRef]
Walther, T.; Novo, M.; Rössger, K.; Létisse, F.; Loret, M.-O.; Portais, J.-C.; François, J.M. Control of ATP homeostasis during the respiro-fermentative transition in yeast. Mol. Syst. Biol. 2010, 6, 344. [CrossRef]
Pinson, B.; Ceschin, J.; Saint-Marc, C.; Daignan-Fornier, B. Dual control of NAD+ synthesis by purine metabolites in yeast. Elife 2019, 8, 43808. [CrossRef] [PubMed]
Fowler, J.D.; Dunlop, E.H. Effects of reactant heterogeneity and mixing on catabolite repression in cultures of Saccharomyces cerevisiae. Biotechnol. Bioeng. 1989, 33, 1039–1046. [CrossRef] [PubMed]
Reifenberger, E.; Boles, E.; Ciriacy, M. Kinetic Characterization of Individual Hexose Transporters of Saccharomyces cerevisiae and their Relation to the Triggering Mechanisms of Glucose Repression. JBIC J. Biol. Inorg. Chem. 1997, 245, 324–333. [CrossRef] [PubMed]
Boender, L.G.M.; de Hulster, E.A.F.; van Maris, A.J.A.; Daran-Lapujade, P.A.S.; Pronk, J.T. Quantitative Physiology of Saccharomyces cerevisiae at Near-Zero Specific Growth Rates. Appl. Environ. Microbiol. 2009, 75, 5607–5614. [CrossRef] [PubMed]
Jules, M.; Beltran, G.; François, J.M.; Parrou, J.L. New Insights into Trehalose Metabolism by Saccharomyces cerevisiae: NTH2 Encodes a Functional Cytosolic Trehalase, and Deletion of TPS1 Reveals Ath1p-Dependent Trehalose Mobilization. Appl. Environ. Microbiol. 2008, 74, 605–614. [CrossRef] [PubMed]
Youk, H.; Van Oudenaarden, A. Growth landscape formed by perception and import of glucose in yeast. Nature 2009, 462, 875–879. [CrossRef]
Teusink, B.; Diderich, J.; Westerhoff, H.V.; van Dam, K.; Walsh, M.C. Intracellular Glucose Concentration in Derepressed Yeast Cells Consuming Glucose Is High Enough To Reduce the Glucose Transport Rate by 50%. J. Bacteriol. 1998, 180, 556–562. [CrossRef]
Bosdriesz, E.; Wortel, M.T.; Haanstra, J.R.; Wagner, M.J.; Cortés, P.D.L.T.; Teusink, B. Low affinity uniporter carrier proteins can increase net substrate uptake rate by reducing efflux. Sci. Rep. 2018, 8, 5576. [CrossRef]
De Alteriis, E.; Cartenì, F.; Parascandola, P.; Serpa, J.; Mazzoleni, S. Revisiting the Crabtree/Warburg effect in a dynamic perspective: A fitness advantage against sugar-induced cell death. Cell Cycle 2018, 17, 688–701. [CrossRef]
Woolford, J.L.; Baserga, S.J. Ribosome Biogenesis in the Yeast Saccharomyces cerevisiae. Genetics 2013, 195, 643–681. [CrossRef]
Nissen, T.L.; Schulze, U.; Nielsen, J.; Villadsen, J. Flux Distributions in Anaerobic, Glucose-Limited Continuous Cultures of Saccharomyces cerevisiae. Microbiology 1997, 143, 203–218. [CrossRef] [PubMed]
van den Brink, J.; Canelas, A.B.; van Gulik, W.M.; Pronk, J.T.; Heijnen, J.J.; de Winde, J.H.; Daran-Lapujade, P. Dynamics of Glycolytic Regulation during Adaptation of Saccharomyces cerevisiae to Fermentative Metabolism. Appl. Environ. Microbiol. 2008, 74, 5710–5723. [CrossRef] [PubMed]
Jansen, M.L.A.; Diderich, J.A.; Mashego, M.; Hassane, A.; de Winde, J.H.; Daran-Lapujade, P.; Pronk, J.T. Prolonged selection in aerobic, glucose-limited chemostat cultures of Saccharomyces cerevisiae causes a partial loss of glycolytic capacity. Microbiology 2005, 151, 1657–1669. [CrossRef]
Mashego, M.R.; Jansen, M.L.; Vinke, J.L.; Van Gulik, W.M.; Heijnen, J.J. Changes in the metabolome of Saccharomyces cerevisiae associated with evolution in aerobic glucose-limited chemostats. FEMS Yeast Res. 2005, 5, 419–430. [CrossRef]
Navas, M.A.; Gancedo, J.M. The regulatory characteristics of yeast fructose-1, 6-bisphosphatase confer only a small selective advantage. J. Bacteriol. 1996, 178, 1809–1812. [CrossRef]
Zaman, S.; Lippman, S.I.; Schneper, L.; Slonim, N.; Broach, J.R. Glucose regulates transcription in yeast through a network of signaling pathways. Mol. Syst. Biol. 2009, 5, 245. [CrossRef]
Saliola, M.; Tramonti, A.; Lanini, C.; Cialfi, S.; de Biase, D.; Falcone, C. Intracellular NADPH Levels Affect the Oligomeric State of the Glucose 6-Phosphate Dehydrogenase. Eukaryot. Cell 2012, 11, 1503–1511. [CrossRef]
Weber, C.A.; Sekar, K.; Tang, J.H.; Warmer, P.; Sauer, U.; Weis, K. β-Oxidation and autophagy are critical energy providers during acute glucose depletion in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA 2020, 117, 12239–12248. [CrossRef]
Thevelein, J.M. Regulation of Trehalose Mobilization in Fungi. Microbiol. Rev. 1984, 48, 42–59. [CrossRef]
Nijkamp, J.F.; Broek, M.V.D.; Datema, E.; de Kok, S.; Bosman, L.; Luttik, M.A.; Daran-Lapujade, P.; Vongsangnak, W.; Nielsen, J.; Heijne, W.H.; et al. De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology. Microb. Cell Factories 2012, 11, 36. [CrossRef]
Ljungdahl, P.; Daignan-Fornier, B. Regulation of Amino Acid, Nucleotide, and Phosphate Metabolism in Saccharomyces cerevisiae. Genetics 2012, 190, 885–929. [CrossRef] [PubMed]
Hardie, D.G.; Ross, F.A.; Hawley, S.A. AMPK: A nutrient and energy sensor that maintains energy homeostasis. Nat. Rev. Mol. Cell Biol. 2012, 13, 251–262. [CrossRef]
Wu, G.; Yan, Q.; Jones, J.A.; Tang, Y.J.; Fong, S.S.; Koffas, M.A. Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications. Trends Biotechnol. 2016, 34, 652–664. [CrossRef] [PubMed]
Celton, M.; Sanchez, I.; Goelzer, A.; Fromion, V.; Camarasa, C.; Dequin, S. A comparative transcriptomic, fluxomic and metabolomic analysis of the response of Saccharomyces cerevisiae to increases in NADPH oxidation. BMC Genom. 2012, 13, 317. [CrossRef] [PubMed]
Verduyn, C.; Postma, E.; Scheffers, W.A.; van Dijken, J.P. Effect of benzoic acid on metabolic fluxes in yeasts: A continuous-culture study on the regulation of respiration and alcoholic fermentation. Yeast 1992, 8, 501–517. [CrossRef] [PubMed]
de Koning, W.; van Dam, K.; de Koning, W. A method for the determination of changes of glycolytic metabolites in yeast on a subsecond time scale using extraction at neutral pH. Anal. Biochem. 1992, 204, 118–123. [CrossRef]
Pepper, M.E. Designing a Minimal-Knowledge Controller to Achieve Maximum Stable Growth for an Escherichia coli Bioprocess. ProQuest Diss. Publ. 2015.
Theobald, U. Untersuchungen zur Dynamik des Crabtree-Effektes, Reihe 17; Fortschrittberichte/VDI: Düsseldorf, Germany, 1995.
Parrou, J.L.; Francois, J. A Simplified Procedure for a Rapid and Reliable Assay of Both Glycogen and Trehalose in Whole Yeast Cells. Anal. Biochem. 1997, 248, 186–188. [CrossRef]
Suarez-Mendez, C.A. Dynamics of Storage Carbohydrates Metabolism in Saccharomyces cerevisiae: A Quantitative study. TU Delft Repos. 2015.
Feith, A.; Teleki, A.; Graf, M.; Favilli, L.; Takors, R. HILIC-Enabled 13C Metabolomics Strategies: Comparing Quantitative Precision and Spectral Accuracy of QTOF High-and QQQ Low-Resolution Mass Spectrometry. Metabolites 2019, 9, 63. [CrossRef]
Frank, C.; Teleki, A.; Jendrossek, D. Characterization of Agrobacterium tumefaciens PPKs reveals the formation of oligophosphorylated products up to nucleoside nona-phosphates. Appl. Microbiol. Biotechnol. 2020, 104, 9683–9692. [CrossRef]
Zimmermann, M.; Sauer, U.; Zamboni, N. Quantification and Mass Isotopomer Profiling of α-Keto Acids in Central Carbon Metabolism. Anal. Chem. 2014, 86, 3232–3237. [CrossRef] [PubMed]
Junghans, L.; Teleki, A.; Wijaya, A.W.; Becker, M.; Schweikert, M.; Takors, R. From nutritional wealth to autophagy: In vivo metabolic dynamics in the cytosol, mitochondrion and shuttles of IgG producing CHO cells. Metab. Eng. 2019, 54, 145–159. [CrossRef] [PubMed]
Wijaya, A.W.; Ulmer, A.; Hundsdorfer, L.; Verhagen, N.; Teleki, A.; Takors, R. Compartment-specific metabolome labeling enables the identification of subcellular fluxes that may serve as promising metabolic engineering targets in CHO cells. Bioprocess Biosyst. Eng. 2021, 44, 2567–2578. [CrossRef] [PubMed]
Wakamatsu, A.; Morimoto, K.; Shimizu, M.; Kudoh, S. A severe peak tailing of phosphate compounds caused by interaction with stainless steel used for liquid chromatography and electrospray mass spectrometry. J. Sep. Sci. 2005, 28, 1823–1830. [CrossRef]
Gower, J.C. Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika 1966, 53, 325. [CrossRef]
Buchholz, J.; Graf, M.; Freund, A.; Busche, T.; Kalinowski, J.; Blombach, B.; Takors, R. CO2/HCO3− perturbations of simulated large scale gradients in a scale-down device cause fast transcriptional responses in Corynebacterium glutamicum. Appl. Microbiol. Biotechnol. 2014, 98, 8563–8572. [CrossRef]