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Optimization of Biomass and 5-Aminolevulinic Acid Production by Rhodobacter sphaeroides ATCC17023 via Response Surface Methodology

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Abstract

Microbial 5-aminolevulinic acid (ALA) produced from wastewater is considered as potential renewable energy. However, many hurdles are needed to be overcome such as the regulation of key influencing factors on ALA yield. Biomass and ALA production by Rhodobacter sphaeroides was optimized using response surface methodology. The culturing medium was artificial volatile fatty acids wastewater. Three additives were optimized, namely succinate and glycine that are precursors of ALA biosynthesis, and D-glucose that is an inhibitor of ALA dehydratase. The optimal conditions were achieved by analyzing the response surface plots. Statistical analysis showed that succinate at 8.56 mmol/L, glycine at 5.06 mmol/L, and D-glucose at 7.82 mmol/L were the best conditions. Under these optimal conditions, the highest biomass production and ALA yield of 3.55 g/L and 5.49 mg/g-biomass were achieved. Subsequent verification experiments at optimal values had the maximum biomass production of 3.41 ± 0.002 g/L and ALA yield of 5.78 ± 0.08 mg/g-biomass.

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Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (51278489).

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Authors and Affiliations

  1. School of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450000, China

    Shuli Liu

  2. School of Environment and Resource, Renmin University of China, Beijing, 100872, China

    Guangming Zhang

  3. School of Municipal and Environmental Engineering, Harbin Institute of Technology, Huanghe Road 73, Harbin, 150090, China

    Guangming Zhang, Jianzheng Li, Xiangkun Li & Jie Zhang

Authors
  1. Shuli Liu
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  2. Guangming Zhang
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  3. Jianzheng Li
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  4. Xiangkun Li
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  5. Jie Zhang
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Correspondence to Guangming Zhang.

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Liu, S., Zhang, G., Li, J. et al. Optimization of Biomass and 5-Aminolevulinic Acid Production by Rhodobacter sphaeroides ATCC17023 via Response Surface Methodology. Appl Biochem Biotechnol 179, 444–458 (2016). https://doi.org/10.1007/s12010-016-2005-z

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  • DOI: https://doi.org/10.1007/s12010-016-2005-z

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