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%), K 2 HPO 4 (0.05 M), KH 2 PO 4 (0.05 M), (NH 4 ) 2 SO 4 (0.05 M), NaNO 3 (0.1 M), MgSO 4 ·7H 2 O (0.002 M), and CaCl 2 (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, NaNO 3 , MgSO 4 ·7H 2 O, 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.