Statistical optimization of chitinase production by Streptomyces rubiginosus SP24 and efficacy of purified chitinase to control root-knot nematode infection in Vigna radiata under controlled conditions

Abstract

Abstract Background Actinomycetes are known to produce various lytic enzymes such as chitinase which have different biotechnological aspects. Plackett–Burman design (PBD) was applied to evaluate significant components of medium to improve chitinase enzyme production. Response surface methodology was implemented to select those variables which are highly fitted in the model and helped in increasing enzyme production. A second experiment was performed as one of the major applications of statistically optimized and purified chitinase (method and result of purification not shown) for reducing root-knot nematode infection in roots known for causing diseases in plants. Among them, Meloidogyne sp. is one which is responsible for decreasing plant growth and fruit yield. For this, Meloidogyne sp. (1000 J2 stage) was used to infect Vigna radiata (mung) plant which was previously coated with formulations of purified chitinase and organic manure ratio—1:1 (w/w). Results From ANOVA results of PBD data analysis, it was confirmed that colloidal chitin, xylose, peptone and dihydrogen potassium phosphate were found to be significant components for chitinase enzyme production. The best combination of factors (gm) to accomplish the optimum response was found to be colloidal chitin: 1.0, xylose: 2.0, peptone: 0.6 and dihydrogen potassium phosphate: 0.25 for predicted response of 3.81. Seeds coated with purified chitinase (5 ml) isolated from Streptomyces rubiginosus SP24 helped in enhancing plant growth promotion and decreasing root-knot nematode infection. Conclusion Statistically optimized seeds of mung coated with purified chitinase was used for suppressing plant-parasitic nematodes which cause severe harm to the production of crops in terms of plant growth and fruit yield.

Keywords

• Chitinase
• Plackett–Burman design
• Response surface model
• Statistical approach
• Optimization
• Streptomyces rubiginosus SP24