Statistical Optimization of Polyhydroxybutyrate Production by Bacillus Pumilus H9 Using Cow Dung as a Cheap Carbon Source by Response Surface Methodology
A gram positive bacterium (designated strain H9) found to be a potential polyhydroxybutyrate (biodegradable polymer) producer was isolated from the soil samples of a stress prone environment (municipal waste areas). This bacterium was identified as Bacillus pumilus H9 from its morphological, physiological and 16S rRNA gene sequence analysis. A four-factor central composite rotary design was employed to optimize the medium and to find out the interactive effects of four variables, viz. concentrations of cow dung, sucrose, peptone and pH on PHB production. Using response surface methodology, a second-order polynomial equation was obtained by multiple regression analysis and a yield of 2.47 g/L of PHB dry weight was achieved from the optimized medium at pH 7. Here, we report cow dung as a cheap carbon source for the production of PHB. Further, phbA, phbB and phbC genes were amplified by polymerase chain reaction which confirms the bacterium to be able to produce polyhydroxybutyrate.
KeywordsBacillus pumilus H9 Polyhydroxybutyrate Composite rotary design Response surface methodology Cow dung
The authors are thankful to Department of Biotechnology, India for providing instruments to Microbial Molecular Biology Laboratory, Department of Biotechnology, Assam University, Silchar, Assam, India which were used in this work. The authors would also like to acknowledge Pintubala Kshetri, ICAR Research Complex for NEH Region, Manipur Centre, Lamphelpat, Imphal, India for her help in analysis of the Design Expert 6 software.
- 8.Kiyasudeen SK, Ibrahim MHB, Ismail SA (2015) Am-Euras J Agric Environ Sci 15(8):1700–1709Google Scholar
- 15.Holt JG, Krieg NR, Sneath PHA, Staley JT, Williams ST (1994) Bergey’s manual of determinative bacteriology, 9th edn. Williamsons and Wilkins, BaltimoreGoogle Scholar
- 19.Palleroni NJ, Kreig NR, Holt JG (1984) Bergey’s manual of systematic bacteriology. Williams & Wilkins, BaltimoreGoogle Scholar
- 24.Soam A, Singh AK, Singh R, Shahi SK (2012) Curr Discov 1(1):27–32Google Scholar
- 25.Khiyami MA, Al-Fadual SM, Bahklia AH (2011) J Med Plants Res 5(14):3312–3320Google Scholar
- 38.Haaland PD (1989) Statistical problem solving In: Haaland PD (ed) Experimental design in biotechnology. Marcel Dekker Inc, New York, pp 1–18Google Scholar
- 39.Berekaa MM (2012) Life Sci J 9(4):518–529Google Scholar