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Additional file 2: Table S1. Native (original) and codon-optimized sequences of the used ectD genes.

Additional file 1: Table S1. Primers used for plasmid construction. Figure S1. Phylogenetic analysis of the donor organisms used in this work. The phylogenetic tree of the donor organisms and the host C. glutamicum ATCC 13032 was compiled using the phyloT online tool (biobyte solutions GmbH, Heidelberg, Germany) and trees visualized with the iTOL online tool [85]. Figure S2. Screening for optimal 5-hydroxyectoine production in recombinant C. glutamicum. The C. glutamicum type strain ATCC 13032 episomally expressed the codon optimized ectD genes from Pseudomonas stutzeri A1501 (PST), Mycobacterium smegmatis ATCC 19420 (MSM), Streptomyces coelicolor A3(2) (SCO), Halomonas elongata ATCC 33173 (HEL) and Virgibacillus salexigens ATCC 700290 with amino acid exchanges A163C and S244C (VSA) [32], in the pClik 5a (pClik) and pCES-PLPV (pCES) vector. Cultures were grown on minimal glucose medium in a microbioreactor and analyzed for growth (on-line measurement of OD620) and the conversion of ectoine into hydroxyectoine (final titers after depletion of glucose). Screening for different initial ectoine concentrations at 30 °C (a). Screening at different temperatures with 1 mM and 5 mM initial ectoine, respectively (b, c). n = 2. Figure S3. Impact of ectoine and 5-hydroxyectoine on intracellular metabolite levels in C. glutamicum. For 5-hydroxyectoine production C. glutamicum ATCC 13032, episomally expressed a codon-optimized ectD gene from Pseudomonas stutzeri A1501 (PST), Mycobacterium smegmatis ATCC 19420 (MSM), or Virgibacillus salexigens ATCC 700290 (VSA), respectively. The gene from V. salexigens encoded for an enzyme variant that carried the amino acid exchanges A163C and S244C [32]. All strains were cultivated at 37 °C on minimal glucose medium, supplemented with 5 mM ectoine. Cells were analyzed for intracellular metabolite levels during the mid-exponential phase (10 h). As a control, C. glutamicum, harboring the empty vector, investigated during growth on glucose and ectoine (Control) and during growth on glucose alone (Control -Ect), respectively. n = 3. Figure S4. Production of 5-hydroxyectoine in a fed-batch process in stirred tank bioreactors using metabolically engineered C. glutamicum. The C. glutamicum type strain ATCC 13032 episomally expressed the codon optimized ectD genes from Mycobacterium smegmatis ATCC 19420 (a, b) and Pseudomonas stutzeri A1501 (c, d). The process was operated in fed-batch-mode at 37 °C with initial sucrose and ectoine concentrations of 100 g L−1 and 60 g L−1, and the addition of feed (600 g L−1 sucrose, 15 g L−1 yeast extract) at constant rate, respectively. n = 2. Figure S5. Impact of process conditions on fed-batch production of 5-hydroxyectoine using strain C. glutamicum Ptuf ectDMSM in a stirred tank bioreactor at 37 °C. In each setup one process parameter was varied as compared to the standard layout (Fig. 7) to study its impact (from left to right): DO maintained at 60%, twice as high as in the control; supplementation of batch and feed medium with l-glutamate; limited nitrogen supply by elimination of small amounts of yeast extract from the batch medium and the use of a nitrogen-free feed; addition of ectoine at a reduced initial amount (10 g L−1) plus separate feeding later on. n = 1. Figure S6. Production of 5-hydroxyectoine in a batch process in stirred tank bioreactors using C. glutamicum Ptuf ectDPST which episomally expressed codon optimized ectD from Pseudomonas stutzeri A1501. The strain was cultivated in batch-mode with initial sucrose and ectoine concentrations of 100 g L−1 and 50 g L−1 (a, b), 150 g L−1 and 75 g L−1 (c, d) and 200 g L−1 and 100 g L−1 (e, f) at 37 °C. n = 2. Figure S7. Selectivity of 5-hydroxyectoine production in a batch process using C. glutamicum Ptuf ectDPST. The C. glutamicum type strain ATCC 13032 episomally expressed codon-optimized ectD from Pseudomonas stutzeri A1501. It was cultivated with initial sucrose and ectoine concentrations of 100 g L−1 and 50 g L−1 (a), 150 g L−1 and 75 g L−1 (b), and 200 g L−1 and 100 g L−1 (c) at 37 °C. n = 2.

Additional file 1: Figure S1. Development of a suitable workflow for photomixotrophic growth of Synechocystis 6803 in 13C metabolic flux analysis. Cultivation profile with indicated threshold for growth with sufficient light supply, A estimation of the maximal cell concentration that provides sufficient light for maximum growth, B light absorption of cultures, incubated in BG11 medium, at varied cell concentration and depth, the values are normalized to 100% for the maximum illuminance, C modelling of the relative light intensity as function of cell concentration and light passage (depth) using the Lambert–Beer law, D the orange lines indicate that a culture at OD = 2 and the light path length for the conducted shake flask cultures (42 mm) receives 70% of the supplied illumination, while absorbing the remaining 30%. n = 3. Figure S2. Time profile of 13C amino acid labelling patterns (given at different cell concentrations) during photomixotrophic cultivation of Synechocystis 6803, grown on [1-13C] glucose (left) and [U-13C] glucose (right). Figure S3. Computational evaluation of previous approaches, based on single isotope experiments and (mainly) GC/MS analysis of proteinogenic amino acids, for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803. The tested approaches had been applied for photomixotrophic flux analysis of Synechocystis, before the full network was known [44] or had focused on a subnetwork of the microbe [45]. A third approach had been derived to analyze heterotrophic Synechocystis sp. PCC6803 without an active CBB cycle [55]. There, the approaches analyzed for the achievable precision and accuracy to determine fluxes in a scenario with 0% (left), 5% (middle), and 50% (right) flux through the ED and the PK pathways. The show the outcome of a Monte-Carlo simulation that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. Displayed are key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. The color indicates the determinability of a flux parameter. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S4. Computational evaluation of different setups for 13C metabolic flux analysis of Synechocystis 6803. The aim of the simulations was to identify optimum strategies for flux analysis in the photomixotrophic microbe. Different setups using different tracer substrates and labelling data were analyzed for the achievable precision and accuracy to determine a flux scenario with low flux (5%) through the ED and the PK pathway. Key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. Each setup was evaluated by a Monte-Carlo approach that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. Double, triple, and quadruple tracer studies were evaluated. The substrates shown here, were [1-13C], [3-13C], [6-13C], and [13C6] glucose for the following reasons. The combination of [1-13C] glucose and [6-13C] glucose well discriminated the fluxes through the EMP, the PP, and the ED pathway in glucose-grown pseudomonads, revealing a similarly cyclic pathway architecture as cyanobacteria [42]. Metabolization of [3-13C] glucose (based on the underlying carbon transitions) via the ED route should selectively lead to 13C label enrichment at the C1 of pyruvate (and amino acids derived therefrom), providing a sensitive readout, should this pathway be active. The use of [13C6] glucose appeared beneficial, likely because it helped to estimate the relative uptake of 13C sugar versus (non-labelled) CO2, as previously demonstrated for Basfia succiniciproducens, grown on sucrose under high rates of CO2 assimilation [22]. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S5. Computational evaluation of different setups for 13C metabolic flux analysis of Synechocystis 6803. The aim of the simulations was to identify optimum strategies for flux analysis in the photomixotrophic microbe. Different setups using different tracer substrates and labelling data were analyzed for the achievable precision and accuracy to determine a flux scenario with medium flux (25%) through the ED and the PK pathway. Key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. Each setup was evaluated by a Monte-Carlo approach that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S6. Computational evaluation of different setups for 13C metabolic flux analysis of Synechocystis 6803. The aim of the simulations was to identify optimum strategies for flux analysis in the photomixotrophic microbe. Different setups using different tracer substrates and labelling data were analyzed for the achievable precision and accuracy to determine a flux scenario with high flux (50%) through the ED and the PK pathway. Key fluxes of upper and lower carbon metabolism, i.e., through ED, PP, EMP, and PK pathways, CBB cycle, and TCA cycle, are shown. Each setup was evaluated by a Monte-Carlo approach that mimicked 100 repetitions of the corresponding flux study while taking experimental errors into account. The color indicates flux determinability: green, < 0.1%, yellow < 1%, orange < 10%; and red, > 10%. Figure S7. Sensitivity of selected mass isotopomer ratios to a variation of individual flux parameters using alternative single 13C labelled glucose as input. The most sensitive change is highlighted. Figure S8. Goodness-of-fit for 13C metabolic flux analysis of Synechocystis 6863. The data reflect measured and model predicted (simulated) data for the best-fit solution: 388 mass isotopomers from amino acids, sugars, and sugar derivatives, measured by GC-MS (A) and on basis of 388 mass isotopomers from amino acids, sugars, and sugar derivatives, measured by GC-MS, plus 168 positional 13C enrichments, obtained by NMR (B). Figure S9. In vivo flux distribution of Synechocystis 6803 during photomixotrophic growth on glucose and CO2 determined by GC-MS and NMR based 13C metabolic flux analysis. Fluxes are normalized to the glucose uptake (100%, 0.421 mmol g−1 h−1). The thickness of the arrows denotes the amount of flux. The errors for the fluxes reflect standard deviations, estimated by Monte-Carlo simulation. The anabolic fluxes into biomass are shown as triangles. The complete flux data set is given in Table S2, where also the 95% confidence intervals from the Monte-Carlo analysis are provided. GLC_ex extracellular glucose; G6P glucose 6-phosphate; F6P fructose 6-phosphate; DHAP dihydroxyacetone phosphate; GAP glyceraldehyde 3-phosphate; 3PG 3-phosphoglycerate; PEP phosphoenolpyruvate; PYR pyruvate; AcCoA acetyl coenzyme A; ICI isocitrate; 2OG 2-oxoglutarate; SucA succinate-semialdehyde; SUC succinate; FUM fumarate; MAL malate; OAA oxaloacetate; 6PG 6-phosphogluconate; KDPG 2-keto-3-deoxy-6-phosphogluconate; Ri5P ribose 5-phosphate; Ru5P ribulose 5-phosphate; X5P xylose 5-phosphate; S7P sedoheptulose 7-phosphate; E4P erythrose 4-phosphate; CO2_EX extracellular carbon dioxide;CO2 intracellular carbon dioxide. The flux estimation yielded an excellent quality of fit for the considered mass isotopomers of amino acids, sugars, and sugar derivatives and NMR-derived positional enrichments (Additional file 1: Table S3). The variance-weighted sum of squared residuals (SSR) was 583 and thus within the expected range (511; 621) of the chi-square test at 95% confidence level. n = 4. Figure S10. Goodness-of-fit for the 13C metabolic flux analysis of Synechocystis 6863 deletion mutants. The data reflect the fest-fit solution and show measured and simulated GC-MS data (388 mass isotopomers from amino acids, sugars, and sugar derivatives) for strains Δeda (A), ΔpfkAB (B), and Δxfp1/xfp2 (C). Figure S11. Evaluation of the light supply during cultivation in glass tubes with 3.5 cm diameter that were illuminated from the front and the back side and were mixed by air, bubbled from the bottom [37]. Simulating the light supply for this geometry, using the obtained Lambert-Beer correlation (Additional file 1: Fig. S1), revealed large inner zones of insufficient illumination, when considering the determined threshold of 35 μE m−2 s−1. Already at OD750 = 1, cells largely faced limiting light supply, and the light-limited areas became even more pronounced at higher cell concentrations, comprising up to more than 90% of the culture volume. The show modelled light intensity profiles in 200 mL Kniese tubes, illuminated with 50 μE m−2 s−1 from the back and the front side, during cultivation of Synechocystis 6830. The calculation was based the measured relationship between cell concentration and light absorption (Fig. 2). The relative light intensities are encoded by different colour and range from green (100%) to black (0%). The colour code shows all areas, illuminated with a light intensity below 35 μE m−2 s−1 and shown to limit growth, in dark. The values were calculated at a spatial resolution of 0.1 mm. Figure S12. Southern blot of wildtype (WT) and Δgap2. The Southern blot was performed in order to verify the completed segregation of Δgap2. The probe detected a fragment in the size of 4916 bp in the wildtype (WT) and of 780 bp in Δgap2 as expected. This result confirmed that Δgap2 was segregated and that no wild type copies were left. In addition, an unspecific fragment of about 4500 bp was detected in Δgap2 as well. Figure S13. Southern blots of wildtype (WT), Δxfp1 and Δxfp1/Δxfp2. Southern blots were performed with probes against xfp1 and xfp2 in order to check segregation of Δxfp1, Δxfp2, and Δxfp1/Δxfp2. The probe against xfp1 was expected to detect a fragment size of 1420 bp in the wildtype and of 2013 bp in Δxfp1 (top). The probe against xfp2 was expected to detect a fragment in the size of 970 bp in the wildtype and of 731 bp in Δxfp2 (bottom). Lanes 4 and 5 in the bottom gel, right from the three strains, are not relevant. The southern blots thus confirmed that Δxfp1, Δxfp2, and Δxfp1/Δxfp2 were segregated and that no wildtype copies were left. Table S1. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit was statistically acceptable. The variance-weighted sum of square residuals (SSR) was 377 and thus within the expected range (342; 434) of the chi-square test at 95% confidence level. Table S2. Flux distributions in Synechocystis 6863 and related deletion mutants. The data represent the best-fit-solution for each strain and include the estimated fluxes (Mean), the standard deviation (SD) and the corresponding 95% confidence intervals (LB lower boundary; UB upper boundary). The 13C labelling data, considered for flux estimation, were taken from GC-MS and from GC-MS plus NMR analysis. The reaction numbers refer to the biochemical network model (Additional file 1: Table S9). Table S3. Measured and simulated GC-MS and NMR 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) GC-MS mass isotopomer distributions of amino acids, sugars, and sugar derivatives plus positional enrichments from NMR analysis. Regarding GC-MS analysis, the specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. Data has been corrected for natural occurring isotopes. For NMR, the assessed carbon atom is given. The flux fit was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 583 and thus within the expected range (511; 621) of the chi-square test at 95% confidence level. Table S4. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803 Δeda. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit of this mutant was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 394 and thus within the expected range (343; 435) of the chi-square test at 95% confidence level. Table S5. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803 ΔpfkA/ΔpfkB. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit of this mutant was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 401 and thus within the expected range (343; 435) of the chi-square test at 95% confidence level. Table S6. Measured and simulated GC-MS 13C labelling data for 13C metabolic flux analysis of photomixotrophic Synechocystis 6803 Δxfp1/Δxfp2. The approach involved four parallel isotope studies on different 13C glucose tracers. The data represent the best-fit solution after minimizing the variance-weighted sum of square residuals and display the experimentally measured (exp) and model simulated (sim) mass isotopomer distributions of amino acids, sugars, and sugar derivatives. The specified fragments represent the ion clusters considered for the analysis, whereby the number denotes the corresponding monoisotopic mass. The flux fit of this mutant was statistically acceptable. The variance-weighted sum of squared residuals (SSR) was 415 and thus within the expected range (344; 436) of the chi-square test at 95% confidence level. Table S7. Primers used to construct a phosphoketolase double deletion mutant Δxfp1/Dxfp2 and a single gene deletion mutant Δgap2 from wild type. In addition, the corresponding annealing temperature (AT) is given. Table S8. Cellular composition used for metabolic flux analysis of Synechocystis 6863. The data for wild type (WT) were also used for the strains ΔpfkAB and Δxfp1/Δxfp2. For strain Δeda, the data reflect the increased glycogen content. Table S9. Biochemical reaction network for 13C metabolic flux analysis of Synechocystis 6803 including reaction stoichiometry, atom transition, and reaction directionality. F unidirectional (forward only) reaction; FR reversible reaction; B biomass. The reactions R1 (v1) to R34 (v34) refer to the carbon core network of the microbe (Fig. 1). The reactions R35 to R84 represent biomass forming reactions. In Fig. 1 they lumped into the corresponding anabolic fluxes (vx).

Additional file 1: Figure S1. Confirmation of the deletion of the catB gene in Corynebacterium glutamicum ATCC 13032, using colony PCR. To this end, C. glutamicum ATCC 13032, had been transformed with the integrative plasmid pClik int sacB ΔcatB, followed by recombination and selection. The primers ΔcatB TS1 FW and ΔcatB TS2 RV were used for the PCR (Table 1). The positive clone, indicated by the white arrow, revealed the small fragment, size expected for the deletion. It was designated C. glutamicum LIMA-1. M, 1 kb DNA ladder; 1, blank; 2, positive clone; WT, wild type. Figure S2. Growth and cis–cis-muconic acid (MA) production from small aromatics, using Corynebacterium glutamicum MA-1. The yield for MA on benzoic acid (20 mM), catechol (10 mM), and phenol (5 mM) was obtained from measurement of substrates and product at the beginning and after 24 h of incubation (A). The tolerance to catechol was obtained from cell growth measurement (B). All data represent mean values and standard deviations from three biological replicates. Figure S3. Tolerance of Corynebacterium glutamicum MA-1 against benzoic acid (A), catechol (B), and phenol (C). In addition, glucose was added as growth substrate. The final cell concentration was measured after 24 h of cultivation. The data represent mean values and standard deviations from three biological replicates. Figure S4. Kinetics and stoichiometry of cis–cis-muconic acid (MA) production, using the first generation producer Corynebacterium glutamicum MA-1. The aromatics benzoic acid (A), catechol (B) and phenol (C) were used for production. In addition, glucose was added as growth substrate. The data represent mean values and standard deviations from three biological replicates. Figure S5. Characteristics of catechol-1,2-dioxygenase (CatA) in Corynebacterium glutamicum, grown on different aromatics. The data comprise the specific enzyme activity of the first generation producer C. glutamicum MA-1 (A), the kinetics of the enzyme with a fit of the experimental data from benzoate-grown cells to a Michaelis–Menten type kinetics (B), and the specific enzyme activity of the second generation producer C. glutamicum MA-2 (C). The data represent mean values and standard deviations from three biological replicates. Figure S6. Confirmation of overexpression of the catA gene under control of the tuf promoter in Corynebacterium glutamicum MA-2, using colony PCR. To this end, C. glutamicum MA-1 had been transformed with the integrative plasmid pClik int sacB PtufcatA, followed by recombination and selection. The primers Pef-tu-catA TS1 FW and Pef-tu-catA TS2 RV were used for the PCR (Table 1). The positive clone, indicated by the white arrow, revealed the increased fragment size, expected for the promoter exchange. M, 1 kb DNA ladder; 1, positive clone; WT, wild type. Figure S7. Kinetics and stoichiometry of cis–cis-muconic acid (MA) production, using the second generation producer Corynebacterium glutamicum MA-2 on catechol (A) and the first generation producer C. glutamicum MA-1 on catechol and benzoic acid (B). Glucose was added as growth substrate. The data represent mean values and standard deviations from three biological replicates. Figure S8. Fed-batch production of cis–cis-muconic acid (MA) from catechol by metabolically engineered Corynebacterium glutamicum MA-2. Substrate consumption, growth and MA formation (A). Volumetric productivity (B). Yields for MA from catechol, and for MA from catechol plus glucose (C). Pulse-wise feeding of catechol (D). Glucose was added continuously to maintain the glucose level in the range between about 5 to 15 g L−1 (A). The vertical lines represent individual catechol feed pulses (D). The feed frequency was variably adjusted, depending on the signal of dissolved oxygen, which precisely indicated the time point of catechol depletion. As example, feeding was halted once during the initial phase, corresponding to transient catechol accumulation and was accelerated later in response to the faster conversion. The data represent mean values from two replicates.