- Mycolic Acid-Containing Actinomycetes Associated with Activated Sludge Foam
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Seong, Chi Nam , Kim, Young Sook , Baik, Keun Shik , Lee, Soon Dong , Hah, Yung Chil , Kim, Seung Bum , Goodfellow, Michael
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J. Microbiol. 1999;37(2):66-72.
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 Abstract
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Mycolic acid-containing actinomycetes associated with extensive foaming in the aeration basin of the activated sludge process were isolated and analyzed by phenotypical, chemotaxonomical and phylogenetic methods. Whole cell sugar patterns of two isolates were pattern A. The nearly complete sequences of the 16S rRNA genes (rDNAs) of the isolates were determined and compared by using several tree-making algorithms. With polyphasic methods, strain SCNU1 was identified as Gordona sputi, and strain SCNU5 assigned to the genus Tsukamurella. The presence of opportunistic pathogens of chronic lung infections within foams can cause public health problems and render waste-treatment processes inefficient.
- Construction of probability identification matrix and selective medium for acidophilic actinomycetes using numerical classification data
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Seong, Chi Nam , Park, Seok Kyu , Goodfellow, Michael , Kim, Seung Bum , Hah, Yung Chil
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J. Microbiol. 1995;33(2):95-102.
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Abstract
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A probability identification matrix of acidophilic Streptomyces was constructed. The phenetic data of the strains were derived from numerical classification described by Seong et al. The minimum number of diagnostic characters was determined using computer programs for calculation of different separation indices. The resulting matrix consisted of 25 clusters versus 53 characters. Theoretical evaluation of this matrix was achieved by estimating the cluster overlap and the identification scores for the Hypothetical Median Organisms (HMO) and for the representatives of each cluster. Cluster overlap was found to be relatively small. Identification scores for the HMO and the randomly selected representatives of each cluster were satisfactory. The matrix was assessed practically by applying the matrix to the identification of unknown isolates. Of the unknown isolates, 71.9% were clearly identified to one of eight clusters. The numerical classification data was also used to design a selective isolation medium for antibiotic-producing organisms. Four chemical substances including 2 antibiotics were determined by the DLACHAR program as diagnostic for the isolation of target organisms which have antimicrobial activity against Micrococcus luteus. It was possible to detect the increased rate of selective isolation on the synthesized medium. The results show that the numerical phenetic data can be applied to a variety of purposes, such as construction of identification matrix and selective isolation medium for acidophilic antinomycetes.
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