Phosphate limited fed-batch processes: impact on carbon usage and energy metabolism in Escherichia coli.

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Phosphate limited fed-batch processes: Impact on carbon usage and energy metabolism in Escherichia coli

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Tom Schuhmacher 1 , Michael Löffler, Thilo Hurler 2 , Ralf Takors ∗

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Q2 Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, D-70569 Stuttgart, Germany

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Article history: Received 3 February 2014 Received in revised form 22 April 2014 Accepted 28 April 2014 Available online xxx

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Keywords: Phosphate starvation Energy metabolism 16 Flux balance analysis 17 Transcriptome analysis 18 19 Q3 Tryptophan synthesis Escherichia coli 20 14 15

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Phosphate starvation is often applied as a tool to limit cell growth in microbial production processes without hampering carbon and/or nitrogen supply alternatively. This contribution focuses on the interplay of process induced phosphate starvation and microbial performance studying an l-tryptophan overproducing Escherichia coli strain as a model for highly ATP demanding processes in comparison with an E. coli wildtype strain. To enable a time-resolved analysis, constant phosphate feeding strategies were applied to elongate the transition from phosphate saturated to phosphate limited cell growth. With increasing phosphate limitation, a reduced cellular efficiency of ATP formation via respiratory chain activity and the ATP synthase complex was found for both strains. Process balancing, transcriptome analysis and flux balance analysis are pointing toward a multi-stage decoupling scenario, which in essence deteriorates the stoichiometric ratio of ATP formation to proton translocation, thereby affecting ATP availability from respiration and carbon usage. Starting off with a potential influence on ATP-synthase efficiency (stage 1), decoupling is further increased by modified respiratory activity (stage 2) and byproduct overflow (stage 3) finally resulting in a metabolic breakdown entering complete phosphate depletion (stage 4). The decoupling is initiated by phosphate limitation; further effects are mainly mediated on metabolic level through ATP availability and energy charge, additionally affected by ATP demanding product synthesis. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Escherichia coli dry mass typically contains about 2–3% (w/w) phosphorus (Bauer and Ziv, 1976), which appears to be low compared to the occurrence of the main elements C, N, O and H. However, the oxidized form phosphate (Pi ) plays a dominant role for cellular energy management and growth (Makino et al., 2003). This is expressed by its crucial function in energy transfer as a key, free Gibbs energy offering compound in nucleotide triphosphates (NTPs), its presence in RNA and DNA and as an activator of enzymes and signal proteins. Consequently, sophisticated regulation of phosphate or phosphorus uptake is essential for living cells, as the accessible amount is usually less than the demand (Ishige et al., 2003; Krol and Becker, 2004). Besides, the appropriate control of phosphate supply represents an attractive tool

∗ Corresponding author. Tel.: +49 7 11 68 56 45 74; fax: +49 7 11 68 56 51 6. E-mail addresses: [email protected] (T. Schuhmacher), Michael.loeffl[email protected] (M. Löffler), [email protected] (T. Hurler), [email protected] (R. Takors). 1 Present address: Evonik Industries AG, Kantstraße 2, Halle, Germany. 2 Present address: Sandoz GmbH, Kundl, Austria.

for running bioprocesses not only in lab- but also in industrial scale. Usually, microbial activity of aerobic fed-batch cultivations needs to be limited in late process phases to fulfill technical constraints like maximum oxygen transfer. Limiting cellular growth by phosphate shortness offers the chance to run bioprocesses at amble carbon and nitrogen conditions, provided that the cellular fitness and product formation are not hampered. Application examples are given for multiple products (Clarke et al., 2010; Johansson et al., 2005; Zheng et al., 2010). Obviously, this fermentation strategy requires a thorough understanding of phosphate induced metabolic control. For E. coli, the response to phosphate starvation was studied at molecular, proteome and transcriptome level (Baek and Lee, 2007; Hsieh and Wanner, 2010; VanBogelen et al., 1996; Wanner and Boline, 1990; Wanner and Chang, 1987). It was reported that phosphorous metabolism is highly interconnected with other regulatory circuits including energy and central carbon metabolism (Baek and Lee, 2007). The best known system to detect and respond to changes of environmental phosphate is the PhoR–PhoB two-component regulatory circuit. More recently, it was suggested that Pi signaling is mediated by a seven component system composed of PhoR/PhoB, PstSCAB and PhoU (Hsieh and Wanner, 2010). Previously, novel members of the phosphate regulon were discovered, further supporting the role of PhoB as

http://dx.doi.org/10.1016/j.jbiotec.2014.04.025 0168-1656/© 2014 Elsevier B.V. All rights reserved.

Please cite this article in press as: Schuhmacher, T., et al., Phosphate limited fed-batch processes: Impact on carbon usage and energy metabolism in Escherichia coli. J. Biotechnol. (2014), http://dx.doi.org/10.1016/j.jbiotec.2014.04.025

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a global regulator and the interconnection of phosphate response with various metabolisms (Marzan and Shimizu, 2011; Richards and Vanderpool, 2012; Yoshida et al., 2012). Despite the close interactions of phosphate response with various metabolisms, it has been previously reported that phoB/phoR deletion is not lethal even under phosphate limiting conditions (Marzan and Shimizu, 2011). Therefore, the mechanism of phosphate limitation affecting essential cell functions such as the respiratory chain activity, energy metabolism and biomass formation is not fully uncovered. Nevertheless, phosphate based fermentation strategies are a promising tool for successful bioprocess development. Therefore, this contribution focuses on the interplay of phosphate limited fed-batch conditions with cellular regulation, respiratory activity and ATP metabolism. To illustrate the proposed inefficiency of ATP generation through oxidative phosphorylation under phosphate limitation, so called “decoupling scenarios” will be discussed. Theoretically, decoupling can be caused by changes of the H+ to ATP stoichiometric ratio of the ATP synthase complex, modified respiratory chain activity resulting in reduced proton motive force generation, membrane integrity issues affecting proton gradients or a redirection of the carbon flow toward byproduct synthesis. The proposed decoupling may further affect ratios of glucose uptake, respiratory activity and growth. To investigate the interplay of process conditions and microbial performance, an l-tryptophan (TRP) overproducing E. coli strain (K12 JP6015/pMU91) is studied. This strain serves as a model to investigate the impacts of additional metabolic burden and ATP demands (Akashi and Gojobori, 2002; Camakaris et al., 1996). In addition, an E. coli wildtype strain (K12 W3110) is used to elucidate fundamental properties of phosphate limitation. Specially designed fermentation conditions will be presented to provide insights into metabolic flux distributions in conjunction with energy management.

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2. Materials and methods

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2.1. Bacterial strains

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The bacterial strains used for this study were E. coli K12 JP6015/pMU91 (DSM10123) (Camakaris et al., 1996) and E. coli K12 W3110 (DSM 5911). 2.2. Cultivation All media for shaking flasks, precultivation and main cultivation were developed based on a previously described approach (Camakaris et al., 1996). The concentrations of tetracycline (2.5 mg l−1 for E. coli K12 JP6015/pMU91 only in precultures, no tetracycline for W3110), yeast extract (omitted for precultivation medium) and phosphate were modified compared to the original approach. The composition of the pre- and main fermentation medium was evaluated using a one-dimensional screening approach (50 ml bioreactor) to exclude additional limitations by other trace-, micro- and macro elements (data not shown). Cells were transferred from cryo-cultures (optical density of 2, culture medium, 20% glycerol (v v−1 )) into shaking flasks containing seed medium. After 24 h cultivation at 33 ◦ C and 90 rpm, 150 ml of the cell suspension was inoculated into a 2 l jar reactor with 1.35 l prefermentation medium. After 16 h, 1.5 l cell suspension was transferred into a 30 l reactor containing 10.5 l main fermentation medium following a 32 h fed batch cultivation. Prefermentation was performed at pH 6.8 and 30 ◦ C, main fermentation at 33 ◦ C and pH 6.9. The temperature for the main fermentation was increased to allow higher activity while maintaining plasmid stability (Camakaris et al., 1996). For all fermentations, dissolved oxygen was maintained above 30% through adjustment of the agitator

speed. For pH control, 24% aqueous ammonium hydroxide solution was used. For the main fermentation, the initial phosphate concentration was 0.6 g l−1 (reference main fermentation: 1 g l−1 ). Glucose saturation (above 3 g l−1 , below 14 g l−1 ) was sustained throughout the main fermentation by glucose feeding (620 g l−1 glucose solution). For fermentations with additional phosphate feeding, the constant phosphate feed was started as soon as the phosphate concentration dropped below 0.2 g l−1 . One feeding scenario (40 g l−1 phosphate solution, 22 ml h−1 ) was selected for both E. coli strains (K12 JP6015/pMU91 and K12 W3110). All measurements were performed in triplicates. Rates were determined differentially as average rates for the given process intervals. Error bars (error range) indicate the standard deviation of the triplicate measurements, the error range of calculated factors was determined through error propagation. To ensure reproducibility, process scenarios were performed at least in duplicates. 2.3. RNA sampling and microarray analysis For RNA sampling, cell suspension was sampled into 3 volumes of RNAprotect bacterial reagent (Qiagen) and processed according to the manufacturer’s protocol. Cell pellets were stored at −70 ◦ C for maximum 4 weeks. The initial sampling time point (unlimited growth) was set after 2.5 h process time for all fermentations. For the initial differential gene expression analysis (data not shown), following time points (phosphate limitation) were normalized on the initial time point through median averaging (phosphate excess). RNA preparation, labeling, hybridization and microarray analysis using Affymetrix E. coli 2 gene chips were performed by mft-services using standardized protocols (www.mftservices.de). Those standard protocols include RNA extraction via the Qiagen RNeasy kit and RNA concentration and quality assessment via NanoDrop (ND 1000) and Agilent Bioanalyzer (2100). RNA was further processed applying Affymetrix standard protocols for GeneChip expression analysis. Microarray raw data extraction was performed using the robust multichip analysis algorithm (RMA) (Irizarry et al., 2003). Previously, transcriptome time series experiments were performed on two reference fermentations (without phosphate feeding) covering 5 sampling time points (2.5, 6, 11, 24, 32 h). To further increase the resolution of the transition into phosphate starvation, time series transcriptome analysis covering 10 sampling time points (2.5, 4, 6, 8, 11, 17, 22, 25, 28, 32 h) was performed using a modified fermentation strategy (phosphate feeding). The selection of the sampling time points was based on fermentation dynamics of biomass, products and byproducts. Notably, each first sample mirrored phosphate excess conditions thus serving as a reference for subsequent phosphate limitation dynamics. To analyze microarray raw data, the commercially available software Genedata Analyst 7.0 was used. Only transcripts with at least 2-fold differential expression in all fermentations (including reference fermentations) were selected for the final clustering analysis. The k-means clustering approach (centroid calculation: median, distance: positive correlation, 100 maximum iterations, 8 clusters) was applied on the high resolution data set (phosphate feeding, 10 sampling time points). The complete transcript data set is accessible under the GEO accession number GSE56447 (NCBI, GEO database). 2.4. Other measurements Exhaust gas analysis was performed using BCP-CO2 und BCP-O2 gas analysis devices (Bluesense). For glucose feed control, glucose concentration was monitored online using a PROCESStrace probe (TRACEbiotech AG). Offline glucose and acetate measurements were performed using enzymatic assays (R-Biopharm), according


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to the manufacturer’s protocol. Amino acids were measured by RPHPLC with fluorescence detection (Henderson and Brooks, 2006). 2.4.1. Energy charge determination For energy charge measurement sample preparation, 4 ml cell suspension was sampled into 1 ml −30 ◦ C cold perchloric acid/EDTA solution (35% (w/w) perchloric acid, 80 ␮M EDTA) for quenching and extraction. After 15 min incubation at 6 ◦ C, 1 ml of the quenching solution was transferred on a preconditioned cation exchange solid phase extraction column (Machery-Nagel Chromabond SA, 3 ml). The nucleotides were then eluted through addition of 1 ml 5% acetic acid solution. For the eluted fraction, pH was adjusted to 6–8 using a potassium hydroxide solution. Finally, a centrifugation step (12,000 × g, 10 min) was performed to remove precipitated salts and cell debris. The supernatant was quantified by RP-HPLC with diode array detection. The lower detection limit for adenine nucleotides was 0.5 mM. Since sample preparation did not affect nucleotide ratios, determined ATP, ADP and AMP concentrations (cATP , cADP , cAMP ) were used to calculate the adenylate energy charge (EC) as follows: EC =

0.5cADP + cATP cATP + cADP + cAMP

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The EC was determined separately for each measurement. The error range represents the standard deviation of the final calculated values. Phosphate and ammonia were quantified using the commercially available kits LCK348 and LCK303 (Hach-Lange), respectively, according to the manufacturer’s protocol. All measurements were performed in triplicates. Error bars represent the standard deviation.

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2.5. Flux balance analysis

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The software Insilico Discovery 3.5 (insilico biotechnology) was used for metabolic modeling. The applied stoichiometric model contained 143 balanced compounds and 143 transformers (see supplementary material, model files) and was based on previously published models (Chassagnole et al., 2002; Schmid et al., 2004). Transport steps, biomass composition and oxidative phosphorylation were modified. The model included the central carbon metabolism (glycolysis, pentose phosphate pathway, and citrate cycle), uptake systems for glucose, ammonia, sulfate and phosphate, as well as building blocks for biomass (fatty acids, nucleotides, proteins, mureine and polysugars). 2.5.1. Biomass composition adjustment Biomass composition for unlimited growth was adapted from Taymaz-Nikerel et al. (2010). Under given process conditions, biomass phosphorus content is affected by phosphate limitation, mainly mediated through declining total RNA content (Makino et al., 2003). To account for that, total RNA content was determined for several process time points of the reference process. Based on the RNA data, a logarithmic function approximating relative total RNA changes in relation to the extracellular phosphate concentrations was fitted. The latter was used to adjust the biomass RNA content in the model for each process interval (see supplementary material, model adjustments and constraints). 2.5.2. Additional constraints, ATP turnover simulation Growth rate as the objective function and experimentally determined phosphate uptake rates together with substrate and product exchange rates as additional constraints were used for flux balance analysis. In addition to the biomass composition adjustments, an artificial ATP hydrolyzing reaction was introduced to account for both maintenance and imbalances in ATP-metabolism. To simulate the proposed decoupling scenario affecting ATP-synthesis

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on various levels, the ATP/H+ import stoichiometry was modified (reduced) to minimize the ATP overflow (artificial hydrolysis reaction). To account for maintenance energy demands, a remaining flux (at least 10% of the total turnover) through the artificial ATP hydrolysis reaction was retained for all scenarios. Furthermore, to consider the experimentally determined elevated respiratory quotient (RQ), the stoichiometry of the cytochrome oxidase was modified in a way that 10% less oxygen was used (all simulations). To evaluate the transition phase flux distribution scenarios, simulated growth rate (objective function), oxygen and carbon dioxide transfer rate (not constrained) were compared to experimentally determined rates.

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3. Results

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3.1.1. Reference fermentations, E. coli K12 JP6015/pMU91 Experimental studies started with the implementation of the reference fermentation given in Camakaris et al. (1996), using the l-tryptophan producer E. coli K12 JP6015/pMU91. Following the slightly modified fermentation protocol, 19.55 ± 0.21 g l−1 biomass, 12.4 ± 0.56 g l−1 l-tryptophan, 3.52 ± 0.21 g l−1 acetate, 2.17 ± 0.13 g l−1 l-glutamate and 1.76 ± 0.06 g l−1 l-phenylalanine and 1.96 ± 0.12 g l−1 l-tyrosine could be detected after 32 h. The reference fermentation enabled carbon balancing with 83% overall carbon recovery. The highest determined specific l-tryptophan production rate was 0.065 ± 0.01 g (g h)−1 , observed after 10 h. After reaching this peak performance, the specific TRP production rate dropped rapidly, remaining at around 0.015 g (g h)−1 toward the end of the fermentation. The fermentations showed unexpected phosphate sensitivity. Cell growth was significantly reduced already at phosphate levels above 0.2 g l−1 (see supplementary Fig. 1).

3.1.2. Phosphate feeding fermentations, E. coli K12 JP6015/pMU91 Based on the findings of the reference fermentation, a phosphate feeding scenario was implemented. Constant phosphate feeding enabled an elongated transition into phosphate starvation (more than 20 h) for the TRP producer (E. coli K12 JP6015/pMU91) and wildtype strain (W3110). The fermentation characteristics were monitored by transcript sampling (TRP producer), carbon balancing and energy charge measurements. Since advanced analytics revealed saturated levels of glucose, oxygen and nitrogen, additional limitations of macro elements could be excluded. Furthermore, media composition studies ensured that none of the media supplements, mineral salts and trace elements limited either (data not shown). The final titers of the phosphate feeding fermentation for the TRP producer were 22.3 ± 0.02 g l−1 biomass, 15.12 ± 0.42 g l−1 ltryptophan, 6.32 ± 0.001 g l−1 acetate, 5.65 ± 0.22 g l−1 l-glutamate, 1.38 ± 0.11 g l−1 l-valine and 0.41 ± 0.2 g l−1 l-phenylalanine after 32 h fermentation time. The specific growth rate was reduced after 3.2 h reaching zero growth after 26 h. The specific glucose uptake rates decreased sub-proportionally compared to growth rates. The highest biomass specific l-tryptophan production rate was 0.056 ± 0.0052 g (g h)−1 after 9.4 h (Fig. 1). The specific tryptophan production rate decreased slowly during 17 h to 0.027 ± 0.0029 g (g h)−1 , followed by a sudden drop to almost zero production (Fig. 1). While the specific acetate production rate was low during early transition phase, toward fermentation end, a rapid increase was observed (Fig. 1). Shortly before, glutamate production started.


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Fig. 1. Specific growth (circle), acetic acid production (square), glutamic acid production (diamond), tryptophan production (triangle) rates and the phosphate concentration (×) for the phosphate feeding process using the TRP producer.

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3.1.3. Phosphate feeding fermentations, E. coli K12 W3110 Identical fermentation conditions with E. coli K12 W3110 resulted in 34.51 ± 0.21 g l−1 biomass and 2.81 ± 0.02 g l−1 acetate. Similar to the TRP-producer, the growth rate dropped immediately after the onset of phosphate limitation (3.2 h). However, remaining growth was observed until the end of the fermentation (Fig. 2). The specific glucose uptake rate also decreased sub-proportionally in comparison to the specific growth rate. Besides CO2 , acetate was the only byproduct detectable, remaining at low specific production rates after the onset of phosphate limitation (Fig. 2). For all phosphate feeding fermentations, an overall carbon balance closure of more than 90% was achieved (see supplementary Fig. 2). 3.1.4. Carbon usage For both strains, a significant growth independent glucose uptake (qs ) was observed. This is modeled by the well-known Pirt equation: qs = (/Yxs ) + k. Here, Yxs mirrors growth-dependent and k growth independent glucose uptake. Parameter regression applied on experimental data from phosphate feeding fed batch fermentations (transition into phosphate limitation) resulted at: JP6015/pMU91, k = 0.169 ± 0.018, Yxs = 0.64 ± 0.045; W3110 (Fig. 3). Compared to published results for wild type strains (TaymazNikerel et al., 2010), the k value is significantly high. Noteworthy, it should not be taken as cellular maintenance demand alone because additional carbon needs for product and byproduct formation are not separated. Since the product and byproduct formations were not proportional to glucose uptake, they should be the result of superimposing metabolic and/or transcriptional regulations occurring during the transition into phosphate limitation. Additionally, carbon balancing revealed that CO2 formation contributes up to 70% (see supplementary Fig. 2). This significant fraction anticipates

Fig. 2. Specific growth (circle), acetic acid production (square) rates and the phosphate concentration (×) for the phosphate feeding process using the wildtype strain.

Fig. 3. Specific glucose uptake rates of the TRP producer (diamond) and wildtype strain (circle) in relation to the specific growth rate, including linear regression (Pirt relation, phosphate feeding processes).

a high activity of the respiratory chain and the ATP-synthase complex. Adenylate energy charge (EC) measurements showed a drop of the EC shortly after growth rate reduction, remaining at this low energy status until the end of the fermentation (Fig. 4). 3.2. Transcriptome analysis, E. coli K12 JP6015/pMU91 3.2.1. k-means clustering In total, 1069 transcripts matching the filtering criteria were selected for further analysis. Transcripts could be grouped into 8 clusters through 100 maximum iterations by k-means clustering based on their characteristics over fermentation time. Cluster 1 and 6 contained transcripts related to phosphate starvation response (Baek and Lee, 2007). After a short delay at process start, transcripts of both clusters showed an upregulation peaking at mid fermentation phase. Toward the final fermentation phase, transcripts of cluster 1 showed a slight upregulation, while cluster 6 indicated a significant downregulation. Cluster 6 (89 transcripts) contained the pho-operon (phoABEHRU) and phosphate starvation related genes like the AMP nucleosidase gene amn (Zhang et al., 2004), the acid phosphatase transcriptional regulator appY (Atlung et al., 1997), the high affinity phosphate uptake system genes pstABC, the glycerol-phosphate transporter genes ugpABCEQ (Brzoska et al., 1994), phosphonate catabolism genes (Wanner and Metcalf, 1992), the metal ion stress response gene yibD (Baek and Lee, 2006) and the phosphate starvation induced genes psiEF (Kim et al., 2000). Additionally, genes related to acid response, osmotic stress, various other stress responses and biofilm formation. Like Cluster 6, Cluster 1 (63 transcripts) contained genes reported to be under phosphate starvation control (pepN, phnD),

Fig. 4. Carbon dioxide emission rates (CER, TRP producer: black, solid line; wildtype: gray solid line) and energy charge (EC, TRP producer: black circle; wildtype: gray circle) for the phosphate feeding processes.


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transcripts related to acid response, osmotic stress, biofilm formation and multiple stress indicators. Clusters 2 (46 transcripts) and 5 (118 transcripts) showed expression tendencies opposite to clusters 1 and 6, suggesting a correlation between downregulation and phosphate limitation. Cluster 2 contained transcripts related to iron metabolism, chemotaxis and sulfur metabolism. Cluster 5 contained a large group of genes related to amino acid metabolism, transport and chemotaxis. In addition, biotin synthesis genes (bioABF), ribosome related genes, various transporters and genes encoding for the succinate dehydrogenase (sdhAB) were assigned. In total, 318 transcripts showed characteristics coinciding with phosphate starvation response. The characteristics of the remaining 751 transcripts assigned to clusters 3, 4, 7 and 8 did not correlate with the characteristic of phosphate starvation response related transcripts. Many transcripts involved in essential cell functions like the ATP metabolism, respiratory chain compounds, amino acid and nucleotide synthesis were assigned to this group.

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Flux balance studies were performed for fermentations with extended transition phase into phosphate limitation. The approach was used to supplement fermentation balancing and to simulate flux scenarios focusing on the ATP metabolism. The goal was to decipher particularities of metabolic flux patterns induced by phosphate limitation and the interplay of growth and product synthesis with fermentation conditions. Therefore, phase-wise pseudo-stationarity within the indicated intervals was assumed applying a metabolic model. Growth rate as the objective function and additional constraints were used as explained in Section 2.5. The approach was well suited to predict growth rates during the mid-transition phases (15–22 h), while for the early transition (6–12 h) and toward the end of the fermentation, the stationarity assumption did not match the given fermentation dynamics (data not shown). For the mid-transition interval, oxygen uptake rates (OUR) and carbon dioxide emission rates (CER) (not constrained) could be simulated equally well. Average discrepancies between simulated and measured rates for growth, OUR and CER (15–22 h) were below 10% (see supplementary Fig. 3). The approach gave insight into possible cofactor balancing scenarios that were not accessible otherwise. Caused by the high glucose uptake and respiration rates, scenarios with P/O ratios greater than 1 (oxidative phosphorylation, all transition intervals) resulted in significant ATP excess requiring for a high artificial ATP hydrolysis to balance the overall ATP turnover. Noteworthy, this simulated ATP

3.2.2. Respiratory chain regulation Given the high respiratory activities observed for both strains, one particular effect (clusters 4, 7) related to the respiratory chain is worth mentioning. Shortly before the onset of the increased byproduct formation after 25 h fermentation time, transcripts encoding for the NADH-dehydrogenase 1 (NDH-1) and the bo-Cytochrome oxidase were significantly down regulated (see supplementary Table 1, transcriptome analysis).

Glc 100 PEP PYR G6P 0.2 0.3 62 66 38 33 F6P 0.5 0.05 74 87

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Fig. 5. Schematic illustration of the stoichiometric metabolic model used for flux balance analysis including selected simulated molar fluxes (decoupling scenario; black, TRP producer, gray: wildtype) for the process interval at around 18 h process time. All molar fluxes are normalized on a glucose uptake rate of 100. Substrate uptake, central carbon metabolism (glycolysis, pentose phosphate pathway and citrate cycle) and an artificial ATP hydrolysis reaction as well as modules for the respiratory chain and biomass components are shown. The dotted line represents the cell membrane. Bold gray arrows: combined reactions or reactions not directly related to the metabolic pathway; Metabolites: Glc, d-glucose; PEP, phosphoenolpyruvate; PYR, pyruvate; G6P, ␣-d-glucose 6-phosphate; F6P, ␤-d-fructose 6-phosphate; R5P, d-ribose 5-phosphate; E4P, erythrose 4-phosphate; G3P, d-glyceraldehyde 3-phosphate; DHAP: dihydroxyacetone phosphate; AcCoA, acetyl coenzyme A; Ace, acetic acid; CIT, citrate; 2OG, 2oxoglutarate; SUCCoA, succinyl coenzyme A; SUC, succinate; FUM, fumarate; OAA, oxalacetate; Trp, tryptophan; Phe, phenylalanine; Glu, glutamic acid.


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excess coincided with significant drops of the experimentally determined adenylate energy charge. Motivated by this discrepancy, the stoichiometric ratio of H+ uptake to ATP formation of the ATP-synthase complex was increased (decoupling) to simulate and quantify possible inefficiencies in ATP synthesis. As a result of the increasing growth-independent glucose uptake, ATP turnover for both E. coli strains during the mid-transition phases turned out to be fully equilibrated only by ATP supply from substrate level phosphorylation. Fig. 5 shows simulated flux distributions during mid-transition phase (18 h) for the TRP producer and the wildtype strain. 4. Discussion 4.1. Transcriptional response The E. coli wildtype strain as well as the TRP producer showed a reduction of cell growth as soon as phosphate levels of 0.3 g l−1 were reached, which was also mirrored by the transcriptional response (TRP producer) of members of the pho-regulon. Out of 1069 differentially expressed transcripts matching the applied filtering criteria, 318 showed a change (positive or negative) simultaneous with the phosphate sensor and response system. An increasing number of pho-regulon members have been identified for various E. coli strains (Alteri et al., 2011; Baek and Lee, 2006; Richards and Vanderpool, 2012; Yoshida et al., 2012). Several acid and metal ion response systems have been reported to interact with phosphate starvation (Baek and Lee, 2006, 2007; Marzan and Shimizu, 2011; Suziedeliene et al., 1999). Contrary to previously published results (Bougdour and Gottesman, 2007), no evidence of stringent response activity was found for the TRP producer. The regulator relA, common targets of the stringent response and the assumed mediator of stringent response under phosphate limitation iraP did not show any significant expression change, while spoT was even downregulated. Nevertheless, it has to be mentioned that any amino acid overproducing strain might have developed a modified stringent response through various selection steps, although this is not stated in the original patent (Camakaris et al., 1996). In accordance with previously published studies (Marzan and Shimizu, 2011), the performed qualitative correlation analysis of transcriptome dynamics based on k-means clustering revealed no (transcriptional) regulatory connection of the phosphate starvation response to essential metabolic activities like the respiratory chain, oxidative phosphorylation or nucleotide synthesis. Therefore, it is likely that the control of those essential cell functions was mediated through alternative mechanisms (Baek et al., 2007). The immediate reduction of growth and the impact on product and byproduct formation entering phosphate limitation suggested a direct link between phosphate availability and those essential pathways. In context of the high respiratory activity observed during mid-transition phase, the significant down regulation of transcripts encoding for the NADH-dehydrogenase 1 (NDH-1) and the boCytochrome oxidase after 25 h is remarkable. The down regulation of respiratory chain enzymes (while the overall respiratory activity was almost maintained) has also been reported for ATP-synthase deficient E. coli strains (Noda et al., 2006). 4.2. Energy management Both E. coli strains showed dropping adenylate energy charge (EC) values from the physiological level of 0.9 while growing nonlimited to low energy status (0.5) coinciding with the beginning of phosphate limitation. Concomitantly, uptake rates of oxygen (OUR) and emission of carbon dioxide (CER) as well as glucose uptake were relatively high compared to growth rates. In contrast, acetate

formation decreased during early and mid-transition phase. Elevated glucose uptake and CO2 emission rates were also reported for other E. coli strains under phosphate limiting conditions (Johansson et al., 2005; Marzan and Shimizu, 2011). For a shikimic acid producing strain, Johansson et al. (2005) proposed that energy excess conditions occurring under phosphate limitation are causing an uncoupling of catabolism and anabolism and futile cycling (Russell and Cook, 1995), finally resulting in high CO2 yield and elevated energy demand (Johansson and Liden, 2005). On the contrary, the determined energy charge of the given fermentations outline that ATP availability was in fact sensitive (Chapman et al., 1971) which was deduced from the tight correlation of dropping energy charge levels with decreasing growth and product formation rates. The phenotypic behavior of the cells resembled previously published scenarios for E. coli strains with defective ATP synthesis (Noda et al., 2006) or additional ATPase activity (Koebmann et al., 2002), suggesting an influence of phosphate availability on ATP synthesis efficiency. The assumption was further supported by previous kinetic studies of the E. coli ATP-synthase complex, revealing a surprisingly high km -value for phosphate (4.2 mM) (Iino et al., 2009). Since E. coli does not accumulate large amounts of polyphosphates (Kulaev, 1975), extracellular phosphate concentrations should directly affect ATP synthesis. While phosphate-limited ATP synthase activity may be one piece of puzzle, this illustration does not yet explain the high NADH oxidation rates observed. Actually, the monitored high respiratory activity during phosphate limitations anticipated equally high ATP-synthase activity to balance proton translocation. However, in vitro experiments of D’Alessandro et al. (2008, 2011) provided evidence that phosphate and ADP availability directly affect ATPsynthase/ATPase activity even decoupling proton translocation from ATP-synthase/ATPase activity at extreme conditions. Given the reported half maximal coupling effect at phosphate concentrations of 0.51 mM (0.05 g l−1 ), phosphate levels are supposed to become sensitive (in our experiments) already after 5 h fermentation time. Given the proposed decoupling effects, cells would still be able to import protons with a high rate while the ATP synthesis is hampered by phosphate availability. As a consequence, this could lead to an increasing glucose demand and elevated respiration rates to maintain required ATP levels. The additional export of weak organic acids, amino acids, futile cycling of protons or membrane deficiencies could enhance this effect (Russell, 2007). Furthermore, simulated flux scenarios accounting for complete decoupling of the ATP synthesis through oxidative phosphorylation revealed a fully equilibrated ATP turnover for the mid-transition fermentation intervals. Given the high prediction accuracies for growth, oxygen uptake and carbon dioxide formation during the transition intervals, the authors qualify the simulation results as reliable (see supplementary Fig. 3). Hence, simulated ATP flux variations are discussed as indications for dynamics in cellular ATP management. Theoretically, unknown byproduct synthesis may consume some of the simulated ATP excess. However, since carbon balance closure was improving to more than 93% with increasing phosphate limitation, it is rather unlikely that significant ATP consumers are missed. Instead, with respect to the findings on kinetic parameters and in vitro studies of the ATP-synthase complex discussed above, decoupling mechanisms of ATP formation are likely to cause the observed effects. The resulting EC drop could serve as a trigger to start further cellular countermeasures. Koebmann et al. (2002) and Noda et al. (2006) already reported that ATP levels influence glucose uptake and glycolytic activity. The initial decoupling phenomenon, probably caused by phosphate limitation, was observed for both the wildtype and the TRP producer. Both strains showed an increased glucose uptake (growth independent) after ATP-synthase decoupling that may be taken as a cellular countermeasure to compensate missing ATP. For


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Fig. 6. Schematic illustration of the decoupling scenario (stage 1–3). Dashed arrows: negative effect; dotted arrows: feedback effects (positive); solid arrow: positive effect.

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the TRP producer, the additional ATP demand for the tryptophan synthesis further amplified the effect. This was observable by the accelerated transition, the further increased growth independent glucose uptake and additional byproduct synthesis.

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4.3. Decoupling scenario

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Combining all impacts, the occurrence of ATP synthesis decoupling accompanied by increased growth independent glucose uptake can be understood as a four-step sequence as follows (Fig. 6): Step 1 (ATP synthase decoupling): reduced phosphate concentrations lead to the initial decoupling of the ATP-synthase activity resulting in increased substrate demand (growth independent glucose uptake) and alleviated respiratory activity (growth independent) mediated through reduced ATP levels (energy charge). High NADH formation via increased metabolic activity occurs. Step 2 (respiratory chain decoupling): As a consequence of Step 1, NADH and NADPH formation (the latter is linked to NADH formation via transhydrogenase activity, PntAB) are kept on high levels although the cellular demand (anabolic reactions) is steadily decreasing. To deal with the raised NADH presence, cells may shift NADH (NADPH) regeneration activity in the respiratory chain toward less efficient dehydrogenases and oxidases with less contribution to the proton motive force (for instance NDH-1 to NDH-2 and bo-oxidase to bd-oxidase). As a consequence, the ATP synthesis efficiency of the ATP-synthase complex is further reduced. This scenario was supported by transcriptome analysis (TRP producer) and was previously reported for an ATP-synthase deficient strain (Noda et al., 2006).

Step 3 (overflow metabolism): If the countermeasures of step 2 are not sufficient to compensate NADH (NADPH) accumulation, overflow metabolites are excreted to maintain the cellular reduction status. While acetate is a typical overflow metabolite for E. coli, the observed excretion of glutamate by the TRP producer may surprise. However, it was suggested previously, that NADPH excess is responsible for glutamate overproduction observed during tryptophan synthesis in E. coli (Dodge and Gerstner, 2002). Fermentation results anticipate high carbon fluxes through the TCA cycle which coincide with equally high NADPH rates via isocitrate dehydrogenase (Csonka and Fraenkel, 1977). Consequently, the anabolic reduction equivalent NADPH should be well available since both growth and production rate were significantly reduced. Additionally, transcript analysis revealed that the membrane-bound transhydrogenase (pntAB; cluster 4) was down regulated at this stage of development. Hence, the cells may produce and excrete glutamate to re-oxidize NADPH. Interestingly, glutamate excretion has recently been reported for an NDH-1, bo-oxidase double mutant E. coli strain (Kihira et al., 2012), further supporting the decoupling scenario of the respiratory chain in step 3. Step 4 (metabolic breakdown): the final phase occurs as soon as the cells enter total phosphate depletion, resulting in massive global down regulation of transcription and significantly reduced metabolic activities. This state was not reached by the phosphate feeding fermentations but could be observed for the reference fermentations. 5. Conclusions For both E. coli strains, the TRP producer and the wildtype, it was possible to extend the transition from phosphate saturation


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into phosphate limitation by applying constant phosphate feeding strategies. The approach allowed high-resolution insights into the cellular regulatory responses on transcript and metabolic level. Surprisingly, the strains showed sensitivity to phosphate limitation magnitudes above the expected concentration threshold based on the kinetic parameters of the phosphate uptake systems. The transition into phosphate limitation caused a high growth independent glucose uptake and respiratory activity. Transcriptome analysis revealed manifold interactions of the phosphate response with various stress replies and phosphorous salvage systems but could not determine a direct connection to the observed dynamics of growth, respiration and product formation. Energy charge measurements, fermentation dynamics and flux balance analysis indicate a direct correlation of phosphate availability with the energy metabolism. In addition, increased ATP demand (TRP producer) affected the transition into a low ATP state, triggering additional decoupling effects. Both the wildtype and the TRP producer showed growth and production independent glucose uptake and high respiratory activity while entering phosphate limitation although the energy charge was constantly dropping. The observed effects were initially caused by phosphate limitation and presumably mediated by intracellular ATP levels and energy charge. The proposed combined 4-step scenario consisting of (1) ATP synthase decoupling, (2) respiratory chain decoupling, (3) overflow metabolism and (4) metabolic breakdown is able to explain the observed phenotypic dynamics. Since only the first decoupling step is directly related to phosphate limitation, the following stages could occur under different glucose excess conditions using E. coli strains with elevated ATP demand or other growth limiting strategies affecting the ATP metabolism. Given the significant carbon loss through ATP synthesis inefficiency, the illustration presented may serve as a blueprint for investigating alternative limitation scenarios for fermentation optimization.

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Acknowledgements

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The authors like to thank the company Evonik Industries AG, namely Thomas Durhuus, Dr. Robert Gerstmeir, Dr. Mechthild 626 Q4 Rieping and Dr. Ralf Kelle as well as the German Funding Agency 627 BMBF for project support (grant number: 0313917A). Furthermore, 628 the authors like to thank Dr. Oliver Vielhauer for helpful hints on 629 sample preparation and metabolomics. 630 625

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Appendix A. Supplementary data

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Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/ 10.1016/j.jbiotec.2014.04.025.

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