Synthetic control of metabolic states in Pseudomonas putida by tuning polyhydroxyalkanoate cycle

Synthetic Control of Metabolic States in Pseudomonas putida by Tuning Polyhydroxyalkanoate Cycle Maria-Tsampika Manoli, a , b Juan Nogales, a , c Auxiliadora Prieto a , b a Interdisciplinary Platform for Sustainable Plastics Towards a Circular Economy–Spanish National Research Council (SusPlast-CSIC), Madrid, Spain b Polymer Biotechnology Group, Microbial and Plant Biotechnology Department, Biological Research Centre Margarita Salas, CIB-CSIC, Madrid, Spain c Systems Biotechnology Group (SBG), Department of Systems Biology, National Centre for Biotechnology (CNB-CSIC), Madrid, Spain ABSTRACT Polyhydroxyalkanoates (PHAs) are polyesters produced by numerous microorganisms for energy and carbon storage. Simultaneous synthesis and degradation of PHA drives a dynamic cycle linked to the central carbon metabolism, which modulates numerous and diverse bacterial processes, such as stress endurance, pathogenesis, and persistence. Here, we analyze the role of the PHA cycle in conferring robustness to the model bacterium P. putida KT2440. To assess the effect of this cycle in the cell, we began by constructing a PHA depolymerase (PhaZ) mutant strain that had its PHA cycle blocked. We then restored the flux through the cycle in the context of an engineered library of P. putida strains harboring differential levels of PhaZ. High-throughput phenotyping analyses of this collection of strains revealed significant changes in response to PHA cycle performance impacting cell number and size, PHA accumulation, and production of extracellular (R)-hydroxyalkanoic acids. To understand the metabolic changes at the system level due to PHA turnover, we contextualized these physiological data using the genome-scale metabolic model iJN1411. Model-based predictions suggest successive metabolic steady states during the growth curve and an important carbon flux rerouting driven by the activity of the PHA cycle. Overall, we demonstrate that modulating the activity of the PHA cycle gives us control over the carbon metabolism of P. putida, which in turn will give us the ability to tailor cellular mechanisms driving stress tolerance, e.g., defenses against oxidative stress, and any potential biotechnological applications. IMPORTANCE Despite large research efforts devoted to understanding the flexible metabolism of Pseudomonas beyond the role of key regulatory players, the metabolic basis powering the dynamic control of its biological fitness under disturbance conditions remains largely unknown. Among other metabolic hubs, the so-called PHA cycle, involving simultaneous synthesis and degradation of PHAs, is emerging as a pivotal metabolic trait powering metabolic robustness and resilience in this bacterial group. Here, we provide evidence suggesting that metabolic states in Pseudomonas can be anticipated, controlled, and engineered by tailoring the flux through the PHA cycle. Overall, our study suggests that the PHA cycle is a promising metabolic target toward achieving control over bacterial metabolic robustness. This is likely to open up a broad range of applications in areas as diverse as pathogenesis and biotechnology. KEYWORDS metabolic robustness, Pseudomonas, polyhydroxyalkanoates, PHA metabolism, PHA depolymerase, oxidative stress, flux balance analysis The group Pseudomonas comprises a heterogeneous and large number (.100) of Gram-negative, aerobic gammaproteobacterial species (1). Pseudomonas has a robust metabolism and is physiologically versatile, which enables fast adaptation to fluctuating environments and evolvability while facilitating colonization of diverse niches, Editor Arash Komeili, University of California, Berkeley Copyright © 2022 Manoli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Address correspondence to Juan Nogales, [email protected], or Auxiliadora Prieto, [email protected]. The authors declare no conflict of interest. Received 29 July 2021 Accepted 11 December 2021 Published 18 January 2022 January/February 2022 Volume 13 Issue 1 e01794-21 ®mbio.asm.org 1 RESEARCH ARTICLE Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. many of them often hostile to other bacterial genera (2). Most of these relevant traits of pseudomonads are powered by their primary and accessory metabolisms and are in their core genome, which comprises around 1,000 genes (3–7). Boosted by these exceptional metabolic features, Pseudomonas spp. have emerged as a notable bacterial group, sparking growing interest in fields as diverse as plant and human diseases (8, 9), agriculture, biodegradation, and industrial biotechnology (5, 10, 11). Despite said interest and the intense scrutiny of Pseudomonas’metabolism in recent years (4, 12), the molecular basis underpinning dynamic control of their physiology under disturbance conditions remains largely unknown. Among this diverse group, Pseudomonas putida is a paradigm of the “cosmopolitan bacterium”that is well-known for its robust metabolism and stress resilience (13–15). P. putida is often isolated in polluted soil and aquatic environments, a fact that has driven extensive research into its stress tolerance mechanisms and adaptability. Strain KT2440 is considered a microbial biocatalyst and has been used in multiple metabolic engineering endeavors, supported by rational genetic modifications using an everincreasing number of genetic tools and genome-scale models (GEMs) (12, 16–18). Among other industrial applications, KT2440 is a paradigmatic model for production of bioproducts such as bacterial polyesters or polyhydroxyalkanoates (PHAs) (10, 19, 20). PHAs are accumulated as reserve storage granules in the cell cytoplasm, mainly under nutrient imbalances like carbon excess coupled to limited availability of nutrients such as nitrogen and phosphorous, among others (21, 22). Granules are coated by the granule-associated proteins (GAPs) involved in PHA metabolism and regulation, i.e., polymerases, depolymerases, phasins, and others with similar functions (23–26). In P. putida, some of these GAPs and the PhaD transcriptional activator are coded in a pha gene cluster, which is well conserved throughout the mcl-PHA (medium-chain-length) producer strains (Fig. 1) (26). The bacterial PHA metabolic machinery is closely connected to central and peripheral metabolic pathways supplying (R)-3-hydroxyacyl-coenzyme A [(R)-HA-CoA] as a substrate for PHA polymerases. In the case of pseudomonads, PHA metabolism relies on the b -oxidation pathway and de novo fatty acid biosynthesis for the conversion of fatty acid and non-fatty acid precursors into different (R)-HA-CoAs for mcl-PHA biosynthesis. PHA metabolism is controlled via a multilevel regulatory network driven by global regulators linked to central carbon metabolism and pha-specificregulators in the pha cluster (Fig. 1A and recently reviewed in reference 20). Key GAPs mediating the PHA cycle in P. putida are the PHA polymerases (PhaC1 and PhaC2) and the PHA depolymerase (PhaZ). The former synthesize and the latter degrades PHA by releasing 3-hydroxyalkanoic acids [(R)-HAs or free monomers]. AcylCoA synthetase (FadD1) subsequently reactivates the free monomers into (R)-HA-CoA in an ATP-dependent reaction (20, 26) (Fig. 1B). This process implies PHA turnover, where both synthesis and degradation of the polymer are active simultaneously (19, 23). Consequently, it has been suggested that, in addition to its primary carbon storage function, this bidirectional flux could provide a certain buffering capability, granting the PHA cycle the ability to control carbon and energy spillage in P. putida (22). Along these lines, it has also been suggested that the PHA cycle acts as a homeostatic cycle, providing stability and metabolic fitness under environmental perturbations. In other words, the PHA cycle might be a metabolic capacitor connecting catabolism and anabolism with P. putida’s central metabolism. Such cycles have been defined as robustness cycles (27–30). The production of PHA is a metabolic feature largely present in Pseudomonas, highlighting the role of PHA metabolism in the evolutionary success of this important bacterial genus (6). In an attempt to find metabolic features providing dynamic control over metabolism in Pseudomonas, in this work we assess the role of the PHA cycle as a robustness cycle in P. putida KT2440. This required a multidisciplinary approach involving systems and synthetic biology to tune the flux of carbon through the PHA cycle by adjusting the flux through the PhaZ reaction. We also provide solid evidence that the PHA cycle Manoli et al. ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 2 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. plays a key metabolic role in the induction of carbon-flux rerouting under perturbations such as oxidative stress. RESULTS Engineering a synthetic tuning of PHA turnover. PHA turnover functionality ensures that the dynamic flux of (R)-HAs toward the central metabolism is available when needed. To study the effect of a defective PHA cycle on the physiology of P. putida, we started by constructing a host strain that was missing the PhaZ depolymerase-encoding gene and therefore was unable to hydrolyze PHA. Since the pha cluster is arranged in two convergent operons (phaC1ZC2D and phaFI), deleting the phaZ gene might trigger polar effects on the transcription level of the whole cluster due to the defective expression of the transcriptional activator, phaD, that controls the activity of promoters P C1 and P I (31) (Fig. 1A). Therefore, the scar sequence of the phaZ deletion mutant (KT40Z) strain was carefully designed and verified via sequencing to ensure the in-frame expression of the remaining pha genes (see Fig. S1 in the supplemental material). The absence of polar effects was further verified by quantitative reverse transcription-PCR (qRT-PCR) experiments, monitoring the transcription levels of phaF and phaI genes in KT40Z and KT2440 cells growing under PHA accumulation conditions (mid-exponential phase) (Fig. 1C). No major differences were observed between the strains; hence, the innocuous genotype of the KT40Z strain was validated. The growth profile of KT40Z (see below) was similar to that of the wild-type strain. However, the phaZ mutant strain did not release (R)-HAs (Table 1), confirming the generation of an interrupted PHA cycle. We then analyzed the phenotype of the knockout strain in terms of FIG 1 Construction and initial phenotyping of phaZ null depolymerase mutant. (A) pha gene cluster in P. putida KT2440. Both operons, phaC1ZC2D and phaIF, are transcripted divergently (31). The transcription of these genes is driven by global and effector-specific regulators (reviewed in reference 20). (B) PHA cycle in P. putida, where the key players are the PHA polymerases (PhaC1 and PhaC2), PHA depolymerase (PhaZ), and acyl-CoA synthetase (FadD). (R)-3-hydroxyacyl-CoA is the substrate for PHA polymerases and for the enzymes responsible for the metabolism of fatty acids. (C) Quantification of phasin transcription levels by qRT-PCR experiments (phaF, shaded light pink bars, and phaI, shaded dark pink bars) for the wild type (wt) and KT40Z. Strains were monitored after 6 h of growth under PHA accumulation conditions. One-way ANOVA was performed, and no significant differences on the phasin transcription levels were observed between the two strains. (D) Transmission electronic microscopy (TEM) pictures of wild-type (wt; KT2440) and KT40Z strains after 24 h of growth under PHA accumulation conditions are shown. The scale bar is 0.5 m m. PHA Cycle Impact on Pseudomonas putida Physiology ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 3 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. PHA accumulation and granule cell localization using transmission electronic microscopy. As expected, no effect on PHA production was observed, and the KT40Z cells were able to accumulate PHA as efficiently as the wild type after 24 h of growth (Fig. 1D). To finely tune the flux through the PHA cycle, we set up a library of P. putida strains harboring differential PhaZ production levels (Fig. 2). We began constructing a collection of plasmids where the only variable was the strength of the constitutive promoters driving the expression of the phaZ gene (Fig. 2A and Table S1). The collection of vectors was then specifically integrated in the genome of P. putida KT40Z using mini-Tn7transposons, resulting in a library of P. putida strains with expected differential expression of phaZ (Fig. 2B). The strains were named M0 to M4, where M0 displayed the lowest and M4 the highest promoter strength. Finally, the library of P. putida strains was validated using Western blot analysis to monitor the production of PhaZ from whole-cell extracts recovered during mid-exponential growth (OD 600 of 0.6) (Fig. S2). As expected, a positive correlation was found between the promoter strength and the PhaZ levels, where M0 and M4 exhibited the lowest and the highest promoter activity and PhaZ levels, respectively. PhaZ production could not be detected in the M0 strain, while M1 to M4 strains successfully produced increasing levels of PhaZ (Fig. 2C). On the other hand, PhaZ was not detected in cell extracts from the wild-type strain (data not shown), which confirmed the low transcription rate of the phaZ gene under these growth conditions (32). Increasing the flux through the PHA cycle leads to significant physiological and phenotypical changes in P. putida.To investigate the impact of differential PhaZ production levels on the metabolism of P. putida, we next carried out a battery of high-throughput phenotypic analyses that involved growing M0 to M4 strains under optimal PHA accumulation conditions (see Materials and Methods) (19). Key growth parameters were monitored along the growth curve using the wild-type strain and a PHA-defective strain with the pha cluster entirely deleted (KT2440 Dpha) as controls (Fig. 3 and Table 1). We observed large differences regarding PHA production properties and growth performance among the different strains. According to these observations, the strains could be classified into two main categories, (i) strains that were able to accumulate PHA (KT2440, KT40Z, M0, and M1) and (ii) strains lacking this ability (M2, M3, M4, and KT2440 Dpha). While PHA-accumulating strains reached 70 to 72% PHA cell dry weight (CDW) after 24 h of growth, P. putida strains harboring high constitutive PhaZ production did not accumulate PHA and instead released higher concentrations of (R)-HAs due to a higher PHA depolymerization rate (described below). As expected, the control KT2440 Dpha strain resulted in no PHA accumulation and no (R)-HAs were released, since the whole pha machinery, including the PhaC polymerase, was missing (Fig. 3 and Table 1). Monitoring total biomass in these experiments returned mixed information, because the PHA produced (grams per liter) adds to the cell biomass (free of PHA), which is referred to as residual biomass. Hence, the higher PHA production capabilities of strains KT2440, KT40Z, M0, and M1 translate into greater total biomass TABLE 1 Physiological data after 24 h of growth under PHA accumulation conditions a Strain Total biomass (g/L) PHA Residual biomass (g/L) (R)-HA concn (g/L) No. of viable cells (10 8 /ml) Growth rate (h 21 )% CDW Concn (g/L) KT2440 1.3 60.1 71.7 63.1 1.0 60.1 0.4 60.0 0.2 60.0 1.8 60.6 0.31 60.02 KT40Z 1.4 60.0 72.3 63.3 1.0 60.1 0.4 60.0 0.0 60.0 1.2 60.5 0.31 60.01 KT2440 Dpha 0.6 60.1 0.0 60.0 ,0.01 0.6 60.1 0.0 60.0 22.3 64.8 0.34 60.00 M1 1.3 60.1 70.2 63.7 0.9 60.1 0.4 60.0 0.3 60.0 4.1 60.3 0.30 60.01 M2 0.6 60.0 0.3 60.4 ,0.01 0.6 60.0 0.5 60.1 16.5 63.1 0.29 60.00 M3 0.7 60.1 0.4 60.4 ,0.01 0.7 60.1 0.6 60.0 16.8 63.1 0.31 60.00 M4 0.5 60.0 0.7 61.0 ,0.01 0.5 60.0 0.5 60.0 15.4 63.6 0.35 60.02 a Sodium octanoate (mM) was not detected in the culture supernatant of any of the strains tested. Manoli et al. ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 4 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. compared to strains M2 to M4. Residual biomass reached concentrations between 0.4 and 0.7 g/L, with some differences found among the strains (Table 1). Overall, M2 to M4 and KT2440 Dpha strains displayed a higher proportion of residual biomass coupled to a lack of PHA accumulation due to increased depolymerization in the former and absence of the pha machinery in the latter. Interestingly, these strains’inability to accumulate PHA led to a log increase in the number of viable cells after 24 h of growth compared to the strains that were able to accumulate PHA (e.g., KT2440 and KT40Z) (Table 1). In strains M2 to M4, the trend was toward a decrease in optical density at 600 nm (OD 600 ) and smaller cell size. In fact, strains M2 to M4 were half the size of the wild-type strain after 8 h of growth (Fig. 3). The decrease in optical density is explained by the absence of PHA accumulation, since PHA producers generally display an opaque phenotype. In this sense, since PHA content disturbs cell’s optical density, the evolution of residual biomass was used for the calculation of growth rates, which turned out to be similar among the strains (Table 1). To fully understand how the carbon cycle functions in our strains, we monitored levels of residual octanoate and secreted (R)-HAs in the supernatants all along the growth curves. At time zero, residual biomass for all strains was between 0.08 and 0.09 g/L, with no PHA or (R)-HAs observed either in the culture pellet or the supernatant. FIG 2 Construction and validation of a library of strains driving differential PhaZ production levels. (A) Structural organization of the pBG derivative plasmids, including their origin of replication (oriR6K; light pink), the origin of transfer (oriT; yellow), the kanamycin-resistant marker (Km r ; orange), a Tn7module with two transposase recognition sites (Tn7L, Tn7R; light blue), a module bearing two terminators (T1, T0; dark blue), and a gentamicin-resistant marker (Gm r ; pink). The cargo includes three modules, i.e., the synthetic promoter, the translational coupler (BCD2; gray), and phaZ (green). Key restriction enzymes flanking the functional modules are also indicated (green letters). (B) Schematic representation of the chromosomal integration of pBGderivative vectors in KT40Z background. (C) Relative PhaZ production levels in culture are indicated and measured by Western blotting (Fig. S2). The library of strains named M0 to M4, from lowest (M0) to highest (M4) promoter activity. Strain KT40Z was used as a negative control. The signal intensities of PhaZ production levels were quantified using Image J software. For the calculations, the OD 600 equivalent load in each case was taken into account, and the data were normalized to M4 production levels (Fig. S2). PHA Cycle Impact on Pseudomonas putida Physiology ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 5 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. No significant amounts of octanoate were detected after 24 h of growth, which suggests that the carbon source was depleted in all cases. Regarding (R)-HAs released as a consequence of PhaZ activity, M2 to M4 strains produced up to 0.6 g/L of (R)-HAs compared to 0.2 g/L in the wild-type strain after 24 h of growth (Table 1). As anticipated, no (R)-HA production was observed in the absence of phaZ. Overall, the smaller total biomass observed with strains M2 to M4, even when accounting for the carbon transformed into (R)-HAs, indicates a loss of carbon in the form of extracellular metabolite accumulation [other than (R)-HAs] and/or as increased CO 2 production. To fill this gap, the culture supernatant of all strains after 24 h of growth was analyzed using high-performance liquid chromatography (HPLC) (see Materials and Methods). No significant amounts of any of the metabolites tested were detected (e.g., acetate, pyruvate, succinate, etc.). However, respirometry experiments targeting in vivo determination of CO 2 revealed increased CO 2 production levels in strains M2 to M4 compared to the wild type after 24 h of growth. In the absence of FIG 3 Growth characteristics under PHA accumulation conditions. (A) Growth curves. (B) Viable cell number over time. (C) Cell size quantification over time. (D) Optical microscopy pictures after 8 and 24 h of growth. (E) CO 2 production rate normalized to wild-type values. The strains tested were KT2440 (green), KT40Z (orange), M4 (blue), KT2440 Dpha (black), and M0 to M3 (shaded gray). The scale bar of the pictures is 2 m m. PHA granules are indicated with arrows. Manoli et al. ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 6 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. PHA accumulation, these strains reached 2.8 to 3.6 times higher CO 2 production than the wild type (Fig. 3E), which strongly suggested a carbon loss due to increased PhaZ flux. Construction and validation of condition-specific metabolic models. To further understand the effect that increasing the flux through the PHA cycle might have on the physiology of P. putida, the experimental data collected under PHA accumulation conditions (Table 1 and Table S3) were contextualized using the metabolic model iJN1411. Condition-specific models were set up using a well-known step-by-step procedure (33). Interestingly, we noticed three distinct growth phases, possibly corresponding to three different steady states under PHA accumulation conditions (Fig. 4A). In phase I (between 0 and 5 h of growth; early exponential phase), the bacteria largely grew, and there was a slight PHA accumulation and/or (R)-HA production. In phase II (between 5 and 10 h of growth; late exponential phase), bacteria were mainly FIG 4 Condition-specific model validation for KT2440, KT40Z, KT2440 Dpha, and M1 to M4 strains using flux balance analysis (FBA). The defined phases I (light pink), II (light blue), and III (light green) are indicated with background shaded colored panels. The in silico and in vivo data are shown with dotted lines and cycles, respectively. Residual biomass data (blue), PHA data (red), (R)-HAs (green), and residual octanoate consumption (mM, black) are shown. PHA Cycle Impact on Pseudomonas putida Physiology ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 7 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. producing PHA or (R)-HAs, and low levels of growth were recorded. Finally, in phase III (between 10 and 24 h of growth; stationary phase), growth was either small or nonexistent and/or production of PHA/(R)-HA free monomers was registered. We set up three independent growth phase condition models for each strain (early exponential, late exponential, and stationary phases) to match the existence of these three consecutive steady states. Overlapping all three steady states, we were able to model the entire growth curve and define the metabolic processes taking place in each phase (e.g., PHA and free monomer production, cell growth, and octanoate consumption). Regarding model construction, we transformed experimental data into flux rates (mmolgCDW 21 h 21 ) for compatibility and used octanoate uptake rate, growth rate, initial residual biomass, and PHA and (R)-HA production rates for the three different phases as model constraints for iJN1411. Flux balance analysis (FBA) at optimum growth levels was used to generate condition-specific model predictions. Results showed a high level of agreement with our experimental data irrespective of the strain (Fig. 4). In fact, the models accurately predicted what happens in vivo in terms of octanoate consumption, growth rates, and PHA and/or (R)-HA production. Therefore, our models are powerful computational tools to study the impact of PhaZ doses on P. putida’s metabolism under the given experimental conditions. Model-based phenotyping data contextualization highlights large metabolic changes on central metabolism in response to increasing flux through PHA cycle. To further analyze the impact of increasing levels of PhaZ on P. putida’s metabolism at the system level, the solution space in each growth phase model was randomly sampled using the Markov chain Monte Carlo approach (34). Thus, the probabilistic flux value for each reaction in the network was computed using a random set of points from the solution space as a proxy of the entire space. Results obtained for strains KT40Z and M4 and their comparison with the wild-type strain are summarized in Fig. 5 (see also Fig. 7). The carbon flux distribution predictions for the rest of the strains are listed in Data Set S1. According to model predictions, deleting the depolymerase reaction led to significant carbon flux distribution changes compared to the wild-type strain in phase I (early exponential) (Fig. 5A). As might be expected, strain KT40Z displayed a complete PHA cycle blockage due to the absence of flux through the PHA polymerase reaction (PHAP2C80). Interestingly, the PHA polymerase substrate, (R)-HA-CoA, was not completely incorporated into nascent PHA but instead was significantly funneled to the b -oxidation pathway through the reaction catalyzed by 3-oxoacyl-ACP reductase, FabG (RHACOAR80), and subsequently transformed into acetyl-CoA, feeding the tricarboxylic acid (TCA) cycle and oxidative metabolism. Key reactions of the TCA cycle, including citrate synthase (CS), aconitate dehydratase (ACONTa/b), and malate dehydrogenase (MDH), resulted in 1.8 to 2.2 times higher flux than the wild-type model. Additionally, during this phase KT40Z showed a 1.3-fold higher flux through the glyoxylate shunt reactions, e.g., isocitrate lyase (ICL) and malate synthase (MALS), thus providing higher levels of C 4 metabolites from acetylCoA. According to model predictions, this excess of C 4 metabolites was rerouted to biomass building blocks and sugars via gluconeogenesis. In fact, 1.5 to 1.8 increased flux was predicted in this pathway compared to the wild-type strain (glyceraldehyde-3P-dehydrogenase, GAPD; phosphoglycerate kinase, PGK; phosphoglycerate mutase, PGM; phosphopyruvate hydratase, ENO reactions). Accordingly, the KT40Z model predicted 1.7-fold ATP production compared to the wild type under this high level of activity of the TCA cycle (Data Set S1). Finally, because of this high oxidative metabolism, KT40Z produced 3.2 times more CO 2 and registered a 2.2-fold higher respiration rate than the wild-type model (Data Set S1). On the other hand, increasing the PhaZ concentration in phase I had no major effects on either the predicted carbon flux distribution around the TCA cycle or gluconeogenesis compared to the wild-type strain (Fig. 5B). Interestingly, model-based predictions suggested different pathways providing (R)-HA-CoA. Hence, while 3-oxoacyl-ACP reductase (RHACOAR80) was predicted to provide (R)-HA-CoA in strain M4, enoyl-CoA hydratase, Manoli et al. ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 8 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. PhaJ (RECOAH3), acted as a major source of (R)-HA-CoA in the wild-type strain. Since the 3-oxoacyl-ACP reductase (RHACOAR80)-based pathway is closely assisted by enoyl-CoA dehydratase, FadB (ECOAH3), and 3-hydroxyacyl-CoA dehydrogenase and FadB (HACD3i) exchanges a mole of NADH-NADPH per mole of (R)-HA-CoA produced, it is tempting to speculate that this alternative pathway in strain M4 is a consequence of a putative balancing of the reducing equivalent (described below). Concerning the predicted carbon flux distribution during phase II (late exponential), no significant differences were observed between KT40Z and the wild-type strain. However, we did register differential production of (R)-HA-CoA when strain KT40Z used mainly the enoyl-CoA hydratase (RECOAH3) reaction, while the wild-type strain synthesized (R)-HA-CoA through the 3-oxoacyl-ACP reductase (RHACOAR80) reaction (Fig. 6A and Data Set S1). In contrast, the M4 model shows significant differences in terms of flux distribution compared to the wild-type model (Fig. 6B and Data Set S1). Overall, the model predicts FIG 5 Impact of PhaZ depolymerase dosage on the distribution of central metabolite fluxes, as determined by Monte Carlo random sampling in phase I (0 to 5 h). The diagrams summarize the reaction networks in cells growing under PHA accumulation conditions. Modifications in the carbon flux resulting from the deletion (A) or the overexpression (B) of the phaZ gene are indicated with red arrows (reduced flux) and green arrows (increased flux), and gray arrows point to unaffected fluxes. The numbers next to the arrows indicate the net flux (mmolgCDW 21 h 21 ) in the mutant and wild-type strain (mutant/wild type). The two (R)-HAs correspond to the intracellular and extracellularcompounds.Abbreviations:Ac-CoA,acetyl-CoA;PEP,phosphoenolpyruvate;OAA,oxaloacetate;CIT,citrate; ISC, isocitrate; KG, a-ketoglutarate; SCC, succinate; FUM, fumarate; MAL; malate; GLX, glyoxylate. PHA Cycle Impact on Pseudomonas putida Physiology ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 9 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. accumulation conditions). A volume of 1 ml of cells was added to a less concentrated agar (0.7% soft agar) with the same medium composition and poured onto the plates. When the top agar was solidified, homogeneous filters (Whatman qualitative filter paper, grade 1) were placed in the middle of the plate, and 5 m l of 30% H 2 O 2 was added. The plates were incubated at 30°C for 24 h and then photographed. The halo inhibition area was quantified using Image J software. At least three technical and biological replicates were carried out. The data were normalized to M4 sensitivity. Intracellular ATP measurements. Intracellular ATP levels were determined using an ATP bioluminescence assay kit (ATP biomass kit HS; Biothema, Sweden) per the manufacturer’s instructions. To measure intracellular ATP, 1 ml of P. putida cells was centrifuged for 1 min at 13,000 gand 4°C, and the pellet was resuspended in 1 ml of saline solution (0.85% NaCl) to remove any extracellular ATP. Methanolysis process and GC-MS analysis for PHA determination. For composition and total cellular PHA content quantification, standard gas chromatography-mass spectrometry (GC-MS) approaches of the methanolyzed polyester were used (19). Briefly, 2 to 5 mg of lyophilized samples (culture pellets) was resuspended in 2 ml of methanol containing 15% sulfuric acid and 2 ml of chloroform containing 0.5 mg/ml 3-methylbenzoic acid (3MB) as an internal standard and then incubated using a screw-cap tube at 100°C for 5 h. After cooling, 1 ml of distilled water was added to the mixture to extract most cell debris and any remaining sulfuric acid. A two-phase extraction process was performed to completely remove the water phase to prevent fouling of the GC column. Finally, a small amount of Na 2 SO 4 powder was added to dry the chloroform phase and to remove any remaining water. The organic phase containing the resulting methyl esters of monomers was analyzed by GC-MS. An Agilent (Waldbronn, Germany) series 7890A gas chromatograph coupled with a 5975C MS detector (EI; 70 eV) and a split–splitless injector were used for the analyses. An aliquot (1 m l) of organic phase was injected into the gas chromatograph at a split ratio of 1:50. For this work, a DB-5HTDB-5HT column (400°C; 30 m by 0.25 mm by 0.1m mfilm thickness) was used. Helium was used as the carrier gas at a flow rate of 0.9 ml/min. The injector and transfer line temperature were set at 275°C and 300°C, respectively. For efficient peak separation, the oven temperature program was set to start at 80°C for 2 min and then rise to 175°C at a rate of 5°C min 21 . EI mass spectra were recorded in full scan mode (m/z 40 to 550). The retention time for each methyl ester monomer obtained in this work was 3.5 min (C 6 ), 7.2 min (C 8 ), and 6.1 min (3MB; internal standard). Octanoate consumption quantification using GC-MS. To quantify extracellular octanoate using GC-MS, the lyophilized supernatants of the strains growing under PHA accumulation conditions were derivatized. Approximately 5 mg of lyophilized sample was weighed, and 100 m l of pyridine and 50 m lof N¨N¨BSTFA [bis(trimethylsilyl)trifluoroacetamide] was added. The mixture was incubated for 45 min in a sand bath at 70°C with agitation. A volume of 50 m lof10mMn-decane dissolved in pyridine was then added to the mixture as an internal standard. A standard curve of sodium octanoate was plotted using the same procedure (0 to 15 mM octanoate). An HP-5MS 5% phenylmethyl Silox (400°C; 30 m by 0.25 mm by 0.1m mfilm thickness) column was used. The transfer line temperature was set at 280°C. The oven temperature program was set to a starting temperature of 80°C for 0 min and then from 20°C/min up to 200°C for 0 min. For efficient separation of peaks, the overall duration of the run was 6 min. Retention time was 2.4 min and 4 min for the internal standard (decane) and octanoate, respectively. Extracellular (R)-HA content quantification using HPLC-MS. P. putida strains were cultivated under PHA accumulation conditions. At different time points, 40 ml of culture medium was centrifuged for 45 min at 3,000 gat 4°C in previously tared 50-ml Falcon tubes. Supernatants were rapidly frozen at 280°C and freeze-dried for 72 h in a lyophilizer. The tubes were later weighed for further calculations. The lyophilized supernatant was homogenized and further resuspended in a methanol-water solution (50%, vol/vol) at 10 mg/ml; 25 m l of this mixture was injected into the chromatographic system for determination of (R)-HA (free monomer) content using a Finnigan Surveyor pump coupled to a Finnigan LXQ TM ion trap mass spectrometer (HPLC-MS) (Thermo Electron). Separation was performed using a 2.1by 150-mm (3.5m m particle size) XTerra MS C 18 column (Waters) at a flow rate of 100 m l/min and an injection volume of 25 m l. The mobile phase was 0.1% ammonium hydroxide in water (A), 0.1% ammonium hydroxide in methanol (B), and 0.1% ammonium hydroxide in acetonitrile (C). The elution program was set as the following: at the onset, 95% A and 5% B; after 3 min, the percentage of B was linearly increased to 95% over 20 min, kept constant for 5 min, and, after that, percentage of C was increased from 0% to 45% to clean the column. Finally, it was ramped to the original composition over 5 min and then balanced for 10 min. Samples were introduced into the electrospray ionization (ESI) source in negative mode by continuous infusion using the instrument’s syringe pump at a rate of 3 ml/min. The source was operated at 4.5 kV, and the capillary temperature was set to 200°C. All spectra were recorded in full scan mode (m/z 50 to 1,500). A standard curve of commercial 3-hydroxyoctanoic acid (HO; Sigma-Aldrich, Merck, Germany) was used, and we observed a deprotonated HO monomer (m/z 159) at a retention time of 15.4 min, a dimer adduct of HO-HO (m/z 603) at a retention time of 21.5 min, a trimer adduct of HO-HO-HO (m/z 887) at a retention time of 29.7 min and a tetramer adduct of HO-HO-HO-HO (m/z 1,171) at a retention time of 35.6 min (32). Sample analysis revealed two major peaks, i.e., a deprotonated HO monomer (m/z 159) and a dimer adduct of HO-HO (m/z 603). Identification of extracellular metabolites using HPLC. A standard HPLC approach was used (60) to detect extracellular metabolites. Reference compounds used in this work were fructose, acetate, citrate, succinate, pyruvate, propionate, malate, formate, fumarate, oxoalacetate, ketoglutarate, butyrate, glucose, and sucrose (Sigma-Aldrich, Merck, Germany). All compounds were quantified using an Agilent Series 1260 Infinity II (Agilent, CA, USA) HPLC on an Aminex HPX-87H column (Bio-Rad, Hercules, CA, Manoli et al. ® January/February 2022 Volume 13 Issue 1 e01794-21 mbio.asm.org 16 Downloaded from https://journals.asm.org/journal/mbio on 26 January 2022 by 161.111.109.235. USA) at 40°C with a 0.5-ml/min flow rate and a 25m l injection volume. The mobile phase was 2.5 mM H 2 SO 4 applied on an isocratic regimen, and compounds were detected by means of a refractive index detector. Retention times for the aforementioned compounds were the following: sucrose (8.4 min), ketoglutarate (8.6 min), citrate (8.9 min), malate (9.5 min), oxoalacetate (9.6 min), pyruvate (9.9 min), glucose (9.9 min), fructose (10.9 min), succinate (13.4 min), formate (15.7 min), fumarate (15.8 min), acetate (17.3 min), propionate (20.4 min), and butyrate (25.8 min). Analyses of PhaZ production. LB precultures of P. putida strains driving differential PhaZ production levels were grown to an OD 600 of 0.6 in fresh LB medium at 30°C under vigorous shaking (200 rpm). Immunological techniques were applied to whole-cell extracts to determine PhaZ production levels. Western blot analysis was performed as previously described (32). Briefly, the primary anti-PhaZ antibody (1:5,000) previously washed overnight at 30°C with shaking set at 200 rpm was used with the sonicated KT40Z strain (32). A commercial anti-rabbit (1:10,000) was used as a secondary antibody (GE Healthcare). The expected PhaZ size was 31 kDa. The Western blot signal intensities were quantified using Image J software, and the resulting intensities were normalized to the M4 strain considering the OD 600 equivalent load of each sample. Microscopy assays. Cultures were routinely visualized with a 100phase-contrast objective (Nikon microscope) and images taken with an attached camera (Leica DFC345 FX). Forty individual cells from at least three individual experiments were size quantified at different time points along the bacterial growth curve using Image J software. For transmission electron microscopy (TEM) assays, P. putida cells previously grown under PHA accumulation conditions for 24 h were harvested and washed twice in 1phosphate-buffered saline (PBS). The protocol previously implemented by our laboratory (19) was subsequently applied to sample staining and further processing for TEM image acquisition. Constraint-based flux analysis and in silico data contextualization. We based our constraintbased flux analysis on previous work (33, 61). Briefly, iJN1411 was analyzed using the COBRA Toolbox v2.0 within the MATLAB environment (The MathWorks Inc.). The constraint-based model consists of a 2,087 by 2,826 matrix containing all stoichiometric coefficients in a model comprising 2,087 metabolites and 2,826 reactions (S). FBA was used to predict growth and flux distributions (62). Experimental data for in silico data contextualization were collected from our strain growth assays under PHA accumulation conditions. The variables used were residual biomass, growth rate, octanoate uptake rate, extracellular (R)-HA production, and PHA production rate. Different condition-specific models were obtained and validated using FBA and dFBA analysis. For carbon flux prediction, we used Monte Carlo random sampling for each of the condition-specific models. Mixed fractions of 0.53 to 0.58 were obtained for all condition-specific models, which suggests that the solution space for these models was uniformly sampled (34). The median value from the carbon flux distribution was used as the most probable flux value. SUPPLEMENTAL MATERIAL Supplemental material is available online only. DATA SET S1, XLSX file, 0.2 MB. FIG S1, TIF file, 2.3 MB. FIG S2, TIF file, 2.1 MB. TABLE S1, DOCX file, 0.05 MB. TABLE S2, DOCX file, 0.04 MB. TABLE S3, DOCX file, 0.04 MB. ACKNOWLEDGMENTS This research was funded by the European Union’s Horizon 2020 research and innovation program under grant agreement number 633962 (P4SB) and 870294 (MIXup). We also acknowledge financial support from the Spanish Ministry of Science, Innovation, and Universities through projects BIO2017-83448-R_TECMABIO and RobExplode PID2019-108458RB-I00 (AEI/10.13039/501100011033). We thank Lars Blank and Victor de Lorenzo for providing the strains used to construct the library of promoters. We also acknowledge support from Maria Virginia Rivero Buceta with the GC-MS and HPLC-MS assays and Clive A. Dove with critical reading of the manuscript. Ana Valencia’s technical work is also greatly appreciated. Finally, we thank the Spanish National Research Council (CSIC) and the Margarita Salas Center for Biological Research (CIB) for its scientific support and the use of its facilities. J.N. and A.P. conceived the study and the experimental approach. 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