Optimization and production of antifungal hydrolysis enzymes by streptomyces aureofaciens against Colletotrichum gloeosporioides of mango


We isolated naturally occurring actinomycetes with an ability to produce metabolites having antifungal property against, Colletotrichum gloeosporioides, the causal agent of mango anthracnose. One promising strain was strong antifungal activity, was selected for further studies. Based on the physiological and biochemical characteristics, the bacterial strain was identical to Streptomyces aureofaciens. Culture filtrate collected from the exponential and stationary phases inhibited the growth of fungus tested, indicating that growth suppression was due to extracellular antifungal metabolites present in culture filtrate. Isolate highly produced extracellular chitinase and β-1,3-glucanase during the exponential and late exponential phases, respectively. In order to standardize the metabolite production some cultural conditions like different incubation time in hours, pH, carbon sources and concentrations and nitrogen source were determined. During fermentation, growth, pH and hydrolysis enzymes production were monitored .Treatment with bioactive components exhibited a significantly high protective activity against development of anthracnose disease on mango trees and increased fruit yield.

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Haggag, W. , Mohamed, E. and Azzazy, A. (2011) Optimization and production of antifungal hydrolysis enzymes by streptomyces aureofaciens against Colletotrichum gloeosporioides of mango. Agricultural Sciences, 2, 146-157. doi: 10.4236/as.2011.22021.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] [1] Chou, K.C. (2002) A new branch of proteomics: Predic-tion of protein cellular attributes. In Weinrer, P. W. and Lu, Q. Eds., Gene Cloning & Expression Technologies, Eaton Publishing, Chapter 4, Westborough, MA, 57-70.
[2] [2] Chou, K.C. (2004) Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry, 11(*), 2105-2134.
[3] [3] Chou, K.C. (2006) Structural bioinformatics and its impact to biomedical science and drug discovery. Frontiers in Medicinal Chemistry, 3(1), 455-502.
[4] [4] Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K. and Watson, J.D. (1994) Molecular Biology of the Cell. 3rd Edition, Garland Publishing, Chapter 1, New York & London.
[5] [5] Lodish, H., Baltimore, D., Berk, A., Zipursky, S.L., Ma-tsudaira, P. and Darnell, J. (1995) Molecular Cell Biology, 3rd Edition, Scientific American Books, Chapter 3, New York.
[6] [6] Nakai, K. and Kanehisa, M. (1991) Expert system for predicting protein localization sites in Gram-negative bacteria. Proteins: Structure, Function and Genetics, 11(*), 95-110. doi:10.1002/prot.340110203
[7] [7] Nakashima, H. and Nishikawa, K. (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. Journal of Molecular Biology, 238(1), 54-61. doi:10.1006/jmbi.1994.1267
[8] [8] Cedano, J., Aloy, P., P'erez-Pons, J.A. and Querol, E. (1997) Relation between amino acid composition and cellular location of proteins. Journal of Molecular Biol-ogy, 266(3), 594-600. doi:10.1006/jmbi.1996.0804
[9] [9] Nakai, K. and Horton, P. (1999) PSORT: A program for detecting sorting signals in proteins and predicting their subcellular localization. Trends in Biochemical Science, 24(*), 34-36. doi:10.1016/S0968-0004(98)01336-X
[10] [10] Chou, K.C. and Elrod, D.W. (1998) Using discriminant function for prediction of subcellular location of pro-karyotic proteins. Biochemical and Biophysical Research Communications, 252(*), 63-68.
[11] [11] Reinhardt, A. and Hubbard, T. (1998) Using neural net-works for prediction of the subcellular location of pro-teins. Nucleic Acids Research, 26(9), 2230-2236. doi:10.1093/nar/26.9.2230
[12] [12] Chou, K.C. and Elrod, D.W. (1999) Protein subcellular location prediction. Protein Engineering, 12(2), 107-118. doi:10.1093/protein/12.2.107
[13] [13] Yuan, Z. (1999) Prediction of protein subcellular loca-tions using Markov chain models. FEBS Letters, 451(1), 23-26. doi:10.1016/S0014-5793(99)00506-2
[14] [14] Collins, C.H., Lyne, P.M. and Granje, J.M. (1995) In: Microbiological methods. Butterworth and Heinemann Publishers, London, 129-131.
[15] [15] Prapagdee, B., Kotchadat, K., Kumsopa, A. and Visarathanonth, N. (2007) The role of chitosan in protection of soybean from sudden death syndrome caused by Fusarium solani f. sp. glycines. Bioresource Technology, 98(7),1353-1358. doi:10.1016/j.biortech.2006.05.029
[16] [16] Boller, T. A Gegri, F.Mauch, and Vogeli,U (1983). Chiti-nase in bean leaves: induction by ethylene, purification, properties and possible function. Planta, 157(*), 22-31. doi:10.1007/BF00394536
[17] [17] Nelson, N.J. (1955) Colorimetric analysis of sugars. Methods Enzymol, 3(*), 85-86.
[18] [18] Koomen, I. and Jeffries, P. (1993) Effects of antagonistic microorganism on the post harvest development of Col-letotrichum gloeosporioides on mango. Plant Pathol-ogy,42(2) 23-237. doi:10.1111/j.1365-3059.1993.tb01495.x
[19] [19] Vaidya, R.J., Vyas, P. and Chhatpar, H.S. (2003) Statistical optimization of medium components for the production of chitinase by Alcaligenes xylosoxydans. Enzyme and Microbial Technology, 33(*), 92-96. doi:10.1016/S0141-0229(03)00100-5

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