Screening and identification of significant media components using Plackett-Burman design for enhanced extracellular protease production by Bacillus sp. MTCC 511 under mutagenic stress conditions.

Abstract

Background: Statistical screening of media components is essential for identifying the key variables that govern protease yield from Bacillus species. We used a 12-run Plackett-Burman Design (PBD) to screen ten independent variables-dipotassium phosphate, monopotassium phosphate, ammonium sulphate, sodium nitrate, magnesium sulphate heptahydrate, calcium chloride, carbon source, nitrogen source, agitation speed, and pH-for their effects on extracellular protease production by Bacillus sp. MTCC 511 under four mutagenic stress conditions: thermal treatment at 70 °C and 80 °C (10 min each) and UV irradiation (2 and 3 min).

Results: Enzyme activity was quantified by Folin-Ciocalteu assay against a tyrosine standard (R2 = 0.998). The highest protease activity, 8.66 U/mL, was recorded under 3-min UV irradiation in a medium containing starch (1%), yeast extract (0.1%), K₂HPO₄ (0.05 M), KH₂PO₄ (0.05 M), (NH₄)₂SO₄ (0.05 M), NaNO₃ (0.1 M), MgSO₄·7H₂O (0.002 M), and CaCl₂ (0.002 M) at pH 9 and 120 rpm, representing a 20.6-fold improvement over the unsupplemented basal medium. ANOVA showed R2 > 97% for the 70 °C, 80 °C, and UV-3 min models; the UV-2 min model was statistically inadequate (R2 = 56.52%). Pareto analysis pointed to agitation speed, NaNO₃, MgSO₄·7H₂O, and carbon source as the most influential factors. Pearson correlation between thermal and UV-3 min responses (r = 0.72-0.83) is consistent with previously described σ^B-dependent stress-response pathways.

Conclusions: The PBD screening efficiently identified significant factors whose effects are consistent with established regulatory mechanisms including nitrogen catabolite repression, carbon catabolite repression, and dissolved oxygen-dependent secretion. Follow-up response surface optimization is warranted to characterize factor interactions and identify the global optimum.