Four hundred and seventy-five strains, which included 394 type cultures of Streptomyces and representatives of 14 other actinomycete genera, were studied. Overall similarities of these strains for 139 unit characters were determined by the SSM and SJ coefficients and clustering by the UPGMA algorithm. Test error and overlap between the phena defined were within acceptable limits. Cluster-groups were defined by the SSM coefficient at the 70.1% similarity (S) level and by the SJ coefficient at the 50% S-level. Clusters were distinguished at the 77.5% SSM and 63% SJ S-levels. Groupings obtained with the two coefficients were generally similar, but there were some changes in the definition and membership of cluster-groups and clusters. The phenetic data obtained, together with those from previous diverse studies, indicated that the genera Actinopycnidium, Actinosporangium, Chainia, Elytrosporangium, Kitasatoa and Microellobosporia should be reduced to synonyms of Streptomyces, while Intrasporangium, Nocardioides and Streptoverticillium remained as distinct genera in the family Streptomycetaceae. Nocardiopsis dassonvillei also showed strong phenetic affinity to Streptomyces, despite its chemotaxonomic differences. Actinomadura sensu stricto was phenetically distinguishable from Streptomyces and 'Nocardia' mediterranea was recognized as a taxon distinct from both these genera and from Nocardia sensu stricto. Most of the Streptomyces type cultures fell into one large cluster-group. At the 77.5% SSM S-level, they were recovered in 19 major and 40 minor clusters, with 18 strains recovered as single member clusters. The status of the latter as species was therefore confirmed. Most of the minor clusters, consisting of two to five strains, can also be regarded as species. The major clusters varied in size (from 6 to 71 strains) and in there homogeneity. Therefore, it is suggested that they be regarded as species-groups until further information is available. The results provide a basis for the reduction of the large number of Streptomyces species which have been described. They also demonstrate that the previous use of a limited number of subjectively chosen characters to define species-groups or species has resulted in artificial classifications.

Numerical Classification of Streptomyces and Related Genera

Bacterial systematics has not yet reached a consensus for defining the fundamental unit of biological diversity, the species. The past half-century of bacterial systematics has been characterized by improvements in methods for demarcating species as phenotypic and genetic clusters, but species demarcation has not been guided by a theory-based concept of species. Eukaryote systematists have developed a universal concept of species: A species is a group of organisms whose divergence is capped by a force of cohesion; divergence between different species is irreversible; and different species are ecologically distinct. In the case of bacteria, these universal properties are held not by the named species of systematics but by ecotypes. These are populations of organisms occupying the same ecological niche, whose divergence is purged recurrently by natural selection. These ecotypes can be discovered by several universal sequence-based approaches. These molecular methods suggest that a typical named species contains many ecotypes, each with the universal attributes of species. A named bacterial species is thus more like a genus than a species.

What are Bacterial Species

The aerobic actinomycetes are soil-inhabiting microorganisms that occur worldwide. In 1888, Nocard first recognized the pathogenic potential of this group of microorganisms. Since then, several aerobic actinomycetes have been a major source of interest for the commercial drug industry and have proved to be extremely useful microorganisms for producing novel antimicrobial agents. They have also been well known as potential veterinary pathogens affecting many different animal species. The medically important aerobic actinomycetes may cause significant morbidity and mortality, in particular in highly susceptible severely immunocompromised patients, including transplant recipients and patients infected with human immunodeficiency virus. However, the diagnosis of these infections may be difficult, and effective antimicrobial therapy may be complicated by antimicrobial resistance. The taxonomy of these microorganisms has been problematic. In recent revisions of their classification, new pathogenic species have been recognized. The development of additional and more reliable diagnostic tests and of a standardized method for antimicrobial susceptibility testing and the application of molecular techniques for the diagnosis and subtyping of these microorganisms are needed to better diagnose and treat infected patients and to identify effective control measures for these unusual pathogens. We review the epidemiology and microbiology of the major medically important aerobic actinomycetes. Images

The medically important aerobic actinomycetes: epidemiology and microbiology.

Profound changes are occurring in the strategies that biotechnology-based industries are deploying in the search for exploitable biology and to discover new products and develop new or improved processes. The advances that have been made in the past decade in areas such as combinatorial chemistry, combinatorial biosynthesis, metabolic pathway engineering, gene shuffling, and directed evolution of proteins have caused some companies to consider withdrawing from natural product screening. In this review we examine the paradigm shift from traditional biology to bioinformatics that is revolutionizing exploitable biology. We conclude that the reinvigorated means of detecting novel organisms, novel chemical structures, and novel biocatalytic activities will ensure that natural products will continue to be a primary resource for biotechnology. The paradigm shift has been driven by a convergence of complementary technologies, exemplified by DNA sequencing and amplification, genome sequencing and annotation, proteome analysis, and phenotypic inventorying, resulting in the establishment of huge databases that can be mined in order to generate useful knowledge such as the identity and characterization of organisms and the identity of biotechnology targets. Concurrently there have been major advances in understanding the extent of microbial diversity, how uncultured organisms might be grown, and how expression of the metabolic potential of microorganisms can be maximized. The integration of information from complementary databases presents a significant challenge. Such integration should facilitate answers to complex questions involving sequence, biochemical, physiological, taxonomic, and ecological information of the sort posed in exploitable biology. The paradigm shift which we discuss is not absolute in the sense that it will replace established microbiology; rather, it reinforces our view that innovative microbiology is essential for releasing the potential of microbial diversity for biotechnology penetration throughout industry. Various of these issues are considered with reference to deep-sea microbiology and biotechnology.

Search and Discovery Strategies for Biotechnology: the Paradigm Shift

Seven soil samples and seven groundwater samples from a site contaminated with fuel-oil were investigated using several chemical and microbiological techniques. In soil samples, 500 to 7,500 mg/kg of total hydrocarbons were found. These samples contained no n-alkanes but iso- and branched chain alkanes. No polychlorinated biphenyls could be detected. Microbiological investigations included estimations of total cell counts, viable cell counts on different media, and numbers of methylotrophic, denitrifying, sulphate reducing, anaerobic (with the exception of methanogenic organisms), and hydrocarbon degrading bacteria. Viable and hydrocarbon degrading bacteria were found in all samples. A total of 1,366 pure cultures was characterized morphologically and physiologically and identified by numerical identification using a data base of more than 4,000 reference strains. Groundwater samples were dominated by gram-negative bacteria of the generaPseudomonas, Comamonas, Alcaligenes, andAcinetobacter, which were also found in soil samples. In addition, more grampositive bacteria belonging to the generaArthrobacter, Nocardia, andBacillus could be isolated from soil samples.

Microbiological characterization of a fuel-oil contaminated site including numerical identification of heterotrophic water and soil bacteria.

The character state data obtained for clusters defined at the 77.5% SSM similarity level in the phenetic numerical classification described by Williams et al. (1983) were used to construct a probabilistic identification matrix. The 23 phena included were the major clusters (19 Streptomyces, 2 Streptoverticillium and 'Nocardia' mediterranea) and one minor cluster (Streptomyces fradiae). The characters most diagnostic for these clusters were selected using Sneath's CHARSEP and DIACHAR programs. The resulting matrix consisted of 41 characters x 23 phena. Identification scores, determined by Sneath's MATIDEN program were used to evaluate the matrix. Theoretical assessment was achieved by determination of the cluster overlap (OVERMAT), the identification scores for the Hypothetical Medium Organism of each cluster (MOSTTYP), and the scores for randomly selected cluster representatives using the classification data of Williams et al. (1983). The matrix was evaluated practically by the independent re-determination of the characters for the same cluster representatives, which also provided a measure of test error. Finally it was used to identify unknown isolates from a range of habitats. The results showed that the matrix was theoretically sound. Test error was within acceptable limits and did not distort identifications. Of the unknown isolates, 80% were clearly identified with a cluster. It is suggested that the matrix could form the basis for a more objective identification and grouping of the large number of Streptomyces species which have been described.

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A probability matrix for identification of some Streptomycetes.

A numerical phenotypic taxonomic study of 315 strains of Neisseria and some allied bacteria examined for 155 phenotypic tests showed 31 groups, most of which were reasonably distinct. These fell into four major areas. Areas A, B and C contained species of Neisseria, whereas area D contained the organisms known as ‘false neisserias’ together with Branhamella, Moraxella and Kingella species. Area A contained N. gonorrhoeae (which showed two subgroups), N. meningitidis (with two subgroups, and N. cinerea closely associated), N. polysaccharea, N. elongata subsp. glycolytica and N. lactamica. Area B contained mainly organisms from the human nasopharynx, and the nine groups were not very distinct: only three, N. mucosa, N. perflava and N. sicca could be recognized by the presence of type strains, and there was little relationship between taxonomic position and species epithets. Area C contained several groups from animals, N. animalis, N. canis and two phenons that may be justified as new species of Neisseria, one from lizards and the other from dental plaque of herbivores. Area C also contained N. elongata, N. subflava (with N. flavescens), type strain of Morococcus cerebrosis and the CDC groups M-5 (N. weaveri) and EF-4. Area D contained Branhamella catarrhalis, a combined group which consists of strains of the ‘false neisserias’ N. caviae and N. cuniculi, the ‘false neisseria’ N. ovis, and a group of Moraxella strains. A small group representing Kingella kingae is included in area D. Mean test error was 1.7%.

A numerical phenotypic taxonomic study of the genus Neisseria.

The character state data obtained for clusters defined in a previous phenetic classification were used to construct two probabilistic matrices for Streptomyces species. These superseded an original published identification matrix by exclusion of other genera and the inclusion of more Streptomyces species. Separate matrices were constructed for major and minor clusters. The minimum number of diagnostic characters for each matrix was selected by computer programs for determination of character separation indices (charsep) and a selection of group diagnostic properties (diachar). The resulting matrices consisted of 26 phena x 50 characters (major clusters) and 28 phena x 39 characters (minor clusters). Cluster overlap (overmat program) was small in both matrices. Identification scores were used to evaluate both matrices. The theoretically best scores for the most typical example of each cluster (mosttyp program) were all satisfactory. Input of test data for randomly selected cluster representatives resulted in correct identification with high scores. The major cluster matrix was shown to be practically sound by its application to 35 unknown soil isolates, 77% of which were clearly identified. The minor cluster matrix provides tentative probabilistic identifications as the small number of strains in each cluster reduces its ability to withstand test variation. A diagnostic table for single-membered clusters, constructed using the charsep and diachar programs, was also produced.

New probability matrices for identification of Streptomyces.

Summary: The character state data for clusters defined at the 83% simple matching coefficient (SSM
) similarity level in a previous phenetic classification were used to construct a probabilistic identification matrix for Streptoverticillium species. The 24 phena included consisted of 10 clusters containing from 2 to 17 strains and 14 single-member clusters. Characters most diagnostic for the clusters were selected from the 185 used in the classification, using previously developed computer programs for determination of character separation indices (CHARSEP) and selection of group diagnostic properties (DIACHAR). The resulting matrix consisted of 41 characters x 24 phena, and identification scores, provided by a program for the identification of unknowns against an identification matrix (MATIDEN), were used for its evaluation. Cluster overlap, calculated by a program for determination of overlap between groups in a matrix (OVERMAT), was generally very small, and the best identification scores possible for most typical examples of each group (MOSTTYP program) were very satisfactory. Input of test data for randomly selected cluster representatives resulted in correct identifications with good scores for the three coefficients provided by the MATIDEN program.

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P. H. A. Sneath

Papers

Numerical Classification of Streptomyces and Related Genera

A probability matrix for identification of some Streptomycetes.

A numerical phenotypic taxonomic study of the genus Neisseria.

New probability matrices for identification of Streptomyces.

Probabilistic Identification of Streptoverticillium Species