Engineering E. coli for large-scale production

Metabolic Engineering 38 (2016) 73–85

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Metabolic Engineering journal homepage: www.elsevier.com/locate/ymben

Original Research Article

Engineering E. coli for large-scale production – Strategies considering ATP expenses and transcriptional responses Michael Löffler a,1, Joana Danica Simen a,1, Günter Jäger b, Karin Schäferhoff b, Andreas Freund a, Ralf Takors a,n a b

University of Stuttgart, Institute of Biochemical Engineering, Allmandring 31, 70569 Stuttgart, Germany University of Tübingen, Institute of Medical Genetics and Applied Genomics, Calwerstr. 7, 72076 Tübingen, Germany

art ic l e i nf o

a b s t r a c t

Article history: Received 16 April 2016 Received in revised form 20 June 2016 Accepted 30 June 2016 Available online 1 July 2016

Microbial producers such as Escherichia coli are evolutionarily trained to adapt to changing substrate availabilities. Being exposed to large-scale production conditions, their complex, multilayered regulatory programs are frequently activated because they face changing substrate supply due to limited mixing. Here, we show that E. coli can adopt both short- and long-term strategies to withstand these stress conditions. Experiments in which glucose availability was changed over a short time scale were performed in a two-compartment bioreactor system. Quick metabolic responses were observed during the first 30 s of glucose shortage, and after 70 s, fundamental transcriptional programs were initiated. Since cells are fluctuating under simulated large-scale conditions, this scenario represents a continuous on/off switching of about 600 genes. Furthermore, the resulting ATP maintenance demands were increased by about 40–50%, allowing us to conclude that hyper-producing strains could become ATP-limited under large-scale production conditions. Based on the observed transcriptional patterns, we identified a number of candidate gene deletions that may reduce unwanted ATP losses. In summary, we present a theoretical framework that provides biological targets that could be used to engineer novel E. coli strains such that large-scale performance equals laboratory-scale expectations. & 2016 International Metabolic Engineering Society. Published by Elsevier Inc.

Keywords: Scale-up/scale-down Glucose limitation Escherichia coli Maintenance ATP expense Transcriptional response

1. Introduction Microorganisms such as Escherichia coli are frequently exposed to rapid changes in nutrient availability. The ability to sense and successfully respond to such dynamic conditions is crucial for their survival. This is true not only for wild-type strains in their natural habitat but also for metabolically engineered hyper-producers in large-scale industrial cultivation. As a result, microbes have developed balanced systems for sensing their extracellular state and transducing the corresponding signals to orchestrate a multi-level, networked regulatory response (e.g., reviewed by Chubukov et al. (2014) and Ferenci (2001)). The majority of our fundamental knowledge about key regulatory elements and their functions has been acquired from shaking flask experiments (Murray et al., 2003; Shimada et al., 2011; Traxler et al., 2011), and studies that address their interactions in large-scale bioreactors are rare (Larsson et al., 1996). Instead, scale-up/scale-down studies have been used to investigate cellular performance under simulated n

Corresponding author. E-mail address: [email protected] (R. Takors). 1 These authors contributed equally to this work.

large-scale conditions (Buchholz et al., 2014; Delvigne et al., 2009; Lara et al., 2006a; Neubauer et al., 1995b; Schweder et al., 1999) which unraveled impaired process performance. However, the overall effects that result from changing process conditions and/or various external stimuli make interpreting these observations very challenging. The ultimate goal of metabolic engineering is to design strains for use in industrial scale production processes. As a prerequisite, it is assumed that large-scale performance equals laboratory-scale expectations, and consequently, there is a need to understand the intrinsic regulatory mechanisms of engineered strains that may cause deterioration of their cellular capacities in large-scale bioreactors. In this study, we focused on the time-resolved interaction of major regulatory mechanisms that have enabled commercial strains, such as E. coli, to adapt to changes in substrate availability, namely glucose shortage. Glucose has been chosen as a ‘model trigger’ for cellular regulation because it is commonly used as sole carbon source, often controlling the cellular growth in fed-batch processes and thus creates short-term starvations in non-ideally mixed large-scale production fermenters (Lapin et al., 2006; Neubauer et al., 1995b; Takors, 2012). Accordingly, regulatory studies on varying glucose availabilities may serve as a pattern for likewise tests with other substrates such as nitrogen sources.

http://dx.doi.org/10.1016/j.ymben.2016.06.008 1096-7176/& 2016 International Metabolic Engineering Society. Published by Elsevier Inc.

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M. Löffler et al. / Metabolic Engineering 38 (2016) 73–85

These results will not only elucidate the basic regulatory systems, but will also provide guidelines for strain and process engineering that will minimize unwanted large-scale impact. Particular emphasis will be given to the fine resolution of metabolic and transcriptional dynamics triggered by glucose shortage. In addition to studying metabolic and transcriptional interactions, the resulting energy expense for the cells was estimated relative to the translation costs. The latter should be of particular interest for the application of E. coli in large-scale bioreactors, where spatial inhomogeneity is frequently observed (Bylund et al., 1998; Humphrey, 1998; Lapin et al., 2006; Takors, 2012). Large-scale inhomogeneities are basically the consequence of non-sufficient mixing because of limited power input. As a result, gradients of substrates are created that are frequently crossed by the cells and represent triggers for cellular regulation. Notably, related ATP demands add to the already enhanced energy demands of engineered producers (Schuhmacher et al., 2014; Wu et al., 2016; Zhao et al., 2013) ultimately contributing to unwanted losses during large-scale production. Our approach differs from established scale-up/scale-down experiments by the introduction of key metabolic engineering perspectives, which have previously only been utilized to improve performance under intermitted oxygen limitation (Lara et al., 2006b). Although we applied the conventional setup of a stirred tank reactor (STR) connected to a plug flow reactor (PFR) to impose glucose gradients on E. coli W3110, we also established steady-state conditions in the STR prior to and during connection of the PFR cell recycle loop. In doing so, we established a distinct reference state that was not included in other STRPFR studies described to date. The volume ratio PFR-to-STR was designed as 1:3 for translating simulation results of Lapin et al. (2006) who had investigated the growth performance of E. coli under well and poorly mixed fed-batch conditions. The selected mean recirculation time between the compartments of 125 s as well as the resulting frequency of cells entering the starvation zone are still consistent with mixing data (Junker, 2004; Noorman, 2011) and previous scale down studies (Amanullah et al., 2001; Limberg et al., 2016; Neubauer et al., 1995b; Schweder et al., 1999). The dilution rate of 0.2 h 1 was chosen as an exemplary value fulfilling the dual demand to enable comprehensive studies and to mimic large-scale conditions. However, the approach chosen is not restricted to these conditions. Smaller growth rates may be installed accordingly. Moreover, we thoroughly sampled the short-term response in the PFR to generate high-resolution data of metabolic and transcriptional dynamics over time. Short-term studies were completed by long-term adaptation analysis due to the chosen steady-state approach. Thus, we were able to derive an in-depth understanding of the tactical (short-term) and strategic (long-term) response of E. coli to varying glucose availability. Furthermore, calculating cellular ATP efforts will allow us to address to what extent large-scale performance losses may be caused by increasing maintenance demands for withstanding heterogeneity in the system. These findings will not only serve as guidelines for designing successful scale-up strategies, but will also identify gene deletion candidates that will help to minimize ATP demands under large-scale production conditions.

2. Material and methods 2.1. Preculture and bacterial strains The Escherichia coli K-12 W3110 LJ110 strain (Jahn et al., 2013; Zeppenfeld et al., 2000) used in all experiments was kindly provided by G. Sprenger (University of Stuttgart). Glycerol stock seed cultures were used to inoculate 2 L baffled shaking flasks with 300 mL of minimal media with the following composition (per liter): 4 g glucose, 3.2 g NaH2PO4 2H2O, 11.7 g K2HPO4, 8 g (NH4)2SO4, 0.01 g thiamine, and trace element solution (0.11 g

Na3C6H5O7, 0.00835 g FeCl3 6H2O, 0.00009 g ZnSO4 7H2O, 0.00005 g MnSO4 H2O, 0.0008 g CuSO4 5H2O, 0.00009 g CoCl2 6H2O, 0.0044 g CaCl2 2H2O, 0.1 g MgSO4 7H2O). Precultures were incubated with agitation (130 rpm) overnight at 37 °C. 2.2. Fermentation Bioreactor cultivations were carried out with a minimal medium containing (per liter) 13.14 g glucose, 1.00 g NaH2PO4 2H2O, 2.6 g K2HPO4, 9 g (NH4)2SO4, and trace element solution with the same composition as that used in the shaking flask minimal medium. One hundred and fifty milliliters of preculture was used as the inoculum. Cultivations were started with an initial volume of VSTR ¼1.5 L at a total pressure of 1.5 bar in a 3 L bioreactor at 37 °C (Bioengineering, Wald, Switzerland) with a six-blade Rushton type impeller (constant Power input of 5 W; see Supplementary Section S1) and constant aeration rate (1.5 L min 1). The reactor was equipped with pH (Mettler Toledo, Columbus, OH, USA) and pO2 (PreSens, Regensburg, Germany) sensors. The pH was adjusted to 7 using 3 M NaOH and 2.5 M H3PO4. Antifoam (Struktol J 647, Schill þ Seilacher, Hamburg, Germany) was added constantly during the chemostat phase at 50 mL h 1. Following the initial batch phase, a constant dilution rate of 0.2 h 1 was established and monitored via on-line measurements. After steady-state conditions were achieved (reached after five residence times), the PFR was connected to the STR. Biosuspension from the STR was pumped continuously through the PFR with a diaphragm metering pump (Sigma/1 S1Cb, ProMinent, Heidelberg, Germany) and flow was measured using a Coriolis flow meter (Cubemass DCI RS-485, Endressþ Hauser, Weil am Rhein, Germany). To maintain a constant total reaction volume (1.5 L), the STR volume was reduced by the PFR fraction of 0.38 L. The inner tube diameter of the PFR was 20 mm. The PFR was equipped with five sample ports and an additional air sparger at sample port P1 to prevent unwanted oxygen limitation in the loop (air flow: 0.15 L min 1). Oxygen saturation was controlled close to sample ports P1 and P5 (for additional information about the oxygen saturation in the PFR see Supplementary Section S2). A curing bag (Calorex EPDM, Chemietechnik GmbH and Co., Heidelberg, Germany) and isolation material (HT Armaflex, Armacell International S.A, Luxembourg, Luxembourg) were used to maintain a constant temperature of 37 °C in the PFR. Process control and data processing were performed using LabVIEWs 2010 (National Instruments, Austin, TX, USA). 2.3. Plug flow reactor characterization For PFR characterization, conductivity sensors were placed at every sample port, while distilled water was pumped through the system to establish typical cultivation conditions with the same pump flow rate, aeration (in the PFR) and overpressure. Then, 3 mL of K2HPO4 (3 M) was pulsed (Buchholz et al., 2014) and response curves were recorded using conductometers (LF 521, WTW Wissenschaftlich-Technische Werkstätten GmbH, Weilheim, Germany). The average residence time τ and its variance σ 2 (in min) were calculated for each sample port and the complete PFR according to Levenspiel (2012):

τ=

n− i

ti⋅ci⋅∆ti

n− i ∑i

ci⋅∆ti

∑i

n− i

σ 2=

∑i

ti2⋅ci⋅∆ti

n− i ∑i

ci⋅∆ti

(1)

−τ 2 (2)


where c is the conductivity signal, which corresponds to the tracer concentration at the time point after induction of the tracer pulse t at the measuring interval i . To characterize the degree of backmixing in the PFR, the Bodenstein number Bo was calculated from the mean residence time and its variance as defined by George et al. (1993) and Levenspiel (1999):

Bo=

2τ 2 στ2

(3)

A Bo 410 is generally considered to be approaching plug-flow behavior so that back-mixing effects can be negated (George et al., 1993). 2.4. Determination of biomass, phosphate, and ammonia concentrations Determination of biomass concentrations was performed gravimetrically as cell dry weight (g (DW) L 1) in quadruplicates. Then, 4 5 mL biosuspension was centrifuged (4000 rcf, 4 °C, 10 min), washed twice with isotonic sodium chloride solution, and dried at 105 °C for 28 h. Batch control experiments and calculation of the Kolmogorov length in the STR and PFR demonstrated that the technical setup of the STR-PFR system has no effect on the biomass growth (see Supplementary Section S1). The extracellular phosphate and ammonia content was quantified using Hach Lange Kits LCK 348 and LCK 303 (Hach Lange, Duesseldorf, Germany), respectively. 2.5. Nucleotide analysis For nucleotide analysis, 2 mL biosuspension was sampled directly into 0.5 mL precooled ( 30 °C) perchloric acid (35% (v/v)) and incubated for 15 min at 6 °C with shaking (Cserjan-Puschmann et al., 1999; Theobald et al., 1997). Samples were neutralized by adding KH2PO4 and KOH. After precipitate separation by centrifugation (15 min, 4 °C, 7000 g) samples were analyzed via HPLC (1200 Series, Agilent, Santa Clara, USA) equipped with a RP-C18 (octadecyl) phase column (Supercosil™ LC-18-T, 3 mm, 150 cm 4.6 mm) and a diode array detector (DAD). The gradient (3.5 min, 0% B; 20 min, 30% B; 22 min, 35% B; 40 min, 60%B; 48 min, 100% B; 55 min, 100% B; 60 min 0% B; 67 min, 0% B) was generated at a flow rate of 1 mL min 1 with buffer A (0.1 M KH2PO4, 0.1 M K2HPO4, 4 mM TBAS, pH 6) and buffer B (0.1 M KH2PO4, 0.1 M K2HPO4, 4 mM TBAS, pH 7.2þ30% methanol). Additional information on nucleotide measurements is available in Supplementary Section S4. The adenylate energy charge was calculated as described earlier (Atkinson and Walton, 1967):

AEC =

[ATP ] + 0.5[ADP ] [AMP ] + [ADP ] + [ATP ]

(4)

2.6. Organic acid HPLC For detection of glucose and the byproducts acetate, succinate, lactate, formate, and ethanol, an isocratic HPLC equipped with RI detector (1200 Series, Agilent, Santa Clara, CA, USA) and a Rezex ROA-Organic Acid H þ (250 cm 4.6 mm) column at a temperature of 50 °C was used. H3SO4 (5 mM) was used as the flow agent with a flow rate of 0.2 mL min 1. 2.7. RNA-sequencing and alignment Cells were sampled directly into RNAprotect Bacteria Reagent (Qiagen, Hilden, Germany), centrifuged, and the resulting pellet was stored at 70 °C. Total RNA was prepared using a Qiagen

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RNeasy mini kit allowing RNA longer than 200 bases to be purified, followed by DNase I treatment with RNase-Free DNase (Qiagen, Hilden, Germany) to remove contaminating DNA, according to the manufacturer's protocol. RNA quality was examined by a Lab-on-a-Chip-System Bioanalyzer 2100 (Agilent, Boeblingen, Germany), and the concentration was determined using a NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA). Library Preparation: Ribosomal RNA of 1 μg total RNA was depleted using the Ribo-Zero™ Magnetic Kit (Bacteria) (Epicentre, Madison, WI, USA) and sequencings were obtained using the TruSeq mRNA Library Prep Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Library size (approximately 400 bp) was examined using the Bioanalyzer 2100 and the concentration (approximately 40 ng/mL) was determined using the Qubit Fluorometric Quantitation (Thermo Fisher Scientific, Waltham, MA, USA). The library was denaturated according to the manufacturer's instructions and diluted to 9 pM. Approximately 10 million clusters per sample were sequenced using the Illumina HiSeq 2500 instrument (Illumina, San Diego, CA, USA) in the HighOutput mode with 68 cycles with single end reads. Cluster intensity values were translated into fastq files using Illumina's bcl2fastq Conversion Software v. 1.8.4. (http://support. illumina.com/downloads.html). Raw reads in fastq format were further investigated for remaining sequencing adapters. Cutadapt v. 1.8.3 (Martin, 2011) was used to remove remaining adapter bases for the subsequent alignment step. Reads were aligned against the E. coli K12 W3110 reference genome obtained from NCBI (GenBank: AP009048.1) using the RNA-sequencing aligner STAR v. 2.4.2a (Dobin et al., 2013). On average 97% of the sequenced reads could be mapped against the reference. 2.8. Read count and differential gene expression HTseq-count v. 0.6.1 (Anders et al., 2014) was used in the intersection-nonempty mode to estimate gene expression levels based on the respective annotations available from UCSC genome browser (http://genome.ucsc.edu) for the chosen reference sequence. On average 93% of the sequenced reads could be assigned uniquely to annotated features making up between 9 and 20 million reads per sample covering approximately 90% of all annotated genes by at least 10 reads. The edgeR R-package (Robinson et al., 2010) (v. 3.8.6), available from Bioconductor (http://www. bioconductor.org), was used to perform the differential gene expression analyses. Prior to statistical analysis, all residual nonprotein encoding RNA molecules were removed from the HTseqderived raw count data and a non-specific filter was applied to remove low coverage genes with fewer than 2 counts per million (16–20 reads) in more than 25% of the dataset (Supplementary data). Samples were grouped by replicates and an experimental design was chosen that used sample time and location (STR or PFR) as a combined environmental factor. Genewise dispersion was estimated based on a negative binominal distribution model and incorporated into the edgeR routines to determine differential gene expression within digital gene expression data using a generalized linear model with the described design. Resulting p-values were adjusted for multiple testing according to Benjamini and Hochberg (1995) to control the false discovery rate (FDR). Genes were identified as significantly differentially expressed by applying FDR adjusted p-values o 0.01. To measure mRNA abundance, the estimated fraction of transcripts formed by a distinct gene was computed as previously described (Li and Dewey, 2011). This adjusts the raw read counts for effective gene length and sequencing depth. The proportion was scaled by multiplying by 106 to get transcripts per million (TPM) (Supplementary data). TPM has been shown to be more consistent across samples and is therefore a reasonable measure

76


for the relative molar concentration of genes in a sample (Li et al., 2010; Wagner et al., 2012). 2.9. Multidimensional scaling analysis To limit multidimensional scaling (MDS) analysis to variable genes containing substantial information, the 600 genes with the largest standard deviation between samples were selected. Metric MDS with Euclidean distance as a proximity measure was performed using the plotMDS function implemented in edgeR (Robinson et al., 2010). Confidence ellipses were added by estimating the covariance matrix, assuming the data came from a multivariate t-distribution with a confidence level of 95% (Fox and Weisberg, 2010). To obtain the coefficient of determination R2, the correlation between the original distances and the distances determined from the two-dimensional MDS solution was computed and squared. 2.10. Functional enrichment tests Overrepresentation and gene set enrichment analysis of clusters of up- and downregulated genes were performed using Bioconductor's R packages GAGE (v. 2.16.0) (Luo et al., 2009) and goseq (v. 1.18.0) (Young et al., 2010), respectively. GAGE tests whether the mean fold change of a distinct gene set is significantly different from that of the background using two-sample t-test, whereas goseq is based on an extension of the hypergeometric test (Wallenius, 1963). In both methods, gene sets were considered to be significantly different with an FDRo 0.05 corrected after Benjamini and Hochberg (1995). Functional annotation was derived from the Cluster of Orthologous Groups (COG) database (Tatusov et al., 1997) (http://www.ncbi.nlm.nih.gov/COG, last modified: 4-22015). Additionally, the experimental sigma factor-gene interaction dataset from RegulonDB v. 8.0 (Salgado et al., 2013) (http:// regulondb.ccg.unam.mx/, last modified: 9-15-2015) was used for annotation, as well as gene lists for ppGpp, RpoS, and Lrp regulons identified by mutant studies (Traxler et al., 2011). 2.11. Computation of nucleotide and amino acid costs Metabolic costs for de novo biosynthesis of nucleotides from precursors were determined from metabolic pathway maps of E. coli obtained from the BiGG database (Schellenberger et al., 2010) (Supplementary Table S1). Amino acid biosynthesis costs were adopted from Kaleta et al. (2013) (Supplementary Table S2). Individual nucleotide and amino acid precursor metabolites were taken from Neidhardt (1990) and biosynthesis routes with the lowest ATP consumption were chosen. Similar to Akashi and Gojobori (2002) and Kaleta et al. (2013), costs in terms of ATP, reducing equivalents (NAD(P)H, FADH) and methylenetetrahydrofolate were considered, assuming that the energy content of NADH is equal to that of NADPH. A phosphate/oxygen ratio (P/O) of 1.49 (Taymaz-Nikerel et al., 2010) was considered for aerobic carbon limited growth, using ATP as the energy unit for the conversion of energy equivalents (i.e., ATP, NAD(P)H, FADH). Total ATP costs for each nucleotide or amino acid were calculated by adding the ATP produced during formation of each precursor from glucose to the ATP consumed by biosynthesis (see Supplementary Table S3, Supplementary Table S4). The weighted average costs per amino acid were calculated using the concentrations of all amino acids per g dry-cell mass reported in Neidhardt (1990), to account for the amino acid composition of cellular proteins in E. coli. 2.12. Estimation of ATP cost of gene expression To estimate the overall ATP requirements for transcription

between PFR P5 and the STR, the individual nucleotide triphosphates (NTPs) required for mRNA synthesis and the nucleotide monophosphates (NMPs) produced by mRNA degradation along the PFR were balanced. For this purpose, differentially expressed transcripts (FDR o 0.01) were selected for each time point and divided into up- and downregulated gene sets. Furthermore, RNA polymerases that had initiated transcription prior to leaving the PFR were assumed to continue transcript elongation within the STR (Chen et al., 2015). The nucleotide fractions for each gene in these sets were computed using the individual protein-coding nucleotide sequences obtained from RegulonDB v. 8.0 (Salgado et al., 2013). In the next step, TPM/106 were computed (see Section 2.8) and multiplied by the respective fraction of A, C, G, U nucleotides, leading to the proportion of nucleotides of the transcriptome for a given gene (Li et al., 2010). The mRNA content of total RNA was assumed to be 5%, which is about 0.16 g (DW) 1 for a typical E. coli cell grown in glucose minimal medium (Stouthamer, 1973). This is in line with values found by Taymaz-Nikerel et al. (2010) and Elser et al. (2003) for growth rates between 0.2 h 1 and 0.3 h 1. The average mass per g (DW) for each NTP was calculated by multiplying the mRNA content by the respective nucleotide proportion. The number of molecules of each nucleotide was determined by dividing this number by the corresponding molecular weight, from EcoCyc (Keseler et al., 2013), and the Avogadro constant (6.022 1023 mol 1). The number of NMP molecules produced for each gene set could now be balanced with the NTP required along the PFR. Since the energy for mRNA polymerization comes from the activated building blocks, NMP recycling was assumed to cost 2 ATP per nucleotide. In contrast, for de novo NTP synthesis the ATP costs reported in the Supplementary Table S1 were used. The number of amino acid molecules required for one mRNA translation was estimated from the number of NTP molecules required for mRNA synthesis along the PFR, assuming that a nucleotide triplet is needed for each amino acid. For simplification, co- and post-translational regulation as well as amino acid release from protein degradation was neglected. We further assumed that co-transcriptional translation protects the nascent mRNA (Sanamrad et al., 2014). After mRNA occurrence, 11 proteins were assumed to be translated per mRNA (estimated for m ¼0.2 h 1 from Bremer and Dennis (1996)). Translation costs are 4 ATP per amino acid including activation of the amino acid (1 ATP to 1 AMP) and peptide bond formation on the ribosome (2 GTP) (Stouthamer, 1973). Since there is a net production of 0.1 ATP per amino acid (for calculation see 2.11, Supplementary Table S4), the overall cost of amino acid synthesis and polymerization was estimated to be 3.9 ATP units consumed per residue. By multiplying the combined costs with the number of required amino acid molecules per translation, the overall ATP cost was calculated.

3. Results 3.1. Periodic stimulation using the STR-PFR two-compartment system To monitor the cellular response of E. coli W3110 on periodically changing glucose availability, a STR-PFR two-compartment system, as described by George et al. (1993), was applied. Using sugar availability as the trigger, cells were grown under glucose limited steady-state conditions in the STR, from which a fraction was drained to enter the PFR loop. No glucose was added to the PFR therefore the cellular consumption of the residual sugar shifted the cells from hunger in the STR to starvation mode at the end of the PFR. Afterwards, the cells were returned to the STR via a recycle loop. Short-term (tactic) cellular responses were monitored via five


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Fig. 1. Design of the two-compartment system for periodic stimulation experiments. (A) Schematic representation of the two-compartment simulator consisting of a stirred tank reactor (STR) coupled to a plug-flow reactor (PFR). The limiting substrate is fed into the well-mixed STR (hunger zone) and residual substrate is rapidly consumed when microbial cells enter the tube reactor, leading to the development of a starvation zone in the PFR. White arrow indicates the direction of microorganism flow through the system. Five sample ports (P1–5) along the PFR are indicated, together with their corresponding residence time τ (s), as well as STR sample port S. The residence time in the loop from the STR outlet (sample port P1 ( τ∆ S − P1)) to the PFR outlet (sample port P5 ( τ∆ P 5 − S )) is indicated, leading to a total mean residence time in the PFR of τPFR ¼125 s (B) Schematic outline of the continuous process strategy. A chemostat process strategy was chosen to maintain a constant volume and dilution rate of D ¼0.2 h 1 in the system after PFR addition. The steady state prior to PFR onset at time zero was used as the reference state (S0). Samples were taken at 11 distinct time points over 28 h.

sample ports (P1 to P5) along the PFR, while long-term (strategic) effects were tracked at the STR sample port S over the length of the process time (Fig. 1A). Prior to any culture testing, the technical set up was characterized to ensure plug-flow behavior in the PFR (Bodenstein numer Bo ¼84, for calculation see Section 2.3), and to identify the mean residence times of the cells with τPFR ¼125 s and τSTR ¼6.2 min in the STR and PFR, respectively. In addition, residence times at each sample port along the PFR were determined (Fig. 1A, Supplementary Table S5). Notably, the STR-PFR system was always operating as a chemostat (Novick and Szilard, 1950) under steady-state conditions, with glucose as the limiting substrate. Under operating conditions, the growth rate of the STR-PFR splits up into the STR growth of F 0.3 L h−1 = 1.12 L =0.27 V

0.27 h 1 ( μ = h−1) and the PFR rate of about m ¼0 h 1, due to the absence of an electron donor in the PFR. Essentially this means that cells showed a mean growth rate of

(

)

0.2 h 1 μ = 1.5 L ∙0.27 h−1+ 1.5 L ∙0 h−1=0.2 h−1 in the STR-PFR system. Fermentation always followed a two-step process (Fig. 1B). First, a reference steady-state (S0) was established for the STR without connection to the PFR (reached after five residence times or 25 h). Then, the statistical variance of S0 was measured in each experiment by three-fold sampling during 16 h after steady state conditions have been achieved. Since these measurements characterize the intrinsic steady state fluctuations, we further assumed that putative fluctuations during the 28 h cultivation would be 1.12 L

0.38 L

covered by the described variance. Afterwards, the PFR was connected and the culture was characterized by measuring intracellular nucleotides (ATP, ADP, AMP, GTP, GDP) and alarmones (ppGpp, cAMP), analyzing transcripts using RNA-sequencing and calculating specific uptake (glucose, ammonia, oxygen) and production rates (acetate, carbon dioxide). All cultivations were performed in biological triplicates under identical experimental conditions. Additional details on the cultivations conditions and off gas analysis are given in Supplementary Section S2 and S3, respectively. 3.2. Short-term response to glucose shortage Decreased intracellular ATP and GTP levels were observed after only 30 s (P1) in the PFR. As ADP and GDP levels simultaneously increased, the adenylate energy charge (AEC) decreased from 0.8 in the STR to 0.69 in the PFR during the first 30 s (Fig. 2A–C, Supplementary Fig. S1). The levels of AXP, GDP, and AEC persisted (P2 to P5). The kinetics of the alarmones ppGpp and cAMP were measured, as they have a well-known role in triggering metabolic regulation under carbon limitation and during the transition to starvation. Intracellular ppGpp levels increased along the PFR and showed sigmoidal time profiles with an average rise of about 3.3fold (Fig. 2D, Supplementary Fig. S1). In contrast, cAMP pools (including extracellular and intracellular levels) remained constant at 3.4 mmol g 1 (data not shown).

78


STR

transcriptional response was observed irrespective of process time. Up to 227 genes were up- or downregulated at least 1.5-fold during the first 70 s. Afterwards, the transcriptional response accelerated, showing 400–600 genes up- or downregulated by at least 1.5-fold at the PFR outlet. These gene expression changes represent the cellular reaction after 110 s in the PFR. The database of clusters of orthologous groups (COG) (Tatusov et al., 1997) was used to structure the gene expression patterns by sorting 3365 of the 3908 (86%) transcripts into 21 functional categories. The COG distribution of transcripts reflecting the cellular response to maximum gradient exposure (P5 samples) was determined using GAGE gene set (Luo et al., 2009) and goseq (Young et al., 2010) analysis. Resulting t-values from GAGE for each COG category are depicted in a spider graph (Fig. 3B). Transcripts coding for carbohydrate transport and metabolism (C) and energy production and conversion (G) were always more abundant at the PFR outlet (FDR o0.05 with GAGE and goseq). In contrast, COG groups representing translation and ribosomal structure (J), nucleotide (F) and coenzyme (H) transport and metabolism and replication, recombination and repair (L) were typically downregulated. Genes within the COG categories of amino acid transport and metabolism (E) and of posttranslational modification, protein turnover, and chaperones (O) were also upregulated. Conversely, cell motility genes were transiently downregulated. In addition, the effect of sigma factor mediated regulation was studied. Sigma factor gene interactions were derived from Regulon DB (Salgado et al., 2013) and could be assigned to 3487 of the 3908 genes (89%). For GAGE analysis, the same samples and settings were applied as for COG enrichment (Fig. 3C). Transcripts regulated by sS (RpoS), the sigma factor of the general stress response in E. coli (reviewed in Battesti et al. (2011)), were induced at all time points. However, biologically significant differences in sSmediated control were only detectable after 75 min. Moreover, transcripts with promoters known to be under dual control of the housekeeping sigma factors sD (RpoD) and sS were also found to be slightly increased. The nitrogen stress response sN (RpoN) regulon was significantly enriched at 45, 75, and 120 min

PFR

S

P2

P1

P4

P3

P5

7 6 5 4 3 2 1 0 0.95 0.90 0.85 0.80 0.75 0.70 0.65 0.60 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

3.3. Long-term response to repeated glucose shortage

0

20

40

60

80

100

120

τ/ s Fig. 2. Metabolic short-term response to carbon substrate availability. Time profiles of intracellular purine nucleotide concentrations in mmol g (DW) 1 over residing time τ in the PFR sampled at 28 h after PFR addition.

Analysis of RNA-sequencing data was performed with a primary filtering step to exclude genes that were expressed at very low levels (see Section 2.8). As a result, 3908 transcripts remained for analysis. To resolve fast transcriptional responses, PFR samples were compared to STR equivalents taken at the same process times (25 min, 120 min, and 28 h), from three independent cultures. Within 110 s, we found log2 fold changes ranging from –2.6 to 2.3, which correspond to 6-fold down- and 5-fold upregulation, respectively. About 60% of all transcripts with a false discovery rate (FDR) o0.01 showed marginal fold changes below 1.5. Fig. 3A depicts the numbers of differentially expressed genes (DEGs) with fold changes Z1.5 along the PFR. Notably, a very quick

Cells that were exposed to glucose shortage in the PFR were returned to the STR via a recycle loop where they intermixed with the remaining culture. The 28 h-lasting adaption process from the initial steady-state S0 (in STR) to the novel steady-state S1 (STRPFR) was monitored by STR sampling as indicated in Fig. 4A. During the course of adaption, the biomass concentration of E. coli W3110 increased from 4.55 7 0.073 g L 1 to 4.947 0.056 g L 1, coinciding with a slight impact on specific glucose and ammonia uptake rates (Supplementary Table S6 and Supplementary Section S3). No further changes were observed after 25 h, indicating that a new stationary state had been reached. As the culture adapted to the new conditions, energy charges ranged from 0.79 to 0.84 and intracellular ppGpp concentrations in the STR ranged from 0.28 mmol g (DW) 1 to 0.52 mmol g (DW) 1; however, these changes were minor compared to the distinct profiles observed along the PFR (see Supplementary Fig. S2). Shortly after connecting the PFR with the STR, transcriptome patterns in the STR changed, as illustrated by a multidimensional scaling plot (Fig. 4A, upper panel). The 95% confidence ellipse of the biological triplicates clearly defined the starting steady-state S0 before PFR connection. Transcriptional dynamics then slowed down throughout the duration of the experiment, finally converging to a new transcriptional steady-state (S1). The latter was characterized by a distinct 95% confidence ellipse, comprising the samples from 25, 26, and 28 h. The fact that no overlap was found with the S0 ellipse highlights the transcriptional differences


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Fig. 3. Transcriptional short-term response to carbon substrate availability. (A) Number of DEGs with fold change Z1.5 over residence time τ in the PFR. Time courses are shown for genes whose expression was increased (upper panel) or reduced (lower panel) between PFR P5 and STR sampled at 25 min (squares), 120 min (circles), or 28 h (diamonds) after PFR onset. (B) COG functional categories (Tatusov et al., 1997) and (C) sigma factor regulation (Salgado et al., 2013) pattern for the comparison of PFR sample port P5 vs. S, visualized as spider graphs. No COG or sigma factor annotation was found for 543 and 421 of 3908 genes, respectively. These genes were excluded from the statistical analysis. The t-statistics pattern from GAGE (Luo et al., 2009) is shown for three representative time points: 25 min (turquoise dotted line), 120 min (magenta line), and 28 h (green line) after PFR connection. Sets containing less than 10 or above 500 genes were excluded from the analysis. Functional groups that were significantly changed using GAGE and goseq (Young et al., 2010) with an FDRo 0.05 at a minimum of one time point are indicated with an asterisk.

between S0 and S1. The transition from S0 to S1 is mirrored by 465 transcripts showing log2 fold changes between 5.8 and 4.7 at least once after the PFR connection. After 28 h, 108 and 23 genes were found to be up- or downregulated, respectively. Highly diverging transcriptional patterns were found in samples collected 25 min and 28 h after PFR connection. Only four commonly upand no downregulated genes were found (Fig. 4B). Fig. 4A illustrates that a transcriptional steady-state was also established in the PFR. However, the transcriptional pattern was parallel shifted and thus was different from the one in the STR. As indicated in the lower panel of Fig. 4A, transcriptional changes along the PFR were tracked for each process time, finally linking the STR conditions with the transcriptional patterns found at P5 in the PFR. Accordingly, both compartments could be divided into clearly separate clusters. COG clustering and GAGE/goseq analysis was applied to the STR

data sets from 25 min, 120 min, and 28 h process times. Fig. 4C shows that transcripts from the COG categories of translation, ribosomal structure and biogenesis (J), replication, recombination and repair (L) and nucleotide (F) and coenzyme (H) transport and metabolism were significantly increased during the first 2 h after PFR connection. However, related expression levels reverted almost completely back to the starting conditions. The opposite profiles were found for the COG categories of carbohydrate transport and metabolism (G) and energy production and conversion (C). The initial phase with lower transcript abundance lasted until 120 min; then, the values recovered by the end of the process, and only slightly exceeded the start levels. The new steady state S1 was characterized by significant upregulation of amino acid biosynthesis and metabolism genes (E) and downregulation of cell motility (N) genes. Consistent with this observation was the reduced expression of genes regulated by the

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Fig. 4. Long-term dynamics of carbon substrate availability. (A) Multidimensional scaling analysis of transcriptomes obtained at 11 process time points (upper panel) and over residing time τ in the PFR (lower panel); mean of n¼ 3. Gray arrows follow the adaptation trajectories from STR (squares) S0 (0 h) to S1 (28 h) and PFR P5 (circles) and P1 (28 h), respectively. Ellipses indicate the 95% confidence interval of replicate samples taken at S0 (red) and S1 at 25, 26, and 28 h after PFR addition (green). Proportion of variance in the data accounted for by the MDS solution: R2 ¼0.96. (B) Venn diagram representing overlapping differentially expressed genes with increased or decreased transcription for the comparisons STR 25 min vs. S0 and STR 28 h vs. S0. DEGs with fold change Z1.5 were selected. (C) COG functional categories (Tatusov et al., 1997) and (D) sigma factor regulation (Salgado et al., 2013) pattern for the comparison of STR sample ports S vs. S0, visualized as spider graphs. No COG classification or sigma factor interaction was found for 543 and 421 of 3908 genes, respectively. These genes were excluded from the statistical analysis. The t-statistics pattern from GAGE (Luo et al., 2009) is shown for three representative time points, 25 min (turquoise dotted line), 120 min (magenta line) and 28 h (green line) after PFR connection. Sets containing less than 10 or above 500 genes were excluded from the analysis. Functional groups that were significantly changed using GAGE and goseq (Young et al., 2010) with an FDRo0.05 at a minimum of one time point are indicated with an asterisk.


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3.4. Estimation of ATP requirements

Fig. 5. Schematic outline of additional maintenance requirements due to transcriptional changes along the PFR. NTP demand or NMP release was calculated for every gene with a significant expression change between the PFR and the STR (FDR o 0.01). If the total NTP demand exceeded the NMP release, de novo synthesis costs were used to calculate the energy demand for the newly synthesized nucleotides (Supplementary Table S1). For nucleotide recycling, an energy demand of 2 ATP per nucleotide was assumed. The overall costs for translation arise from amino acid synthesis and polymerization, including tRNA loading. It was assumed that each mRNA is translated 11 times. The average costs (gray box) for recycling, de novo synthesis, and translation were calculated as a percentage of the growthdecoupled maintenance, assuming a P/O ratio of 1.49 and equivalent energy content for NADH and NADPH.

flagellar sigma factor sF (FliA) that manifested after 120 min (Fig. 4D). Furthermore, genes known to be under the control of sS were significantly overexpressed at the end of the cultivation period (S1), while a lower abundance of transcripts was observed during the initial process phase (5–120 min). Genes regulated by either sS or sD displayed a similar, although less pronounced, time profile. The induction of alternative sigma factor gene sets (i.e., sS, sN) was accompanied by a simultaneous reduction of the fraction of differentially expressed genes regulated by the housekeeping sigma factor sD (Supplementary Fig. S3). We further investigated the potential influence of short-term ppGpp induction (Section 3.2) on long-term adaption. Interestingly, we found that a significant number of genes requiring ppGpp and sS for full induction (Traxler et al., 2011), showed STR time profiles similar to genes that are regulated by sS (Supplementary Fig. S4).

In order to analyze the impact of the additional mRNA synthesis along the PFR (monitored by samples from the STR and PFR P5), the ATP requirements for the transcription of every differentially expressed gene (FDR o0.01) were computed (for details see Section 2.12). Fig. 5 summarizes the key factors that lead to the additional ATP requirements for transcription and translation; the values indicate the percent increase in ATP with respect to growth-independent maintenance of 0.0027 mol of ATP g (DW) 1 h 1 given by Taymaz-Nikerel et al. (2010). On average, cells invested 0.18 mmol ATP g (DW) 1 h 1 in mRNA synthesis, which increased the growth-independent maintenance by 6.6%. Based on nucleotide balancing, theoretical translation costs were estimated to increase the growth-independent maintenance demand by an additional 31% on average, even reaching 40–50% at distinct process times. Co-transcriptional translation was assumed (Sanamrad et al., 2014). Genes that were amplified during PFR passaging were further ranked according to individual ATP demands for transcription and translation. Table 1 depicts the top 20 genes, encoding about 9% of the total maintenance increase. According to COG classification (Supplementary Table S7), genes from the categories of cell motility (N), energy production and conversion (C) and amino acid transport and metabolism (E) were the most prominent. The gene encoding flagellin (fliC) was most energy-costly, with a 3% maintenance increase, followed by ompF and aroF, which encode the outer membrane porin and DAHP synthase catalyzing the first committed step in aromatic amino acid synthesis, respectively.

4. Discussion 4.1. Tactical response of E. coli to glucose shortage The short-term responses observed in the PFR may be regarded as the tactical response of E. coli to an extracellular glucose shortage. Importantly, we observed similar cellular responses in the PFR irrespective of the process time. We measured an immediate 13% decrease in AEC levels during the first 31 s in the PFR,

Table 1 Top energy-consuming genes. Differentially expressed genes (FDR o0.01) with the highest estimated energy demands for transcription and translation. Genea

fliC aroF aldA cstA aceA cspD aceB trg groL dnaK yfiA gatC flgL flgK acs mdh kgtP fliA glnH yjdA a b

Add-on to maintenanceb, % mRNA synthesis

TL

∑

2.70 0.67 0.48 0.35 0.34 0.31 0.27 0.27 0.26 0.24 0.18 0.17 0.16 0.13 0.13 0.12 0.11 0.11 0.10 0.10

0.40 0.10 0.07 0.05 0.05 0.05 0.04 0.04 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01

3.10 0.77 0.55 0.40 0.39 0.36 0.31 0.31 0.30 0.27 0.21 0.20 0.19 0.15 0.14 0.14 0.12 0.12 0.12 0.11

COG

Function

N E C T C K C N O O J G N N I C E K E n.a.

Flagellar biosynthesis; flagellin, filament structural protein 2-dehydro-3-deoxyphosphoheptonate aldolase (DAHP synthase) Aldehyde dehydrogenase A, NAD-linked Peptide transporter induced by carbon starvation Isocitrate lyase monomer DNA replication inhibitor Malate synthase A Methyl accepting chemotaxis protein – rib/gal/glc sensing GroEL chaperonin, peptide-dependent ATPase, heat shock protein Chaperone protein DnaK Stationary phase translation inhibitor and ribosome stability factor Galactitol PTS permease – GatC subunit Flagellar biosynthesis; hook-filament junction protein Flagellar biosynthesis, hook-filament junction protein 1 Acetyl-CoA synthetase (AMP-forming) Malate dehydrogenase α-ketoglutarate: H þ symporter RNA polymerase, sigma 28 (sigma F) factor Glutamine ABC transporter – periplasmic binding protein Clamp-binding sister replication fork colocalization protein

Genes which expression was significantly changed between STR and PFR P5 (FDR o 0.01) to all sampling times were selected for the calculations (core genes). Growth-independent maintenance taken from Taymaz-Nikerel et al. (2010).

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which coincided with a similar reduction in ATP and GTP pool sizes (Fig. 2A–C, Supplementary Fig. S1). At the same time, no prominent gene amplification or repression was observed. Therefore, ATP demands due to mRNA synthesis or protein formation should play a negligible role during the first 31 s. Instead, the reduced ATP and GTP levels are likely to mirror ongoing metabolic demands, which are no longer being equilibrated by adequate ATP and GTP formation. STR glucose balances reveal that 0.65 mgglc L 1 s 1 is required by cells when they enter the PFR. Hence, the residual amount of 4 mgglc L 1 (estimated via the detection limit of analytics) must already have been depleted at about 7 s, i.e., before sample port P1 was reached. As a consequence, metabolism became glucose starved and cell growth slowed down. Interestingly, ATP levels almost persisted during the remaining PFR passage which may refer to the consumption of secreted byproducts. However, side products were not detectable in the STR and PFR (Supplementary Section S3). Another possible explanation might the mobilization of storage pools like glycogen. Tian et al. (2013) outlined that slow glycogen degradation occurs under stationary conditions and is controlled by the phosphorylation state of the PTS component HPr. We found slight and significant upregulation of the cluster genes glgBXCAP and genes directly and indirectly involved in carbon storage regulation (glgS, csrB), respectively (Supplementary Table S8). This may pinpoint to the use of glycogen for compensating glucose shortage. Noteworthy, glgCAP, glgS and csrB have been shown to be under the regulation of ppGpp and/or RpoS (Edwards et al., 2011; Hengge-Aronis and Fischer, 1992; Rahimpour et al., 2013; Romeo and Preiss, 1989; Traxler et al., 2008). Cell lysis might be regarded as another putative energy source. However, studies with E. coli (Reeve et al., 1984) suggest certain robustness to prolonged starvation, since it takes 6 days of glucose starvation to reduce cell viability by 50%. Furthermore, genes for known toxins that might trigger programmed cell death under several stress conditions in E. coli (i.e. mazEF, hokB-sokB, chpSB) (Yamaguchi and Inouye, 2011) were not significantly altered (see Supplementary Table S9). Our results are in accordance with early studies by Chapman et al. (1971), who described an AEC value between 0.6 and 0.7 after cessation of growth due to glucose exhaustion. The AEC variation may influence many metabolic pathways due to the central role of adenine nucleotides as substrates and effectors (Atkinson, 1968). ATP is used in all biosynthesis pathways as well as tRNA charging, and GTP is required for translocation and tRNA delivery during translation. Their availability will therefore also alter the demand for protein synthesis by decreasing transcription from rRNA promoters during progression to a stationary phase (Murray et al., 2003; Schneider et al., 2002). The decrease in GTP and ATP was followed by a rapid accumulation of ppGpp, which is produced from these purine nucleotides. A final concentration of about 1 mmol g (DW) 1 was achieved after 90 s (Fig. 2D), which is consistent with intracellular ppGpp levels reported in other studies involving carbon stress conditions (Hardiman et al., 2007; Neubauer et al., 1995a; Teich et al., 1999). In contrast, extra and intracellular levels of the alarmone cAMP remained constant at 3.4 mmol g (DW) 1, which again was comparable to reported values for glucose-limited cultivation conditions (Hardiman et al., 2007; Lin et al., 2004). Alarmones exert distinct regulatory functions on the availability of metabolic precursors (Chubukov et al., 2014). During carbon starvation, accumulation of ppGpp is assumed to initiate the stringent response. cAMP is a component of the Crp-cAMP regulon, thus balancing precursor supply and demand (Hardiman et al., 2007) by amplifying genes coding for catabolic reactions. It is well known that high intracellular cAMP levels initiate glucose transport systems such as malT, mglABC, and lamB to allow the

highest affinity glucose uptake in the presence of low (10 6 M) extracellular glucose concentrations (Ferenci, 2001). In the current study, it is likely that cells experienced even lower extracellular glucose levels during PFR passaging (see glucose balancing above). Thus, a further improvement in glucose uptake, although indicated by amplification of genes belonging to COG category G, may have been of minor importance. Accordingly, total cAMP levels persisted in the PFR (data not shown). While influence of other regulators, especially cAMP/Crp, cannot be ruled out, we conclude that ppGpp was predominantly responsible for the observed regulatory response. The tactical response of E. coli therefore likely started within the first 30 s and was fully initiated after 90 s, when ppGpp levels reached their maximum. This hypothesis is supported by the genome-wide transcript analysis. As outlined in Fig. 3A, there was a boost in gene expression after 70 s, which coincided with increasing ppGpp amounts. The majority of genes (4 80%) that were amplified after 70 s were still overexpressed after 110 s, which suggests the onset of a concerted transcriptional response. Compared to this, gene expression profiles during the first 30 s were much less pronounced, with no distinct pattern, and many genes being assigned to predictive and putative functions. This further supports the idea of concerted transcriptional regulation starting after a time delay, and not reaching detectable expression changes until after 70 s. Notably, the observed transcript levels are consistent with a number of theoretical assumptions. Assuming an RNA polymerase elongation rate of 21–25 nt/s (Chen et al., 2015), PFR residence times would be sufficient to synthesize mRNA with a 2625–3125 nt chain length. This fits well with our findings and is in accordance with previous scale-down studies (Lara et al., 2006a; Schweder et al., 1999). Likewise, downregulation of genes along the PFR should mirror short mRNA lifetimes between 2 and 4 min (Chen et al., 2015) and controlled mRNA degradation (Lara et al., 2006a). A detailed analysis of gene expression changes induced by PFR conditions identified ppGpp-mediated regulation as a key component of the transcriptional changes. Examples of this include the ppGpp-induced sS regulon (Gentry et al., 1993) and the amplification of amino acid biosynthesis genes (Paul et al., 2005), both of which are consistent with our data. In particular, genes reported to be under the dual control of ppGpp and sS were overexpressed in the PFR (Supplementary Fig. S5). Furthermore, the rapid transcriptional downregulation of some of the most energy-demanding processes, including translation, nucleotide biosynthesis and replication (J, F, L in Fig. 3B), for the benefit of stress-protection (C, G, E, O in Fig. 3B) can also be attributed to the stringent response (Paul et al., 2004; Srivatsan and Wang, 2008). Related mechanisms are thought to be part of metabolic restructuring, usually preparing the cell for prolonged starvation. However, in the current study, this process was only induced and may not lead to a complete starvation response, since cells were exposed to less stressful conditions after 125 s when they returned to the STR. As a result, cell circulation caused frequent on/off switching of metabolic and transcriptional states as cells flowed from the STR to the PFR and back. While E. coli would appear to be robust enough to adapt to these frequent changes, while maintaining an overall growth rate of 0.2 h 1, it should be considered that exactly this scenario occurs in large-scale production fermenters when cells are fluctuating between different zones with different substrate availabilities. A question therefore arises regarding the cost at which E. coli withstands the stress scenario. Fig. 5 depicts the average additional ATP demands for transcription and translation calculated for different process times, considering gene expression changes between the PFR (P5) and the STR. Using growth decoupled ATP maintenance as a reference (Taymaz-Nikerel et al., 2010), an additional 6.6% was needed to fulfill the requests of the frequent on/


off switching of the mRNA machinery. However, this additional ATP requirement should not cause a significant metabolic burden for the cells. Instead, one may argue that potential benefits for survival will substantially outweigh the low costs of transcriptional adaptation. For wild-type E. coli, this argument holds true when additional ATP costs for translation are considered, and it was estimated that on average an additional 31% of ATP maintenance was required. Since one mRNA was assumed to be translated up to 11 times (calculated from Bremer and Dennis (1996)), the ATP costs for protein formation are about 5-fold higher than those for mRNA formation. Wild-type E. coli could still endure these costs. However, high-producing industrial strains, which are forced to perform at their metabolic limits, would likely become ATP-limited under such conditions. The genome may be reduced selectively by those genes that demand high switching on/off costs. Not surprisingly, induction of cell motility genes loaded highest on the cells’ energy budget, especially fliC and trg encoding flagellin and the sugar-sensing chemotaxis protein Trg, respectively. These genes are good examples of genome reduction in E. coli. Recently, Lieder et al. (2015) demonstrated that this is also the case for Pseudomonas putida. Other candidates, such as aroF, dnaK, groEL, and cstA, could be core elements of the regulatory network, requiring finely-tuned metabolic engineering for conserving ATP by modulating gene expression. It should be noted that individual genes in Table 1 should also be qualified with respect to the follow-up costs they create once they are activated. Being components of metabolism, they are likely to cause additional ATP demands that are not indicated in the table. 4.2. Long-term adaption and strategic response As indicated by Fig. 4A, repeated stimuli in the PFR ultimately caused a switch from the transcriptional steady-state, S0, to S1 in the STR. S1 can be distinguished from S0 by induction of the sS (RpoS)-dependent general stress response, amino acid biosynthesis, and downregulation of cell motility. The transition from S0 to S1 reveals transient downregulation of carbon and energy metabolism and upregulation of macromolecule synthesis genes, which reset opposite developments in the PFR. This phenomenon underlines the rapid on/off-switching of regulatory systems between the PFR and the STR. The permanent change from high to low levels of ppGpp found in cells leaving the PFR versus those staying in the STR appears to trigger the long term adaption. ppGpp is known to exert regulatory control on different time scales. For example, it may initiate early stationary phase responses quickly, but inhibit ribosomal RNA promoters (i.e., rrn) only after some delay (Murray et al., 2003). In the present study, during the cultivation we identified a shift from genes controlled by housekeeping sigma factor (sD) to alternative sigma factors in the STR (Supplementary Fig. S3). Notably, the competiveness of alternative sigma factors for RNAP is known to be enhanced by ppGpp by redirecting RNAP to specific promoters (Srivatsan and Wang, 2008). ppGpp is also known to simultaneously induce expression of the anti-sD factor Rsd (Jishage and Ishihama, 1999). Thus, ppGpp, Rsd, and the small 6S RNA (ssrS), which inhibits transcription from the sD promoter (Wassarman and Storz, 2000), increase the availability of RNAP. As a result, the transcription of genes by alternative sigma factors, such as sS (Gentry et al., 1993), is favored. Interestingly, rpoS, ssrS and rsd were induced along the PFR. It could therefore be assumed that their translation continued inside the STR, although transcription of novel mRNAs may have ceased (Chen et al., 2015). As indicated above (see Results 3.1), the operating STR-PFR system initiates compartmentalized growth rates of 0.27 h 1 in

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the STR and almost no growth in the PFR. It can be hypothesized whether long term transcriptional responses are caused by the relative growth rate increase in the STR alone. However, the increasing sS-dependent gene levels in STR contradict the hypothesis. sS is known to negatively correlate with increasing growth in the absence of stresses other than nutritional limitation in E. coli (Ihssen and Egli, 2004). Furthermore, we found expression of ppGpp dependent genes in the STR, although ppGpp levels only increased in the PFR and not in the STR. This is taken as a hint that ppGpp mediated regulation is induced in the PFR and propagates into the STR. Fig. 4A also illustrates the formation of spatially resolved, stable transcription patterns in the PFR. They mirror the equal transcriptional dynamics that cells perform during one cycle of the loop. In essence, this means that a multitude of cells with different transcriptional patterns are co-existing. This finding should be applied to large-scale production processes, where the ‘technical loop’ corresponds to hydrodynamic flow inside the bioreactor, and cells with a fluctuating transcriptional status represent the heterogeneous population within large-scale production processes.

5. Conclusion This study demonstrates that E. coli shows a gradual response to glucose shortage within a time frame of 2 min. During the first 30 s, glucose shortage is metabolically sensed (decrease of ATP and GTP) and leads to ppGpp accumulation. This induces a concerted transcriptional response, which is observed after about 70 s. Consequently, in order to prevent a massive regulatory response, successful bioreactor design should restrict mixing times to less than 70 s. An interesting observation is that the ppGpp/RpoS dependent transcriptional response, triggered during short periods of glucose starvation, switches genes on and off, thus also affecting the transcriptional state in the STR. Cellular ATP costs of transcription and translation may increase growth independent maintenance by around 40–50%. Under large-scale conditions, this may lead to ATP limitation in hyper-producing strains. Oscillating gene expression was detected in a number of pathways, and thus could affect several aspects of cellular physiology in genetically engineered strains. The list of the top energy-consuming genes may serve as a guideline for selective genome reduction, which could finally lead to the development of robust E. coli strains for successful large-scale applications.

Acknowledgements We thank Mira Lenfers-Lücker for assistance with the HPLC analyses, as well as Salaheddine Laghrami and Jan Müller for support with the cascade bioreactor fermentations. Moreover, we thank Sven Poths and Vanessa Vosseler for technical assistance in RNA-sequencing as well as Jakob Matthes, Michael Walter and Michael Bonin for valuable comments on the topic and the other members of the "RecogNice" project group for great cooperation: Michael Ederer, David Knies, Samantha Kunz, Julia Lischke, Oliver Sawodny (Institute for System Dynamics, University of Stuttgart), Olaf Riess (Institute of Medical Genetics and Applied Genomics, University of Tübingen), Georg Sprenger, Natalie Trachtmann (Institute of Microbiology, University of Stuttgart). The authors further gratefully acknowledge the funding of this work by the Bundesministerium für Bildung und Forschung (BMBF; Grant FKZ0316178A).

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Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ymben.2016.06. 008.

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