Optimization of Rhodococcus erythropolis JCM3201T Nutrient Media to Improve Biomass, Lipid, and Carotenoid Yield Using Response Surface Methodology
Abstract
:1. Introduction
2. Materials and Methods
2.1. Bacterial Strain and Inoculum Preparation
2.2. Cultivation Medium
2.3. Experimental Design
2.4. Growth Analysis
2.5. Carotenoid Extraction
2.6. Fatty Acid Profile
2.7. Response Surface Methodology and Further Statistical Analysis
3. Results and Discussion
3.1. Effect of Different Nitrogen Sources on Biomass, Lipid, and Carotenoid Formation of R. erythropolis
3.2. Effect of Different Carbon Sources on Biomass, Lipid, and Carotenoid Formation by R. erythropolis
3.3. Effect of Nitrogen and Carbon Sources on the Fatty Acid Profile of R. erythropolis
3.4. RSM Model: Effect of Nitrogen and Carbon Concentrations on Biomass, Lipid and Carotenoid Formation of R. erythropolis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Number | Nitrogen Source | Chemical Nature | Carbon Source | Chemical Nature |
---|---|---|---|---|
1 | Ammonium chloride | Defined inorganic | Glucose | Monosaccharides |
2 | Diammonium hydrogen phosphate | Galactose | ||
3 | Ammonium sulfate | Fructose | ||
4 | Potassium nitrate | Lactose | Disaccharides | |
5 | Ammonium nitrate | Sucrose | ||
6 | Yeast extract | Complex organic | Maltose | |
7 | Tryptone/peptone | Sorbitol | Sugar alcohol | |
8 | Urea | Defined organic | Glycerol | |
9 | Ammonium acetate |
Variable | Level | ||||
---|---|---|---|---|---|
−1.414 | −1 | 0 | +1 | +1.414 | |
Carbon (g L−1) | 3.929 | 6 | 11 | 16 | 18.071 |
Nitrogen (g L−1) | 0.017 | 0.1 | 0.3 | 0.5 | 0.583 |
Run | C:N | C g L−1 | N g L−1 | Biomass g L−1 | Lipid Content mg g−1DCW | Carotenoid Content Abs454nmmg−1DCW | Biomass g L−1 | Lipid Content mg g−1DCW | Carotenoid Content Abs454nmmg−1DCW |
---|---|---|---|---|---|---|---|---|---|
140 h | 192 h | ||||||||
1 | 60.00 | 6.000 | 0.100 | 1.44 | 26.90 | 0.0192 | 1.72 | 43.89 | 0.0159 |
2 | 60.00 | 6.000 | 0.100 | 1.23 | 25.86 | 0.0175 | 1.78 | 40.79 | 0.0148 |
3 | 160.00 | 16.000 | 0.100 | 1.74 | 41.83 | 0.0156 | 1.94 | 63.19 | 0.0156 |
4 | 160.00 | 16.000 | 0.100 | 1.73 | 40.09 | 0.0146 | 2.12 | 65.65 | 0.0133 |
5 | 12.00 | 6.000 | 0.500 | 3.21 | 30.61 | 0.0124 | 4.82 | 22.34 | 0.0206 |
6 | 12.00 | 6.000 | 0.500 | 3.48 | 30.26 | 0.0146 | 4.60 | 24.36 | 0.0207 |
7 | 32.00 | 16.000 | 0.500 | 4.02 | 28.18 | 0.0165 | 7.43 | 28.09 | 0.0186 |
8 | 32.00 | 16.000 | 0.500 | 5.12 | 27.60 | 0.0156 | 7.25 | 25.14 | 0.0183 |
9 | 13.10 | 3.929 | 0.300 | 2.49 | 27.11 | 0.0134 | 3.66 | 22.01 | 0.0186 |
10 | 13.10 | 3.929 | 0.300 | 2.33 | 26.76 | 0.0154 | 3.45 | 21.34 | 0.0202 |
11 | 60.24 | 18.071 | 0.300 | 3.19 | 28.53 | 0.0238 | 4.53 | 29.71 | 0.0164 |
12 | 60.24 | 18.071 | 0.300 | 3.61 | 26.63 | 0.0209 | 5.13 | 31.30 | 0.0148 |
13 | 647.06 | 11.000 | 0.017 | 0.63 | 65.72 | 0.0132 | 0.50 | 100.53 | 0.0138 |
14 | 18.87 | 11.000 | 0.583 | 5.37 | 30.13 | 0.0173 | 8.04 | 22.60 | 0.0172 |
15 | 18.87 | 11.000 | 0.583 | 5.50 | 27.77 | 0.0183 | 7.96 | 25.68 | 0.0179 |
16 | 36.67 | 11.000 | 0.300 | 2.73 | 26.96 | 0.0190 | 3.96 | 28.79 | 0.0196 |
17 | 36.67 | 11.000 | 0.300 | 2.83 | 27.42 | 0.0173 | 4.76 | 29.52 | 0.0172 |
18 | 36.67 | 11.000 | 0.300 | 2.80 | 26.18 | 0.0184 | 4.12 | 32.76 | 0.0170 |
19 | 36.67 | 11.000 | 0.300 | 2.71 | 25.23 | 0.0166 | 4.05 | 31.79 | 0.0183 |
20 | 36.67 | 11.000 | 0.300 | 2.77 | 26.80 | 0.0189 | 3.20 | 33.16 | 0.0174 |
Response | Model | p-Value Model | p-Value Lack of Fit | R2 | Adjusted R2 | Predicted R2 | |
---|---|---|---|---|---|---|---|
Biomass | 140 h | Linear | <0.0001 | 0.0318 | 0.9306 | 0.9224 | 0.8976 |
192 h | 2FI | <0.0001 | 0.0722 | 0.9611 | 0.9538 | 0.9416 | |
Lipid | 140 h | Reduced Quadratic | 0.0007 | <0.0001 | 0.7058 | 0.6273 | 0.2220 |
192 h | Reduced Quadratic | <0.0001 | <0.0001 | 0.8725 | 0.8486 | 0.7262 | |
Carotenoids | 140 h | 2FI | 0.1085 | 0.0004 | 0.3080 | 0.1782 | −0.1744 |
192 h | Reduced Quadratic | <0.0001 | 0.2525 | 0.7936 | 0.7549 | 0.6715 |
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Engelhart-Straub, S.; Haack, M.; Awad, D.; Brueck, T.; Mehlmer, N. Optimization of Rhodococcus erythropolis JCM3201T Nutrient Media to Improve Biomass, Lipid, and Carotenoid Yield Using Response Surface Methodology. Microorganisms 2023, 11, 2147. https://doi.org/10.3390/microorganisms11092147
Engelhart-Straub S, Haack M, Awad D, Brueck T, Mehlmer N. Optimization of Rhodococcus erythropolis JCM3201T Nutrient Media to Improve Biomass, Lipid, and Carotenoid Yield Using Response Surface Methodology. Microorganisms. 2023; 11(9):2147. https://doi.org/10.3390/microorganisms11092147
Chicago/Turabian StyleEngelhart-Straub, Selina, Martina Haack, Dania Awad, Thomas Brueck, and Norbert Mehlmer. 2023. "Optimization of Rhodococcus erythropolis JCM3201T Nutrient Media to Improve Biomass, Lipid, and Carotenoid Yield Using Response Surface Methodology" Microorganisms 11, no. 9: 2147. https://doi.org/10.3390/microorganisms11092147
APA StyleEngelhart-Straub, S., Haack, M., Awad, D., Brueck, T., & Mehlmer, N. (2023). Optimization of Rhodococcus erythropolis JCM3201T Nutrient Media to Improve Biomass, Lipid, and Carotenoid Yield Using Response Surface Methodology. Microorganisms, 11(9), 2147. https://doi.org/10.3390/microorganisms11092147