OPTIMIZATION OF MEDIUM COMPOSITION FOR cis,cis-MUCONIC ACID PRODUCTION BY A Pseudomonas sp. MUTANT USING STATISTICAL METHODS

Abstract

cis,cis-Muconic acid (CCMA) is used as a platform chemical for the production of several high-value compounds. For this article, an optimization strategy has been used to optimize medium composition for CCMA production from fairly cheap benzoate by Pseudomonas sp. 1167. The effect of different concentrations of medium components on CCMA production was studied. CCMA yields obtained from Plackett–Burman design (PBD) showed wide variation (3.95–5.87 g/L), and the first-order model indicated that (NH₄)₂SO₄ (P < 0.01) and K₂HPO₄ · 3H₂O (P < 0.02) were the significant components for CCMA production. Then the optimization was performed by steepest ascent design (SAD) and central composite design (CCD), and a validation experiment was conducted to verify the predicted value. The optimal medium composition was: 12 g/L sodium benzoate, 2.5 g/L sodium succinate, 0.7932 g/L (NH₄)₂SO₄, 1.5612 g/L K₂HPO₄ · 3H₂O, 1.2 g/L MgSO₄ · 7H₂O, 0.4 g/L yeast extract, 0.08 g/L FeCl₃ · 6H₂O, and 0.08 g/L ethylenediamine tetraacetic acid (EDTA). Under these conditions, a maximum of 7.18 g/L CCMA was produced per 12 g/L benzoate with a highly efficient process within 11 hr and a molecular conversion yield of 61%. Altogether, our results provide valuable insights into nutritional supplementation of CCMA production by using statistical methods, which may benefit a cost-competitive industrial fed-batch fermentation process using a cheap substrate.

Keywords:

• bioconversion
• central composite design
• cheap substrate
• cis,cis-muconic acid
• optimization strategy
• statistical methods

ACKNOWLEDGMENTS

This work was supported by Guangxi Natural Science Foundation (12237022, 2012GXNSFBA053063 and 2013GXNSFDA019007), Guangxi Academy of Sciences (12YJ25SW01, 12YJ25SW02), and National Natural Science Foundation of China (31160023).

Notes

^a The symbols of the coded variables are the same as those in Table 1.

^b Y, experimental responses (g/L).

^c Ŷ, predicted values calculated from the fitted first-order model (g/L).

^a Y, experimental responses (g/L).

^a Y, experimental responses (g/L).