A study of the ecology and taxonomy of micromonospora in the natural environment

Abstract

Selective isolation procedures were developed for the isolation of Micromonospora from natural environments. Methods developed involved the use of heat treatment and the use of antibiotic incorporated media using lincomycin and novobiocin. It was found that heat treating air dried soil samples in a dry oven at 120°C for 60 minutes drastically reduced the number of both bacteria and streptomycetes occurring on the isolation plates thereby allowing for the selective isolation of Micromonospora. Additionally the most effective concentration of antibiotics for the selective isolation of Micromonospora was found to be lOpil/ml novobiocin and 10/xl/ml lincomycin. A spore specific extraction procedure was also developed, again exploiting the ability of Micromonospora spores to withstand heat treatment, to follow the fate of Micromonospora spores and mycelia in artificial soil microcosms. Comparison of the survival of both the spore and mycelial component of M. chalcea and M. Julvopurpurea populations indicated that heat treating lOg microcosms for 20 minutes at 70°C allowed the maximum recovery of spores present whilst deselecting completely the mycelial population. The ability of Micromonospora chalcea and M. Julvopurpurea to survive in sterile soil microcosms was studied over 30 days. Both species showed marked germination and sporulation cycles mirroring streptomycetes. Both species consistantly showed significant germination at Day 1 with spore numbers starting to increase by Day 2 (ca. 10^ spores/g soil). Following rapid sporulation at Day 5. a plateau at ca. 10^ to 10^ c.f.u./g soil was attained. Using phenotypic data, including antibiotic resistance profiles, Micromonospora strains, comprising both type strains and wild isolates, were clustered using numerical taxonomic methods. Clustering of the largest set of data (121 strains/179 characteristics) using the NTSYS clustering package, gave 14 distinct species-groups. The character state data obtained for clusters defined at the 77.5% Ssm similarity level were then used to develop a probabilistic identification matrix for the rapid identification of Micromonospora