Optimization of Spore and Antifungal Lipopeptide Production During the Solid-state Fermentation of Bacillus subtilis
- 350 Downloads
Bacillus subtilis strain TrigoCor 1448 was grown on wheat middlings in 0.5-l solid-state fermentation (SSF) bioreactors for the production of an antifungal biological control agent. Total antifungal activity was quantified using a 96-well microplate bioassay against the plant pathogen Fusarium oxysporum f. sp. melonis. The experimental design for process optimization consisted of a 26−1 fractional factorial design followed by a central composite face-centered design. Initial SSF parameters included in the optimization were aeration, fermentation length, pH buffering, peptone addition, nitrate addition, and incubator temperature. Central composite face-centered design parameters included incubator temperature, aeration rate, and initial moisture content (MC). Optimized fermentation conditions were determined with response surface models fitted for both spore concentration and activity of biological control product extracts. Models showed that activity measurements and spore production were most sensitive to substrate MC with highest levels of each response variable occurring at maximum moisture levels. Whereas maximum antifungal activity was seen in a limited area of the design space, spore production was fairly robust with near maximum levels occurring over a wider range of fermentation conditions. Optimization resulted in a 55% increase in inhibition and a 40% increase in spore production over nonoptimized conditions.
KeywordsSolid-state fermentation Optimization B. subtilis Lipopeptides Spores Biocontrol
We thank Dr. Gary Bergstrom and Dr. Thomas Zitter in the Department of Plant Pathology, Cornell University, for providing the B. subtilis and F. oxysporum cultures, respectively. This research was supported in part by a USDA MGET (Multi-Disciplinary Graduate Education Traineeship) program grant.
- 1.Yamada, S., Takayama, Y., Yamanaka, M., Ko, K., & Yamaguchi, I. (1990). Journal of Pesticide Science, 15, 95–96.Google Scholar
- 2.Vanittanakom, N., Loeffler, W., Koch, U., & Jung, G. (1986). Journal of Antibiotics, 39, 888–901.Google Scholar
- 13.Thimon, L., Peypoux, F., Magetdana, R., Roux, B., & Michel, G. (1992). Biotechnology and Applied Biochemistry, 16, 144–151.Google Scholar
- 18.Kuehl, R. O. (2000). Design of experiments: Statistical principles of research design and analysis. Pacific Grove, CA: Duxbury.Google Scholar
- 19.Montgomery, D. C. (1991). Design and analysis of experiments. New York: Wiley.Google Scholar
- 20.Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1978). Statistics for experimenters: An introduction to design, data analysis, and model building. New York: Wiley.Google Scholar
- 21.Myers, R. H., & Montgomery, D. C. (1995). Response surface methodology: Process and product optimization using designed experiments. New York: Wiley.Google Scholar
- 22.Pryor, S. W., Gibson, D. M., Krasnoff, S. B., & Walker, L. P. (2006). Transactions of the ASABE, 49, 1643–1649.Google Scholar
- 23.Bergstrom, G. C., & da Luz, W. C. (2005). Biocontrol for plants with Bacillus subtilis, Pseudomonas putida, and Sporobolomyces roseus. US Patent, 6, 896–883 B2.Google Scholar