Presentation Information
[P02-286]Estimating effects of fermentation medium composition for Sophorolipid production by Starmerella bombicola
○Ankhmend Battsengel1, Yuwa Inaba1, Takuto Nakajima1, Kazuki Watanabe2, Tomoko Kagenishi2, Masaaki Konishi2 (1. Graduate School of Engineering, Kitami Institute of Technology (Japan), 2. Kitami Institute of Technology (Japan))
Keywords:
Sophorolipid,Starmerella bombicola,Medium profiling
[Purpose]
Sophorolipid (SLs) are a microbial amphiphilic glycolipid with applications in pharmaceutical, cosmetic, petroleum, and food industries due to their excellent detergency, low cytotoxicity, and readily biodegradability. Starmerella bombicola is one of the famous SL-producing yeasts, which produces abundant amounts of SLs up to 200 g/L. Yeast extract (YE) and peptone (PP) are often used in production medium as nitrogen sources in SL production. It has been reported that high contents of free amino acids in YE have increased the SL production. However, the effects of individual amino acids on SL production have been unknown. Therefore, we attempted to find the significant amino acids in the nitrogen sources for efficient SL production, using a metabolomics-like technique.
[Methods]
Six brands of YE, named YE1, YE2, YE3, YE4, YE5, and YE6, and six brands of PP, named PP1, PP2, PP3, PP4, PP5, and PP6, were analyzed. A total of 102 components were quantified using multiple analytical techniques, including HPLC-OPA, GC-MS/MS, LC-MS, IC, and ICP-MS. Starmerella bombicola NBRC10243 was cultured in medium containing olive oil (50 g/L), glucose (50 g/L), YE or PP (1.0 or 0.5 g/L), NaNO3, KH2PO4, and MgSO4·7H2O at 28°C for 4 days. Biomass was measured by dry cell weight (DCW), and SL was extracted and quantified by dry weight. Principal Component Analysis (PCA) was used to compare medium composition. Partial Least Squares Regression (PLS-R) was performed to relate 71 quantified components to SL production and DCW. Model performance was evaluated using hold-out validation, and Variable Importance in Projection (VIP) scores were calculated.
[Results and Discussion]
Based on multiple analyses, YE was composed of 13.28–64.50% free amino acids, 9.86–41.62% peptide derived amino acids, 0.740–12.55% nucleic acids, 0.64–6.24% organic acids, 1.80–24.61 % saccharides, 0.007–0.117% vitamins, 15.50–26.20% minerals, and 0.21–0.27% urea. In contrast, PP contained 13.11–42.14% free amino acids, 6.89–65.63% peptide derived amino acids, 0.054–4.50% nucleic acids, 0.004–10.18% saccharides, 0.15–18.68% organic acids, 0–0.15% vitamins, 4.58–54.82% minerals, and 0.160–0.190% urea. According to SL production, SL yields varied depending on YE or PP type. The highest SL production (36.3 ± 1.7 g/L) was observed with PP1, while the lowest (9.6 ± 2.1g/L) was observed with YE6. PCA suggested that YE6 and PP2 contained lower levels of most amino acids, indicating possible nutrient limitation. PLS-R analysis showed strong predictive performance (R² = 0.96; Q² = 0.90). The high variable importance in projection score (≧1.0) was observed in tryptophan, lysine, serine, ornithine, methionine, pantothenic acid, cytosine, and nitrate. According to validation tests in YE6, lysine, serine, methionine, ornithine, and cytosine obviously increased SL production. Furthermore, when all important components were added in combination, SL production was further improved to 36.5 ± 2.0 g/L, which was corresponding to that in the best nitrogen source, PP1.
[Conclusion]
Combining comprehensive quantitative medium analysis with machine learning enabled the identification of key components influencing SL production. This omics–machine learning approach offers a practical framework for estimating significant components in complex natural feed stocks and improving industrial SL production.
Sophorolipid (SLs) are a microbial amphiphilic glycolipid with applications in pharmaceutical, cosmetic, petroleum, and food industries due to their excellent detergency, low cytotoxicity, and readily biodegradability. Starmerella bombicola is one of the famous SL-producing yeasts, which produces abundant amounts of SLs up to 200 g/L. Yeast extract (YE) and peptone (PP) are often used in production medium as nitrogen sources in SL production. It has been reported that high contents of free amino acids in YE have increased the SL production. However, the effects of individual amino acids on SL production have been unknown. Therefore, we attempted to find the significant amino acids in the nitrogen sources for efficient SL production, using a metabolomics-like technique.
[Methods]
Six brands of YE, named YE1, YE2, YE3, YE4, YE5, and YE6, and six brands of PP, named PP1, PP2, PP3, PP4, PP5, and PP6, were analyzed. A total of 102 components were quantified using multiple analytical techniques, including HPLC-OPA, GC-MS/MS, LC-MS, IC, and ICP-MS. Starmerella bombicola NBRC10243 was cultured in medium containing olive oil (50 g/L), glucose (50 g/L), YE or PP (1.0 or 0.5 g/L), NaNO3, KH2PO4, and MgSO4·7H2O at 28°C for 4 days. Biomass was measured by dry cell weight (DCW), and SL was extracted and quantified by dry weight. Principal Component Analysis (PCA) was used to compare medium composition. Partial Least Squares Regression (PLS-R) was performed to relate 71 quantified components to SL production and DCW. Model performance was evaluated using hold-out validation, and Variable Importance in Projection (VIP) scores were calculated.
[Results and Discussion]
Based on multiple analyses, YE was composed of 13.28–64.50% free amino acids, 9.86–41.62% peptide derived amino acids, 0.740–12.55% nucleic acids, 0.64–6.24% organic acids, 1.80–24.61 % saccharides, 0.007–0.117% vitamins, 15.50–26.20% minerals, and 0.21–0.27% urea. In contrast, PP contained 13.11–42.14% free amino acids, 6.89–65.63% peptide derived amino acids, 0.054–4.50% nucleic acids, 0.004–10.18% saccharides, 0.15–18.68% organic acids, 0–0.15% vitamins, 4.58–54.82% minerals, and 0.160–0.190% urea. According to SL production, SL yields varied depending on YE or PP type. The highest SL production (36.3 ± 1.7 g/L) was observed with PP1, while the lowest (9.6 ± 2.1g/L) was observed with YE6. PCA suggested that YE6 and PP2 contained lower levels of most amino acids, indicating possible nutrient limitation. PLS-R analysis showed strong predictive performance (R² = 0.96; Q² = 0.90). The high variable importance in projection score (≧1.0) was observed in tryptophan, lysine, serine, ornithine, methionine, pantothenic acid, cytosine, and nitrate. According to validation tests in YE6, lysine, serine, methionine, ornithine, and cytosine obviously increased SL production. Furthermore, when all important components were added in combination, SL production was further improved to 36.5 ± 2.0 g/L, which was corresponding to that in the best nitrogen source, PP1.
[Conclusion]
Combining comprehensive quantitative medium analysis with machine learning enabled the identification of key components influencing SL production. This omics–machine learning approach offers a practical framework for estimating significant components in complex natural feed stocks and improving industrial SL production.
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