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Identification and characterization of C4 dicarboxylic acid transporters in Pichia kudriavzevii CY902 strain

ABSTRACT: RNA-seq libraries were constructed for three samples, including (I) ΔURA3 strain grown in SD medium containing 2% w/v glucose(pH=5.0);(II)ΔURA3 strain grown in SD medium containing 2%w/v glucose and 2% w/v succinic acid(pH=5.0);(III)ΔURA3 strain grown in SD medium containing 2% w/v succinic acid(pH=5.0). For preparation of RNA samples, ΔURA3 cells grown overnight were inoculated into 50 ml liquid Synthetic Dextrose (SD) medium with the initial inoculation amount of OD600= 0.1, and cultured for 24 hours to the logarithmic growth phase (OD600= 2-3). The cells were collected by centrifugation at 6000g for 5 minutes. After washing twice with phosphate buffer saline (PBS), they were inoculated into new 50 mL SD medium containing carbon sources of different combinations of glucose and succinic acid .The pH of the culture sample is adjusted to 5.0 with NaOH. After flask culturing at 30°C and 250 rpm for an additional three hours, the yeast cells were collected by centrifugation for total RNA isolation and Illumina RNA-seq library construction. Total RNA for samples were isolated using TRIzol reagent (Invitrogen, Grand Island, USA), then used for high-throughput RNA sequencing. The 150-nt paired-end RNA-seq libraries were generated commercially at Novogene Biotechnology Co. Ltd (Tianjin, China) by using Illumina’s Hiseq X Ten platform (Illumina, San Diego, USA). Each sample contains two biological replicates.

ORGANISM(S): Pichia kudriavzevii

PROVIDER: GSE159457 | GEO | 2021/05/05

REPOSITORIES: GEO

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Project description:This is the model described in: Bacterial adaptation through distributed sensing of metabolic fluxes Oliver Kotte, Judith B Zaugg and Matthias Heinemann;Mol Sys Biol2010;6:355. doi:10.1038/msb.2010.10; Abstract: The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources. In its current form this SBML model is parametrized for the glucose to acetate transition and to simulate the extended diauxic shift as shown in figure 3 and scenario 6 of the attached matlab file. In this scenario the cells first are grown from an OD600 (BM) of 0.03 with a starting glucose concentration of 0.5 g/l for 8.15 h (29340 sec). Then a medium containing 5 g/l acetate is inoculated with these cells to an OD600 of 0.03 and grown for another 19.7 hours (70920 sec). Finally the cells are shifted to a medium containing both glucose and acetate at a concentration of 3 g/l with a starting OD600 of 0.0005. The shifts where implemented using events triggering at the times determined by the parameters shift1 and shift2 (in hours). To simulate other scenarios the initial conditions need to be changed as described in the supplemental materials (supplement 1) The original SBML model and the MATLAB file used for the calculations can be down loaded as supplementary materials of the publication from the MSB website. (supplement 2). The units of the external metabolites are in [g/l], those of the biomass in optical density,OD600, taken as dimensionless, and [micromole/(gramm dry weight)] for all intracellular metabolites. As the latter cannot be implemented in SBML, it was chosen to be micromole only and the units of the parameters are left mostly undefined. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Project description:To understand cell responses against high succinate stress, a continuous culture was performed for 9 months using a wild Escherichia coli under gradually increasing succinate stress. The final adapted strain against high succinate stress, named DST160, was able to grow with no lag phase in medium containing 1.35 M of succinate stress while wild W3110 showed more than 38 h lag phase. The maximum growth rate and growth yield of DST160 under the stress condition were 0.20 h-1 and 0.192 g-cell/g-glucose, which were 53.8 % and 12.3 % higher than those of W3110 (0.13 h-1 and 0.171 g-cell/g-glucose, respectively). A single colony of E. coli W3110 was inoculated into a 15-mL tube containing 4 mL of LB medium. After 12 h cultivation in a shaking incubator at 37℃ and 220 rpm, pre-culture (1 mL) was transferred to a 250 mL-fermentor (Mini-chemostat fermentor; Biotron Inc., Bucheon, Korea) containing 100 mL medium consisted of M9-glucose minimal medium (3 g glucose , 0.8 g NH4Cl, 0.5 g NaCl, 7.5 g Na2HPO4•2H2O, 3 g KH2PO4 with separately added trace elements of 0.2 g MgSO4•7H2O, 0.1 g CaCl2, 1 mg thiamine) per liter supplemented with 254 mmole of succinate (30 g succinic acid) per liter. The initial pH of the medium was set to be pH 7.5 by adding 6N NaOH. The fermentor was operated at 37℃, 350 rpm with air flushing (100 mL/min.). After 12 h batch mode growth, feeding and outlet pumps were turned on at flow rate of 0.167 mL/min (Dilution rate=0.1 h-1). The fresh medium reservoir (10 L) contained the same contents and pH with the initial medium. Every 10 turnover time (100 h), the reservoir was changed with the same medium composition except succinate concentration. The succinate concentration of the reservoir was gradually increased by 25.4 ~ 84.7 mM (3 ~ 10 g/L of succinic acid) at a time than previous states depending on the cell concentrations at the outlet. When the cell concentration was reduced below O.D.=0.2, succinate feeding pump was temporarily stopped to go back to the slightly lower succinate concentration. The continuous culture was performed until the steady state when succinate in the reservoir reached 1.35 M (160 g/L in succinic acid form). The final adapted cells in 100 mL culture were harvested by centrifuge and frozen rapidly in a liquid nitrogen tank for further analyses including DNA microarray. To understand the change of up/down regulations in the high-succinate adapted E. coli, total mRNAs of control cells and the adapted E. coli cells were extracted using a RNA extraction kit (RNeasy mini kit; Quiagen, Hilden, Germany), and the transcriptomes were compared using DNA microarray. Hybridization and washing were accomplished by a facility of Digital Genomics Co (Seoul, Korea). DNA microarray were scanned using an Axon GenePix 4000B Scanner (Axon Instruments, Union City, CA, USA), and the scanned images were analyzed with GenePix Pro 3.0 software to obtain gene expression ratios.

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Identification and characterization of C4 dicarboxylic acid transporters in Pichia kudriavzevii CY902 strain

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