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Article

Optimization of Propagation Medium for Enhanced Polyhydroxyalkanoate Production by Pseudomonas oleovorans †

by
Daniela Chmelová
1,
Barbora Legerská
1,
Miroslav Ondrejovič
1,* and
Stanislav Miertuš
1,2
1
Department of Biotechnology, Faculty of Natural Sciences, University of SS. Cyril and Methodius, J. Herdu 2, SK-917 01 Trnava, Slovakia
2
ICARST n.o., Jamnického 19, SK-841 01 Bratislava, Slovakia
*
Author to whom correspondence should be addressed.
†
The paper is dedicated to paying tribute to the memory of Professor Federica Chiellini.
Fermentation 2022, 8(1), 16; https://doi.org/10.3390/fermentation8010016
Submission received: 9 December 2021 / Revised: 22 December 2021 / Accepted: 29 December 2021 / Published: 31 December 2021
(This article belongs to the Section Industrial Fermentation)
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Versions Notes

Abstract

:
Polyhydroxyalkanoates (PHAs) represent a promising alternative to commercially used petroleum-based plastics. Pseudomonas oleovorans is a natural producer of medium-chain-length PHA (mcl-PHA) under cultivation conditions with nitrogen limitation and carbon excess. Two-step cultivation appears to be an efficient but more expensive method of PHA production. Therefore, the aim of this work was to prepare a minimal synthetic medium for maximum biomass yield and to optimize selected independent variables by response surface methodology (RSM). The highest biomass yield (1.71 ± 0.04 g/L) was achieved in the optimized medium containing 8.4 g/L glucose, 5.7 g/L sodium ammonium phosphate and 35.4 mM phosphate buffer. Under these conditions, both carbon and nitrogen sources were completely consumed after 48 h of the cultivation and the biomass yield was 1.7-fold higher than in the conventional medium recommended by the literature. This approach demonstrates the possibility of using two-stage PHA cultivation to obtain the maximum amount of biomass and PHA.

1. Introduction

Traditionally used petrochemical-based plastics produced from fossil resources pose a serious problem due to the depletion of fossil resources as well as the accumulation of nondegradable waste in the environment [1,2,3]. Therefore, the search for a suitable alternative is an important area of research. Microbially synthesized polyhydroxyalkanoates (PHAs) as natural biodegradable polyesters are environmentally sustainable substitutes for commercial plastics [4,5] and have no negative impact on the environment [6,7].
PHAs can be synthesized by a variety of wild-type or genetically modified organisms, including the archaea [8,9], bacteria [10,11,12], yeasts [13,14] or algae [15,16]. Particularly important producers are bacteria, which typically require a limiting concentration of a single source, such as nitrogen, oxygen, phosphorus, magnesium or sulfur, and an excess of a carbon source for efficient PHA production [17,18,19]. The genus Pseudomonas is one of these bacterial producers that additionally produces medium-chain-length PHAs (mcl-PHAs) through two biosynthetic pathways [20]. Each biosynthetic pathway uses a different substrate to produce the 3-HA precursors for mcl-PHA synthesis [21,22]. Fatty acids as aliphatic carbon sources are utilized by these bacteria via fatty acid β-oxidation, but they are also able to use de novo fatty acid synthesis to synthesize mcl-PHA monomers from unrelated carbon sources such as glucose, gluconate or ethanol [23,24,25]. Pseudomonas oleovorans produces PHAs containing different mcl monomers (C6–C12) depending on the substrate used [26]; mcl-PHAs have better elastic and flexible properties than other biodegradable polymers [27,28,29] and are therefore applicable in medical devices, food, agriculture and consumer products [30,31]. However, high overall production costs, including sterilization costs, as well as low conversion of carbon substrates to PHA remain a challenge for industrial-scale PHA production [32].
A necessary step to produce a sufficient biomass amount of a producer is propagation, leading to the acquisition of a larger amount of product. However, continuous PHA production is a challenge because PHA synthesis is not associated with growth. Therefore, it is not possible to optimize producer growth and PHA production simultaneously. A prerequisite for successful PHA production is a two-step cultivation adapted to the physiological and kinetic specificities of the biological system [33]. The cost of the set-up process can be reduced by appropriately adjusting the culture conditions, the composition of the culture medium and downstream processing [33,34]. First, it is necessary to know the producer’s requirements as well as the optimum conditions for the growth and PHA production. In the case of Pseudomonas cultivation, several authors [26,35,36,37,38] used a two-step cultivation method to maximize biomass production and subsequently stimulate PHA production. The first phase is the production of bacterial biomass in order to obtain the highest possible density and adjust growth conditions of the producer. The second phase of cultivation involves a nutrient reduction step and is designed to promote PHA accumulation [20,22].
The aim of this study was to prepare a synthetic propagation medium with minimal nutrients in order to maximize the production of P. oleovorans biomass and to optimize the selected independent variables by using response surface methodology (RSM).

2. Results

2.1. Screening the Variable Components Affecting Biomass Production and PHA Yield

We applied the Plackett–Burman model as a statistical tool for screening the variables affecting biomass production, based on the absence of a selected component of the conventional propagation medium. The effect of the absence of each component on the dependent variables (the consumption of glucose and sodium ammonium phosphate, and the biomass and PHA concentrations) is shown in Figure 1.
The absence of any component of the propagation medium decreases biomass and PHA concentrations, except in the absence of KH2PO4 (Figure 1). Both C and N sources were completely or almost completely depleted in the medium without KH2PO4 and in the complete propagation medium. The biomass concentration in the complete propagation medium was 1.09 ± 0.02 g/L and the PHA concentration was 0.24 ± 0.09 g/L. In the absence of KH2PO4, the biomass concentration was comparable (1.04 ± 0.02 g/L), but PHA concentration decreased to 0.05 ± 0.00 g/L. Since KH2PO4 forms a stable pH with K2HPO4 during cultivation and at the same time its presence in the medium has no effect on the biomass concentration, we decided to keep this component in the propagation medium. After the end of cultivation, the pH decreased only minimally (6.64–6.95) from the initial pH of 7.0. The lowest pH was measured in the medium without KH2PO4, namely 6.27 ± 0.03 (data not shown). Since we found that the presence of the other components was important, the effect of different concentrations of glucose, sodium ammonium phosphate, magnesium sulfate and molarity of phosphate buffer on the growth of P. oleovorans and PHA production was investigated in further experiments (Figure 2).
The concentration of glucose and sodium ammonium sulfate and the concentration of phosphates have a significant effect on biomass growth (Figure 2). The pH values decreased from 7.0 to 6.6–7.0 after the cultivation (data not shown). Biomass concentration increased with increasing glucose concentration, with the highest biomass concentration (1.54 ± 0.12 g/L) observed with an excess carbon source of 50 g/L (Figure 2A). However, the glucose concentration was limiting for the growth of P. oleovorans; further increase of glucose concentration led to a growth inhibition. The PHA concentration was comparable in the range of glucose concentrations from 10 to 50 g/L (~0.32 ± 0.02 g/L). As sodium ammonium phosphate concentration increases (Figure 2B), there is an increase in the biomass and PHA yields up to a concentration of 8 g/L. The highest biomass concentration was measured at 4 g/L (1.13 ± 0.02 g/L) and the highest PHA concentration at 8 g/L (0.30 ± 0.07 g/L). A positive effect of increasing phosphate buffer molarity on biomass and PHA concentrations was observed only up to a concentration of 45.4 mM (biomass 1.12 ± 0.09 g/L) (Figure 2D). PHA production was comparable in the phosphate buffer molarity of 22.7–60.5 mM (0.23 ± 0.03 g/L). The influence of magnesium sulfate concentrations (0.1–5 g/L) was observed without a significant effect on the growth (0.98–1.09 g/L) and production of PHA (0.23–0.33 g/L) (Figure 2C). The concentrations of the components significantly affecting biomass yield together with their interactions were further evaluated using the RSM approach.

2.2. RSM Optimization of Production Parameters

The experiments described above showed that biomass and PHA concentrations were not affected by magnesium sulfate concentrations. Therefore, the effect of parameters such as glucose concentration, sodium ammonium phosphate concentration, phosphate buffer molarity on biomass concentration (g/L) and glucose consumption (%) was evaluated using RSM (Table 1). The measured values of biomass concentration were in the range 0.00–1.74 g/L; glucose consumption varied from 7.4 to 100.0%. The experimental data obtained were used to calculate the coefficients of the second-order polynomial equations (see Methods for further detail).
The R2 coefficients were set at 95.6% for biomass concentration and 93.7% for glucose consumption. Three-dimensional graphs for better understanding the interactions between the selected variables are shown in Figure 3.
Table 2 summarizes the regression coefficients and analysis of variances calculated for the biomass concentration and the glucose consumption. The results show that the increase of the phosphate buffer molarity (from 20.0 to 35.4 mM) had a significant positive linear effect (p < 0.05) on the biomass concentration (Figure 3A). Moreover, quadratic effect of all tested factors (AA, BB and CC) significantly affected the biomass concentration (p < 0.05). A significant interaction effect between sodium ammonium phosphate concentration and phosphate buffer molarity was found (p < 0.05). Not surprisingly, glucose concentration had a positive linear effect (p < 0.05) on glucose consumption. The phosphate buffer molarity had a significant positive linear effect (p < 0.05) and a negative quadratic effect (p < 0.05). As the molarity of the phosphate buffer increases up to 40 mM (Figure 3B), the glucose consumption also increases.

2.3. Optimization and Verification of the Model

The optimal cultivation conditions for biomass production by P. oleovorans were determined as follows: 8.4 g/L glucose, 5.7 g/L sodium ammonium phosphate and 35.4 mM phosphate buffer (Table 3).
Complete glucose consumption can be achieved under the following optimal conditions: glucose concentration 7.5 g/L, sodium ammonium phosphate concentration 6.0 g/L and phosphate buffer molarity 40.0 mM (Table 3). Under selected optimal conditions, the predicted biomass concentration and glucose consumption should be 1.71 g/L and 100%, respectively. We verified the optimal conditions and found that there was no significant statistical difference (p < 0.005) between the predicted and experimental values of the dependent variables (Table 3). Moreover, the glucose consumption under optimal conditions for biomass concentration was 98.3 ± 0.09%, but the biomass concentration under optimal conditions for glucose consumption was 1.49 ± 0.04 g/L (data not shown). However, given the aim of the study, we chose the optimal conditions for biomass concentration. In summary, we achieved a 1.7-fold higher biomass concentration in the optimized propagation medium than in the conventional medium recommended by the literature.

3. Discussion

The production of an intracellular metabolite requires a rapid accumulation of biomass, and at the same time, high production of the metabolite in cells. PHA accumulation is initiated by depletion of the essential nutrient, but this is not compatible with high biomass production. Two-step cultivation, possibly fed-batch or continuous cultivation, appears to be an effective way to produce a secondary metabolite [26,35,36,37,38]. This study was aimed to optimize the propagation medium resulting in maximum biomass yield in the first step of bacterial PHA production. The composition of the propagation medium frequently used in published studies [35,36,37,39,40] was chosen. The necessity of individual components present in the medium can be verified using a Plackett–Burman design. The results of the first model (data not shown) suggest that the presence of the trace elements commonly used for other PHA producers [38,41,42] is not necessary. The absence of the trace element solution in the propagation medium did not affect the biomass yield. Other authors obtained the same results in their experiments [38,43,44]. Even the trace element solution did not affect the PHA yield [43,44]. Subsequently, the second analysis according to the Plackett–Burman statistical model (Figure 1) showed that carbon and nitrogen sources and phosphorus-containing buffer had a significant effect on P. oleovorans growth. Although there are few scientific works focusing on the optimization of the P. oleovorans biomass growth (later name P. oleovorans GPo1, now P. putida or P. pseudoalcaligenes) [45], the results can be compared with other producers of the genus Pseudomonas due to the remarkably close similarity of biosynthetic pathways [11,20,46,47]. The frequently used carbon sources for the genus Pseudomonas are mainly various oils and fatty acids [38,48] and phenols [49], but also waste raw materials and wastewaters [48,50]. The choice of C and N sources has a significant impact on biomass yield and PHA production [23,48,51]. However, simple organic carbon sources in suitable concentration such as glucose are used to produce biomass [52,53]. Glucose limitation leads to lower biomass yields due to a bacterial growth reduction [54], as also confirmed by our results (Figure 2c).
The absence of K2HPO4 in the propagation medium had a negative effect on the biomass and PHA concentrations (Figure 1). The absence of KH2PO4 had no effect on both monitored variables, although the initial pH of both propagation media was set to 7.0. The pH value has a significant effect on biomass and PHA yields [53,55,56]. The neutral pH is most suitable for the genus Pseudomonas due to the highest activity of enzymes involved in PHA synthesis [56]. However, the pH after cultivation was lower in the medium in the absence of KH2PO4 (6.27 ± 0.03) than in the medium without K2HPO4 (6.64 ± 0.02). Other media (Figure 1) had pH in the range 6.84–6.95 (data not shown). Therefore, these results are probably related to the amount of phosphorus in the propagation medium. Protein synthesis increased at high phosphorus concentrations, whereas a phosphate deficiency leads to a reduction in protein synthesis rate and affects biomass and PHA yields [54]. Low phosphorus concentration in K2HPO4-free medium significantly affected the biomass production of P. oleovorans (Figure 1). Different concentrations of magnesium sulfate (Figure 2C) had no notable effect on either biomass or PHA yield. This conclusion was confirmed in the studies of Nikel et al. [53] and Sangkharak and Prasertsan [54]. The PHA production was approximately 23 ± 1.7% (Figure 2). In some experiments, specifically when the glucose concentration was increased or the sodium ammonium phosphate concentration was decreased, we obtained 32 ± 0.7% of PHAs (Figure 2). These relatively high PHA yields may indicate culture limitation probably due to oxygen limitation. The problem of maintaining oxygen transfer rate associated with high cell density limitation has been described in the literature [57,58]. This challenge could be circumvented by the use of oxygen-enriched airflow into the propagation medium in the fermenter.
RSM is a frequently used research method to simultaneously assess the influence of multiple cultivation parameters on PHA production [59,60,61]. We chose the carbon and nitrogen source concentration and the molarity of phosphate buffer as independent variables in the optimization matrix. Our experimental data confirmed the predicted value of the biomass yield (1.71 ± 0.04 g/L). We observed higher biomass production than the work of Manso Cobos et al. [62]. They cultivated the wild-type bacterial strain of P. pseudoalcaligenes CECT5344 in medium with sodium octanoate (12.5 mM) as carbon source and ammonium chloride (12.5 mM) as nitrogen source with a biomass yield of 1.18 ± 0.1 g/L [62]. Prieto et al. [63] achieved a yield of 1.67 g/L of genetically modified strain P. oleovorans strain GPo1 (xylS/Pm::phaC1- P. oleovorans POMC1). Higher biomass yield can be achieved by changing the C or N source, but there are increased production process costs. A two-fold increase in biomass yield was observed in the medium using octanoate as a carbon source under optimized P. putida BM01 growth conditions [64]. A comparable increase in biomass yield was also measured in Możejko et al. [52], who cultivated Pseudomonas sp. G101 in medium with glucose and rapeseed oil. These works suggest that the biomass yield can be affected by the ratio C/N. For the PHA production, the suitable ratio ranges from 5 to 15. The C/N ratio is not surprising because PHA production usually starts by an excess of carbon and a lack of nitrogen [52,62,63,64]. However, lower C/N ratios are more suitable for biomass production than higher C/N ratios. In our study, we found that the most suitable C/N ratio for P. oleovorans biomass production is 1.5. However, biomass production is only the first step, although efficient PHA production requires a sufficient number of cells capable of accumulating intracellular metabolites.

4. Materials and Methods

4.1. Microorganism and Inoculum Preparation

The bacterial strain Pseudomonas oleovorans DSM 1045 from the DSMZ collection (Deutsche Sammlung von Mikroorganismen und Zellkulturen, Germany) stored at 4 °C on nutrient agar with 1.0% (w/v) agar, was used for PHA synthesis. Bacterial cells were reinoculated at 4-day intervals and cultivated at 30 °C. The cells were diluted with sterile distilled water to obtain 0.8–0.9 McFarland units (MFU) solution and prepared inoculum was used to inoculate the propagation medium at a final concentration of 2% (v/v).

4.2. Propagation Medium

Conventional liquid propagation medium (20 mL) comprises glucose (5.0 g/L); NaNH4HPO4·4H2O (4.0 g/L); K2HPO4 (5.8 g/L); KH2PO4 (3.7 g/L) and MgSO4·7H2O (0.25 g/L) [29,33,56,57] and microelements stock solution (FeSO4·7H2O (2.78 g/L); MnCl2·4H2O (1.98 g/L); CoCl2·6H2O (4.0 g/L); CuCl2·2H2O (1.0 g/L); CaCl2·2H2O (1.67 g/L); ZnSO4·7H2O (0.29 g/L); Na2MoO4·2H2O (2.0 g/L); Na2B4O7·10H2O (0.23 g/L); Al2(SO4)3·12H2O (1.3 g/L) in 0.1N HCl) mixed with a medium in a ratio of 1:1000 (v/v) [35,36,39,40,65,66,67,68,69,70] was used in the study. Cultivation was performed under dynamic conditions (150 RPM) for 48 h at 30 °C and the initial pH 7.0.

4.3. Growth Pattern

The growth pattern of P. oleovorans was observed in modified propagation medium (20 mL) (glucose (5.0 g/L), NaNH4HPO4·4H2O (4.0 g/L), K2HPO4 (5.8 g/L), KH2PO4 (3.7 g/L) and MgSO4·7H2O (0.25 g/L)) under dynamic conditions (150 RPM) for 48 h at 30 °C and the initial pH 7.0. The biomass concentration was determined after drying at 50 °C to constant weight and the optical density was measured at 600 nm using a microplate reader (BioTek EL 800, Fisher, Germany). Both variables were determined in propagation media after 14, 18, 20, 22, 24, 32, 36, 42, 44, 46, 48 h cultivation and the relationship between these two variables was evaluated (y = 0.5693x + 0.07777; R2 = 0.95).

4.4. Plackett–Burman Design

The significant variables affecting biomass production were selected using the Plackett–Burman design (Table 4) [71]. A total of six variables were tested, and the model was based on the absence of a (−) selected variable. The propagation medium (20 mL) contained 5 g/L glucose, 4 g/L sodium ammonium phosphate, 0.25 g/L magnesium sulfate, and 60.5 mM phosphate buffer (pH 7.0). The cultivation conditions were the same as in the previous experiments.

4.5. Screening the Components Affecting Biomass Production and PHA Yield

The effect of glucose (carbon source) concentration (1.0–100.0 g/L), sodium ammonium phosphate (nitrogen source) concentration (1.0–20.0 g/L), magnesium sulfate concentration (0.1–5.0 g/L) and phosphate buffer molarity (0.6–302.4 mM) was tested on dependent variables (biomass and PHA concentrations, carbon and nitrogen consumptions).

4.6. Optimization of Propagation Medium by RSM

RSM was used to investigate the effect of glucose concentration, sodium ammonium phosphate concentration and phosphate buffer molarity on the dependent variables (biomass concentration and glucose consumption). These three independent factors were tested on five code levels: −1.682, −1, 0, 1 and 1.682 (Table 5).
The second-order polynomial function with respect to the three selected parameters is given by Equation (1):
Y = b 0 + ∑ i = 1 k b i X i + ∑ i = 1 k b i i X i 2 + ∑ i = 1 i < j k − 1 ∑ j = 2 b i j X i X j
where X are independent variables (carbon concentration, nitrogen concentration and phosphate buffer molarity), Y is a response (biomass concentration or glucose consumption) and b are regression coefficients. The interaction between two variables and the effect of these factor levels on biomass yield and carbon consumption were derived from 3D surface response plots. The coefficients of the response surface equation were determined.

4.7. Analytical Methods

The glucose concentration was determined by the DNS (3,5-dinitrosalicylic acid) method [72] at 540 nm using a microplate reader. The glucose concentration was expressed as % of the carbon source consumed during cultivation. The concentration of sodium ammonium phosphate was determined by the Nessler reagent method at 450 nm using a microplate reader and expressed as % of nitrogen source consumed during cultivation. The pH of the propagation medium was determined at the end of cultivation.
The biomass concentration was evaluated using optical density at 600 nm and expressed in grams per liter of propagation medium. Dried P. oleovorans biomass was homogenized for 5 min at 3000 RPM using metal beads and PHAs were extracted into chloroform (15 mL) for 24 h at 22 °C and 150 RPM. The extract obtained was dried by sodium sulfate, filtered and evaporated to dryness on a rotary evaporator (Heidolph, Germany). The dry extract was washed with n-hexane to remove fatty acids, evaporated and the residue represented the crude PHA fraction expressed in grams per liter of propagation medium.

4.8. Statistical Analysis

OriginPro 2016 software (OriginLab Corporation, Northampton, MA, USA) was used to process all experimental data obtained. Statgraphic Centurion XV (Statpoint Technologies, Warrenton, Virginia, VA, USA) was used for statistical analysis of experimental data. All assays were performed in triplicate.

5. Conclusions

In the present study, we demonstrated that statistical design is an important tool in the preparation of cultivation media. The Plackett–Burman statistical design helped identify the factors needed to produce P. oleovorans biomass. Then, using RSM, we optimized the variables that have the greatest impact on biomass yield, and we were able to increase this amount 1.7 times. The optimal conditions were determined as follows: glucose concentration 8.4 g/L, sodium ammonium phosphate concentration 5.7 g/L and phosphate buffer molarity 35.4 mM.

Author Contributions

Conceptualization, D.C. and B.L.; methodology, D.C.; software, D.C.; validation, D.C., B.L. and M.O.; formal analysis, D.C.; investigation, D.C.; resources, M.O.; data curation, D.C.; writing—original draft preparation, D.C. and B.L.; writing—review and editing, M.O. and S.M.; visualization, D.C. and M.O.; supervision, M.O. and S.M.; project administration, M.O.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Slovak Research and Development Agency, grant numbers APVV-18-0420 and APVV-PP-COVID-20-0010.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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Fermentation 08 00016 g001 550
Figure 1. Effect of the absence of the selected component on the propagation medium and the complete propagation medium on the monitored variables.
Figure 1. Effect of the absence of the selected component on the propagation medium and the complete propagation medium on the monitored variables.
Fermentation 08 00016 g001
Fermentation 08 00016 g002 550
Figure 2. The influence of a selected component of the propagation medium (A)—glucose concentration (g/L); (B)—sodium ammonium phosphate concentration (g/L); (C)—magnesium sulfate concentration (g/L) and (D)—phosphate buffer molarity (mM)) on the monitored variables. The concentrations of parameters not investigated in a propagation medium were 5.0 g/L glucose, 4.0 g/L sodium ammonium phosphate, 0.25 g/L MgSO4·7H20 and 60.5 mM phosphate buffer.
Figure 2. The influence of a selected component of the propagation medium (A)—glucose concentration (g/L); (B)—sodium ammonium phosphate concentration (g/L); (C)—magnesium sulfate concentration (g/L) and (D)—phosphate buffer molarity (mM)) on the monitored variables. The concentrations of parameters not investigated in a propagation medium were 5.0 g/L glucose, 4.0 g/L sodium ammonium phosphate, 0.25 g/L MgSO4·7H20 and 60.5 mM phosphate buffer.
Fermentation 08 00016 g002
Fermentation 08 00016 g003 550
Figure 3. Response surface model plots showing the effects of the phosphate buffer molarity and the glucose concentration on the biomass concentration (A) and the carbon consumption (B). The optimal value of the sodium ammonium phosphate concentration was set as a constant.
Figure 3. Response surface model plots showing the effects of the phosphate buffer molarity and the glucose concentration on the biomass concentration (A) and the carbon consumption (B). The optimal value of the sodium ammonium phosphate concentration was set as a constant.
Fermentation 08 00016 g003
Table
Table 1. Experimental design with actual and coded level of independent variables and the measured values of biomass concentration (g/L) and glucose consumption (%).
Table 1. Experimental design with actual and coded level of independent variables and the measured values of biomass concentration (g/L) and glucose consumption (%).
Run No.Glucose Concentration (g/L)Sodium Ammonium Phosphate Concentration (g/L)Phosphate Buffer Molarity (mM)Biomass Concentration (g/L)Glucose Consumption (%)
15 (−1)4 (−1)20 (−1)0.94 ± 0.01100.0 ± 0.0
27.5 (0)6 (0)40 (0)1.61 ± 0.0399.9 ± 0.1
310 (1)8 (1)20 (−1)1.35 ± 0.0481.9 ± 0.5
410 (1)4 (−1)60 (1)1.11 ± 0.0166.2 ± 2.1
55 (−1)8 (1)60 (1)0.42 ± 0.0889.2 ± 0.6
65 (−1)4 (−1)60 (1)0.99 ± 0.02100.0 ± 0.0
75 (−1)8 (1)20 (−1)1.07 ± 0.02100.0 ± 0.0
87.5 (0)6 (0)40 (0)1.56 ± 0.15100.0 ± 0.0
910 (1)8 (1)60 (1)0.00 ± 0.007.4 ± 0.2
1010 (1)4 (−1)20 (−1)1.03 ± 0.0265.1 ± 0.2
113.3 (−1.682)6 (0)40 (0)0.86 ± 0.02100.0 ± 0.0
127.5 (0)6 (0)73.5 (1.682)0.00 ± 0.007.5 ± 1.3
137.5 (0)6 (0)6.5 (−1.682)0.64 ± 0.0053.3 ± 2.1
1411.7 (1.682)6 (0)40 (0)1.44 ± 0.0175.9 ± 0.8
157.5 (0)9.3 (1.682)40 (0)0.89 ± 0.2764.1 ± 0.0
167.5 (0)2.7 (−1.682)40 (0)1.04 ± 0.1979.3 ± 2.1
177.5 (0)6 (0)40 (0)1.74 ± 0.05100.0 ± 0.0
Table
Table 2. Regression coefficients of the predicted second order polynomial models for biomass concentration and glucose consumption.
Table 2. Regression coefficients of the predicted second order polynomial models for biomass concentration and glucose consumption.
EffectFactor 1Biomass ConcentrationGlucose Consumption
Constant −5.37985−111.602
LinearA0.5066956.66772
B0.92137933.5018
C0.1306896.46912
QuadraticAA−0.02377−0.12396
BB−0.05366−1.6428
CC−0.00111−0.05333
InteractionAB−0.00875−0.782
AC−0.00168−0.15655
BC−0.00666−0.27
1 A—glucose concentration, B—sodium ammonium phosphate concentration and C—phosphate buffer molarity. Statistically significant differences at p-value < 0.05 are shown in bold.
Table
Table 3. Predicted and experimentally verified values of biomass concentration and glucose consumption under optimal production conditions.
Table 3. Predicted and experimentally verified values of biomass concentration and glucose consumption under optimal production conditions.
Independent Variable
Dependent VariableGlucose Concentration (g/L)Sodium Ammonium Phosphate Concentration (g/L)Phosphate Buffer Molarity (mM)Predicted ValueExperimental Value
Biomass concentration8.45.735.41.68 g/L1.71 ± 0.04 g/L
Glucose consumption7.56.040.0100%100 ± 0.01%
Table
Table 4. Plackett–Burman experimental design.
Table 4. Plackett–Burman experimental design.
FactorRun *
123456
Glucose−+++++
Sodium ammonium phosphate+−++++
K2HPO4++−+++
KH2PO4+++−++
MgSO4·7H2O++++−+
* (+) presence or (−) absence of the selected nutrient in the propagation medium.
Table
Table 5. Interpretation of coded levels of the three independent variables tested by RSM.
Table 5. Interpretation of coded levels of the three independent variables tested by RSM.
VariablesCode Levels
−1.682−1011.682
Glucose concentration (g/L)3.35.07.510.011.7
Sodium ammonium phosphate concentration (g/L)2.74.06.08.09.3
Phosphate buffer molarity (mM)6.520.040.060.073.4
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Chmelová, D.; Legerská, B.; Ondrejovič, M.; Miertuš, S. Optimization of Propagation Medium for Enhanced Polyhydroxyalkanoate Production by Pseudomonas oleovorans. Fermentation 2022, 8, 16. https://doi.org/10.3390/fermentation8010016

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Chmelová D, Legerská B, Ondrejovič M, Miertuš S. Optimization of Propagation Medium for Enhanced Polyhydroxyalkanoate Production by Pseudomonas oleovorans. Fermentation. 2022; 8(1):16. https://doi.org/10.3390/fermentation8010016

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Chmelová, Daniela, Barbora Legerská, Miroslav Ondrejovič, and Stanislav Miertuš. 2022. "Optimization of Propagation Medium for Enhanced Polyhydroxyalkanoate Production by Pseudomonas oleovorans" Fermentation 8, no. 1: 16. https://doi.org/10.3390/fermentation8010016

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Chmelová, D., Legerská, B., Ondrejovič, M., & Miertuš, S. (2022). Optimization of Propagation Medium for Enhanced Polyhydroxyalkanoate Production by Pseudomonas oleovorans. Fermentation, 8(1), 16. https://doi.org/10.3390/fermentation8010016

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