SELECTIVE ISOLATION, CHARACTERISATION AND IDENTIFICATION OF STREPTOSPORANGIA
Thesissubmitted in accordancewith the requirementsof theUniversity of Newcastleupon Tyne for the Degreeof Doctor of Philosophy by Hong-Joong Kim B. Sc.
NEWCASTLE UNIVERSITY LIBRARY ______093 51117 X ------fn L:L, Iýý:, - L. 51-ý CJ -
Departmentof Microbiology, The Medical School,University of Newcastleupon Tyne December1993 CONTENTS
ACKNOWLEDGEMENTS Page Number
PUBLICATIONS SUMMARY
INTRODUCTION A. AIMS 1 B. AN HISTORICAL SURVEY OF THE GENUS STREPTOSPORANGIUM 5 C. NUMERICAL SYSTEMATICS 17 D. MOLECULAR SYSTEMATICS 35 E. CHARACTERISATION OF STREPTOSPORANGIA 41 F. SELECTIVE ISOLATION OF STREPTOSPORANGIA 62
MATERIALS AND METHODS A. SELECTIVE ISOLATION, ENUMERATION AND 75 CHARACTERISATION OF STREPTOSPORANGIA B. NUMERICAL IDENTIFICATION 85 C. SEQUENCING OF 5S RIBOSOMAL RNA 101 D. PYROLYSIS MASS SPECTROMETRY 103 E. RAPID ENZYME TESTS 113
RESULTS A. SELECTIVE ISOLATION, ENUMERATION AND 122 CHARACTERISATION OF STREPTOSPORANGIA B. NUMERICAL IDENTIFICATION OF STREPTOSPORANGIA 142 C. PYROLYSIS MASS SPECTROMETRY 178 D. 5S RIBOSOMAL RNA SEQUENCING 185 E. RAPID ENZYME TESTS 190 DISCUSSION A. SELECTIVE ISOLATION 197 B. CLASSIFICATION 202 C. IDENTIFICATION 208 D. FUTURE STUDIES 215
REFERENCES 220
APPENDICES A. TAXON PROGRAM 286 B. MEDIA AND REAGENTS 292 C. RAW DATA OF PRACTICAL EVALUATION 295 D. RAW DATA OF IDENTIFICATION 297 E. RAW DATA OF RAPID ENZYME TESTS 300 ACKNOWLEDGEMENTS
I would like to sincerely thank my supervisor, Professor Michael Goodfellow for his assistance,guidance and patienceduring the course of this study. I am greatly indebted to Dr. Yong-Ha Park of the Genetic Engineering Research Institute in Daejon, Korea for his encouragement, for giving me the opportunity to extend my taxonomic experience and for carrying out the 5S rRNA sequencing studies. I am most grateful to Dr. David Minnikin for his advice and practical assistance in chemotaxonomy as well as to Dr. Alan Ward for his encouragement and discussions on computer-assisted identification. However, I am also indebted to Professor Han of the Department of Biology, Inha University in Inchon, Korea for teaching me the importance of microbiology. Penultimately, I also extend thanks to all in the Department of Microbiology at the University of
Newcastle upon Tyne. Finally I am extremely grateful for the never ending support and encouragementof my family and friends. PUBLICATIONS
Someof the work presentedin the thesishas been published:
Kim, H-J. and Goodfellow, M. (1993). Computer-assistedidentification of Streptosporangium. In Identification of Bacteria: Present Trends-Future Prospects. Proceedingsof the FEMS Meeting in Granada,Spain (abstract).
Chun, J., Atalan, E., Kim, S-B., Kim, H-J., Hand, M. E., Trujillo, M. E.,
Magee, J. G., Manfio, G. P., Ward, A. C. and Goodfellow, M. (1993). Rapid identification of streptomycetesby artificial neural network analysis of pyrolysis mass spectra. FEMS Microbiological Letters (in press). SUMMARY
Large numbers of actinomycetes were isolated from composite soil samples using procedures considered to be selective for the isolation of streptosporangia
and related sporoactinomycetes from environmental samples. The highest streptosporangial counts were obtained when suspensionsof air-dried soil were heated in the presence of yeast extract for 20 minutes at 4000 then plated onto
humic acid vitamins agar supplemented with actidione (50mg/1) and nalidixic acid (30mg/1) and incubated for 4 weeks at 30°C. The highest count, 7.94 ± 1.19 x 104
colony forming units per gram dry soil, were obtained from samples of Ginseng
field soil. Representative strains had morphological and chemical properties
consistent with their classification in the genus Streptosporangium. Representativeisolates and marker strainsof the genusStreptosporangium were examined for diagnostic features recommended for computer-assisted identification of unknown streptosporangia. Stringent criteria were adopted for positive identifications of both known and unknown strains following a critical evaluation of identification scores obtained for the marker cultures. Sixty-five of
the seventy marker strains and twelve of the hundred and thirty six unknown streptosporangia were identified to known streptosporangial taxa. A further nineteen of the isolates were assigned to known taxa using less stringent cut-off points for positive identifications.
5S ribosomalRNA sequenceswere determinedfor nine representativesof the genus Streptosporangium including centrotype strains of two taxa, clusters 1 and 2, circumscribed in a recent numerical phenetic survey and two Ginseng field soil isolates. The primary and secondary structure of the resultant sequenceswere of the type characteristic of Gram-positive bacteria with DNA rich in guanine and cytosine. It was evident from the phylogenetic tree that the genus Streptosporangium is heterogeneous as the type strains of Streptosporangium
albidum and Streptosporangium viridogriseum subspecies viridogriseum were
sharply separated from the remaining test strains; a result in good agreement with
current trends in streptosporangial systematics.
Pilot experimentswere designedto determinethe potential of Curie point pyrolysis mass spectrometry and rapid fluorogenic enzyme tests in the classification and identification of streptosporangia. The pyrolysis mass spectral
data supported the taxonomic integrity of clusters 1 and 2 and showed that
Streptosporangium viridogriseum subspecies viridogriseum had little in common with bona fide members of the genus Streptosporangium. Pyrolysis data also
supported the results of the computer-assisted identification exercise as ten
isolates assigned to cluster 1 using stringent cut-off criteria were found to be closely related to representatives of cluster 1. There was evidence that some of the conjugated substrates based on the fluorophores 7-amino-4-methylcoumarin
and 4-methylumbelliferone have potential as taxonomic markers for the classification of streptosporangia and related actinomycetes. INTRODUCTION
A. AIMS
Actinomycetes are an unique source of high value bioactive products,
notably antibiotics, enzymes, enzyme inhibitors and vitamins. In particular, they account for sixty percent, that is, more than seven thousand of the naturally occurring antibiotics that have been discovered (Table 1, page 2). Approaches to
the search for, and discovery of, new bioactive compounds are generally based on screening both naturally occurring actinomycetes and genetically manipulated strains. Current efforts to find the next generation of new antibiotics of
therapeutic value are compromised as the probability of discovering new
compounds is declining as the number of known antibiotics is increasing (Okami
and Hotta, 1988). It is, therefore, important in search and discovery programmes to screen novel and rare actinomycetes in order to raise the probability of finding novel antibiotics (Nolan and Cross, 1988; Bull et al., 1992). Given recent developments in microbial systematics it is now possible to recognise and characterise rare and novel actinomycetes derived from the application of selective isolation procedures by detecting key phenetic taxonomic
markers (O'Donnell, 1986,1988; Goodfellow and O'Donnell, 1989; Bull et al., 1992). In addition, molecular taxonomic methods, such as nucleic acid hybridisation, sequencing and fingerprinting techniques, are available for the accurate description of patent strains (Stackebrandt and Goodfellow, 1991). In addition, information in numerical taxonomic databases can be used to design media formulations for the selective isolation of specific fractions of the actinomycete community from soil and other natural habitats (Vickers et al., 1984; Williams et al., 1984a; Williams and Vickers, 1988; Goodfellow and ODonnell, 1989; Bull et al., 1992). Screening methods, which are increasingly target
I Table 1 Number of antibiotics producedby membersof selectedactinomycete genera*
Genus 1974 1980 1984 1988 1993
Streptomyces 1934 2784 3477 4876 5645 Micromonospora 41 129 269 398 535 Nocardia 45 74 107 262 287 Actinomadura - 16 51 164 248 Actinoplanes 6 40 95 146 169 Streptoverticillium 19 41 64 138 169 Streptosporangium** 7 20 26 39 57
Saccharopolyspora - 4 33 44 55 Dactylosporangium - 4 19 31 40 Amycolatopsis - - - - 23 Kibdelosporangium - - - 7 18 Actinosynnema - - 5 14 17 Microbispora ** 4 6 6 10 15
Streptoalloteichus - 3 4 12 14 14 Kitasatosporia - - - 11 Planobispora ** 10 - - - - 4 Microtetraspora ** - - - - Planomonospora ** 2 - - - -
* Data from Berdy (1974,1984), Nisbet (1982) and Berdy database(August, 1993; Data-wm MedhaRtk ye, Sm°iI,kl11e ßeectiens,$rcckl, aºn PaA, Be#tl,wýrtý,, Suwreq, u.k. ), ** Membersof the family Streptosporangiaceae(Goodfellow et al., 1990a).
2 directed, are also important in the searchfor new products (Okami and Hotta, 1988;Bull et al., 1992). There is evidence that the genus Streptosporangiummay become an increasinglyrich sourceof commerciallysignificant products,notably antibiotics (Table 2, page 4). In addition, cystathionine T-lyase has been detectedin a strain of Streptosporangium(Nagasawa et al., 1984). This enzymecatalyses the a, T-elimination reaction of L-cystathionine and also the 7-replacementof L- homoserinein the presenceof various thiol compounds(Kanzaki et al., 1986a). An efficient methodbased on the reactionof 7-replacementhas been developed for the preparationof L-cystathionine(Kanzaki et al., 1986b),a product with potential value as it has beenshown to be deficient in the brains of homocystinuricpatients (Gerritsen and Waisman, 1964). The proceduredesciibed by Kanzaki and his colleaguesallows the total conversionof L-cysteine into L-cystathionineand 0- succinyl-L-homoserine. The discoveryof additional commerciallysignificant natural productsftom Streptosporangium strains is hindered by the lack of effective procedures for the selective isolation, classification and identification of streptosporangia from environmental samples (Goodfellow, 1991; Hayakawa et al., 1991). Current isolation methods are empirical and depend upon drastic, heat pretreatment of air dried soil samples and the plating out of serial dilutions onto basal media supplemented with selective agents (Nonomura and Ohara 1969a, b; Hayakawa and Nonomura, 1987a; Nonomura and Hayakawa, 1988). Little attempt has been made to evaluate the effectiveness of these selective isolation procedures partly because of the poor taxonomy of the genus Streptosporangium.
In a comprehensive chemical and numerical phenetic survey of the genus Streptosporangium marker and fresh isolates were assigned to five major, seven minor and eighteen single membered clusters (Whitham, 1988; Whitham et al.,
3 Table 2 Members of the genus Streptosporangiumknown to produce novel bioactivecompounds
Name Product Reference
Streptosporangium albidum Aculescimycin Murata et at. (1989) "Streptosporangium Selenomycin U. S. patent3,683,074 brasiliense"
Streptosporangium fragile Anthracycline Sheareret al. (1983)
fragilomycin complex U. S. patent4,293,546
"Streptosporangium indica" Antimicrobial agent Rao et al. (1987)
"Streptosporangium Antimicrobial agent karnatakensis"
Streptosporangium Antitumour antibiotics Umezawaet al. (1976) pseudovulgare
Sporamycin Komiyama et al. (1977)
"Streptosporangium Sibiromycin Brazhnikova et al. (1972) sibiricum"
Streptosporangium PlatomycinsA andB Takasawa et al. (1975) violaceochromogenes
Victomycin Kawamoto et al. (1975)
Streptosporangium Chloramphenicol Tamura et al. (1971) viridogriseum subsp. kofuense
Streptosporangium Sporaviridin Okudaet al. (1966a,b) viridogriseum subsp. viridogriseum
"Streptosporangium sp. " Cystathioniney-lyase Nagasawaet al. (1984)
" ", Speciesnot on the ApprovedLists of Bacterial Names(November, 1993).
4 1993). The results underpinned the taxonomic integrity of the genus
Streptosporangium and most of its constituent species but indicated that the taxon was heterogeneous. Information from the numerical taxonomic databasewas used
to generate a theoretically sound frequency matrix for the computer-assisted identification of unknown environmental isolates belonging to the genus. The initial aim of the present project was to evaluate the selectivity of procedurescurrently recommendedfor the selectiveisolation of streptosporangia from environmentalsairnples (Nonomura and Ohara, 1969a; Nonomura, 1989). Representativeisolates and marker strainsof numerically circumscribedclusters containing streptosporangia(Whitham et al., 1993) were then examined for propertiesconsidered to be diagnosticfor the identification of streptosporangiain order to evaluate the computer-assistedprocedure (Whitham, 1988). The taxonomic statusof authentic and putatively novel speciesof Streptosporangium were then evaluatedusing a rapid automatedenzymatic procedure (Hamid et al., 1993) and Curie-point pyrolysis massspectrometry (Magee, 1993a,b). Finally, the phylogeneticrelationships of representativestreptosporangia were the subject of 5S ribosornalRNA sequencingstudies carried out in collaborationwith Dr. Y- H. Park of the GeneticEngineering Research Institute in Daejon,Korea.
B. AN HISTORICAL SURVEY OF THE GENUS STREPTOSPORANGIUM Couch (1955a) proposed the genus Streptosporangiumfor sporangiate actinomycetes that formed nonmotile sporangiosporeson abundant aerial hyphae. Initially, only one species, Streptosporangium roseum, was recognised. Additional taxa were added to the genus which now includes fourteen validly
described species (Table 3, page 6; Nonomura, 1989; Mertz and Yao, 1990).
Members of these taxa characteristically form aerial hyphae that carry, on either
5 Table 3 Validly describedspecies and subspeciesof the genusStreptosporangium
Taxon Authors Habitat Type Strain Streptosporangium Furumai et al. (1968) Soil, Mount Tonigawa, ATCC 25243 albidum Japan Streptosporangium Nonomuraand Ohara Soil, Japan DSM 43023 album (1960) Streptosporanglum Nonomuraand Ohara Soil, Japan ATCC 33327 amethystogenes (1960) Streptosporangium Mertz and Yao (1990) Soil, River Tana NRRL 18437 carneum Nairobi, Kenya Streptosporangium Williams and Sharples Beachsand, Freshfield, ATCC 29331 corrugatum (1976) Lancashire,U. K. Streptosporangium Sheareret al. (1983) Soil, Anaikota, Sri ATCC 31519 fragile Lanka Streptosporangium Schäfer(1969) Steppesoil, Turkey ATCC 25212 longisporum
Streptosporangium Nonomuraand Ohara Soil, Japan ATCC 27101 nondiastaticum (1969b) Streptosporangium Nonomuraand Ohara Soil, Japan ATCC 27100 pseudovulgare (1969b) Streptosporangium Couch (1955a) Vegetablegarden soil ATCC 12428 roseum Streptosporangium Kawamotoet al. (1975) Swampsoil, Japan ATCC 21807 violaceochromogenes Streptosporangium Nonomuraand Ohara Soil, Yotei, Hokkaido, ATCC 33328 viridialbum (1960) Japan Streptosporangium Nonomuraand Ohara Soil, Japan ATCC 27102 viridogriseum subsp. (1969b) koft ense Streptosporangium Okudaet al. (1966a) Soil, Japan ATCC 25242 viridogriseum subsp. viridogriseum Streptosporangium Nonomuraand Ohara Soil, paddyfield, Anjo, ATCC 33329 vulgare (1960) Aichi Prefecture,Japan ATCC, AmericanType Culture Collection, 12301Parklawn Drive, Rockville, Maryland, U. S.A.; DSM, DeutscheSamn-flung von Mikroorganismenund Zellkulturen, Mascher0derWeg IB, D- 38124 Braunschweig,Federal Republic of Germany;NRItL, Northem Researchand Development Division, United StatesDepartment of Agriculture, Peoria,Hfinois, U.S. A.
6 short or long sporangiophores,single or clustered spore vesicles which are commonly 7 to 20pm,but may be up to 40pm, in diameter. Spore vesicles contain coiled chains of arthrosporesthat are formed by septation of an unbranched,spiral hypha within each expandedsporangiophore sheath(Vobis and Kothe, 1985). Since sporeformation is not endogenous,the term "spore vesicle" has greater precision than the original term "sporangium" (Cross 1970; Sharples et al., 1974; Goodfellow, 1991). Studies on spore maturationhave shown that the sporesin both sporevesicles and sporechains are formed in essentially the sameway. In each case, sporesare differentiated by fragmentationof a hypha within a sheath,the latter either expandsto form the envelopeof the sporevesicle or remainsaround the sporechain (Lechevalieret al., 1966;Sharples et al., 1974;Vobis and Kothe, 1985;Goodfellow, 1991). Streptosporangiahave a wall chemotype III (M. P. Lechevalier and Lechevalier, 1970a), that is, they have meso-diaminopimehcacid in the wall peptidoglycanbut do not contain characteristicsugars other than madurose(3-0- methyl-D-galactose;Lechevalier and Gerber, 1970) which can be detected in whole-organismhydrolysates. The peptidoglycanis of the A17 type (Schleifer and Kandler, 1972). The organismsare rich in iso-, anteiso-, saturated,unsaturated, and methyl-branchedfatty acids (pattern 3C; Kroppenstedt,1985; Kudo et al., 1993; Stackebrandtet al., 1993;Whitham. et al., 1993),contain dihydrogenated and tetrahydrogenatedmenaquinones with nine isoprene units as predominant isoprenologues(Kroppenstedt, 1985; Kudo et al., 1993;Stackebrandt et al., 1993; Whitham et al., 1993), and have phospholipid patterns characterised by glucosamine-containing lipids together with phosphatidylethanolamine, diphosphatidylglycerol and phosphatidylinositol (phospholipid pattern IV; Lechevalier et al., 1977,1981; Kudo et al., 1993; Stackebrandtet al., 1993; Whitharn et al., 1993). The DNA base composition is between 69 and 71
7 mol % guanine (G) plus cytosine (C) Oonesand Bradley, 1964; Tsyganovet al., 1966;Yamaguchi, 1967; Farina andBradley, 1970;Stackebrandt et al., 1993). Streptosporangiumspecies can be distinguished on the basis of spore vesicle size, sporangiophorelength, spore shape, and aerial spore mass and substratemyceliurn pigmentation (Table 4, page9). They may also be subdivided according to the nature of their vesicular walls. At one extreme, the spore vesicular membraneof Streptosporangiumfragile is so thin that is cannot be detectedby light microscopy (Sheareret al., 1983);this may lead to difficulty in differentiating such organismsfrom Actinomaduraand Microtetraspora, as some strains may produce "pseudosporangia"covered by a slimy substance(Nonomura and Ohara, 1971b). In contrast,the sporevesicles of Streptosporangiumalbidum and Streptosporangiumviridogriseum have thick and strong walls that enclosea itsheathed" chain of spores (Nonomura and Ohara, 1969b). The genus Kibdelosporangium (Shearer et al., 1986) bears a close morphological resemblanceto thesestreptosporangia but hasa wall chemotypeIV, that is, strains containedmeso-diaminopimelic acid and the sugarsarabinose and galactose (M. P. Lechevalierand Lechevalier,1970a). The wall componentsof Streptosporangium albidus and Streptosporangiumviridogriseum strains need to be re-examinedto clarify the relationship of these organismsto the genus Kibdelosporangiumand other taxa assignedto the family Pseudonocardiaceae(Embley et al., 1988). Streptosporangiumcorrugatum produces characteristically small, club-shaped spore vesicles and thoseof the remaining speciesthin vesicularmembranes that are readily disruptedin water (1,echevalier et al., 1966a; Williams et al., 1973; Sharpleset al., 1974). Streptosporangiumstrains usually grow well between 25T and 30T thoughStreptosporangium nondiastaticum and Streptosporangiumpseudovulgare grow better at 42'C and 55'C, respectively (Nonomura and Ohara, 1969b).
8 Table 4 Characteristicsdifferentiating validly described speciesof the genus Streptosporangium *
14
13 Q
Colour of substmit myceftum Brown-black + Red or orange + + + + + Yellowish brown to brown + + + + + + + + + + + + + Colour of sporemug Greenishgmy + + + Pink + + + + + + + + + White + + + spore Vesicle size 1-5 Pm + 6-10 pm + + - + + + + + + + H 11-20 pm (+) - - + + + + 21-30 pm + + 31-50 pin + Sporxnglophore sin Short (10 pm) + + + + + + + + + + + + Long (50 pm) - + - + + + Sporeshape Spherical-oval + + + + + + + + + + + + + Rod + + Solublepigmezifta + + + B vitarnin required + + + + + + + Growth st:* 420C + + + + + 500C + (+) - Gelatin liquefiction + ND ND + + + d + + d lodinin production + d Nitrate reduction + + + + + + + + Starchhydrolysis + + + - + + + + + + + Utlysadon or- Adonitol + ND + + + + + + ND + - ND Arabinose + ND ND + + + + + + + ND + + + Galactose + ND - + + + - + Glycerol ND ND + + ND + + + Inositol ND + + (+) + + + + mamlitol + ND ND + + + - ND + + + (+) + + (+) + Rhamaose - ND + + + Tumnose + ND ND ND + + ND + + +
* Data taken from Nonomura (1989), Mertz and Yao (1990), Goodfellow (1991) and Whitharnet al.(1993). d, Symbols: +, positive reaction; (+), weak positive reaction; -, negative reaction; doubtful; ND, not determined. aOtherthan pale yellow-brown. "Streplosporangiumalbum subsp. thermophilus" (Manachini et al., 1965) is a thermophilic organism that was wrongly classified as it belongs to the genus Thermoactinomyces(Goodfellow and Cross, 1984). Most Streptosporangium strainsgrow well at pH 6.8 to 7.0. Differences in gelatin liquefaction, nitrate reduction, starch hydrolysis and the production of iodinin crystals have also been recommended for the identification of streptosporangia. However, little credence can be placed in the predictiveness of such properties given the small sample of strains and tests
examined. Nevertheless the status of most validly described species of
Streptosporangium was supported in the numerical phenetic survey of Whitham et
al. (1993) though it was evident that the Streptosporangium viridogriseum strains
clustered apart from the other streptosporangia.
Little is known about the metabolism and genetics of streptosporangia. Protoplasting and regeneration protocols have been developed for Streptosporangiumviridogriseum (Oh et al., 1980); plasmids have also been isolated from this organism (Fare et al., 1983) though attemptsto isolate phage have been unsuccessful (Prauser, 1984). Plasmid pSg V-1 from Streptosporanglumviridogriseum had an estimatedMr of 54 x 106whereas the pSg B-I plasmid was found to be phenotypicallycryptic. An unusualexpressed trait resemblingphage plaqueshas been associatedwith the Streptosporangium viridogriseumplasmid pSg V- 1. Nonornura and Ohara (1969a) demonstratedthat streptosporangiawere componentsof the actinomycetecommunity in soil. Previousworkers had only isolated these organismsinfrequently from soil, dung (Couch, 1955a) and leaf litter (Van Brummelen and Went, 1957; Potekhina,1965), but streptosporangial populations of 104 to 106 colony-forming units (cfu) per gram dry weight of samplewere reported for various Japanesesoils (Nonomuraand Ohara, 1969a;
10 Nonomura, 1984). Recently, Hayakawa et al. (1991) recovered 105
streptosporangial from vegetable field soil in
Japan by plating suspensions of heat pretreated air dried soil onto HV agar
supplemented with leucomycin and nalidixic. acid. The organisms were abundant
in Japanese soils rich in humus and with an acidic reaction (Nonomura and Hayakawa, 1988).
Streptosporangia have also been isolated from lake sediments (Willoughby,
1969a; Johnstonand Cross, 1976), beach sand (Williams and Sharpies,1976), pasture and woodland soils (Whitham et al., 1993), and one strain, "Streptosporangiumbovinum", was reported from infectedbovine hooves (Chaves Batista et al., 1963). "Streptosporangiumindianensis" Gupta, 1965,isolated from an Indian soil, was transferred to the genus Streptomycesas Streptomyces indidensis(Kudo andSeino, 1987)as it doesnot form true sporevesicles (SchAfer, 1969) and has morphological and chemical properties characteristic of streptomycetes(Kudo and Seino,1987; Whitham et al., 1993). It seemslikely that the original author mistook spore aggregates,resulting from autolysis of sporulating aerial hyphae, for spore vesicles. Similarly, strains labelled Streptosporangiumtype I from stream water (Willoughby, 1969b) probably belong to the genus Actinoplanes given their morphological properties and capacityto form motile spores(Goodfellow andCross, 1984). Couch (I 955a) classified Streptosporangium in the family "Actinosporangiaceae" together with sporangiate actinomycetes belonging to the genus Actinoplanes. The family 'Actinosporangiaceae" was subsequently renamed Actinoplanaceae (Couch, 1955b). In addition to Actinoplanes, the type genus, this taxon encompassed the genera Amorphosporangium, Ampullariella, D"tylosporangium, Kitasatoa, Pilimelia, Planobispora, Planomonospora,
Spirillospora and Streptosporangium (Couch and Bland, 1974). Members of all of
11 these genera were considered to form spore vesicles (sporangia). It was subsequently shown that Planobispora, Planomonospora, Spirillospora and Streptosporangiumformed a DNA homology group that was readily separated from a second aggregategroup that encompassedthe genera Actiwplanes, Ampullariella and Dactylosporangium (Farina and Bradley, 1970). The two groups were also separatedby chernotaxonomicmarkers. Organismsassigned to the first group containedmadurose and had a wall chernotypeIR whereasthose in the secondgroup had a wall chernotype11, that is, they containedmeso- and/or hydroxy diaminopimelicacid and glycine (Lechevalieret al., 1971). The genera Actiwplanes, Dactylosporangium,Micromonospora and Pilimelia are now known to have many properties in common and are classified in the family Micromonosporaceae(Krassilnikov, 1938; Goodfellow et al., 1990a). In the meantimethe genusKitasatoa has becomea synonymof the genusStreptomyces (Goodfellow et al., 1986) and the generaAmorphosporangium and Ampullariella have been reduced to synonyms of the genus Actinoplanes (Stackebrandtand Kroppenstedt,1987).
Goodfellow and Cross (1984) assigned the oligosporic genera Actinomadura (H.A. Lechevalier and Lechevalier, 1970), Microbispora (Nonomuraand Ohara, 1957) and Microtetraspora (Thiemann et al., 1968) and the sporangiate genera Planobispora (Thiemann and Beretta, 1968), Planomonospora(Thiemann et al., 1967), Spirillospora (Couch, 1963) and Streptosporangium(Couch, 1955a) to an aggregategroup, the maduromycetes. Apart from Spirillospora, thesetaxa form a recognisablesuprageneric group based on 16S ribosomal RNA cataloguingand sequencingdata (Stackebrandt,1986). The genusSpirillospora is currentlyconsidered to be a genusin searchof a family (Stackebrandtet al., 1981; Goodfellow, 1986,1989a).
12 The taxonomic status of the genera assignedto the maduromyceteswas formalised with the proposal that Streptosporangiumbe recognisedas the type genusof a new supragenerictaxon, the family Streptosporangiaceae(Goodfellow et al., 1990a). In addition to the type genus, this family was introduced to accommodate the genera Microbispora, Microtetraspora (including the Actinontadura pusilla group; Kroppenstedt et al., 1990), Planobispora, Planomonosporaand tentatively Spirillospora. Recently, a seventhmember, Herbidospora, has been added (Kudo et al., 1993). Members of the family Streptosporangiaceae,which may also include the genus Planotetraspora (Runmao et al., 1993), have a pattern of chemical and molecular taxonomic properties that distinguishes them from all other actinomycete families (Goodfellow, 1989a;Kudo et al., 1993;Stackebrandt et al., 1993). Streptosporangiaceaestrains have many chemicalfeatures in commonbut form a morphologically diverse group (Table 5, pages 14 to 15). Nevertheless, strains that bear spore vesicles (Planobispora, Planomonospora and Streptosporangium) are closely related to organisms that form paired (Microbispora) or longer spore chains (Herbidosporaand Microtetraspora) but have little in common with sporangiate actinomycetes (Actinoplanes, Dactylosporangiumand Pilimelia) classified in the family Micromonosporaceae (Goodfellow et al., 1988,1990a;Stackebrandt et al., 1981,1983). It is now apparentthat the genusStreptosporangium is heterogeneousgiven scanningelectron microscopy studies on the morphology of spores and spore vesicles (Nonomura,1989), analysisof the electrophoreticmobility of ribosomal protein AT-L30 (Ochi and Miyadoh, 1992) and analyses of 16S rDNA (Kemmeringet al., 1993) and 5S rRNA (Kudo et al., 1993). Stackebrandtet al. (1993) found that while representativestreptosporangia had many chemical properties in common they fell into two groups on the basis of chemical
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m :5 , 9 Z06 9 O ý5 C .d u e g ä "0 _A 00 N. -: 00 öE lý 9 i2 .A r. m 4) r. 0 00 .S oý ce :. *1In GA "B 3 21U 0 Meu * r- 2 = - Z ä Q CA "-d JS 42. ýi m .25 bA O a U öýA O ýi U .0 -EI QO. Mt W' ý ;.r, «S U H m JD i .3& cý -ti , u 15 differences. Most species, including Streptosporangiumcorrugatum and Streptosporangiumroseum, had a phospholipidpattern type IV and predominant menaquinonesof the MK-9 (1-12)and MK-9 (II, VIR-H4), MK-9 and/orMK-9(H6) type. The secondgroup, which containedStreptosporangium albidum and the subspeciesof Streptosporangiumviridogriseum, were characterisedby principal isoprenoid quinones of the MK-9 (11,111-H4)type and phospholipidstypical of pattern11. Theseresults are in excellentagreement with corresponding16S rRNA sequencingdata (Kemmering et al., 1993). Stackebrandtand his co-workers proposed that Streptosporangiumalbidum, Streptosporangiumviridogriseum subspecies kqfuense and Streptosporangium viridogriseum subspecies viridogriseum be assignedto a new genus, Kutzneria, as Kutzneria albida, Kutzneria viridogrisea and Kutzneria koAensis. There is a wealth of evidenceto support the classification of the genus Kutzneria in the family Pseudonocardiaceae(Ochi and Miyadoh, 1992;Kemmering et al., 1993;Kudo et al., 1993;Stackebrandt et al., 1993)though additionalwork is neededto determine the relationshipof this taxon with establishedmembers of the family. The proposal for the genus Kutzneria leaves Streptosporangiumas a homogeneousgenus encompassingtwelve validly describedspecies. Improved phenotypictests are neededboth for the circumscriptionof new and established speciesof Streptosporangiumand for the rapid identification of streptosporangia if the full potential of these organismsas sourcesof commercially significant naturalproducts is to be realised.
16 C. NUMERICAL SYSTEMATICS 1. CLASSIFICATION
It hasbeen repeatedlyshown in actinomycetesystematics that overreliance on morphological properties can lead to the circumscription of markedly heterogeneoustaxa (Bousfield and Goodfellow, 1976; Williams et al., 1983a; Goodfellow and Cross, 1984; Kroppenstedt et al., 1990). The family Actinoplanaceae(Couch, 1955b; Couch and Bland, 1974) is a casein point as organismssubsequently shown to be unrelatedwere assignedto this taxon solely on the basisof a capacity to form sporevesicles. Similarly, strainsshown to be only distantly related were classifiedin the genusStreptosporangium only on the basisof a few chemical and morphologicalproperties (Ochi and Miyadoh, 1992; Kernmerling et al., 1993;Stackebrandt et al., 1993). Taxonomiesbased on single characteristics,or a series of single characteristics,are termed monothetic classifications (Sneath, 1962). These artificial classifications are notoriously unreliableas they havea low informationcontent and cannot readily accommodate strain variationdue to mutationor test error (Goodfellowand O'Donnell, 1993). It can, for instance,be difficult to distinguish spore vesicles from aggregatesof sporesin someactinomycetes (Nonomura and Ohara, 197 1 b; Nonomura,1989). The structural weaknessesof monotheticclassifications led somebacterial systematists to believe that stable taxonomies could only be achieved when many bacteria were examined for a large balanced set of properties. Such numerical taxonomies have a high information content and are often described as general purpose classifications sensu Gilmour (1937) since they can be of value to many different microbiologists (Goodfellow and O'Donnell, 1993). Sneath (1957a) noted that scientific classification was based upon the assumption that there is a natural order to the microbial world that can be discovered by careful investigation.
17 A reliable and relatively quick way of establishingcentres of variation amongst bacteria is to examine many strains for a large number of equally weighted characters. This is the basis of the numerical taxonomic method introducedto bacteriology by P.H. A. Sneath(1957a, b) and subsequentlywidely applied (Sneathand Sokal, 1973; Goodfellow and Dickinson, 1985; Macdonell and Colwell, 1985; Sackinand Jones,1993). The theoreticalbasis of numerical taxonomy is well documented(Sneath, 1971,1972; Sneath and Sokal, 1973; Goodfellow, 1977; Sneath, 1978a,b; Sackin and Jones, 1993) and will only be briefly describedhere. An outline of the operationalprocedure involved is given in Figure 1, page 19. Classificationsderived from an examinationof a large numberof organisms andmany charactersare polythetic as they havehigh information contentsand are basedon a completeset of recordedcharacters not on the presenceor absenceof seriesof singlecharacters. Polythetic classifications can accommodatea degreeof strain variation and are objective in the sensethat they are not sensitiveto the addition of more strainsor characters. The entities to be classified, such as strains,species or genera,are referred to collectively as operationaltaxonomic units (OTU's). The latter should include type strains, well studied referencestrains and duplicated cultures to provide a check on test error. In practice,numerical phenetic studies should be basedon at least sixty but preferablymore strains(Sneath and Sokal, 1973;Sackin and Jones, 1993).It is importantto selecttests that yield charactersthat aregenetically stable and not sensitive to experimental or observationaluncertainties (O'Brien and Colwell, 1987). The usual practice is to choose a set of biochemical, cultural, morphological and physiological charactersto representthe entire phenome,that is, the genomeand phenotype(Sneath, 1978a, b). It has beenrecommended that
18 Figure 1 Operational numerical taxonomic procedure
SELECTION OF OTU's t individuals I COLLECTION OF DATA n characterson t individuals I CODING AND/OR SCALING OF DATA
RAW DATA MATRIX I ESTIMATION OF TEST ERROR I FINAL DATA MATRIX 14 N ORDINATION CLUSTERING (non-hierarchic) (hierarchic) I I REPRESENTATION OF TAXONOMIC REPRESENTATIONOF TAXONOMIC STRUCTURE STRUCTURE 2 or 3 dimensionaldiagrams or models dendrogram,shaded diagram 4. I EVALUATION EVALUATION clusteroverlap, cophenetic correlation, intra- and inter- groupsimilarity I I ANALYSIS OF RELATIONSHIPS DEFINITION OF TAXA AND LINK II N le _N MULTI- DISCRIMINANT TAXON SELECTION OF IDENTIFICATION DIMENSIONAL ANALYSES RADIUS REPRESENTATIVE SCHEMES ANALYSES MODEL STRAINS FOR FURTHER STUDY lic N HYPOTHETICAL CLUSTER MEDIAN CENTROTYPE ORGANISM
19 between100 and 200 charactersshould be studiedwith a lower limit of about60 (Sackinand Jones, 1993). The similarities or dissimilajitiesbetween test strainscan be estimatedonce the final data matrix has been obtained.Many different resemblancecoefficients have beenused but only a few havefound favour in microbial taxonomy(Sneath, 1972,1978a; Austin and Colwell, 1977;Sackin and Jones,1993). The two most commonly usedresemblance coefficients are the simplematching (Ssm; Sokal and Michener, 1958) and the Jaccard(Sj; Jaccard,1908) coefficients which measure similarity between OTU's basedon binary data. The Ssm is used to calculate similarities based on both positive and negativematches whereaswith the Sj coefficient negativematches are ignored. Thus, taking two OTU's, x and y, data for all the characterstates can be summarisedas shownbelow:
n=a+b+c+d
Thus, the Ssmcoefficient can be defined as Sxy = (a + d) / n, that is, the ratio of the total numberof matchesto the total numberof charactersincluded in the data is, matrix. Similarly, the Sj coefficient can be defined as Sxy =a/ (n - d), that the ratio of the total numberof positive matchesto the total numberof characters. A third and particularly useful coefficient usedto estimaterelationships is the patterndifference coefficient (Dp; Sneath,1968). This measuresdissimilarity
20 betweenstrains and omits componentsof dissimilarity due to differencesin vigour. Sneath (1968) divided the total difference between two OTU's into two componentstermed "vigour" and "pattern" differences. The pattern difference coefficient correspondsto the proportion of the total number of differences betweenOTUs (DT) minus the number of differencesdue to vigour (Dv). These valuesare defined by the following equations: c+b c-b DT_ Dv _ nn
The Dp coefficient is expressedas DP2= DT2- Dv 2= 4bc:/n2 in order that the termsare additive andDp is a positive value. Dp values can be convertedto a measureof similarity, Sp, by using the formula:
Sp = (I - Dp) x 100 Conversion of Dp to similarity values (Sp) facilitates comparisons with classificationsbased upon the Ssmand Sj coefficients. The patterncoefficient has been effectively used in studies on Actinomadura (Athalye et al., 1985), Actinoplanes (Goodfellow et al., 1990a),Mycobacterium (Runyon et al., 1974) and Streptosporangium(Whitham et al., 1993) where test strains have included both slow andfast growing organisms. Operationaltaxonomic units are assignedto groups (clusters)on the basis of sharedsimilarities. Several techniquesare available for clustering organisms into groups (Austin and Colwell, 1977; Sackin and Jones, 1993). The single linkage methodis the simplestclustering algorithm as the similarity betweentwo clustersis defined as that of the most similar pair, only one pair per cluster being join considered(Sneath, 1957b; Sackin and Jones,1993). Thus, two clustersmay merely becausetwo constituentOTU's sharea higher overall similarity with one linkage another than with any of the remaining test strains. With the single
21 algorithm, groups of OTU's tend to be linked at relatively low similarities by "chains"of OTU's lying betweenthem. Consequently,this methodfails to resolve relatively distinct groupsshould "intermediate"OTU's be present. The most commonly usedalgorithm is the averagelinkage method, the most popular variant of which is the unweightedpair group method with arithmetic averages(LJPGMA; Sokal and Michener, 1958). This algorithm gives equal weight to all of the clusters formed regardlessof the numbers of OTU's they contain (Sneath,1978a). The similarity betweentwo clusters is defined as the averageof the similarities between all pairs of OTU's from each cluster. In general,clusters formed using averagelinkage algorithmsare more compactthan thosebased on the single linkagetechnique.
Hierarchical clustering techniquesimpose structureson data that may or may not be true representationsof the original relationshipsbetween OTU's as implied by their similarity values. The suitability of test data for hierarchical clusteringcan be assessedby determiningthe copheneticcorrelation coefficient (r; Sokal and Rohlf, 1962;Sneath, 1978a; Sackin and Jones,1993). The coPhenetic correlationvalue betweenOTU's is the differencebetween their actualsimilarities, calculatedusing any of the various similarity or distancecoefficients, and their observedsimilarities as seenon a hierarchicaldendrogram. In practice,complete agreementbetween dendrograms and resemblancematrices cannot be achieved given the taxonomic distortion introduced when representingmultidimensional datain two dimensionalform. Typical copheneticcorrelation values vary from 0.6 to 0.95 (Jonesand Sackin, 1980; Sackinand Jones,1993). Scoresabove 0.85 are consideredgood whereasthose below 0.7 imply that only limited confidencecan be given to relationshipsdepicted in dendrograms.Farris (1969) demonstratedthat of all possible clustering algorithms the UPGMA technique gave the highest
22 cophenetic:correlation values. This observationhelps to explain why the UPGMA algorithm is widely employedin numericalphenetic studies. Once OTUs have been assignedto individual clustersseveral calculations can be performedto yield taxonomicallyuseful information. The compactnessof clustersand the degreeof separationbetween them can be determinedfrom intra- and inter- cluster similarities,respectively. In addition the 95% taxonomicradius of each cluster can be calculated. The latter representsthe distance from the centroid of eachcluster within which 95% of the membersof the clusterwould be expectedto fall assuminga Gaussiandistribution of strains. A high intra-cluster similarity and low 95% taxonomic radius is indicative of a tight, homogeneous cluster andhigh inter-clustersimilarity denotespoor separationof clusters. Numerical taxonomies,irrespective of the statisticsused, are only as good as the data upon which they are based. Test error in polythetic taxonomies, althoughless seriousthan in monotheticclassifications, serves to lower observed similarities between OTU's and if high erodes the taxonomic structure of the classification (Sneathand Johnson, 1972; Jones and Sackin, 1980; Sackin and Jones, 1993). It is now common practice in numerical taxonomic studies to determinetest error by examining duplicated cultures under code and using the averageprobability of error in an analysisof test variance(Sneath and Johnson, 1972). The assessmentof test error is especiallyimportant if datafrom more than one operator is used to generatea numerical classification. In general, inter- operator generationtends to be higher, with values of 8% to 15% (Sneathand Johnson, 1972; Sneath, 1974), than intra-operatortest error where values are usually below 4% (Sneath,1974; Whitham et al., 1993). Taxonomic structure can also be determinedusing ordination techniques. Principal componentsand principal coordinates analyses are two of several ordination techniquesavailable for this purpose (Alderson, 1985). Principal
23 componentanalysis (PCA) was first proposedfor use with continuousdata but the technique can also be applied to binary data (Gower, 1966). Each OTU is representedas a point in multidimensionalspace where the numberof dimensions is equal to the numberof variablesexamined. The points are then projectedonto a line through spacethe direction of which is calculatedto representthe maximum variation betweenOTU's. This line is referredto as the first principal component. A secondline is then plotted to accountfor as much of the remainingvariation as possible. Additional principal componentsare determinedin the same way. Scores for the first two or three principal componentsare often plotted on orthogonalaxes and the distribution of OTU's in the 2 or 3 dimensionsmay reveal valuable information on the taxonomic structureof the OTU's (Dunn and Everitt, 1982). Principal componentanalysis is only suitablewhen the distancesbetween OTU's in the original multidimensionaldata are Euclidean. An alternativeordination techniqueknown as multidimensionalscaling is concernedwith the distributionof pointsin Euclideanspace. This methodreflects the relationshipsbetween OTU's whether Euclideanor not and is achievedusing principal coordinatesanalysis. The resultsobtained are the sameas for PCA when the observed distances are Euclidean. The products of principal coordinates analysis can also be representedin the form of two or three-dimensionalplots. The method is considered satisfactory if the variation in the plotted principal coordinatesis at least 40% of the original total variation (Sneathand Sokal, 1973; Alderson, 1985). Ordination techniques have been used successfully to represent relationshipsbetween large groupsbut suchanalyses can distort affinities between close neighbours(Alderson, 1985). In contrast, hierarchicalclustering methods do are reliable when depictingrelationships between closely relatedorganisms but
24 not always satisfactorilyrepresent affinities betweenlarge heterogeneousgroups (Sneathand Sokal, 1973;Sneath, 1978a). Numerical classfications need to be interpreted with care as similarity valuesbetween strains can be distortedby factorssuch as test and samplingerror, the statistics used, and failure to allow for differences in growth rates and metabolic activity (Sneathand Johnson, 1972; Goodfellow et al., 1979,1990a). Most confidencecan usually be placed in the major centresof variationdefined in numericalanalyses, it is the relationshipsof strainslying towardsthe peripheryof clusters that are not always clear (Goodfellow and O'Donnell, 1993). It is, therefore, important to evaluate numerical taxonomiesin the context of other taxonomic methodssuch as chernotaxonomicand molecular systematics. It can also be important to identify OTU's that are most typical of each cluster and thereforesuitable for representingclusters in additional studies. The OTU which is the most typical of a phenonand lies closestto the centroid of the clusteris the centrotype,this organismshows the highestaverage similarity of all the OTU's in the cluster. Centrotype, type and additional representativestrains should be included where appropriate in analyses designed to evaluate numerical taxonomies. In most numericaltaxonomic surveys the majority of test strainshave been assigned to a small number of major clusters that are often equated with taxospecies (Goodfellow and Dickinson, 1985; Goodfellow et al., 1990a; Goodfellow and O'Donnell, 1993). Single memberedclusters that include only a few strains tend to be overlooked. These minor or single memberedclusters, however, may representnuclei of novel groups, genetically unstable strains or organismsof establishedtaxa lacking plasmids (Goodfellow et al., 1987a), and needto be given more considerationwhen interpretingnumerical taxonomies.
25 2. IDENTWICATION
Numerical taxonomicsurveys provide data on the test reactionsof strains within each taxon circumscribed in the classification. Results are usually expressedas the percentageof the strainsin eachcluster that give a positive result for the charactersused to generatethe taxonomy. Diagnosticcharacters can then be selectedfrom the percentagepositive frequency table, that is, by a posteriori weighting, andused to generatedichotomous keys, diagnostic tables and computer identification matrices. Computer-assisted identification is prefeffed to conventionalkeys and tables as it is relatively quick and simple (Lapageet al., 1970;Hill, 1974;Priest and Williams, 1993)with chancesof misidentificationdue to effoneousresults greatly reduced (Sneath, 1974a). Surprisingly few numerical classifications have been supported by probabilistic identification schemes. Theoretically sound,workable schemesare available for the identification of slow-growing mycobacteria(Wayne et al., 1980),neutrophilic streptomycetes(Williams et al., 1983b;Langharn et al., 1989), streptosporangia(Whitham, 1988), streptoverticillia (Locci et al., 1986), vibrios (Dawson and Sneath, 1985; Bryant et al., 1986), aerobic, endospore-forming bacilli (Priest and Alexander, 1988;Alexander and Priest, 1990) and for bacteria isolated from Alaskan outer continental shelf regions (Davis et al., 1983). Probabilisticidentification schemesusing lesscomprehensive data than is provided by numerical phenetic studiesare also available for the identification of Gram- negative aerobic rods (Bascombet al., 1973), anaerobic bacteria (Kelley and Kellog, 1978) and aerobic endospore-formingbacilli (Willemse-Collinet et al., 1980;Priest, 1989). The first stagein the generationof a frequencymatrix is the selectionof a smallnumber of diagnostictests that are sufficient to differentiateall of the taxa in the numericaltaxonomic database. Programsavailable for this purposeinclude
26 CHARSEP (Sneath,1979b) and DIACHAR (Sneath,1980a). CHARSEPis used to calculate valuesfor five separationindices, the most useful of which, the VSP index, gives high scoresfor presumptivediagnostic tests. The DIACHAR program is used to calculate diagnostic scoresfor each of the charactersincluded in the databasewith tests then being ranked in order of descending score. The DIACHAR programhas been included in the TAXON programwhich was written in order to facilitate analysis of numerical taxonomic data (Ward, unpublished data, Appendix A). A soundfrequency matrix containssufficient information to define eachtaxon by severaldiagnostic properties. The importance of evaluating identification matrices has been stressed (Sneath and Sokal, 1973; Sneath, 1978b; Williams et al., 1985b; Priest and Williams, 1993). The computerprogram OVERMAT (Sneath,1980c) can be used to determine the degree of overlap between taxa representedin identification matrices. Unknown strains cannot be unambiguouslyidentified when there is considerable overlap between clusters. OVERMAT determines both the disjunction index (W) for eachpair of taxa (Vg) from the percentagepositive data. Any pair of taxa,which overlap by more than a chosencut-off value (Vo), usually I%, will have a value for W which is less than that for the cut-off point. Further testsselected using the DIACHAR programare then addedto the matrix in order to distinguishbetween taxa that overlapby more than the chosencut-off value. The computer program MOSTTYP (Sneath, 1980b) is used to calculate identification scores for the most typical organisms,that is, the hypothetical median organism (HMO), in each constituent cluster. When identification matricesare soundthe HMO of eachcluster will be assignedto its taxon with very high identification scores. The calculationof identification scoresfor HMO's can be achieved using the procedureCOMPARE in the TAXON program (Ward, unpublished data; Appendix A). Probabilistic identification matrices can be
27 further assessedby treating strains included in the original numericaltaxonomic study as known organismsthen calculating identification scoresusing the original classificationdata obtainedfor the diagnostictests. The MATIDEN program (Sneath,1979a) is often used to obtain the best identification scores for known or unknown strains against defined groups in frequency matrices (Williams et al., 1983b; Dawson and Sneath,1985; Locci et al., 1986;Priest and Alexander, 1988;Alexander and Priest, 1990). Three of the five identification coefficientsincluded in this programare commonly used:
(i) Willcox probability (Willcox et aL, 1973). This is the likelihood of an unknown (u) againsttaxon J divided by the sum of the likelihood's of u against all q taxa. Scoresapproaching 1.0 denotea good fit betweenthe unknownto a group in the frequencymatrix.
Willcox probability =Luil I",Lui j=j
whereLu, = rj Cui+ Pij -I i=1 for the m charactersconsidered, where Ui is the characteri and Pij is the proportionof positivesfor characteri of taxon J (Sneath,1974).
(ii) Taxonondc distance (d): This expressesthe distance of an unknown (u) from the centroidof the group with which it is being comparedhence low scores indicaterelatedness.
m d= [ý (Ur - Pu)2 / m]
28 The taxonomicdistance should not be significantly morethan the 95% taxonomic radius of the cluster(Williams et A, 1983b).
(iii) The standard error of taxonomic distance (S.E. [d]). This coefficient is basedon the assumptionthat the organismsare in hypersphericalnormal clusters. An acceptablescore is less than about2.0 to 3.0, and abouthalf of the members of a taxon will havenegative scores, that is, they are closer to the centroid than average. The criteria chosenfor a successfulidentification are somewhatarbitrary (Williams et al., 1985a;Priest and Williams, 1993). Thoseadopted by Williams et aL (1983b)for theidentification of streptomyceteswere:
0) A Willcox probability greater than 0.850 with low scoresfor taxonomic distanceand its standarderror. (H) All first scoressignificantly better than thosefor the next best two alternative clusters. (M) A small numberof charactersof the unknownlisted as beingatypical of those of the clusterin which it was placed.
All of thesecriteria were derivedfrom the applicationof the MATIDEN program (Sneath,1979a). More stringentWillcox probabilities have been recommended for the identification of unknown Gram-negativebacteria (Lapage et al., 1973), corynebacteria(Hill et al., 1978) and slow growing mycobacteria(Wayne et al., 1980). The algorithms used to calculate Willcox probability and taxonomic distance values are part of the MENTIFY procedurein the TAXON program (Ward, unpublished data; Appendix A). Two additional values, the 95%
29 taxonomic radius of clusters and the Gaussiandistance probability coefficient, can also be used for the identification of unknown strains to taxa included in probabilistic identification matrices. The Gaussiandistance probability is a measureof the percentageprobability of membersof the cluster to which the unknown strain is being comparedlying further away from the centroid of that cluster than the unknown strain. The derivation of these two additional coefficientsis given in Figure 2, page3 1. The two additional identification values offer some advantagesover the existing coefficientsas the homogeneityand compactness of eachcluster within a frequencymatrix is expressedas a numericalvalue. A high taxonomicdistance may be acceptableif the cluster to which an unknown strain is identified is heterogeneousor containsa relatively smallnumber of strainsand therefore has a large 95% taxonomic radius. In contrast, compact clusters, that is, those containing a large number of strains with a very high overall similarity will require a low taxonomicdistance for good identification. The definition of an acceptableidentification should therefore include a comparisonbetween the taxonomic distanceof an unknown strain from the cluster centroid and the 95% taxonomicradius of the cluster. Probabilisticidentification proceduresallow a probability to be fixed to an identification so that a measureof confidencecan be placedin the identification of unknownorganisms. In general,such identifications are not normally affectedby an occasionalerroneous result. Numericalidentification procedurescan alsocope with missing information, that is, adequateidentifications can be obtainedwhen only a proportion of tests have been performed. Automated proceduresfor microbial identification and commercial identification kits are based on the concepts underpinning numerical identification as they require an unknown
30 Figure 2 Derivationof the 95% cluster radiusand Gaussian distance probability
0 tailed test TD
C, cluster centroid; f, number of strains; TR, 95% taxonomicradius; TD, taxonomic distancefrom cluster centroid; TDu, taxonomic distanceof u from cluster centroidand u, unknown strain.
The figure shows a single cluster representedas a number of strains (. ) whose phenetic position in multidimensional spaceis representedin two dimensions. The lower part of the figure shows how the 95% taxonomic radius of the cluster was found using a one-tailedtest-, a Gaussiandistribution of strains is assumed.
The position of the unknownstrain (u) is shown. The areaunder the Gaussiandistribution curvehaving a greater taxonomicdistance away from the cluster centroid is hatched. This value, the Gaussiandistance probability, is expressedas a percentageof the total areaunder the curve to give the percentprobability of a strain lying further away from the cluster centroid than u.
31 organism to be comparedagainst information in databases(Priest and Williams, 1993).
3. COMPUTER-ASSISTED CLASSIFICATION AND IDENTIFICATION OF STREPTOSPORANGIUMAND RELATED TAXA a. Classiflcation Numerical taxonomic procedures have been successfully used in the reclassificationof several actinomycetetaxa, notably Actinomadura (Athalye et al., 1981),Actinomyces (Schofield and Schaal,1981), Actinoplanes (Goodfellow et al., 1990a), Corynebacterium(Jones, 1975), Gordona (Goodfellow et al., 1991), Mycobacterium(Goodfellow and Wayne, 1982), Nocardia (Goodfellow, 1971; Orchard and Goodfellow, 1980),Rhodococcus (Goodfellow et al., 1990b), Streptomyces(Williams et al., 1983a;KýUnpfer et al., 1991), Thermomonospora (McCarthy and Cross, 1984) and Tsukamurella(Goodfellow et al., 1991). In contrast, relatively few numerical taxonomic surveys have included, let alone focusedon streptosporangiaand relatedstrains. Members of the fan-dly Streptosporangiaceaehave featured, albeit peripherally, in severalbroadly basednumerical phenetic studies (Silvestri et al., 1962;Jones and Bradley, 1964;Goodfellow and Pirouz, 1982) but little credence can be given to numericaltaxonomic relationships based on single representatives of specifictaxa (Wilkinson andJones, 1977; Whitharn et al., 1993). This is borne out by the fact that Streptosporangiumstrains included in broadly basedstudies have been reported to have relatively high overall similarities with organismsas taxonomically diverse as Nocardia (Silvestri et al., 1962), Actinoplanes and Micromonospora (Jones and Bradley, 1964) and Thermomonosporaand Spirillospora (Goodfellowand Pirouz, 1982).
32 It has already been pointed out that streptosporangiaand related actinomycetes were the subject of an extensive numerical phenetic survey (Whitham, 1988; Whitham et al., 1993). One hundred and twenty-two streptosporangia,including isolatesfrom naturalhabitats, and 37 marker strainsof Microbispora, Microtetraspora, Planobispora, Planomonospora and Streptosporangiumwere examined for 199 unit characters. The data were examined using the pattern, simple matching and Jaccard coefficients and clustering achieved with average (UPGMA), complete and single linkage clusteringalgorithms. Two reduceddata setswere also examinedusing the same proximity coefficients and the UPGMA clustering algorithm. Good agreement was obtainedbetween the classificationsbased on the combineddata set and with the latter minus the antibiotic sensitivity data. Cluster composition was not markedly affected by the statistics used or by test error, which was low at 2.4%.
The product of the Dp, UPGMA analysis on the whole data set was considered in
detail and the five aggregate groups defined were equated with the genera Microbispora, Microtetraspora, Planobispora and Planomonospora, and
Streptosporangium. The streptosporangia were recovered in five major, seven minor and twenty single membered clusters. Ten of the multimembered clusters contained putatively novel environmental isolates.
b. Identification
Identificationof actinomycetescan be consideredas a two-foldprocess (Goodfellow, 1986,1989a). Reliable criteria are neededto assign unknown organismsto family and generic rank prior to the use of diagnostic tests for identification to specieslevel and below. Identification to the genus level and above can usually be achievedby using a combination of morphological and chemicalproperties (Lechevalier, 1989), but few reliable andwell testedschemes
33 are available for the separation of species and biotypes. Most of the recommendedprocedures tend to be labour intensiveand henceare of little value for ecologicalstudies that involve many strains. Membersof the family Streptosporangiaceaecan be distinguishedfrom all other actinomycetesusing a combinationof chemicaland morphologicalfeatures. Unidimensional thin-layer chromatographic analysis of whole-organism hydrolysatesis used to determinewhether an organismcontains diaminopimelic acid and,if so, whetherthis componentis in the IL or meso-form (Lechevalierand Lechevalier, 1980; Kroppenstedt,1985). The presenceof meso-diaminopimelic acid and madurosewith the absenceof 46-Y sugarsserves to separate Streptosporangiaceaestrains from those of Actinoplanes and related genera, Nocardia and related genera,Pseudonocardia and related genera,Nocardiopsis and Thermomonospora,but not from the generaDermatophilus and Frankia. The latter can readily be distinguishedfrom Streptosporangiumand allied taxa on morphologicalgrounds. In an extension of the survey outlined above a frequency matrix was generatedfor the identification of unknownStreptosporangium strains. Twenty- six characters,selected using the DIACHAR program(Sneath, 1980a), served to distinguish between twelve multimemberednumerically circumscribedclusters encompassingstreptosporangia. Application of the OVERMAT (Sneath,1980c) and MOSTrYP (Sneath, 1980b) programs showed the frequency matrix to be theoreticallysound.
34 D. MOLECULAR SYSTEMATICS
The bacterialgenome is a rich sourceof taxonomicdata (Stackebrandtand Goodfellow, 1991; Stackebrandtand Liesack, 1993; Palleroni, 1993). DNA sequencingof large sectionsof the genomeshould eventually provide dataof great value for bacterial systematicsbut large-scalesequencing studies are cuffently time-consumingand expensive.However, encouraging results have been obtained from comparativesequence analyses of genesencoding elongation factors EF-Tu and P-subunitsof F, F0 type ATP synthases(Amarm et al., 1988;Schleifer and Ludwig, 1989;Ludwig et al., 1993). One of the major limitations of taxonomic methods such as chernotaxonomyis that they are sensitiveto changesin the growth regime of the test organisms(ODonnell, 1988a,b; Suzukiet al., 1993). Thus,when comparing bacteria for chemical markers, such as the discontinuouscontribution of fatty acids, it is important that any variation observedis an expressionof genetic differencesand not a result of the differential effects of the growth conditions. This problem can be containedby growing cultures under identical conditions and, in somecases, to the samestage of the growth cycle (Saddleret al., 1986), but this approachis difficult, indeedsometimes impossible, when physiologically diverse organisms are being compared. In sharp contrast, the chemical compositionof chromosomalDNA and RNA is not affectedby growth conditions though the amountsof these macromoleculesmay fluctuate with growth rate. Nucleic acidsare, therefore,the only macromoleculesthat can be usedto compare andcontrast very diversemicroorganisms (Stackebrandt and Goodfellow,1991).
The bacterialchromosome can be analysedat two levels:
35 0) The grosscomposition of the four nucleotidebases in DNA can be estimated. The guanine(G) plus cytosine(C) contentin bacterialDNA variesfrom 24 to 76 mol% (Stackebrandtand Liesack, 1993). (H) Sequencerelatedness between DNA macromoleculesfrom two bacteriacan be estimatedby determining the extent of renaturationof DNA moleculesfrom the two strainsin DNA "reassociation"or relatednessexperiments (Johnson, 1985b, 1991;Stackebrandt and Liesack, 1993).
The % G+C content of DNA has been determinedfor membersof many bacterialtaxa using well describedmethods (Johnson, 1985a; Owen and Pitcher, 1985; Tamaoka,1993). Indeed,the analysisof the meannucleotide composition of DNA (De Ley, 1970;Johnson, 1985 a, b; Tamaoka,1993) can be consideredto be an integral part of the minimal descriptionof bacterialtaxa (Uvy-Fr6bault and Portaels, 1992). It is, however, important that the results of % G+C determinationsbe interpretedin the light of other taxonomic data as unrelated organismscan have identical basecompositions. Generally, strains with DNA basecompositions that differ by more than 5% mol G+C shouldnot be classified in the samespecies and thosethat differ by more than 10%should not be assigned to the same genus (De Ley, 1970; Owen and Pitcher, 1985; Goodfellow and O'Donnell, 1993). Conversely, two organisms that have DNA with widely different basecompositions will only be distantly related. In the first instance, DNA basecompositions of representativesof numerically circumscribedclusters provide a way of assessingtheir homogeneity. Assessmentof basesequence similarity betweenDNA from two organisms may be readily achieved by DNA reassociation experimentswhere DNA is renderedinto single strandsby alkaline or thermaldenaturation and subsequently allowed to reanneal in the presenceof a second denaturatedDNA molecule
36 (Johnson,1985b, 1991). If the nucleotidesequences of the two DNA samplesare homologous,hybrid duplexeswill be formed by basepairing. In contrast,duplex formation will be negligible if there are few sequencesin common. The amount of molecular hybrid formed and its thermal stability provides a measure of homology. Heterologous duplexes are less stable than their homologous counterpartsdue to imperfectbase pair matching. It hasbeen estimated that if I% of the bases are unpaired within a heteroduplex then its thermal melting temperature(Tm value) is loweredby 1 to 1.5% (Britten andKohne, 1966). It has been recommendedthat genornicspecies should encompassstrains with approximately70% or more DNA: DNA relatednesswith a differenceof 50C or less in thermal stability (A Tm; Wayne et al., 1987). Values from 30 to 70% reflect a moderate degree of relationship while values become increasingly unreliable once they fall below the 30% level. DNA relatednessexperiments, therefore,provide a quantitativeestimate of DNA sequencerelatedness between organismsand have becomethe gold standardfor the recognition of bacterial species(Goodfellow and O'Donnell, 1993). Severaldetailed protocols are recommendedfor the determinationof DNA relatedness(Owen and Pitcher, 1985; Johnson, 1985b, 1991; Stackebrandtand Liesack, 1993). In all casesit is important that DNA reassociationassays be carefully standardisedif reproducibleresults are to be obtainedsince the extent and specificity of reassociationis influenced by external conditions and the physical state of the DNA. Given careful attention to these parametersDNA reassociationstudies are usually accurateand repeatablewith congruentresults beingobtained. DNA reassociationtechniques have limitations. In particular, they do not lend themselvesto rapid, automatedprocedures. Further, due to the labour intensivenature of the work, full similarity (reassociation)matrices with estimates
37 of DNA relatednessbetween each and every strain are rare (Grimont et al., 1982; Goodfellow and O'Donnell, 1993). Most DNA: DNA relatednessstudies tend to be restricted to comparisonsbetween judiciously chosenreference strains and a variety of test organisms. Generally, this approachis reliable but it is important that reference strains are representativeof the taxa under study (Hartford and Sneath,1988).
DNA relatednessstudies have contributed to improved classification of several actinomycetetaxa, notably rhodococci (Zakrzewska-Czerwfnskaet al., 1988), "sporangiate"actinomycetes (Farina and Bradley, 1970; Stackebrandtet al., 1981) and streptomycetes(Mordarski et al., 1986;Labeda and Lyons, 1991a, b; Labeda, 1992). DNA: DNA pairing studies,however, are mainly of value in establishing relationshipsbetween closely related speciesas DNA relatedness valuesfall to low levels,below 20%,for speciesthat are only moderatelydifferent phenotypically(Goodfellow and O'Donnell, 1993). Ribosomal (r) RNA sequencinganalyses and DNA: rRNA pairing studies have been widely used to establish supragenericrelationships between bacteria (Woese et al., 1985; Woese, 1987; Stackebrandtand Liesack, 1993) as base sequencesof rRNA cistrons are more highly conservedthan most genesin the bacterial genome(Doi and Igarashi, 1965; Dubnau et al., 1965). The methods used to determine DNA: RNA similarities have been extensively reviewed (Kilpper-Bdlz, 1991). DNA: rRNA hybridisation techniqueshave not been as widely used as DNA: DNA relatednessprocedures despite their undoubted significance in unravelling relationships between taxa within the class Proteobacteria(De Vos et al., 1989). A major breakthrough in determining relationships between distantly related bacteria was achievedby sequenceanalysis of linear rRNA. Initially, partial cataloguesof oligonucleotide sequences,derived from TI ribonuclease
38 digestionof purified 16SrRNA, were generatedfor test strains(Fox et al., 1977a, b, 1980; Stackebrandtet al., 1980,1985). Oligonucleotidesproduced by the digestionprocedure were sequencedin their entirety to give nucleofidecatalogues. An averagecatalogue consisted of about 80 fragments(7-20 nucleotidesin length) that were evenly distributedover the primary structureaccounting for between35 and45% of a completesequence. The relationshipbetween any two given strains was expressedas a similarity coefficient (SAB) calculatedon the basis of the proportion of identical nucleotidesin the respectivecatalogues. The resultant matricesof SkB values were examinedusing appropriatealgorithms to generate dendrograrns. As new cataloguesbecame available they were comparedto all previous cataloguesregardless of the laboratory of origin. This continually growing databaserepresented a significant advantageover hybridisationmethods as the latter rely on comparisonsagainst a few judiciously chosenreference strains usually at one point in time. The need to derive more information from 16S rRNA, and to extend sequencing studies to the larger 23S rRNA macromolecules,led to the developmentof the reversetranscriptase sequencing technique (Qu et al., 1983; Lane et al., 1985). This method was derived from the deoxynucleotidechain- terminating, copying method of Sanger et al. (1977). In this method an oligonucleotideis annealedto a templatenucleic acid underconditions such that it annealsat only one location. The oligonucleotideserves as a primer for the synthesisof a DNA copy of the templateextending from the 3' terminusof the primer that is incorporatedinto the reversetranscript. The template strand is copied from 3' to 5' and the synthesisedDNA strandfrom 5' to 3'. Inclusion of low levels of one dideoxynucleotidetriphosphate in the reactiontogether with all four deoxynucleotidetriphosphates results in the random terminationof reverse transcriptaseat positions correspondingto the nucleotide componentof that
39 dideoxynucleotide in the templatestrand. The sequencesobtained can be usedto homology calculate values for the construction of phylogenetic trees, and to chooseprimers for amplification of 16S DNA genesusing the polymerasechain reaction (Saiki et al., 1988;Ludwig, 1991).
5S rRNA has also been sequencedfor taxonomic purposes (Fox and Stackebrandt,1987; Hori and Osawa, 1986;Stackebrandt and Liesack, 1993) but the small size of this molecule has tendedto detractfrom its value in measuring distant phylogenetic relationships. Nevertheless, comparative 5S rRNA sequencingstudies have been usedto clarify relationshipsbetween closely related bacteria, including actinomycetes(Park et al., 1987a, b, 1991,1993). Two techniquesare applied to achieve the necessarybase-specific cleaves of RNA, namely the chemical(Peattie, 1979; Waldmannet al., 1987;Zhang et al., 1987) andenzymatic methods (Donis-Keller et al., 1977;Krupp and Gross,1979,1983). The genus Streptosporangiumhas been the subject of few molecular systematic studies. Stackebrandtet al. (1993) undertook 16S rDNA / RNA analysesof five membersof the genus Streptosporangium. The type species Streptosporangium roseum, Streptosporangium nondiastaticum and Streptosporangiumpseudovulgare formed an highly related cluster with Streptosporangium corrugatum peripherally associated. In contrast, Streptosporangiumvirldogriseum subspecies viridogriseum fell within the radiation of the family Pseudonocardiaceaeshowing a close similarity to Saccharothrixaustraliensis. The resultswere in good agreementwith thoseof an earlier study on the electrophoreticmobility of ribosomal proteins which also showed that streptosporangiacould be assigned to three groups (Ochi and Nfiyadoh, 1992). The first group contained Streptosporangiumalbidum, Streptosporangiumviridogriseum subspecieskofuense and Streptosporangium viridogriseum subspeciesviridogriseum, the secondStreptosporangium album,
40 Streptosporangium amethystogenes, Streptosporangium fragile, Streptosporangium nondiastaticum, Streptosporangium roseum, Streptosporangiumviolaceochromogenes and Streptosporangium vulgare, and the third Streptosporangiumcorrugatum. Kudo et al. (1993) carried out 5S rRNA sequencingstudies on Herbidospora strainsand found that one of them had very similar sequencesto those of Streptosporangiumroseum and Planobispora longispora strains.
E. CHARACTERISATION OF STREPTOSPORANGIA 1. RAPID ENZYME TESTS
a. Background The discontinuousdistribution of enzymesbetween members of microbial species can provide infon-nation of value for classification and identification (Manafi et al., 1991;Goodfellow and James,1993; James, 1993). Indeed,for the best part of a century,diagnostic tests have been usedwhich are dependenton the presenceor absenceof particular enzymes. Most early enzymatic tests were applied empirically, their underlying biochemicalbasis only becomingclear at a later date. Early biochemical tests included examination for enzymesof the hydrolase,lyase and oxidoreductasegroups, as well as for the end-productsof metabolicpathways. The use of enzymesas taxonomic markers offers advantagesover some other taxonomic methods. These include easeof performance,flexibility in a variety of situations such as in agar and liquid media, ability to test diverse organismsin the samestudy, for example,fast andslow growing organismsin the samemicrotitre plate, and the capacityto acquiredata quickly. In addition, tests designedto detectindividual enzymesmay be rapidly performedand are simple in operationoften without the needfor reagentadditions.
41 One of the outstandingproperties of enzymesis their specificity. Some enzymeshave almost absolutespecificity for a given substrateand do not attack evenclosely relatedmolecules; others are less so and act on a classof compounds. The specificity of enzymescombined with their ability to catalysereactions of substratesat low concentrationsis significant in biochemicalevaluation. As with other bacterialproteins, the type of enzymeproduced by membersof a particular taxon is an expressionof their genetic potential though microorganismscan regulate the amount and activity of the enzymesthat they produce. Thus, for example, enzyme tests carried out on an organism grown on different culture media can give differing resultsdue to the induction of catabolicenzymes or the repression of biosynthetic pathways hence standardisationof enzyme test proceduresis essential.
b. Enzymesas Taxonomic Markers Enzymes are not distributed uniformly amongstprokaryotes hence their discontinuousdistribution can be weighted for classificationand identification. Among the oxidoreductases,catalase, cytochrome oxidase and nitrate reductase havelong been useful for classificationand identification. Catalaseis presentin most cytochromecontaining aerobic and facultatively anaerobicbacteria, with the notable exception of Streptacoccus species, and is responsible for the decomposition of hydrogen peroxide to water and oxygen. The usual test procedure is simply to add hydrogen peroxide to a colony and observe the emissionof oxygen bubbles(McFaddin, 1980). However, aromaticamines and phenols may also be used as oxygen acceptors. Hanker and Rabin (1975) developeda test whereby dopamine and phenylenediaminewere oxidised to a colouredderivative by hydrogenperoxide in the presenceof catalase.
42 The cytochrorne oxidase enzyme mediates the oxidation of reduced
cytochrome by molecular oxygen which in turn acts as an electron acceptor in the terminal stage of the electron transfer system. In this test,
tetramethylphenylenediamine is oxidised by molecular oxygen in the presence of cytochrome C to give a coloured compound, Wurster's blue (Kovacs, 1956).
Nitrate reductase catalyses the reduction of nitrate to nitrite. The nitrite reductase
test relies on the formation of an azo dye from nitrite, sulphanilic acid and naphthylamine (Bachmann and Weaver, 1951).
Enzymes of the lyase group that are commonly used in systematics include decarboxylases,tryptophanase and tryptophan deaminase. The decarboxylase enzymesare numerousand each is totally specific for a given substrate. In bacterial classification and identification arginine, lysine and ornithine are generally usedsince they are decarboxylatedto producediamines which may be readily detectedusing a pH indicator. Tryptophanaseand tryptophandeaminase form products which may be readily detected. Indole producedby the former reacts with 4-dimethylaininobenzaldehydeto give a red-violet compound (McFaddin, 1980) andphenylpyruvic acid producedby the latter complexeswith ferric chloride to producea greencolouration (Blazevic andEderer, 1975). One of the first teststo be used in bacterialclassification which involved the detection of a hydrolaseenzyme was that for P-glucosidase.The naturally occurringcompound esculin (6-0-p-D-glucosyloxy-7-hydroxycoumarin) was used as the test substrate(Meyer and Sch6nfeld,1926). This compoundis cleavedin the presenceof P-glucosidaseand the 6,7-dihydroxycoumarin(esculetin) released forms a dark-brownchelate with ferric ions in the growthmedium. In the searchfor new diagnostic tests chromogenichydrolase substrates developedfor biochemicalapplications were adaptedfor bacterialidentification. Phenolphthaleindiphosphate has been used to detectalkaline phosphataseactivity
43 (Lewis, 1961) and0-nitrophenyl-p-D-glucoside has been employed as a rapid test for lactose fermenting bacteria (Lowe, 1962). Muffic: (1967) demonstratedthe value of aminopeptidasesin the classification of mycobacteria;naphthylamide substrateswere employed and free naphthylaminereleased in the reaction was visualisedwith a diazoniumsalt. Fluorescenttechniques provide a much more sensitive way of detecting enzymeactivity than colorimetric basedprocedures. The latter may be adapted for fluorometry by adding a fluorophore with a spectrumthat is quenchedby a product of the enzymaticreaction or by using fluorescentindicators, notably 7- amino-4-methylcoumarin(7-AMC) and 4-methylumbelliferone(4-MU). When conjugatedderivatives of thesemolecules are cleavedby the relevant hydrolytic enzymethe parentmolecules are released. The latter are intenselyfluorescent in the visible region of the electromagneticspectrum whereas the corresponding derivativesare only weakly fluorescentin this region(Figure 3, page45). Maddocks and Greenan (1975) introduced a simple test procedurethat involved the use of 4-methylumbelliferyl glucosides to differentiate between Escherichiacoli and Pseudomonasaeruginosa strains. A restricted number of fluorogenicprobes have been used to classify andidentify actinomycetes,notably gordonae(Goodfellow et al., 1991), mycobacteria(Grange, 1978; Grange and Clark, 1977; Slosarek,1980), streptomycetes(Goodfellow et al., 1987b) and a range of carboxydotrophic and mycolic acid containing actinomycetes (Goodfellow et al., 1988,1990b, 1991; O'Donnell et al., 1993). The ability of actinomycetes to produce endo- and exo-peptidases from 7-amino-4- methylcoumarinconjugated substrates has also beendemonstrated (Goodfellow et al., 1987c, 1990b, 1991). These preliminary studiesindicate that fluorogenic probesprepared from 7-AMC and 4-MU provide a simple,rapid and inexpensive way of detectingspecific enzymes in small amountsof whole actinomycetes.
44 (a) H I 'C-N 00H Nc 11 1H020 02+ Enzyme 0 CH CH 3
weakly fluorescent stronglyfluorescent
(b)
RO 000 H20 -40. R+ Enzyme CH CH 3
weakly fluorescent stronglyfluorescent
Figure 3 Cleavage of conjugated substrates to release (a) 7-amino-4- methylcoumarinand (b) 4-methylumbelliferone. Abbreviation:R, fatty acid or sugar;R', amino acidor peptide,chain.
45 c. Types of Biocheniical Tests 1) Coloximetric Liquid Media
Originally most biochemicaltests were carried out using bottles or tubes containing broth test media. Preparationtime and costs made it impossible to carry out effective test schemesroutinely on large numbersof isolatesand in an effort to solve the problem diagnosticcompanies developed test kits. The latter are usually basedon a seriesof microcupulescontaining dehydrated sterile media. A suspensionof test organismis addedto theseand the strip or plate incubated. After reagent addition, if required, colour formation is estimated visually or measuredusing a colorimeter. Most tests in kits used to be of the classical biochemical type but these have been replaced by chromogenic hydrolase substrates. Esteraseand glycosidase substratesare usually based on para- nitrophenol althoughabsorption of the free chromophoreis fairly weakmaking it difficult to seelow levels of hydrolysiswith the nakedeye. In the Zyme kit marketedby API (API SystemS. A., La BalmeLes Grottes, France)the esteraseand glycosidasesubstrates are basedon naphthylainine.The free amineor phenolreleased in the reactionsare coupled with a diazoniumsalt to producea stronglyabsorbing chromophore. Zyme kits havefound widespreaduse in the classification and identification of various groups of actinomycetes, including Actinomyces (Kilian, 1978), Brevibacterium (Freney et al., 1991), Gordona (Goodfellow et al., 1991), Nocardia (Boiron and Provost, 1990), Rhodococcus(Goodfellow et al., 1991),Streptomyces (KAmpfer et al., 1991a,b) and carboxydotrophic actinomycetes(ODonnell et al., 1993). This system provides information on nineteenenzyme activities in four hours. Encouraging results have also been obtained with the API Coryne strip developedfor the identification of Gram-positive rods, notably clinically important brevibacteria andcorynebacteria (Freney et al., 1991).
46 2) Multipoint Inoculated Agar Plates
Many biochemical tests are carried out using agar plates. An indicator incorporated in the medium or a reagent added after incubation leads to the production of coloured rings around positive test colonies. With multipoint inoculation devicesup to forty different microbial strainsmay be inoculatedonto a single agarplate leadingto inexpensivedata acquisition. Mast (Mast International Ltd., Bootle, U. K. ) market an identification systembased on agarplates. Resultsmay be interpretedby eye, more objectively, using an image analyser. Unfortunately, somereaction productsreadily diffuse through the media and confusion can occur if a coloured zone spreadsover adjacent colonies. Tests that generatehighly insoluble products are, therefore, preferred. Esculin hydrolysis is useful in this respect as the ferric-esculetin complex producedis poorly soluble and forms an easily discerniblering around positive colonies. Another chelating compound,8-hydroxyquinoline, has been used for enzyme testing (Fishman and Green, 1955). It produces a highly insoluble black chelate with a large molar extinction coefficient. Jamesand Yeoman (1987,1988) examinedmembers of the family Enterobacteriaceaewith 8-hydroxyquinoline-o-D-glucosideand 8-hydroxyquinoline-P-D-glucuronidein agarplates and achievedmuch more compactzones around positive coloniesthan wereobtained with esculin.
3) Fluorometric Testing
Fluorescence of molecules is caused by absorption of electromagnetic radiation, i.e. ultraviolet, visible and infra-red, leading to promotion of electrons from the groundto an excited state. The latter has a finite lifetime during which some loss of vibrational energy occurs. The residual energy is either lost by collisional deactivationor by re-emissionof radiant energy (fluorescence). The
47 loss of vibrational energy meansthat fluorescenceenergy is less than the energy of absorption and the wavelength maximum correspondinglylonger than the absorptionmaximum. Absorptionof energyleads to electronicexcitation. In most casessubstrate specificity is wide enoughto permit a variety of groups on one side of the bond to be broken. Artificial substratesmay thus be used with a chromophoreor fluorophore attachedto the group of interest. A chromophoreshould havestrong absorptioncapacity to maximise sensitivity and it is beneficial if this absorptionis in the visible spectrum. Generally,wavelength and intensity of absorption are increasedif conjugation in the molecule is increased. Electron donating groups such as the halogens also increase the wavelengthof absorption. If, however,another molecule is attachedto the amino or hydroxy groups the electron donatingeffect is reducedand absorptionis at a lower wavelength. A substrateformed by such a linkage will have a lower wavelengthof absorptionthan the free chromophoreso that enzymeactivity may be readily followed. Fluorescenceis enhancedby electronic conjugationand by the planarity (flat, stable nature) of the molecule. It is for this reason that most highly fluorescentsubstances have rigid aromaticring structures.Fluorescent emission is shifted to longer wavelength by electron donating groups and fluorogenic substratesmay be createdin a similar mannerto their chromogeniccounterparts. The cournarinic compounds7-AMC and 4-MU have structural features which predispose towards fluorescence. These include planarity, rigidity, electron delocalisationvia an efficient conjugationsystem and the presenceof at leastone electronreleasing group. A restricted range of 4-MU linked substrateshave been used in the classificationand identification of actinomycetesincluding mycobacteria(Grange, 1978; Grangeand Clark, 1977;Hamid et al., 1993),renibacteria (Goodfellow et
48 al., 1985) and streptomycetes(Goodfellow et al., 1987b). Kenneth et al. (1992) havedeveloped a rapid and sensitive assayfor chitinolytic activity basedon the use of fluorogenic 4-methylumbelliferoneglycosides of N-acetylglucosamine oligosaccharidesin order to characterisethe enzymesassociated with chitinolytic microorganisms,notably exo- and endo-chitinasesand N-acetylglucosamine. Similarly, enzyme activity profiles of mycobacteriaand related actinomycetes have been obtained using peptide hydrolase substratesbased on 7-amino-4- methylcoumarin(Goodfellow et al., 1987c;Hamid et al., 1993). Fully automatedsystems are neededto determinethe full potentialof rapid enzyme tests in bacterial systematics. Microscan (Sacramento,California, U. S.A. ) and Sensititre Ltd. (East Grinstead, U. K. ) market automated systems. With the
Sensititre procedure conjugated enzyme substrates are held in microtitre plates and after inoculation results are read quantitatively using a fluorometric plate reader attached to a computer for instant data acquisition. The raw data are interpreted using appropriate software. A variant of the Sensititre system has been developed and applied to the classification and identification of rapidly growing mycobacteria and nocardiae (Hamid et al., 1993). This latter procedure was used in the present study to assign unidentified streptosporangia from soil to artificial groups. d. Enzymesas Taxonomic Markers in StreptosporangialSystematics Little attempt has been made to determine the enzymatic profile of representativesof the genusStreptosporangium. The restrictednumber of enzyme tests currently used in streptosporangial systematics (Nonomura, 1989; Goodfellow, 1991) can be attributed to the traditional emphasisplaced on morphological and physiological criteria and to the problem of detecting small amountsof endproduct in large volumesof testmedia.
49 The most comprehensivestudy to date was carried out by Whitham et al. (1993) who examinedone hundred and twenty-two streptosporangia,including isolates from soil, and 37 marker strains of Microbispora, Microtetraspora, Planobispora,Planomonospora and Streptosporangium,for their ability to cleave twenty-six 4-MU conjugatedderivatives. Someof the testswere of presumptive diagnostic value for the delineation of the taxa classified in the family Streptosporangiaceae.Most of the microbisporaewere characterisedby their ability to cleave 4MU-P-D-glucuronide;the planobisporaeand planomonosporae were unusualin cleaving 4MU-pyrophosphate.All of the streptosporangiawere active against 4MU-2-acetwnido-2-deoxy-p-D-galactopyranoside,4MU-P-D- fucopyranoside, 4MU-P-D-galactopyranoside,4MU-(x-D-glucopyranoside and 4MU-P-D-glucopyranoside,but gave different patternsof reaction with 4MU-2- acetamido-2-deoxy-p-D-glucopyranoside, 4MU-N-acetyl-p-D-glucosainide, 4MU-(x-L-arabinofuranoside, 4MU-(x-L-arabinopyranoside, 4MU-0-D- cellobiopyranoside,4MU-a-D-galactopyranoside, 4MU-P-D-glucuronide, 4MU-a
-D-mannopyranoside,4MU-P-D-mannopyranoside, 4MU-bisphosphate, 4MU- pyrophosphate,4MU-heptanoate, 4MU-nonanoate and 4MU-palmitateindicating that theseenzymes were of potential value for the classificationand identification of streptosporangia.
2. CHEMOSYSTEMATICS
Simple rapid methodshave been developedto detect the distribution of taxonomically useful componentsof the bacterial wall peptidoglycan. These qualitativetechniques are widely usedfor primary classificationand identification of actinomycetesas they can be applied to large numbersof strains(Schaal, 1985; Williams et al., 1989;Suzuki et al., 1993). More analyticaltechniques have been usedto elucidatethe primary structureof actinomycetecell walls but they involve
50 proceduresthat arenot readily applicableto more than a few strains(Schleifer and Kandler, 1972; Schleifer and Seidl, 1985;Kodama et al., 1992; Hancock, 1993). However, quantitative data on wall amino acid composition can be obtained relatively quickly by gaschromatography (ODonnell et al., 1985). Qualitative analysesof componentsof cell walls andwhole-organisms led to the classification of actinomycetesinto eight aggregategroups or wall chemotypesbased on the discontinuousdistribution of mai or wall amino acids and sugars (Table 6, page 52; Becker et al., 1965; Lechevalier et al., 1966b; Lechevalier and Gerber, 1970; M.P. Lechevalier and Lechevalier, 1970a, b; Minnikin and O'Donnell, 1984; Suzuki et al., 1993). Membersof the family Streptosporangiaceaecontain meso-DAPas the major wall diaminoacid but lack any characteristic sugars and hence belong to wall chemotype IH (M. P. Lechevalierand Lechevalier,1970a, b). The isomersof diaminopimelicacid are key chemicalmarkers particular sincethe detectionof the LL-isomer distinguishes streptomycetesfrom all other sporoactinomycetes. Current methods for the detectionof DAP by thin layer chromatography(TLC) are not satisfactoryas it can be difficult to distinguish the LL-, DD- and meso-DAPisomers from one anotheron TLC plates. Peptidoglycans have been classified according to their chemical composition by Ghuysen (1968) and Schleifer and Kandler (1972). Both classificationsstress the importanceof the mode of cross linkage, but the tri- digital system proposed by Schleifer and Kandler (1972) is the one that is commonlyused (Suzuki et al., 1993). The variation in peptidoglycancomposition has provided invaluable information for the reclassification of actinomycetes (Goodfellow andCross, 1984), notably for the so called corynefonnactinomycetes (Goodfellow, 1989a). Members of the family Streptosporangiaceae
51 Table 6 Cell wall chemotypesof someactinomycete taxa* Wall MajorConstituents" Families/ Genera Chemotype
I LL-diaminopimelicacid, glycine Streptomycetaceae
H meso-diaminopimelicacid, glycine Micromonosporaceae
1HA meso- diaminopimelic acid, madurose Dermatophilaceae Frankiaceae Streptosporangiaceae
III B meso-diarninopimelic acid Brevibacteriaceae Thennomonosporaceae
IV A meso-dimninopimelic acid, arabinose, Corynebacteriaceae galactose,mycolic acids Mycobacteriaceae Nocardi"eae
IV B meso-diaminopimelic acid, arabinose, Pseudonocardiaceae galactose v lysine, omithine Actinomycesisraelii vi lysine (asparticacid, galactose)*** Microbacterium Oeskovia vu diaminobutyricacid, glycine (lysine) Agromyces Clavib"ter vin ornithine Curtobacterlum Cellulomonas
*, Data modified from M.P. Lechevalierand Lechevalier (1970b) and Goodfellow andO'Donnell (1989). **, All wall preparationscontain major amountsof alanineand glutamic: acid; variableconstituents.
52 have a peptidoglycantype A17 (Schleifer and Kandler, 1972; Nonomura, 1989; Goodfellow, 1991).
Lipid analyseshave yielded valuable data for bacterialclassification and identification (Mayer et al., 1985; Tornabene,1985; Jantzenand Bryn, 1993), especially for actinomycete systematics (Minnikin, 1982; Minnikin and O'Donnell, 1984; O'Donnell, 1985,1988a, b; Saddleret al., 1987; O'Donnell et al., 1993; Suzuki et al., 1993). Most attentionhas focusedon the use of fatty acids,isoprenoid quinones and polar lipids aschemical markers. A functional plasmamembrane requires the presenceof a suitablemixture of both relatively fluid and solid fatty acids esterified to polar head groups. Severaldifferent types of fatty acid mixtures are found in actinomycetes.At one extreme,straight chain fatty acidsoccur with fluid monounsaturatedcomponents and acids biosynthetically derived from unsaturated fatty acids such as tuberculostearicand cyclopropaneacids (e. g. Actinomyces,Corynebacterium and Mycobacterium). In contrast, other actinomyceteshave iso- fatty acids as their main relatively solid basealthough smaller amountsof straightchain components are usually present. The fluid elementsin patterns of this type are composed solely of anteiso-fatty acids (e.g. Actinopolyspora, Cellulomonas,Oerskovia, Thermomonospora,Saccharomonospora and Streptomyces). Membersof the genusStreptosporangium have complex fatty acid patterns consistingof major amountsof saturatedstraight chain, iso- andmethyl- branched fatty acids and minor and variable amountsof unsaturatedand anteiso- acids, respectively (Kroppenstedt, 1985; Poschner et al., 1985; Kudo et al., 1993; Whitharn et al., 1993). There is also evidencethat quantitativeanalyses of fatty acids extracted from representativestrains of the family Streptosporangiaceae grown under carefully standard conditions will yield valuable data for the
53 classification and identification of the constituenttaxa (Mertz and Yao, 1990; Kudoet al., 1993;Stackebrandt et al., 1993). Information of value for actinomycetesystematics has also been derived from two-dimensional thin-layer chromatographic analyses of polar lipids (Minnikin and O'Donnell, 1984; Suzuki et al., 1993). These lipids form the structural basis of bacterial plasma membranes,phospholipids are the most common type found in actinomycetes.Phospholipids commonly detected in actinomycete wall envelopes include phosphatidylglycerol (PG), diphosphatidylglycerol (DPG), phosphatidylethanolatnine (PE) and phosphatidylinositol (PI). Methylated deiivatives of PE, such as phosphatidylmethylethanolamine(PME) and phosphatidylcholine(PC) are less commonly encountered. Other polar lipids of diagnostic potential which lack phosphorusgroups include the amphipaticglycolipids and acylated long-chain omithine and lysine amides(Minnikin and O'Donnell, 1984). Actinomyceteshave been assignedto five groups on the basis of the discontinuousdistribution of certain nitrogenousphospholipids (Lechevalier et al., 1977,1981; Goodfellow, 1989b).
Polar lipid dataprovide further evidenceof a closerelationship between the genera Microbispora, Microtetraspora, Streptosporangium, Planobispora and Planomonospora;strains in thesetaxa havea type IV phospholipidpattern sensu Lechevalier et al. (1977), that is, they contain glucosamine. Whitham et al. (1993) found that nearly all of their streptosporangiacontained major amountsof diphosphatidylglycerol,phosphatidylethanolamine and phosphatidylglycerolwith most also characterised by the presence of phosphatidylinositol, phosphatidylinositolmannosides and phosphatidylmethylethanolamine.Simflar findings were reportedfrom earlier studies(Kroppenstedt, 1985; Mertz and Yao, 1990) though the failure of some investigators (Lechevalier et al., 1977;
54 Hasegawa 1979) et al., to detectphosphatidylglycerol can be attributedto the use of different media andcultivation regimes. Actinomycetescan also be assignedto severalaggregate groups on the basis of the types of menaquinonesfound in their plasmamembranes. Menaquinones vary in the length and degreeof hydrogenationof doublebonds of their isoprene units (Collins and Jones, 1981; Collins, 1993; Suzuki et al., 1993). Initial menaquinone analyses provided qualitative or semi-quantitative information (Minnikin et al., 1978;Minnikin and O'Donnell, 1984)but it is now commonplace to derive quantitative profiles by using high perfonnanceliquid chromatography (Collins et al., 1984;Kroppenstedt, 1982,1985; Suzuki et al., 1993). Quantitative menaquinonedata, however, have to be interpretedwith care as Saddler et al. (1986) found that the menaquinonecomposition of Streptomycescyaneus NCIB 9616 varied at different stagesof the growth cycle. Most Streptosporangiaceae strainshave profiles rich in MK-9 (HO),MK-9 (112)and MK-9 (H4) (Poschneret al., 1985;Stackebrandt et al., 1993;Whitham et al., 1993). Membersof the genus Streptosporangiumare characterisedby the presenceof major amountsof di- and tetra-hydrogenatedmenaquinones with nine isopreneunits (Collins et al., 1984; Kroppenstedt,1985; Mertz and Yao, 1990;Kudo et al., 1993;Stackebrandt et al., 1993;Whitham et al., 1993). The sites of hydrogenationof the isoprenyl side chains of menaquinones also provide useful taxonomic information. Prior to the application of mass spectrometrý/massspectrometq (MS/MS; Tamaoka, 1987) the hydrogenated positions of bacterial menaquinoneswere determinedby NMR analysis. Di-, tetra-, hexa- and octahydrogenatedmenaquinones tend to be hydrogenatedat positions U, '11and RF, '11,111and VIR', and '11,111,VIII and IN, respectively (Suzuki et al., 1993). The order of hydrogenationwould appearto be 11 >111 >VRI AV. Streptosporangiacontain major amounts of tetrahydrogenated
55 menaquinoneswith nine isoprene units hydrogenatedat positions H and Vul (Stackebrandtet al., 1993).
3. PYROLYSIS MASS SPECTROMETRY
The needto classify, identify andtype microorganismsis a centraltheme in microbiology but many of the methods currently used for these purposesare complex, expensive in labour and materials and quite often give poor reproducibility. There is a real need to develop rapid, reproducibleand cost- effective methods for the classification, identification and typing of microorganisms. It has already been demonstratedthat chemical and molecular techniques have helped to generate a lot of new information for microbial classification and identification (Goodfellow and O'Donnell, 1993) and have provided valuable data for the revision of many bacterial taxa, including those assignedto the order Actinomycetales(Williams et al., 1989;Kroppenstedt et al., 1990; Manchesteret aL, 1990; Ochi and Miyadoh, 1992; Stackebrandtet al., 1993; Whitharn et al., 1993). However, many of these techniquesare relatively expensivein both cost per sampleand in time. This is not the casewith analytical chemical techniques,notably Curie-point pyrolysis mass spectrometry(Magee, 1993a,b).
Curie-point pyrolysis mass spectrometry(PyMS) of whole-organismswas introduced by Meuzelaar (1974) to facilitate rapid characterisation and identification of microorganisms.Pyrolysis, the thermaldegradation of materialin an inert atmosphereor vacuum,leads to the productionof volatile fragmentsfrom non-volatile materials such as microorganisms. Under controlled conditions the breakdown is reproducible and the fragmentsare characteristicof the original material hencethe pyrolysateis a "fingerprint" of the original sample. Following pyrolysis, the fragmentsare ionised then separatedby mass spectrometry, The
56 methodinvolves organismstaken directly from growth media. Samplepreparation and total running time are very fast and inexpensive. The analytical system for pyrolysis and data acquisition can also be automated thereby allowing the pyrolysis of multiple samples. Pyrolysis mass spectrometry was first used to investigate complex biopolymerssuch as albumin andpepsin (Zemany,1952). Interestsoon waned in PyMS as workers turned to pyrolysis gas-liquid chromatography(PyGS) as this system was less expensive (Davison et aL, 1954). Pyrolysis gas-liquid chromatographywas used to separatemolecules on the basis of their relative polarity, that is, the constituentcomponents of the pyrolysatewere separatedby the difference in retention time of each fragmentin the chromatographiccolumn. This system was used in the late 1960's and early 70's (Gutteridgeand Nonis, 1979) but it's applicationto microbial systematicswas limited given problemsof long-term reproducibility, lack of speedand inadequatedata handling facilities (Gutteridge,1987). An automatedPyGC systemwas subsequentlydeveloped, but not used,for the detectionof possibleextra-terrestrial life in the Surveyorseries of lunar landings(Wilson et aL, 1962). The National Aeronautics and Space Administration (NASA) later developeda pyrolysis mass spectrometerfor the detectionof microorganismsin dust sampleson Mars. The techniqueinvolved heating lunar dust samplesand analysing the resultant degradativeproducts but convincing traces of organic material were not found (Gutteridgeand Norris, 1979). An improved pyrolysis mass spectrometerwas introducedby Meuzelaarand Kistemaker(1973) for the characterisationof complex biological samplesincluding microorganisms. The procedure was seen as rapid, universally applicable, and relevant for the characterisationof organismsat generic, specific and subspecificlevels but the two commerciallyavailable machines gave data that were poorly reproducible. In
57 addition, the cost of running the instrumentswas high and the analysisof samples could not be achievedin lessthan 5 minutes. A major breakthrough in pyrolysis mass spectrometrycame with the introduction of the Horizon PyMS-20OX,an instrument based on the PyMS quadrupolemass spectrometric system of PruteeLimited (Afies et aL, 1986). The superior performanceof this machineover the earlier modelscan be attributedto an improved electronmultiplier, which led to faster analysistimes (2 minutesper sample),enhanced reproducibility and an upgradedstatistical packagefor data analysis. The operational procedure has been describedin detail by Magee (I 993a,b) and hencewill not be consideredhere. Curie-point pyrolysis mass spectrometryis increasinglybeing applied in microbial systematics(Magee, 1993a, b). The procedurehas beenused to confirm the homogeneity within and discrimination between taxa circumscribed using conventional taxonomic methods (French et aL, 1989). Good correlation was found when PyMS data were comparedwith those obtainedusing conventional clustering techniques, for example, in comparative taxonomic studies of FusobacteriumuIcerans (Adriaans and Shah,1988; Magee et aL, 1989b).In some instances, however, as Gutteridge and Norris (1979) anticipated, pyroclassificationsdiffer from more conventionally based taxonomies thereby providing anotherperspective on the classificationof testorganisms (Hindmarch et aL, 1990). Excellent resultshave been obtainedin applicationsof PyMS to microbial identification. Pyrolysis mass spectrometric identification of clinically significant mycobacteria (Wieten et al., 1981a, b; 1983) was shown to be reliable given >
90% agreement with conventional procedures in discriminating between organisms in the Mycobacterium tuberculosis complex. In these studies challenge sets were compared with marker organisms included in each batch of analyses, a technique
58 termedoperational fingerprinting. Identification of staphylococcito speciesusing the PyMS procedure also showed around 90% agreementwith conventional identification systems(Magee et aL, 1983;Hindmarch and Magee, 1987). Saddler et aL (1989) used PyMS in the selection of unusual actinomycetes for pharmacologicalscreening programmes. The most important application of Curie-point PyMS, to date, has been in the area of microbial epidemiology (Magee, 1993a). A broad range of species have been studied including Bacteroides ureolyticus (Duerden et al., 1989), Candida albicans (Magee et al., 1988), Mycobacteriumxenopi (Sisson et al., 1992), Pseudomonasaeruginosa (Sisson et al., 1991), Salmonella enteritidis (Freemanet al., 1990a)and Staphýlococcusepidermidis (Freeman et al., 1991). Studiesof three Streptococcuspyogenes outbreaks (Magee et al., 1989a;Freeman et al., 1990b) showedalmost total agreementwith conventionaltyping; interestin this speciesreflects the dire consequencesof misjudgementin potential hospital outbreaks. Many authorscite the speedand lack of necessityfor species-specific modification of methods with PyMS as clear advantagesover traditional techniques. The application of PyMS to actinomycetesystematics has mainly been restricted to examinationof a few clinically significant isolates. Meuzelaaret al. (1976) were able to distinguishbetween several mycobacterial species but were unableto show differencesbetween Mycobacterium avium, Mycobacteriumbovis and Mycobacteriumxenopii strains. Wieten et al. (1981a, b) was able to separate Mycobacterium bovis, Mycobacterium bovis BCG and Mycobacterium tuberculosisstrains using a PyMS generateddatabase derived from a small set of molecularmasses. Curie-pointpyrolysis massspectrometry has also beenused to characteriseMycobacterium kansasii (Wieten et al., 1979),Mycobacterium leprae
59 (Wieten et al., 1982) and Mycobacteriumstrains (Sisson et al., 1992), and to distinguish betweenCorynebacterium species (Hindmarch et al., 1990). Pyrolysis mass spectrometry has also been used in the classification, identification and typing of industrially significant actinomycetes(Saddler et al., 1988; Sanglier et al., 1992). Sanglier et al. (1992) pyrolysed membersof representativeactinomycete genera in order to study the effects of media design, incubation time, sample preparationand the effects of sporulating versus non- sporulating actinomycetestrains on experimentaldata. It was concluded that reproducibledata could be obtainedgiven vigorousstandardisation of growth and pyrolysis conditions. These workers were also able to distinguish between actinomycetestrains at and below the specieslevel. In particular, representatives of three closely related Streptomycesspecies, namely Streptomycesalbidoflavus, Streptomycesanulatus and Streptomyceshalstedii were distinguished. The separationof thesenumerically circumscribedstreptomycete species (Williams et al., 1983a;KAmpfer et al., 1991a; Goodfellow et al., 1992) suggeststhat PyMS may be a useful way of evaluating clusters defined in numerical taxonomic surveys. It is essential to perform multivariate statistical analyses to interpret pyrolysis massspectral data. The mathematicalprocedure involves severalstages; pre-processingto remove the effects of variation in the amount of sample analysed;univariate analysis to removemasses with poor reproducibilityand some form of multivariate analysisto resolve the complex statistical structure of the remaining data to yield results that can be used for either classification or identification. It can be difficult to representthe large amountsof information derived from PyMS studiesand this may necessitatefurther processingto obtain three dimensionalscatter plots anddendrograms. Much of this data handlingcan now be rapidly performed on micro-computersusing commercial software.
60 Statistical Packagesused to analyse PyMS data include ARTHUR (Kowalski, 1975), BMDP (Dixon, 1975), GENSTAT (Nelder, 1979) SIMCA (Wold, 1976) andSPSS (Nie et aL, 1975). The multivariate technique,principle componentsanalysis, is commonly used to analyse PyMS data. Principle components analysis involves data reduction to obtain a two or three dimensional graphical representation of multidimensional data in order to display relationships within datasets and to detect outliers (Gutteridge et al., 1979; McFie and Gutteridge, 1982; Shute et al., 1985; Magee,
1993a, b). Canonical variate analysis is used in PyMS to determine relationships between the groups defined by principle components analysis. In PyMS analysis
replicate samples, usually three, of a single strain are considered as a group. Canonical variate analysis ininunises intra-replicate variation and maximises differences between strains.
The use of artificial neural networks (ANNs) for the analysis of pyrolysis mass spectraldata provides an even more robust and effective approachtowards patternrecognition (Goodacreet al., 1992;Chun et al., 1993a,b; Freemanet al., 1993). Artificial neuralnetworks are composedof processingelements which are analogousto neuronsin the brain. Each of the processingelements (PE) has a numberof weightedinputs that are summedto give the internal activationlevel of the PEs. Artificial neural networks use many processingelements which are usually arrangedin three layers: an input layer, one or more hiddenlayers and an output layer. Such networks learn by modifying the weights or strengthsof the connectionsbetween the PEs though finding ways of teaching/trainingnetworks were a major stumbling block during early research(Morris and Boddy, 1992). The artificial neural network systemoffers considerableadvantages over current practicein the analysisof PyMS dataas it is not necessaryto examineduplicate or triplicate samplesor to analysereference and unknown strains in a singlerun.
61 Artificial neural networks are increasinglybeing used to detect complex, non-linearrelationships in multivariate data. They havebeen used to analysedata generatedby PyMS (Goodacre et al., 1992) in order to discriminate between bacterial species(Bungay and Bungay, 1991),in fermentationcontrol (Lant et al., 1990), and in gene (Wu et al., 1990) and protein classification (Qian and Sejnowski, 1988; Wu et al., 1991). In the case of bacterial identification the underlying rationale is that it is possibleto acquire setsof multivariate data from representativesof taxa and train ANNs using the known identities as the derived outputs. Once neuralnetworks have been trained they can be usedto discriminate between the taxa under study (Chun et al., 1993a, b; Freemanet al., 1993). Artificial neural networks havebeen shown to provide a rapid, reliable and cost- effective methodof identifying Streptomycesspecies (Chun et al., 1993a,b).
F. SELECTIVE ISOLATION OF STREPTOSPORANGIA LBACKGROUND
Actinomycetesare a successfulgroup of bacteria that live in a variety of naturaland man-made environments. Most are strict saprophytesbut someform parasiticor mutualisticassociations with animalsand plants (Goodfellowand Williams, 1983;Williams et al., 1984b;Schaal and Lee, 1992). The primary naturalreservoir of actinomycetesis soil wherestrains are believed to havea role in the recyclingof nutrients(Williams et al., 1984b;McCarthy and Williams, 1992). Soil particlescarrying sporesare widely dispersedwhich meansthat actinomycetescan be isolated from most natural habitats. Actinomycetes are usually isolated from enviromnental samples by applying appropriate selectivepressures at various stagesof the dilution plate procedure(Williams and Wellington, 1982; Williams et al., 1984a;Goodfellow and Williams, 1986; Goodfellow and O'Donnell, 1989; Bull et al., 1992).
62 Samplesmay be taken at randomor from habitatswhere the microbial community is adapted to relatively extreme external factors. It is usually necessaryto eliminate or reduce fungal and unwantedbacterial growth on isolation media in order to selectively isolate actinomycetes from natural habitats. Fungal contaminants are usually controlled by supplementingisolation media with antifungalantibiotics such as cycloheximide(actidione; Phillips and Hanel, 1950; Cork and Chase, 1956; Porter et al., 1960), nystatin or pimaricin (Porter et al., 1960;Tsao andThieleke, 1966).
2. CHOICE OF ENVIRONMENTAL SAMPLES
Many thousandsof actinomyceteshave been isolated from the environment but relatively little is known about the ecologicalor geographicaldistribution of even the better known neutrophilic streptomycetes(Goodfellow and Simpson, 1987; Bull et al., 1992). Ecological studieshave been few in number and have tended to concentrateon the detailedtaxonomic analysisof a limited number of strains isolated from a few environmentalsamples (Orchard and Goodfellow, 1980; Goodfellow et al., 1990a;Whitharn et al., 1993). The lack of convincing evidencefor the production of antibiotics and other useful metabolitesin nature compoundsthis problem (Williams, 1982). Given this situation,it is difficult to predict the sites where particular actinomycetetaxa or strains occur. Notable exceptions include the association of thermotolerant and then-nophilic actinomycetesinvolved in the turnover of self-heating composts and other vegetablematerial (Lacey, 1988) and the coprophilouslife cycle of Rhodococcus coprophilus (Rowbothamand Cross, 1977). Neverthelessthe selectionof macro- or micro- habitats as a source of commercially useful actinomycetesremains somethingof a lottery.
63 3. PRETREATMENT OF SAMPLES
A wide range of pretreatmentregimes have been used to enhancethe isolation of actinomycetesfrom environmentalsamples (Cross, 1982;Williams et al., 1984a; Goodfellow and O'Donnell, 1989). These include physical and chemicaltreatments or enrichmentsof samples.Many pretreatmentregimes exert a clear selectivity for the isolationof membersof particularactinomycete taxa, but the scientific basisfor their action is rarely evident. A novel pretreatmentmethod involves the addition of a mixture of polyvalent streptomycetephage to soil suspensionsto selectivelyreduce the growth of streptomycetecolonies on isolation plates and thereby raise the proportion of other genera isolated (Williams and Vickers, 1988).
Membrane filtration and centrifugation can be used to concentrate actinomycetePropagules in soil, water and sedimentsamples (Trolldenier, 1966; Goodfellow and Haynes, 1984). Similarly, nutrient enrichmentof environmental samples and soil suspensionshave been used to increase the number of streptomycetesand streptosporangiaprior to isolation (Williams and Mayfield, 1971;Nonomura and Hayakawa, 1988). A usefuldeparture from the dilution plate procedureinvolves the isolation of thermophilic actinomycetesfrom dry, self- heatingplant material.The latter is shakenin a wind tunnel (Gregory and Lacey, 1963) or sedimentationchamber (Lacey and Dutkiewicz, 1976) and the spore clouds obtainedimpacted onto surface-driedisolation platesheld in an Andersen (1958) sampler. This methodhas been successfullyused to isolate mycolateless wall chemotype IV actinomyceteswhich are a source of several important antibiotics(Whitehead, 1989). Hirsch and Christenson(1983) usedmembrane filters to reducethe number of contaminatingbacteria on isolation plates. Soil dilutions were inoculatedonto the surfaceof membranefilters placedonto the surfaceof isolationmedia. After
64 incubation for three days, the filters were removedand the platesre-incubated for a further sevendays. Actinomycetespores can germinateand grow through pores in membranefilters onto the media below whereas non-mycelial bacteria are restrictedto the upper surfaceof filters and are thereforeremoved with them. A variety of baiting procedureshave been developed for the selective isolationof specificactinomycetes from environmentalsamples. The "baits", once colonised, are removedand placed onto plates of nutrient media for subsequent culturing and examination.A variety of baits havebeen employed for the isolation of members of the genera Planobispora, Planomonospora,Spirillospora and Streptosporangium(Couch, 1954;Schiffer, 1973; Goodfellow, 1991a). A method based on the chernotacticresponse of motile spores was successfullyused to isolate Spirillospora strainsfrom soil (Palleroni,1980). Actinomycete propagules are more resistant to desiccation than most vegetativebacteria so that the simple practiceof air-drying soil samples,prior to plating suspensionsonto suitable selectivemedia, can significantly increasethe chancesof isolatingspore-forming actinomycetes, especially when isolation media are not highly selective(Meiklejohn, 1957; Williams et al., 1972;Hunter, 1978; Nonomura and Hayakawa, 1988; Hayakawa et al., 1991). Resistance to desiccationis usually accompaniedby a measureof heat resistance;dry soils can be heatedto over 1000Cto reduce the number of unwantedbacteria in order to enhancethe recoveryof rareactinomycetes (Nonomura and Ohara,1969a; Athalye et al., 1981). Lessextreme heat pretreatment regimes have been recommended for the isolation of micromonosporae(Goodfellow and Haynes, 1984), nocardiae (Orchard, 1975), rhodococci (Rowbothamand Cross, 1977) and streptomycetes (Williams et al., 1972; Goodfellow and Haynes, 1984). It is not known why actinomycetepropagules such as spores(e. g. streptomycetes),spore vesicles (e. g. streptosporangia)and hyphal fragments(e. g. rhodococci) are more resistant to
65 heat and desiccationthan vegetativecells of Gramnegative bacteria. Someof the physical pretreatments used to isolate members of the family Streptosporangiaceaeare shownin Table 7, page67. The effectivenessof chen-dcalpretreatment regimes depends on the higher resistance of actinomycete propagules to antimicrobial compounds such as chlorine (Burman et al., 1969), phenol (Speer and Lynch, 1969; Pantier et al., 1979; Nonomura and Hayakawa, 1988) and quaternary ammonium compounds (Phillips and Kaplan, 1976; Du Moulin and Stottineier, 1978). Although toxic such compounds have been found useful in the reduction of unwanted bacteria on isolation plates. Sodium dodecyl sulphate (0.05%) and yeast extract (6%) have also been used inhibit the growth of bacteria, they enhance germination of actinomycete spores (Nonomura and Hayakawa, 1988).
4. CHOICE OF ISOLATION MEDIA
The selectivity of an isolation medium is influenced by its nutrient composition,PH, the addition of selectiveinhibitors and by incubationconditions (Williams et al., 1984a;Hayakawa and Nonomura, 1987a). These factors determine which fractions of natural populationsfrom environmentalsamples will compete successfullyand therebybe recoveredon selectiveisolation media. Innumerable media formulationshave beenrecommended for the isolation of actinomycetesin generaland for selectedgenera in particular. Surprisingly, many "general" or "non-selective" media were designed without regard either to the nutrient properties or tolerances of the target actinomycetes. Thus, media such as colloidal chitin (Lingappaand Lockwood, 1961; Hsu and Lockwood, 1975), half-strength nutrient (Gregory and Lacey, 1963),glucose-asparagine (Crook et al., 1950),glycerol-arginine (Benedict et al., 1955),M3 (Rowbothamand Cross, 1977) and starch-casein-nitrateagars (Mister
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67 and Williams, 1964) are widely used though little attempt has been made to evaluate their effectiveness(Williams et aL, 1984a). This point was clearly demonstratedwhen the use of various objectively formulated selective media resulted in the isolation of different streptomycetespecies from the same sand dune sample(Vickers et aL, 1984). The media recommendedby Nonomuraand Ohara (1969a, b) for the isolation of Microbispora and Streptosporangiumwere empirical.
It is now apparentthat most "non-selective" media recommendedfor the isolation of actinomycetes are selective for streptomycetes, notably for those
belonging to the Streptomyces albidoflavus (griseus) group (Vickers et al., 1984). This observation helps to explain why actinomycetes such as streptosporangia
have only occasionally been isolated on convenfional isolation media such as cellulose, chitin, starch-ammonium or starch-casein agars (Van Brummelen and Went, 1957; Potekhina, 1965; Willoughby, 1969b; Williams and Sharples, 1976).
5. TAXONOMIC APPROACH TO SELECTIVE ISOLATION Selective media can be designed on objective criteria given recent improvementsin actinomycetesystematics (Vickers et al., 1984; Williams et al., 1984a; Goodfellow and O'Donnell, 1989; Bull et al., 1992). Indeed,numerical taxonomic databases,which contain extensiveinformation on the biochemical, nutritional, physiological and antibiotic sensitivity profiles of constituent actinomycete taxa are ideal resourcesfor the formulation of isolation media deemedselective for industrially significantactinomycete taxa. Further,improved diagnostic methods allow selective isolation media to be evaluated as representativecolonies can be identified with confidence.
68 a. Formulation and Evaluation of New SelectiveMedia The discovery that Diagnostic Sensitivity Test agar supplementedwith tetracyclines was selective for Nocardia asteroides(Orchard and Goodfellow, 1974) was based on antibiotic sensitivity data (Orchard, 1975; Orchard and Goodfellow, 1974) and the product of an earlier numerical phenetic survey (Orchardand Goodfellow, 1980). A logical extensionof this work was to visually scanpercentage positive frequencytables for antibiotics that could form the basis of selective isolation media. In one such numerical taxonomic survey streptoverticillia were found to have a higher resistance to neomycin and oxytetracyclinethan neutrophilic streptomycetes(Williams et al., 1985a). This observationraised the possibility of reducingthe numberof streptomycetes,and other unwanted bacteria on isolation plates, with a view to isolating streptoverticillia, a commercially significant group of actinomycetes rarely recoveredon "non-selective"isolation media. Hanka et al. (1985) were able to raise the proportion of Streptoverticillium colonies on isolation plates using an agarmedium supplemented with oxytetracyclineand the membrane-filterstripping method of Hirsch and Christenson(1983). Hanka and Schaadt(1988) further enhancedthe recoveryof streptoverticilliaby the addition of lysozyme,as well as oxytetracycline,to the agarmedium. Another approachto selective isolation involves tailoring media to the nutritional requirementsof target organisms. This has also been achievedby taking the relevant information from numerical taxonomicdatabases. The most extensivestudies have been canied out by Williams andhis colleagues(Vickelyet al., 1984; Williams et al., 1984a; Williams and Vickers, 1988) who used particular combinations of carbohydrateand amino acid, with and without antibacterial antibiotics, to favour the growth of uncommon streptomycetes previously shownto be a promising sourceof antibiotically active metabolites,or
69 to discourage the growth of the ubiquitous Streptomycesalbidoflavus. The selective agents were chosen objectively by examining the neutrophilic streptomycetedatabase (Williams et al., 1983b) using the DIACHAR program (Sneath,1980a). The highest diagnosticscores were given by characterswhich were all positive or negativefor strainsin one cluster when comparedwith all of the other numericallydefined taxa (Vickers et al., 1984). Other workers have also used the growth responsesof actinomycetesto sole nitrogen and/or carbon sources to selectively isolate other antibiotic- producing taxa. To this end, selective media have been designed to isolate specific fractions of the acidophilic streptomycetemicroflora (Goodfellow and Simpson,1987) and mycolatelesswall chernotypeIV actinomycetes(Whitehead, 1989)found in natural habitats(Table 8, page71). It is essential to evaluate,and if necessarymodify, computer-generated media formulations by altering combinations of selective agents in order to enhancethe recovery of target organisms (Goodfellow and O'Donnell, 1989). With neutrophilicstreptomycetes, the employmentof objectively designedmedia, and subsequentcomputer-assisted identification of isolates,showed that it was possibleto increasethe numberof particular speciesand decreaseothers but not with all of the soils tested(Vickers et aL, 1984). It was notedthat somespecies increasedor decreasedin a manner that could not be predictedby comparison with information in the data matrix. This is not altogethersurprising for the surface of an isolation plate is the site of intense competition between many bacterial colonies and the outcomeof the struggle for survival will not only be influencedby the compositionof the mediumbut also by the mix of speciesin the inoculum that are able to grow. This apparentproblem can be turned to good advantagewhen selectivepressures generated by the mediumreveal the presence of rare and novel speciesin soil samples. Thus, the role of computer-assisted
70 Table 8 Objectively formulated media basedon selective agentsderived from numericaltaxonomic databases using the DIACHAR program
SelectiveAgents Target Strains References
Adenineand streptomycin 'Saccharopolyspora'-Iike Goodfellow(unpublished organisms data) Raffinoseand histidine Streptomyces chromofuscus, Vickerset aL (1984), S. cyaneus and S. rochei Williams et aL (1984a) Rifampicin Streptomyces atroolivaceus, Vickerset aL (1984), S. diastaticus Williams et aL (I 984a) Sodiumchloride Streptomycesalbidoflavus, Williams andVickers S. atroolivaceus (1988) Rifampicin Acidophilic streptomycetes- Simpson(1987) cluster25 Butyleneglycol, Rhodococcusrhodochrous- lbomas (1991) clilorotetracyclineHCI and cluster 8 sodiumchloride Arginine, glycerol andstarch Streptomyceslividans Heffon andWellington (1990) Neomycin, streptomycin, Streptomyceslividans Wellingtonet aL (1990) thiostrepton,viomycin Aminobutyric acidand rhainnose Streptomycesalbidoflavus, Williams andVickers S. violaceusniger (1988)
71 methods in the development of targeted selective media must not be underestimated.Indeed, such methods are a vital elementfor the isolationof novel andtarget isolatesfrom natural habitats.
6. INCUBATION REGIMES
The temperatureand length of incubation also contribute to selectivity. Incubation at 250C to 300C favours mesophilic bacteria and incubation at 550C enhancesthe chancesof isolating thermotolerantand thermophilic actinomycetes. Novel and unusual isolates may be overlooked unless incubation periods are extended. Nonomura and Ohara (1971a) succeededin isolating several new speciesof less commonactinomycete genera when they incubatedisolation plates at 300C and 400C for up to one month. Little attention has been paid to the selectiveisolation of psychrophilic,anaerobic or autotrophicactinomycetes which represent potential sources of commercially important novel compounds. However, large numbers of carboxydotrophicactinomycetes have been isolated using carbon monoxideas a sole carbon sourceand shown to form a distinct and taxonomicallydiverse group (Falconeret al., 1993;O'Donnell et al., 1993).
7. SELECTION OF COLONIES
The ability to recognisemembers of actinomycetetaxa on primary isolation platesrequires the use of a microscopefitted with a high powered,long working distanceobjective. Continueddevelopments in image analysisand the application of sophisticatedsoftware may pave the way for automatedsystems capable of recognisingcertain colony typesdirectly on isolationplates. However,in general, it is not possibleto distinguishbetween members of speciesof the samegenus on isolationplates, a fact which may result in seriousduplication andwasted effort in industrial screeningprogrammes. Much subsequentduplication of effort can be
72 avoided by a preliminary grouping of isolates. With some genera this can be achievedvery easily. Thus, most streptomycetesoil isolates,grouped together on the basisof their easily determinedpigmentation characteristics, were identified to samecluster (taxospecies)in a frequencymatrix designedfor the identification of unknownstreptomycetes (Williams and Vickers, 1988).
8. SELECTIVE ISOLATION OF STREPTOSPORANGIA
Isolation procedures may combine one or more selective regimes (Goodfellow and Williams, 1986; Goodfellow and O'Donnell, 1989). Thus,
enrichment or pretreatment of environmental samples is often followed by
incubation on an appropriate selective isolation medium. Nonomura and Ohara (1969a) introduced a combined technique for the selective isolation of
streptosporangia from soil. Air-dried soil samples were passed through a 2mm sieve, ground using a pestle and mortar, spread on filter paper and heated in a hot air oven at 1200C for one hour prior to the preparation of soil dilutions and surface incubation of arginine-vitamins (AV) agar plates (Nonomura and Ohara, 1969a). Inoculated plates were incubated at 300C for four to six weeks when colonies bearing spore vesicles on an abundant aerial mycelium, that is, streptosporangia,
were observed on isolation plates. Nonomura and Ohara (I 969a) isolated streptosporangiafrom several Japanese soils using arginine-vitamins (AV) medium in numbers up to 2.0 x 104 colony forming units per gram dry weight of soil. They also recovered large numbers of Microbispora strains, 2.8 x 104 colony forming units per gram dry weight, from the same soils. Hayakawa and Nonomura (1987a, b) successfully used humic acid-vitamins agar for the isolation of large numbers of actinomycetes belonging to the genera Dactylosporangium, Microbispora, Micromonospora,
Microtetraspora, Nocardia, Streptomyces, Streptosporangium and
73 Thermomonospora.Humic acid-vitamins(HV) agar supplementedwith nalidixic acid (30mg/1)and cycloheximide (50mg/1)was subsequentlyused to selectively isolate Microbispora and Streptosporangiumstrains (Nonomura and Hayakawa, 1988). Recently, Hayakawaet al. (1991) introduceda new procedurefor the selective isolation of streptosporangia which involved heat pretreatment (120OC/Ihour) of air-dried soil with benzethoniumchloride (0.01%) prior to plating out onto HV agarsupplemented with leucomycin(I mgA) andnalidixic acid 30rng/l. Little attempt has been made to evaluate the effectivenessof these proceduresfor the selectiveisolation of streptosporangia.
74 NIATERJALS AND NWTHODS
A. SELECTIVE ISOLATION, ENUMERATION AND CHARACTERISATION OF STREPTOSPORANGIA 1. ENVIRONMENTAL SAMPLES
Soil samples collected from diverse habitats were used to prepare composite samples (Table 9, page 76) that were used for the isolation of streptosporangia.
2. SOIL REACTION
The pH of all of the soil sampleswas determined using the methodof Reed and Cummings(1945). Each sample(ca. 20-25g) of fresh material was placed into a 100 ml beakerand deionized water addedslowly while agitating until the samplewas thoroughlywetted, that is, when a thin layer of waterhad appearedon the surfaceof the sample. Samplesprepared in this way were left to equilibrate for one hour when the pH was determinedusing a glass electrode pH meter (Model 292, Pye Unicarn Ltd., Cambridge,England, U. K. ). The electrodewas pushed well down into the sample and the reading allowed to stabilise before being taken. The final pH was takenas an averageof severalreadings since small local variationsmay exist within samples.
3. PRETREATMENT AND DILUTION PLATE PROCEDURES
The compositesoil sampleswere dried at room temperaturefor four weeks. Some of the samples were then heated at 1200C for an hour in a hot air oven (Nonornuraand Ohara, 1969a). A seriesof dilutions were made as shown in Table 10,page 77 using both heatpretreated and non-heat pretreated preparations. Compositesoil samples(ca Ig) were accuratelyweighed and aseptically addedto sterile Universal bottles of known weight. Both treatedand untreated
75 Table 9 Sourceof soil samplesused for selectiveisolation of streptosporangia
Numberof Source Time of Sampling LaboratorySoil
433-434 Gardensoil, Hibuya Park,Tokyo, Japan May, 1989 435-436 Gardensoil, TsukubaUniversity, May, 1989 Tsukuba,Japan 443-444 Gardensoil, IMTECH, Chandigarh, April, 1990 India 482-489 Tropical rainforestsoil, Meru Betini, July, 1991 Indonesia 512-513 Rim of crater,Mount Bromo, Bromo, July, 1991 Indonesia 515-516 Gardensoil, Yogyakarta,Indonesia July, 1991 576-577 Soil rich in humus,Keswick, England, September,1991 U. K. 579-581 Ginsengfield (post harvest),Kumsan, September,1991 Republic of Korea 583-584 Ginsengfield (post harvest),Kumsan, September,1991 Republicof Korea 585-587 Ginsengfield (youngplant), Kumsan, September,1991 Republicof Korea 604-605 Woodlandsoil, Mount Sorak,Republic September,1991 of Korea A2 Subtropicalrainforest soil, Brazil
76 Table 10 Pretreatmentand dilution plate regimes*used for the selectiveisolation of membersof the genera Microbispora and Streptosporangiumfrom diverse compositesoil samples
Pretreatmentof Soil 10-1Dilution
Heat 120OC/Ihour Salinesolution (0.9ml, 0.85%, w/v)
Heat 120OC/Ihour Phenol(0.9ml, 1.5%,w/v, 300030 minutes)
Sodiumdodecyl sulphate(0.05%, w/v, Salinesolution (0.9ml, 0.85%, w/v) 40OC/20minutes)
Yeast extract(6%, w/v, 40OC/20minutes) Salinesolution (0.9ml, 0.85%,w/v)
Procedurerecommended by Nonomuraand Hayakawa (1988).
77 1 10- suspensionswere shakenon a Griff ,n flask shaker(Griff in and GeorgeLtd., Manchester,England, U. K. ) for 30 minutesat speedsetting 8 to dispersebacteria. Tenfold dilutions of the suspensionswere made by transferring Iml samples aseptically, using automatic pipettes (PlOOO;Gilson, Anachem Ltd., Luton, Bedfordshire,England, U. K. ) fitted with sterile tips, to steriletest tubescontaining 9.0 ml. of sterile saline solution (0.85%, w/v) and mixing on a Vortex mixer (FisonsScientific ApparatusLtd., Loughborough,Leicestershire, England, U. K. ).
Aliquots (O.Iml) of each dilution were aseptically,pipetted, using an automaticpipette (P200; Gilson, AnachemLtd. ) fitted with sterile tips, onto the surfaceof humic acid and vitamins (HV) agar (Nonomura,1984; Hayakawa and Nonomura, 1987a) supplementedwith nalidixic acid (30mg/1)and actidione (50mg/1)(Nonomura and Hayakawa,1988). Four plates were preparedfor each dilution, aliquots (0.1 ml) of the 10-1toIO4 dilutions were dispersedover room dried agar surfacesof the isolation medium (Vickers and Williams, 1987),using sterile glass spreaders. Inoculatedplates were incubatedat 300C for up to four weeks. The number of target organisms,and the total number of actinomycetes, growing on the isolation plateswere recordedand expressedas the meannumber of colony forming units (cfu) per gram dry weight soil.
4. SELECTION, PURIFICATION AND MAINTENANCE OF ISOLATES
After incubation,isolation plates were examinedboth by eye and using a binocular microscopefitted with long distance objectives(X400, magnification; Nikon Kogaku K. K., Tokyo, Japan). Organismswere tentativelyassigned to the genusMicrobispora if they producedaerial hyphaewith sporophoresbearing two spores,to the genusMicrotetraspora if aerial hyphaecarried four spores,to the genusStreptosporangium if sporevesicles were detected on aerial hyphae,and to a catch all group labelled "unknownactinomycetes" if they did not fall into any of
78 the previous catagories. It was observedthat many of the organismsgrowing on the selective isolation plates had long spore chains like those characteristicfor some Microtetraspora, Saccharopolysporaand Streptomycesspecies; these organisms were categorisedas "unknown actinomycetes". Photographswere taken of thirty-six putative streptosporangiagrowing on isolation plates using a Nikon camera(35mm, Nikon Kokaku K. K., Tokyo, Japan)fitted to the binocular microscope. One hundredand fifty-three putative streptosporangia,that is, all that were detected,were takenfrom the isolation plates(Table 20, pages 133to 136) using sterile tooth-picks and inoculated onto HV agar plates (Nonomura, 1984; Hayakawaand Nonomura,1987a), which were incubatedat 300Cfor two weeks. All of the strainswere checked for purity both by eye andthe using the binocular microscope,single colonies were then picked from the platesusing sterile loops and inoculated onto HV plates (Nonomura, 1984; Hayakawa and Nonomura, 1987a) which were incubated for two weeks at 300C. This procedure was repeateduntil pure culturesof all of the isolateswere obtained. In addition, 46 randomly selectedunknown strains growing on isolation plateswere purified and maintainedas frozen glycerol suspensions(Table 11, page80). The pure cultures were maintained on modified Bennett's agar slopes (Agrawal, unpublisheddata) and stored at room temperature. Each slope was usedto inoculatetwo modified Bennett'sagar plates that were incubatedat 300C for 10 days. Glycerol suspensionswere preparedby scrapingaerial and substrate mycelium from the incubatedplates and making heavy suspensionsin 1.5ml of glycerol (20%, v/v, BDH) in each of two Cryo vials (2.5ml; Whatman Ltd., Williams, Maidstone,England, U. K.) that were stored at -250C (Wellington and 1978). One glycerol suspensionwas usedas a working suspensionand the other kept as a stock culture. The frozen glycerol suspensionsserved as both a
79 Table II Sourceof unknownactinomycetes isolated using HV agarsupplemented with actidione (50mg/1)and nalidixic acid (30mg/1)and incubatedat 300Cfor 4 weeks
Strain Number(A) Soil Numbers SelectiveIsolation Procedure
001,003-005,007,433-434 Dried soil heated at 120OC/Ihour 009-011 013-015,018-021 Dried soil heated at 120OC/Ihour and treated with phenol (1.5% w/v; 30OC/30minutes) 022-024,026,029- Dried soil treated with sodium dodecyl 030 sulphate (0.05% w/v; 40OC/20minutes) 033-036,039,041- Dried soil treated with yeast extract (6% 042 w/v; 40OC/20minutes)
046-047,049 443-444 Dried soil heated at 120OC/Ihour 050-052,055 Dried soil heated at 120OC/Ihour and treated with phenol (1.5% w/v; 30OC/30minutes) 056-058,061-062 Dried soil treated with sodium dodecyl sulphate (0.05% w/v; 40OC/20minutes) 064-067,069-070 Dried soil treated with yeast extract (6% w/v; 40OC/20minutes)
80 convenientmeans of long term preservationand as a sourceof instant inoculum. Working inocula were obtained by thawing glycerol suspensionsat room temperaturefor about 15 minutes when they were treatedas conventionalbroth cultures. After use, the glycerol suspensionswere promptly frozen and stored once again at -250C. Although repeatedfreezing and thawing decreasescell viability, this methodof preservationcompares favourably with other techniques for storing cultures,including Iyophilisation(Wellington and Williams, 1978).
5. CHARACTERISATION
a. Analysis for Diandnopimelic Acid 1) Growth and Harvesting One hundredand thirty-six of the 153 putativestreptosporangia (Table 12, pages 82 to 83) and the 46 unknown actinomycetes(Table 11, page 80) were examinedfor the presenceof isomersof diaminopimelicacid. Thawed glycerol suspensionswere usedto inoculatesterile cellulose nitrate membranefilters (0.45 Wn pore size, 47mm diameter;Whatman Ltd., Maidstone,England, U. K. ) which had been placed in the centreof Petri disheson modified Bennett'sagar plates (Agrawal, unpublisheddata) which were incubatedfor 2 weeksat 300C. After incubation,the test strainswere scrapedfrom the surfacesof the membranefilters and put into sterileUniversal bottles. The sampleswere kept in a freezer(-200C) for Ihour then Iyophilised (Edward's High Vacuum, Model EF03, Crawley, England,U. K. ).
2) Whole-Organism Hydrolysis
Dried biomass(ca 20mg)was hydrolysedwith Iml of 6N HCI in a screw- cappedtube at IOOOCfor 18 hours. After cooling, the hydrolysatewas filtered (Whatinan No. 1 Filter Paper, Whatman Ltd., Maidstone, England, U.K. ) and
81 Table 12 Source of Streptosporangiumisolates examinedfor the presenceof diaminopimelic acid and for diagnostic characters needed for numerical identification
Strain Number (HJ) Soil Numbers Selective Isolation Procedure
001-002 585-587 Dried soil heatedat 1200C/lhour 005-006 Dried soil heatedat 1200C/lhourand treated with phenol(1.5% w/v; 30OC/30minutes) 008 Dried soil treatedwith sodiumdodecyl sulphate(0.05% w/v; 40OC/20minutes) 009-011 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
012-017,019-024 579-581 Dried soil heatedat 120OC/lhour 025-033 Dried soil heatedat 1200C/lhourand treated with phenol(1.5% w/v; 30OC/30minutes) 034-051 Dried soil treatedwith sodiumdodecyl sulphate(0.05% w/v; 40OC/20minutes) 052-087 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
090-092 583-584 Dried soil heatedat 120OC/Ihour 093-094,096-097 Dried soil heatedat 120OC/Ihour andtreated with phenol(1.5% w/v; 30OC/30minutes) 098-099 Dried soil treatedwith sodiumdodecyl sulphate(0.05% w/v; 40OC/20minutes) 100-103 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
104 443-444 Dried soil heatedat 120OC/Ihour 105-106 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
82 Table 12 continued Strain Number(HJ) Soil Numbers SelectiveIsolation Procedure
107-109 433-434 Dried soil heated at 120OC/Ihour III Dried soil treated with yeast extract (6% w/v; 40OC/20minutes)
112-114 435-436 Dried soil heated at 120OC/Ihour 115-116 Dried soil treated with yeast extract (6% w/v; 40OC/20minutes)
117-118 482-489 Dried soil heatedat 1200C/lhour 122 Dried soil heatedat 1200C/lhourand treated with phenol(1.5% w/v; 30OC/30minutes) 123-124 Dried soil treatedwith sodiumdodecyl sulphate(0.05% w/v; 40OC/20minutes) 125-126,128-129 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
130-132,133 515-516 Dried soil heatedat 120OC/Ihour 135 Dried soil heatedat 120OC/Ihourand treated with phenol(1.5% w/v; 30OC/30minutes) 138-141 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
143 604-605 Dried soil heatedat 120OC/Ihour 144,146-147 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
148-149 576-577 Dried soil treatedwith sodiumdodecyl sulphate(0.05% w/v; 40OC/20minutes) 150-153 Dried soil treatedwith yeastextract (6% w/v; 40OC/20minutes)
83 washed twice with Iml sterile distilled water. The combined filtrates were concentratedto drynessusing a vacuumpump. The dried extractwas dissolvedin Iml of distilled water and dried again until the smell of FIG was lost. The residue was then redissolved in distilled water (0.3ml) and transferredto an Eppendorftube.
3) One-DimensionalTbin Layer Chromatography
Eight or nine samplesof residuewere applied as aliquots (5gl) onto the base line of a cellulose TLC plate (20x2Ocm, Merck 5716, Merck Ltd., Warwickshire,England, U. K. ). In addition, a standardwas also applied,namely 591mim-d T,-wfs -f oex-diaminopimelic acid (Sigma 1377). The plates were developedin methanol:water: ION HCI:pyridine (80:26.25: 3.75: 10, v/v) for up to 4 hours, that is, until the solventfront had nearly reachedthe top of the plate. The plates were dried in a fume cupboardand spots visualisedby sprayingwith ninhydrin in acetone(0.2%, w/v) followed by heating at 1000Cfor 5 minutes. The diaminopimelic isomers were detected after about 3 minutes as brown colouredspots; the remainingamino acidswere visualised as blue-violetcoloured spots. The spots correspondingto the diaminopimelic acid isomers became yellow after 24 hours.
b. Morphological Studies
Three of the putative Streptosporangiumstrains, namely HJ47, IU84 and FU94, isolated from Ginseng field soil (Kumsan, Republic of Korea), were examined to determine the morphology of spore vesicles using a Scanning Electron Microscope (Cambridge S240, Cambridge Instrument Ltd., Cambridge,
England, U. K. ). All of the test strains produced abundant aerial myceliurn on arginine-vitamins (AV) agar plates (Nonomura and Ohara, 1969a).
84 Plugs (5 mm diameter)of the agarmedium were taken from platesof the test strains grown on AV agar for 2 weeks at 300C, Placed into 2 ml of gluteraldehyde(25%) /Ca codylate (IM) held in bijoux bottles and kept in the fixative solution for 3 hours at 200C. The fixed sampleswere dehydratedin a graded alcohol series (10%, 25%, 50%, 75%, 90%, 100%) for 10 minutes consecutively,then dried in a Biorad Critical Point Drier (BIORAD CDP750,VG Microtech Ltd., East Sussex,England, U. K. ) using liquid C02 as the transition fluid. The plugs were then mounted onto stubs using 'silver dag' adhesiveand coated with gold by means of a Polaron Sputter Coater (E 5100, Fisons InstrumentsLtd., East Sussex,England, U. K.). The gold-coatedspecimens were observedby ScanningElectron Microscopy using a acceleratingvoltage of 12KV andphotographed. Photographs were taken with an arnplicationrange of 638X to 11.6KX magnification.
B. NUMERICAL IDENTIFICATION 1. PRACTICAL EVALUATION OF THE STREPTOSPORANGIUM FREQUENCY MATRIX
Seventy strains representing the twelve major Streptosporangium clusters (Table 13, pages 86 to 90) defined in the numerical taxonomic survey of Streptosporangiumand related taxa (Whitham, 1988;Whitham et al., 1993)were examined under code for each of the diagnostic tests recommendedfor the computer-assistedidentification of streptosporangia(Whitham, 1988; Table 14, pages91 to 93). All of the test strainswere examined in duplicate. Identification scoreswere determinedusing the IDENTIF-Yprocedure in the TAXON program (Ward, unpublisheddata; Appendix A).
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v vli vi ý M) 0 IOZ 9 9U0u4 Z 2 .-t a. ý 1: 92 C, C) 0 C) C, men CD CD sag C, 00 tr 88 C, oczo 0 0 000 ý-0 e en ý 0 Cf 0gZ oý oý O\ 00 a Ao 00 NA , 00 O\ ýV, 00 00 C% a, 'Z CS kor) mu 93 2. IDENTIFICATION OF UNKNOWN STREPTOSPORANGIA One hundred and thirty-six putative streptosporangiafrom diverse soil samples(Table 12, pages 82 to 83) were examinedin duplicate as described above. 3. CHARACTERISATION OF STRAINS All of the test strains, that is, the marker strains and fresh isolates,were examinedfor twenty-six unit characters(Whitham, 1988). Tests were repeated where ambiguousor clearly unexpectedresults were obtained. Details of media, their preparation and sterilisation are given in Appendix B. Unless otherwise stated,all testswere carried out at pH 7.0. Where possible, tests were carried out using a multipoint inoculation procedurethat involved the use of an automaticmultipoint inoculator (Denley- Tech; Denley Instruments Ltd., Daux Road, Billingshurst, Sussex,England, U. K. ). This apparatusallows standardised,multiple surfaceinoculation of 90mm diameterPetri dishes(Sterilin Ltd., Teddington,Middlesex, England, U. K. ) with either twenty, twenty-five or thirty different organisms. In this study, the multipoint inoculator was used with an inoculation head that containedtwenty pins for culture transfer and a marker pin that provided a referencepoint to orientateplates. Inocula werepipetted into sterile,inverted Oxoid capsheld in an orderedarray within a metal templateplaced inside the baseof a 100mmsquare plastic dish (Sterilin Ltd., Teddington, Middlesex, England, U. K. ). For each group of twelve test strains inoculated, control plates of the appropriatebasal mediumwere seededat the beginningand end of the inoculationprocedure. This practicewas followed throughoutto eliminatefalse negativeresults due to loss of inoculum. 94 a. Biochendcal Test 1) Urea Urea is a product of amino acid metabolism. The hydrolysis of urea was detectedusing the medium and methodof Gordon (1966). Broth cultures were incubatedat 300Cand examinedafter 7,14,21, and 28 days. The test medium contained the indicator phenol red. The hydrolysis of urea results in the formationof ammoniaand carbon dioxide: Urease NH2CONH CO 2+H 20 )2NH3+ 2 Urea Water Ammonia CarbonDioxide The consequentialchange in pH from neutral to alkaline is detectedby the phenol red indicator. b. Degradation Test 1) Keratin Keratin is a highly insoluble protein found in hair, wool, hom and skin. Keratin (5g/1)was incorporatedinto AV agar(Nonomura and Ohara,1969a), care being taken to ensurean evendistribution of this insoluble compound. Clearing of the compoundfrom underand aroundthe areaof test straingrowth was taken to denotea positive result. Plateswere examinedafter incubation for 7,14,21 and 28 daysat 300Cto detectthe breakdownof the substrate. 2) Starch Starchoccurs principally as the main reservepolysaccharide in plants and is a composite molecule consisting of (x-D-glucopyranosesubunits in two different structuralconfigurations; amylose, a linear moleculewith ct-1,4-linkages and amylopectin,a (x-1,4-linkedbackbone with cc-1,6-branches.Both ccand P- amylasesdegrade starch. (x-Amylasesrandomly cleave the cc-1,4-glucosidic 95 linkagesso that amylose,for example,is broken down initially to dextrins and then to a mixture of maltoseand glucose. P-Amylasesact on linear (X-1,4-linked glucans and cleave alternate bonds from the non-reducing end of the chain forming maltose. The production of extracellular amylases was detected in AV agar (Nonomura and Ohara, 1969a)supplemented with potato starch (10g/1). The inoculatedplates were incubatedfor 14 days at 300Cwhen thosesupporting good growth were flooded with Lugol's iodine (Cowan and Steel, 1974). Iodine complexes with amylose to form a dark blue starch-iodine complex whereas dextrins,maltose and glucose are unableto do so. A positive result was indicated by a zoneof clearingaround the areaof growth. 3) Deoxyribonudeic Acid DNA is a linear polymer of deoxyribonucleotideswhere the adjacent residuesare linked by Y, 5'-phosphodiesterbridges. Eachresidue in the chain is linked to oneof severalnitrogenous bases, nairnely adenine, thymine, cytosine and guanineand, in someinstances, modifications of thesebases. All bacteriapossess intracellular nucleasesfor the manipulationof their own nucleic acids but only some produce the extracellular enzymes capable of specific or non-specific hydrolysisof extracellularnucleic acids. The degradationof DNA was detectedusing Bacto DNase Test Agar (Difco) which containsDNA at 2 g/l. Inoculatedplates were incubatedfor 14 days at 300Cand checkedfor good growth before flooding with a molar solution of hydrochloric acid. This test dependson the ability of the deoxyribonucleases to reducethe viscosity of solutionsof the appropriatesemi-purified nucleic acid extracts. Degradationproducts from nucleasetreated nucleic acids are acid solublewhereas native DNA is precipitatedby the additionof IM HCL Thus, the 96 diffusion of any nucleasesinto the growth medium from a test strain results in a clear zone around and under the colonies after addition of the acid. Zones of clearingwere scoredas positive results. c. Morphological Tests The test strainswere grown on oatmealagar (Mister, 1959) at 300Cfor 3 weeks when aerial mycelial pigmentationwas recordedusing a binocular plate microscope(Nikon Kogaku K. K. Tokyo, Japan) at X40 magnification. The colours of the aerial mycelium.were recordedand assignedto two colour groups, namelypink andwhite. d. Nutritional Tests The test strainswere examinedfor their ability to utilise three compounds, D(+)galactose,mannitol andturanose, as the sole sourceof carbonfor energyand growth. Thesecarbon sources(10g/1) were preparedas aqueoussolutions and sterilisedby steamingat IOOOCfor 30 minuteson three consecutivedays. The sterilised test compoundswere added to sterile, molten carbon utilisation agar (ISP 9 medium;Appendix B) recommendedby Shirling and Gottlieb(1966). Inocula were preparedfrom test strains grown on sterile cellulose nitrate membranefilters (0.45pmpore size, 47mm diameter;Whatman Ltd., Maidstone, England, U. K. ) which had been placed in the centre of Petri dishes on AV medium (Nonomuraand Ohara, 1969a),inoculation was for two weeksat 300C. After incubation,aerial mycelia and sporeswere transferred to 2ml salinesolution (0.85%, w/v) in bijoux bottles. The freshly preparedsaline suspensionswere used to inoculate Petri dishes containing carbon utilisation agar supplemented with a carbon source,plates of basalmedium supplementedwith glucose(10g/1), the positive control, and plates of basal medium alone, the negative control. 97 Inoculations were carried out using the multipoint inoculation procedure describedearlier with twelve organismsper Petri dish. Inoculated plates were incubated at 300C and examinedfor growth after 7,14 and 21 days. When scoringthe plates,growth on the testmediwn wascompared with that on both the positive and negativecontrols. Growth, when greaterthan that on the negative control, was scoredas positive and growth that was equal to or less than that on the negativecontrol was scoredas a negativeresult. e. Physiological Tests 1) Growth in The Absenceof B-Vitamins The test strains were examinedfor their ability to grow on AV agar (Nonomuraand Ohara, 1969a)without B-vitamins- Plateswere inoculatedfrom saline suspensions(0.85%, w/v) using the multipoint procedurethen incubatedat 300C for 14 days. After incubation, plates were scored by comparisonwith growth on a control plate of AV mediumalone. Growth was scoredas a positive result. 2) Growth in The Presenceof Chemical Inhibitors The test strains were examined for their ability to grow on AV agar (Nonomura and Ohara, 1969a) supplementedwith one of three chemical inhibitors, namely phenyl ethanol(I. Omlll), sodium chloride (40g/1)and thallous acetate(0.005gll). Plateswere inoculatedfrom salinesuspensions (0.85%, w/v) using the multipoint procedure then incubated at 30oC for 14 days. After incubation,plates were scoredby comparisonwith growth on a control plate of AV medium alone.Growth wasscored as a positive result. 98 3) Resistanceto Antibiotics The test strainswere examinedfor their ability to grow in the presenceof antibiotics. All but one of the antibiotics were sterilised by Seitz-filtration of aqueoussolutions. Rifampicin wasdissolved in dimethylformamide(0.2ml; BDH Laboratory Supplies, Warwickshire, England, U. K. ) and then added to the appropriateamount of steriledistilled water. The sterilisedantibiotics wereadded to molten,cooled AV agar,at pH 7.0, to give the appropriateconcentrations. Media were dispensedinto Petri dishes and inoculated immediately after setting. Plates were inoculated using the automatic multipoint inoculator using suspensionsof spores and mycelial fragments in sterile saline solution (0.85%, w/v). Inoculated plates were incubatedat 250Cthen examinedfor growth after 4,7 and 14 days. Growth of culturesin the presenceof eachantibiotic wascompared to that on a control plate consistingof the basalmedium alone. Cultures showingresistance were scored positive. 4) Toleranceto Temperature Organismswere testedfor their ability to grow at 37oC. The test strains were inoculated from saline suspensions(0.85%, w/v), using the multipoint inoculation procedure,onto AV agar medium (Nonomuraand Ohara, 1969a). Inoculatedplates were incubatedat 370C for 14 days and examinedfor growth against the control plate of AV meditun alone. Growth was scoredas positive, lack of growth indicated a negative result provided the organismgrew on the control plate. 99 4. CODING AND COMPUTATION All of the testswere binary and hencewere scored "I" for a positive result and "0" for a negativeone. The binary test results weretyped in a +/- format as input to the TAXON program (Ward, unpublisheddata; AppendixA) andrun on a IBM personal computer; data were stored on hard disc. The results of the duplicated cultures were analysedand the test reproducibility expressedas the test variance(Si2; formula 15; Sneathand Johnson,1972). The +/- results in the final data matrix were analysedusing the IDENTEFYprocedure in the TAXON program (Ward,unpublished data; Appendix A). 5. DETERMINATION OF IDENTIFICATION SCORES The test strains were identified as far as possible using the frequency matrix (Tablel4, pages91 to 93) and algorithmsmodified from the MATIDEN program (Sneath,1979a) and incorporatedinto the IDENTIFY procedurein the TAXON program(Ward, unpublisheddata; Appendix A). IDENTIFY provides the best identification scoresfor known and unknownstrains against a frequency matrix consistingof q taxa and m unit characters. Percentagesin the frequency matrix, with 0 changedto I and 100 to 99% (Lapageet al., 1970),are converted into proportions,Pij, for the ith characterof taxonj. The characterstate values of an unknownorganism (u) are comparedwith eachtaxon in turn and identification coefficientscalculated and printed out for all of the clusters. Four identification coefficientswere calculated,namely Willcox probability, taxonomicdistance (d), the 95% taxonomicradius andGaussian distance probability. 100 C. SEQUENCING OF 5S RI]BOSOMAL RNA i 1. TEST STRAINS Glycerol suspensionsof the test strains(Table 15, page 102) were usedto inoculate modified Bennett's agar plates (Agrawal, unpublished) which were incubatedat 30oC for 7 days. After incubation, the strains were checked for purity by eye and usedto inoculate200ml of modified Bennett'sbroths in 500ml long neck flasks which were shakenat 300Cfor 5 days on a orbital incubatorat 200 rpm. Test strainswere harvestedusing a Beckmanncentrifuge (Rotor JAIO) at 10,000rpm for 20 minutesat 4oC. 2. PREPARATION AND SEQUENCING OF 5S rRNA Wet biomass(ca 5g) washomogenised with aluminiumoxide (2g), mixed with 7ml of buffer containing lOmM Tris hydrochloride (pH 7.5), l()mM MgC12, O.IM KCI and lOgg of DNase 100mg/ml for 10 minutes at PC then centrifuged at 5,000 rpm for 15 minutes at PC Ethanol was then added to the aqueousphase dissolved in 7ml buffer which was kept at -200C. The resultant precipitate was of containing lOmM Tris hydrochloride (pH 7-5), IOMM MgC12, O.IM KCI and 10inl of phenol and the preparation centrifuged at 7,000 rpm for 10 minutes at 200C. The resultant supernatant was kept in ethanol at -2OoC. This procedure was repeated to yield more rRNA. The RNA preparation was examined by electrophoresis on a 12% polyacrylamide, gel containing 7M urea, O.IM Tris- borate (pH 8.3) and ImM EDTA. The electrophoresed preparation was stained with ethidium. bromide(20mg/1), and the 5S rRNA band excised and eluted with 0.5M ammonium acetate-O.ImM EDTA-sodium dodecyl sulphate (0.1%, w/v) at 370C. The 32p labelling of the 5' tenninus of was done with [T -32p]ATP and polynucleotide kinase after pretreatment of the 5S rRNA with alkaline 101 Table 15 History of the strainsexamined in the 5S rRNA sequencingstudies Strain StrainIdentity Strain History / Source Number TW 006 Streptosporangiumalbidum DSM 43870; T. Okuda, MCRL-048; soil, Japan. (Furumai et al., 1968) (ATCC 25243; TO 13901;KCTC 9237) TW 001 Streptosporangium DSM 43179;A. Seino, KCC A-0026; H. amethystogenes(Nonomura Nonomura,FYU S5; soil. (ATCC!33327; CBS and Ohara, 1960) 429.61; NRRL B-2639; RIA 764) TW 004 Streptosporangium DSM 43181; A. Seino,KCC A-0 115; H. pseudovulgare (Nonomura Nonomura,FYU, S2-32;soil. (ATCC 27100; CBS and Ohara, 1969b) 881.70;IIFO 13991; KCTC 9239) TW 021 Streptosporangium DSM 43850; ATCC 25242 viridogriseum subsp. viridogriseum (Okudaet al., 1966a) TW 007 Streptosporangiumvulgare DSM 43802; G. Vobis, MB-TI9; H. Nonomura, (Nonomuraand Ohara, 1960) FYU S- 1; paddyfield, Anjo, Aichi Prefecture, Japan.(ATCC 33329; CBS 433.61; KCC A-0028; NRRL B-2633; RIA 765) TW 166 Streptosporangiumsp. Centrotypestrain of cluster 1; Plot 5, Cockle Park ExperimentalFarm, Northumberland, England, U. K. TW 292 Streptosporangiumsp. Centrotypestrain of cluster2; Plot 10, Cockle Park ExperimentalFarm, Northumberland, England, UX HJ 011 Streptosporangiumstrain Unidentified strain,Ginseng field (youngplant), Kumsan,Republic of Korea HJ 090 Streptosporangiumstrain Identified to cluster 1; Ginsengfield (postharvest), Kumsan,Republic of Korea Abbreviations:ATCC, American Type Culture Collection, 12301 Parklawn Drive, Rockville, Maryland, U. S.A.; CBS, Centraalbureauvoor Schimmelcultures, Baarn, Netherlands; DSM, DeutscheSammIung von Mikroorganismen und ZelIkulturen, Mascheroder Weg IB, D-38124, Braunschweig, Federal Republic of Germany; FYU, Departmentof FermentationIndustries, YamanashiUniversity, Motoganagi- cho, Kofu-shi, Yamanashi-ken, Japan; IFO, Institute of Fermentation, 4-54 Juso Nishino-machi, I-ligashiyodogawa-ku,Osaka, Japan;KCC, Kaken Chemical CompanyLimited, 6-48 Jujodai, I-Chome, Kita-ku, Tokyo 114, Japan;KCTC, Korean Collection of Type Cultures, Genetic Engineering Research Institute, Korean Institute of Scienceand Technology,Republic of Korea; NRRL, Northern Researchand DevelopmentDivision, United StatesDepartment of Agriculture, Peoria, Illinois, U. S.A.; RIA, Research Institute for Ampelology, Budapest,Hungary. 102 phosphatase(Donis-Keller, 1980). The 32p labelling of the 3' tem-dnuswas perfonnedwith [51-32p]pCp with RNA ligase (Peattie,1979). The 5'- or Y-end labelled 5S rRNAs were digested completely by nuclease P, or RNase T2 using thin-layer cellulose plates and autoradiographs (Kuchino et al., 1979). 5S rRNA secondarystructure models were constructedusing the method of Tinoco et al. (1971), as adaptedby Hori and Osawa(1986). 3. PHYLOGENETIC ANALYSIS The evolutionayldistance, Knuc, and the standard error of KnUC' (Ykl betweensequences were calculatedafter Kimura (1980). Knuc correspondsto the number of base substitutionsper nucleofide site that have occurred in the courseof evolution: Knuc =-I log, [(l 2P Q)(I 2Q)112J 2 - - - where P and Q are the fractions of nucleotide sites showing transition- and transversion-typedifferences, respectively. A phylogenetictree wasgenerated by applying the weightedpair group averageclustering method with meanaverages algorithm (Sneathand Sokal, 1973)to the Knuc valuesto determinethe branching orderand relative evolutiondistances. D. PYROLYSIS MASS SPECTROMETRY 1. TEST STRAINS Experimentswere carried out to evaluatethe statusof clustersrecovered in the numericalphenetic survey of Whitharn(1988) and to deter-minethe quality of some of the results obtained in the computer-assistedidentification procedure. The strain historiesof all of the test strainsare shown in Table 16, pages 105 to 103 108. All of the strains were maintained as frozen glycerol suspensionsas describedearlier. (i) Experiment 1: The aim of this experiment was to determine whether representativesof streptosporangialclusters I and2 (Whitham, 1988;Whitham et al., 1993)could be separatedby using Curie-pointpyrolysis massspectrometry. (ii) Experiment 2: This experimentwas designedto determinethe relationships betweenrepresentatives of streptosporangialclusters I and 2 (Whitham, 1988; Whithamet al., 1993)and type strainsof Streptosporangiumspecies. (iii) Experiment 3: The aim of this experimentwas to detenninewhether isolates identified to clustersI and 2 groupedwith representativesof the respectivetaxa. 2. GROWTH CONDITIONS Glycerol stockcultures were usedto inoculatesterile polyvinyl membrane filters (0.45 mm, HV type; Millipore) placed over a mediumoriginally designed to inhibit the sporulationof streptomycetes(20 g, Casaininoacids; 20 g, starch;4 g, yeast extract; 18 g, Bacto agar; I litre distilled water; pH 6.4-6.6; Sanglieret al., 1992). Duplicated preparationswere incubatedfor 3 days at 300C and the growth obtainedused to inoculate a further set of plates which were incubated underidentical conditions. 3. PREPARATION AND ANALYSIS OF SAMPLES Pyrolysis foils and tubes (Horizon Instruments,Heathfield, East Sussex, England,UK. ) were washedin acetoneand dried overnightat 270C. Singlefoils were inserted,with flamed forceps,into pyrolysis tubesso as to protrudeabout 6 104 Table 16 Sourceof strainsexamined by Curie point pyrolysis massspectrometry StrainNumber Strain History / Source EXPERIMENT I a) Cluster 1 (Streptosporangiumsp. ) 166*, 167 Plot 5, Cockle Park Experimental Farm, Northumberland, England, U. K. 256 Plot 9, Cockle Park Experimental Farm, Northumberland,England, U. K. 100,101,109,117,118,121,124,Oak copse, Corbridge, Northumberland, England, U. K. 127,131,140,142,145a,159, 179,225,235,245 369 BurtonBushes, Beverley Westwood, Hull, U.K. b) Cluster 2 (Streptosporangiumsp. ) 269,272,273,274,275,398,399 Plot 3, Cockle Park Experimental Farm, Northumberland,England, U. K. 281,287,292*, 303,308 Plot 10, Cockle Park Experimental Farm, Northumberland,England, U. K. 113 Oak copse,Corbridge, Northumberland,England, U. K. 366 Burton Bushes,Beverley Westwood, Hull, U. K. EXPERIMENT 2 a) Cluster I (Streptosporangiumsp. ) 166* Plot 5, Cockle Park Experimental Farm, Northumberland,England, U.K. 101,109,118,124,142, Oak copse,Corbridge, Northumberland, England, U. K. 145a, 179,225,245 105 Table 16 continued StrainNumber Strain ffistory / Source b) Cluster 2 (Streptosporangiumsp. ) 272,273,274,275,399 Plot 3, Cockle Park Experimental Farm, Northumberland,England, U. K. 287,292*, 303,308 Plot 10,Cockle Park Experimental Farm, Northumberland, England, U.K. 113 Oak copse,Corbridge, Northumberland,England, U. K. 0 Type Strains Streptosporangiumalbidum DSM 43870;T. Okuda,MCRL-048; soil, Japan.(ATCC 25243;EFO (Furumaiet al., 1968) 13901) Streptosporangium DSM43179; A. Seino,KCC A-0026;H. Nonomura,FYU S5;soil. amethystogenes(Nonomura and (ATCC33327, CBS 429.61; NRRL B-2639; RIA 764) Ohara,1960) Streptosporangiumcorrugatum DSM 43316;S. T. Williams,E90; beach sand. (ATCC 2933 1; NCIB (Williamsand Sharples, 1976) 11120) Streptosporangiumfragile ATCC 31519; EFO14311 (Sheareret al., 1983) Streptosporangiumnondiastaticum DSM 43848; ATCC 27101 (Nonomura and Ohara, 1969b) StrePtosPorangiumpseudovulgare DSM 43181; A. Seino, KCC A-01 15; H. Nonomura,FYU, S2-32; (Nonomuraand Ohara, 1969b) soil. (ATCC 27100; CBS 881.70) Streptosporangiumroseum DSM 43021,A. Seino,KCC A-0005;K. Tubaki,NI 9100;1 N. (Couch, 1955a) Couch,UNCC 27B; vegetable garden sod. (ATCC12428; CBS 313.56;IFO 3776;RIA 470) Streptosporangium DSM43849; Kyowa Fermentation Industry, MK-49: soil swamp, violaceochromogenes(Kawamoto Japan.(ATCC 21807) et al, 1975) Streptosporangiumviridialbum DSM 43801; G. Vobis, MB-T8; H. Nonomura,FYU S-20; soil, Yotei, (Nonomuraand Ohara, 1960) Hokkaido, Japan.(ATCC 33328; CBS 432.61; KCC A-0027; NRRL B-2636; RIA 768) 106 Table 16 continued StrainNumber StrainHistory / Source Streptosporangiumviridogriseum DSM 43850. (ATCC 25242) subsp.viridogriseum (Okuda et al., 1966a) Streptosporangiumvulgare DSM 43802; G. Vobis, MB-n; H. Nonomura,FYU S-1; paddy field, (Nonomura and Ohara, 1960) Anio, Aichi Prefecture,Japan. (ATCC 33329; CBS 433.61; KCC A- 0028; NRRL B-2633; RIA 765) EXPERIMENT 3 a) Cluster 1 (Streptosporangiumsp. ) 166* Plot5, CocklePark Experimental Farm, Northumberland, England, U.K. 101,109,118,124,142,145a, Oakcopse, Corbridge, Northumberland, England, U. K. 179,225,245 b) Cluster 2 (Streptosporangiumsp. ) 272,273,274,275,399 Plot 3, Cockle Park Experimental Farm, Northumberland,England, U. K. 287,292*, 303,308 Plot 10, Cockle Park Experimental Farm, Northumberland,England, U.K. 113 Oak copse,Corbridge, Northumberland,England, U. K. 0 Isolates Identilled to clusterl (Streptosporangiumsp. ) 20,36,55,56, Ginsengfield, Kumsan, Republicof Korea 90,98 107,112 Woodland Soil, Tokyo, Japan 123,125 Rainforestsoil, Meru Betini, Indonesia d) Isolates Identifled to cluster2 (Streptosporangiumsp. ) 21,91 Ginsengfield, Kumsan,Republic of Korea 126,129 Rainforest soil, Meru Betini, Indonesia 107 Table 16 continued Strain Number Strain History / Source e) Unidentified Isolates 9,14,64,93 Ginseng field, Kumsan,Korea 131 Gardensoil, Yogyakarta,Indonesia 149 Soil rich in humus, Keswick, England, U. K. *Centrotype strain. Abbreviations: ATCC, American Type Culture Collection, 12301Parklawn Drive, Rockville, Maryland, U. S.A.; CBS, Centraalbureauvoor Schimmelcultures,Baarn, Netherlands;DSM, DeutscheSammlung von Mikroorganismen und Zellkulturen, Mascheroder, Weg IB, D-38124, Braunschweig, Federal Republic of Germany; FYU, Department of Fermentation Industries, Yamanashi University, Motoganagi-cho, Kofu-shi, Yamanashi-ken,Japan; IFO, Institute of Fermentation,4-54 Juso Nishino- machi, Higashiyodogawa-ku,Osaka, Japan; KCC, Kaken Chemical Company Limited, 648 Jujodai, I- Chome,Kita-ku, Tokyo 114, Japan;NCIB, National Collection of Industrial Bacteria, St. Macbar Drive, Aberdeen, U. K.; NRRL, Northern Researchand DevelopmentDivision, United StatesDepartment of Agriculture, Peoria, Illinois, U. S.A.; RIA, ResearchInstitute for Ampelology, Budapest,Hungary. 108 mm from the mouth. For eachstrain, small amountsof biomass(ca. 50 gg) were taken from different areasof the inoculatedplate, using steriledisposable plastic loops, and smeareduniformly onto the surface of the protruding foils. The assembledtubes plus foils were placedin an oven at 800Cfor 15 minutesto dry the biomassonto the foils. The dried foils werethen inserted into the tubes,using a stainlesssteel depth gauge, so that the tip of the foils lay 10 mm from the mouth of the tubes. Viton O-fing collars (Horizon Instruments)were positioned 2mm from the edge of the tubes which were loadedonto the PyMS carousel. Each strain was examinedin triplicate in order to follow the mass characteristicity discriminateanalysis routines. Pyrolysis was carried out using a Horizon InstrumentsPyMS 20OXmass spectrometer(Aries et aL, 1986; Ottley and Maddock, 1986). Prior to the analysis,the inlet heaterwas set at 1600Cand the heatedtube loader at 1200C. The assembledtubes were loaded sequentiallyinto the pyrolysis chamberby a robotic arm. Curie-point pyrolysis was carried out at 5300C for 2.4 seconds under vacuum with a temperaturerise time of 0.6 of a second. The volatile pyrolysis productswere ionisedby collision with a crossingbeam of low-energy (20eV) electronsand the ions separatedin the quadrupolemass spectrometer that scannedthe pyrolysate at 0.35 secondintervals. Integratedion counts at unit mass intervals from 51 to 200 were recorded on hard disc togetherwith the pyrolysis sequencenumber and total ionscount for eachsample. 4. DATA ANALYSIS The GENSTAT statisticalpackage (Nelder, 1979) was used to cany out the multivariate statistical analyses. PYSTAT, a program developedfor the PyMS 20OXby Horizon Instruments,was used to convertthe raw PyMS datainto a fonn suitablefor analysis,and to provide instructionsto GENSTAT as to which 109 analysissteps were to be carriedout. PYSTAT was also usedto pre-treatdata for analysison the OPUS V IBM-PC compatiblemicrocomputer. The major steps involved in theseprocedures are shownin Figure4, page 111. The molecular structure of microorganisms shows a high degree of similarity hencepyrolysis mass spectrafrom different microorganismsare very similar and cannot be differentiated and identified by simple observation. Although the PyMS-20OXcan scandown to a mass-chargeratio of II theselow mass ions tend to be derived from gasesand water which are not only common derivativesfrom many organicmolecules but may alsobe derivedfrom leaksand filament oxidation. This problem was overcomein the presentstudy by omitting standardmasses below 50 from the analyses(Berkeley et aL, 1991). The quantitative ion count for massesfrom a particular sampledepend upon molecular composition and sample size. It is important to control the sample size as mass ion counts can saturatethe detector or at low levels are subjectto sensitivityand random fluctuation effects. Consequently,samples with total mass ion counts exceeding3.000.000 or less than 800.000were excluded from the analyses. To correct for smaller changesin samplesize raw data were normalisedsuch that; 200 mc /I )XIOO ii =51 y where is the corrected mass ion intensity for sample j mass ion i as a percentageof the total ion intensity, and m, is the mass ion intensity for samplej mass ion L Within any set of spectra some mass ion peaks may show little change between spectra from different organisms while others may show large variations between spectra from duplicated samples. Mass ion peaks that are reproducible 110 RAW DATA I PRE-PROCESSING Choiceof massrange I PYSTAT NORMALISATION I MASS SELECTION Characteristicity I AUTOSCALING I DATA REDUCTION PCA/ PCO I EXPLORATORY Multivahate displays ANALYSIS I DISCRIMINATION Canonicalvariate analysis I GENSTAT CLASSIFICATION Cluster analysis Multivariate analysis V N CHEMICAL IDENTIFICATION INTERPRETATION Operational fingerprinting Factor analysis- mass or library matching adding, Graphical rotation Figure 4 Major stepsin handlingpyrolysis massspectrometric data showingthe functionsperformed by the PYSTAT andGENSTAT programs. ill within duplicates but vary across spectra from different organisms best distinguish betweenorganisms. Eshuis et aL (1977) proposedcharacteristicity, the ratio of outer varianceto inner variance(Kruskal 1964a,b), as a measureof this discrimination. Consequently,data sets were reduced by selectingthe 30 massions with the highestcharacteristicity values. The data neededto calculate the inner variancewere derivedfrom the triplicate samples. After datareduction on the smallerset of masses,re-normalisation changes the normalised mass spectraand re-calculation of characteristicitygives new characteristicityvalues. In an iterativere-normalisation procedure the whole data set was normalised and the characteristicityvalues calculated,the five lowest characteristicitymasses were removed and the data set re-normalisedand the characteristicity recalculated. The averagecharacteristicity of the remaining masseswas calculated and the processof removing massesand recalculating repeateduntil the averagecharacterisiticity did not increaseany more. This setof massesis, in somesenses, the most characteristicset of masses. The reduceddata set was then analysedby principal componentsanalysis. Plots of the first two or threeprincipal componentswere produced as plots of the spectralscores, the position of the pyrolysis spectraon the principal component axes. A plot of the mass loadings for the axes gave information about the contribution of massesto the principal component axes. Canonical variate analysesof all of the principal componentsaccounting for more the 0.1% of the total variancewas carried out to give a combinedprincipal component-canonical variateanalysis (PC-CVA). The data from PC-CVA wereplotted as Mahalanobis distances. The Mahalanobisdistance matrix was standardisedby dividing the maximumintergroup distance and was treatedas an ordinary Euclideandistance then convertedto a similarity matrix (Gutteridgeet al., 1985). The valuesin the 112 similarity matrix were examinedusing the unweightedpair group method with arithmetricaverages algorithm (Sneathand Sokal, 1973). E. RAPID ENZYME TESTS 1. TEST STRAINS AND SUBSTRATES The 159 test strainsincluded the centrotypestrains of clusters 1,2,4,6,8 (Whitham, 1988; Whitharn et al., 1993), 18 type strains representingvalidly described speciesof the generaMicrobispora, Microtetraspora, Planobispora, Streptomycesand Streptosporangium and 136putative streptosporangia from soil; seventeenof the strains were duplicated in order to determinetest error. The sourceand strainshistories of the test organismsare given in Table 17, pages114 to 116. The namesand sourcesof the thirty-six 4-methylumbelliferone(4-MU) and thirty-five 7-amino-4-methylcoumarin(7-AMC) derivatives examined are given in Table 18, pages117 to 118. 2. ENZYME TESTS The conjugatedsubstrates were dissolved in 0.5m].of dimethylsulfoxide (DMSO, Sigma),apart from 4MU-lignocerate,4MU-palmitate and 4MU-stearate which were solubilisedin a few drops of acetic acid and DMSO (0.5ml). The conjugated derivatives were then diluted in absolute alcohol to give a final concentrationof 5x 104M. All stocksolutions were storedat -250C. Aliquots of eachof the substrates(50 gl) were transferredto the wells of 96 welled microtitre plates (Sensititre Ltd., East Grinstead, Sussex,England, U. K. ) using an eight chanelledautomatic pipette and the solventevaporated by leavingthe plates in a laminar flow cabinet for 30 minutes. The plateswere then sealedwith a plastic cover (SensititreLtd. ) and storedat 40C until required. Plateswere allowed to equilibrateto room temperatureprior to inoculation. 113 Table 17 Test strainsexamined using the rapid enzymetests Strain Number Strain Identity Strain History / Source A. Streptosporangiumisolates HJ001-002,005-006, Streptosporangiumsp. Ginsengfield (youngplant), Kumsan, 008,009*' 010-011 Korea 1H[J0 12-015,016 *, Ginsengfield (postharvest), Kumsan, 0 17,019-020,021*, Republicof Korea 022-027,028*, 029- 030,031*, 032-034, 035*, 036-040,041*, 042-051,052*, 053- 062,063*, 064-076, 077*, 078-084,085*, 086-087,090-093, 094*, 096-098,099*, 100-103 HJ 104-105,106* Gardensoil, IMTECH, Chandigarh, India HJ 107-109,111 Gardensoil, Hibuya Park,Tokyo, Japan HJ 112-116 Gardensoil, TsukubaUniversity, Tsukuba,Japan HJ 117-118,122-126, Tropicalrainforest soil, Meru Betini, 128,129* Indonesia HJ 130-132,133, Gardensoil, Yogyakarta,Indonesia 135,138-141 HJ 143-144,146-147 Woodlandsoil, Moun Sorak,Republic of Korea HJ 148-153 Soil rich in humus,Keswick, England, U. K. B. Centrotype strains of nunterically circumscribed dusters TW 166 (cluster 1) Streptosporangiumsp. Plot 5, Cockle ParkExperimental Farm, Northumberland,England, U. K. TW 292 (cluster2) Streptosporangiumsp. Plot 3, Cockle ParkExperimental Farm,Northumberland, England, U. K. TW 116 (cluster4) Streptosporangiumsp. Oakcopse, Corbridge, England, U. K. TW 141 (cluster6) Streptosporangiumsp. Oak copse,Corbridge, England, U. K. TW213 (cluster8) Streptosporangiumsp. Oakcopse, Corbridge, England, U. K. 114 Table 17 continued Strain Number Strain Identity Strain History / Source C. Marker strains `I`W038 Microbispora DSM 43165;A. Seino,KCC A-0022; chromogenes H. Nonomura,FYU, M22; soil. (CBS (Nonomuraand Ohara, 304.61;DSM 43165; KCC 3022; 1960) NRRL B-2634) TW 029 Microbispora rosea S.T. Williams, Departmentof Genetics (Nonomuraand Ohara, andMicrobiology, Liverpool 1957) University,Liverpool, U. K.; E6, Japanesesoil TW 041 Microtetrasporafusca DSM 43841;A. Seino,KCC A-3188; (Tbiemannet al., RIA 924; J.E. Thiemann,T457; soil. 1968) (ATCC 23058;CBS 623.67;TO 13915) TW 030 Microtetraspora S.T. Williams, E63; ATCC 23057; glauca (Thiemannet J.E. Thiemann, T158; italian soil. (CBS al., 1968) 624.27;DSM 43311, KCC A-0300; RIA 925) TW 023 Microtetraspora DSM 43174; A. Seino.KCC A-0149; niveoalba(Nonomura H. Nonomura,FYU Mt3; soil. (ATCC and Ohara, 1971 b) 27301;CBS 834.70;DSM 43174) TW 032 Planobispora DSM 43041, A. Seino,KCC A-0092; longispora (Tbiemann J.E. Thiemann Pb-1075; soil, shore and Beretta, 1968) Uramaco,Venezuela. (ATCC 23867; CBS 115.69;TO 13879) TW 008 Streptomyces DSM 43803; G. Vobis, MB-TIO; T. indiaensis(Gupta et Frumai,MCRL; K.C. Gupta,RRI. al., 1965) (ATCC 33330;KCC A-0053) TW 006* Streptosporangium DSM 43870; T. Okuda,MCRL-048; albidum (Furumaiet soil, Japan.(ATCC 25243;TO 13901) al., 1968) Tw 010 Streptosporangium DSM 43023;A-Seino, KCC A-0025; album (Nonomuraand H. Nonomura,FYU, S2-32;soil, Ohara 1960) Japan.(ATCC 27100; CBS 881.70) TW 001* Streptosporangium DSM 43179;A. Seino,KCC A-0026; amethystogenes H. Nonomura,FYU S5; soil. (ATCC (Nonomuraand Ohara, 33327;CBS 429.61;NRRL B-2639; 1960) RIA 764) 115 Table 17 continued Strain Number Strain Identity StrainHistory / Source 71W002 Streptosporangium DSM 43316;S. T. Williams, E90; corrugatum(Willams beachsand. (ATCC 29331; NCEB and Sharples,1976) 11120) Tw 009 Streptosporangium ATCC 31519;IFO 14311 fragile (Sheareret al., 1983) TW 022 Streoptosporangium DSM 43848;ATCC 27101; H. nondiastaticum Nonomura,S 2-31; soil (Nonomuraand Ohara, 1969b) TW 004 Streptosporangium DSM 43181; A. Seino,KCC A-0 115; pseudovulgare H. Nonomura,FYU, S2-32;soil. (Nonomuraand Ohara, (ATCC 27100;CBS 881.70) 1969b) TW 005 Streptosporangium DSM 43021;A. Seino,KCC A-0005, roseum(Couch, K. Tubaki, NI 9 100;J. N. Couch, 1955a) UNCC 27B; vegetablegarden soil. (ATCC 12428;CBS 313.56;IFO 3776; RIA 470) TW 020 Streptosporangium DSM 43851,ATCC 27102 vifidogriseum subsp. kqfuense(Nonomura andOhara, 1969b) TW 021 Streptosporangium DSM 43850; ATCC 25242 viridogriseumsubsp. viiidogriseum (Okuda et al., 1966a) TW 007 Streptosporangium DSM 43802;G. Vobis, MB-T9; H. vulgare (Nonomura Nonomura,FYU S-1; paddyfield, and Ohara, 1960) Anjo, Aichi Prefecture,Japan. (ATCC 33329;CBS 433.61;KCC A-0028; NRRL B-2633;RIA 765) * Duplicatedstrain Abbreviations:ATCC, AmericanType Culture Collection, 12301 Parklawn Drive, Rockville,Maryland, U. S. A.; CBS, Centraalbureauvoor Schimmelcultures,Baarn, Netherlands; DSM, DeutscheSammlung von Mikroorganismenund Zellkulturen,Mascheroder, Weg 1B, D-38124,Braunschweig, Federal Republic of Germany;FYU, Departmentof FermentationIndustries, Yamanashi University, Motoganagi-cho, Kofu-shi, Yamanashi-ken, Japan; IFO, Instituteof Fermentation,4-54 Juso Nishino-machi, Higashiyodogawa-ku, Osaka, Japan; KCC, KakenChemical Company Limited, 648 Jujodai,I -Chome,Kita-ku, Tokyo 114,Japan; NCIB, NationalCollection of IndustrialBacteria, St. Machar Drive, Aberdeen,U. K.; NRRL, NorthernResearch and DevelopmentDivision, United StatesDepartment of Agriculture, Peoria,Illinois, U.S. A.; RIA, ResearchInstitute for Ampelology,Budapest, Hungary. 116 Table 18 The name and source of the 7-wnino-4-methylcoumarinand 4- methylumbelliferoneenzyme substrates used in the rapid enzymetests Substrates Source Substrates Source 7-ainino-4-niethyleournarin 4-Methylumbelliferone (4MU) (7AMC) 1) Endopeptidase substrates 1) Glycosides Boc-L-Leucine-glycine-L-arginine- Bachern 4MU-2-Acetarnido-4,6-o-benzylidene-Sigma 7AMC 2-deoxy-o-D-glucopyranoside Boc-L-Valine-L-leucine-L-lysine- Bachern 4MU-2-Acetamido-2-deoxy-o-D- NBS 7AMC galactopyranoside Boc-L-Valine-L-proline-L-arginine- Bachern 4MU-2-Acetamido-2-deoxy-0-D- NBS HCI-7AMC glucopyranoside Boc-iso-L-Leucine-L-glutainine- Nova 4MU-N-Acetyl-o-D-galactosaniine Sigma glycine-L-arginine-HCI-7AMC Bz-L-Valine-glycine-L-arginine- Bachem 4MU-N-Acetyl-o-D-glucosamine Sigma HCI-7AMC Glutaryl-glycine-glycine-L- Nova 4MU-0-D-Cellobiopyranoside Sigma phenylaianine-7AMC Succinyl-glycine-L-proline-7AMC Nova 4MU-(x-L-Fucopyranoside NBS Succinyl-L4eucine-L-tyrosine- Nova 4MU-0-D-Fucoside Sigma 7AMC Succinyl-L-alanine-L-alanine-L- Nova 4MU-0-L-Fucoside Sigma phenylalanine-7AMC Succinyl-L-leucine-L-leucine-L- Nova 4MU-(x-D-Galactoside Sigma valine-L-tyrosine-7AMC Z-L-Arginine-L-arginine-7AMC CRB 4MU-0-D-Galactoside Sigma Z-Glycine-L-proHne-7AMC CRB 4MU-(x-D-Glucoside Sigma Z-L-Glycine-glycine-L4eucine- Nova 4MU-0-D-Glucoside Sigma 7AMC 2) Other peptidase,substrate 4MU-a-D-Glucuronide Sigma L-Lysine-L-alanine-7AMC Sigma 4MU-0-D-Maltoside AJ L-Alanine-L-phenylalanine-L- Bachem 4MU-a-D-Mannopyranoside Sigma lysine-2TFA-7AMC 3) Exopeptidasesubstrates 4MU-0-D-Mannopyranoside Sigma L-Alariine-7AMC Sigma 4MU-P-D-Ribofuranoside AJ 117 Table18 continued Substrates Source I Substrates Source O-Alanine-TFA-7AMC Bachem 4MU-2,3,5-THo-o-benzyl-a-L- Sigma arabinofuranoside D-Alanine-TFA-7AMC Bachem 4MU-0-D-Xyloside Sigma L-Arginine-7AMC Bachem 4MU-0-D-Xylopyranoside KL Asparate-7AMC Bachem 2) Inorganic esters L-Aspwagine-TFA-7AMC Sigma 4MU-Phosphate Sigma L-Cysteine(Bzl)-7MAC Bachem. 4MU-Pyrophosphate KL L-Glutamine-110-7AMC Bachem 4MU-Sulphate Sigma L-Glydne-HBr-7AMC Bachem bis-(4MU)-phosphate Al L-Mstidine-7AMC Bachem 3) Organic esters iso-Leucine-TFA-7AMC Bachem 4MU-(protected)Acetate AJ L-Leucine-7AMC Bachem 4MU-Eicosanoate AJ L-Methionine-7AMC Bachem 4MU-Elaidate KL L-Proline-HBr-7AMC CRB 4MU-Heptanoate KL L-Pyroglutainate-7AMC Bachem 4MU-Laurate KL L-Serine-HCI-7AMC Bachem 4MU-Lignocerate NBS L-Tyrosine-7AMC Bachem. 4MU-Myristate AJ L-Valine-7AMC Bachem 4MU-Palmitate Sigma L-Glycine-L-proline-IlBr-7AMC Bachem 4MU-Pentadecanoate AJ L-Arginine-L-arginine-3110-7AMC CRB 4MU-Stearate Sigma 4MU-Octadecanoate AJ Boc, tert-butyloxycarbonyl; Bz, benzoyI; Bzl, benzyl; HBr, hydrogen bromide; HCL hydrochloride;TFA, trifluoroacetate;Z, benzyloxycarbonyl. AJ, A. L.James, School of Chemistryand life Science,University of Northumbria at Newcastle, Newcastleupon Tyne, NEI 8ST, U. K.; Bachem,Bachem Feinchemikalien AG Ltd., Hauptstrasse 144, CH-4416 Bubendorf, Switzerland; CRB, Cambridge Research Biochemicals Ltd., 3 Heathcoat Building, Highfields SciencePark, Nottingham, NG7 2QJ; KL, Koch-Light Ltd., Rookwood Way, Haverhill, Suffolk, CB9 8PB, U. K.; NBS, New Brunswick Scientific Ltd., Edison House, 163 Dixons Hill Road, North Mymms, Hatfield AL9 7JE, U. K.; Nova, Calbiochem-NovabiochemLtd., 3 Heathcoat Building, Highfields SciencePark, Nottingham, NG7 2QJ, U. K- 118 The tests strains were examined for their ability to degrade the 71 conjugated fluorogenic: substrates. The organisms were inoculated onto the centre of sterile cellulose nitrate membrane filters (0.4511mpore size, 47mm diameter; Whatman Ltd., Maidstone, England, U. K. ) thq4had been aseptically placed in the centre of modified Bennett's agar plates (Agrawal, unpublished data); the membranes were allowed to absorb surface water and the resultant plates incubated at 300C for 7 days. After incubation, growth was removed from the surface of the cellulose nitrate membranefilters and transferredto universalbottles containing 15 ml of 0-IM MOPS buffer (pH 7.5) and about ten sterile glassbeads (Jencon Scientific Ltd., Leighton Buzzard,Bedfordshire, England, U. K.; gauge5mm diameter). The preparationswere agitatedon a Vortex mixer (Fisons Scientific ApparatusLtd., Loughborough,Leicestershire, England, U. K. ) and the homogeneoussuspensions obtainedad j. usted to 0.2 turbidity on a colorimeter at 600 nm. This procedure yielded suspensionsof betweenca. 6.2 x 107to 108viable colony forming units per ml. Hornogenisedsuspensions of eachtest strain (100gl) were inoculatedinto wells of the microtitre plates (SensititreLtd. ) using an eight channelautomatic pipette. The platescontained negative controls, that is, wells with only inoculum and substratewith buffer, respectively. Inoculated plates were resealedand incubatedat 300Cfor 24 hoursand the resultsread using an automaticfluorescent plate reader(Sensititre Ltd. ) at a wavelengthof 366 nm. The readerwas allowed to warm-up for 30 minutesand calibratedagainst an internal standardbefore the analyses. A negativecontrol plate inoculated with MOPS buffer (pH7.5) was read both at the beginning and at the end of the analysesin order to provide readingsfor backgroundfluorescence and autofluorescenceof the compounds. 119 The results were collected on a PC-AT microcomputervia dedicatedsoftware (SensititieLtd. ). 3. DATA ANALYSIS a) Automatic Data Collection The results of the enzyme tests were collected on an IBM PC computer and analysedusing Quattropro4.0 (Borland InternationalInc. ). The tests were coded positive when the difference in fluorescent intensities betweentest and negativecontrol wells that containedonly cell inoculum andsubstrate with buffer was more than 0[ Rp= V,- V,- V,,b (Rp,positive result; Vr, resultant reaction betweentest strain andenzyme substrate; Vc, value of cell inoculumalone; Vs+b, value of substratewith 0.1Mol MOPS buffer)]. b) Numerical Classificadon All of the testswere scored + for a positive and - for a negativeresult. The binary test data were typed in a +/- format as input into the TAXON program (Ward, unpublisheddata; Appendix A). The +/- resultsin the final data matrix were convertedto a binary format (1/0) and written to a DOS text file using the TAXON program and analysedusing the CLUSTAN 2.1 statistical computer package (Clustan Ltd., Scotland, U. K. ). The CLUSTAN procedure HEERARCHY was used to examine the data using the Dp, Sj and Ssm coefficients. Clustering was achievedusing the unweightedpair group method with ajithmetic averages(UPGMA; Sneathand Sokal, 1973)algorithm. Test Error Seventeenstrains were examined in duplicate(Table 17,pages 114 to 116) and an estimate of test variancecalculated (formula 15; Sneathand Johnson, 120 1972); this was usedto estimatethe averageprobability (p) of an erroneoustest result (Formula4; Sneathand Johnson, 1972). 121 KRUEUSULTS A. SELECTIVE ISOLATION, ENUMERATION AND CHARACTERISATION OF STREPTOSPORANGIA. 1. ENUMERATION The numbers of presumptive microbisporae, microtetrasporae, streptosporangiaand unidentified sporoactinomycetesisolated from the composite soil sampleson humic acid vitamins agar supplementedwith actidione(50mg/1) and nalidixic acid (30mg/1)are shown in Table 19, pages 123 to 129. Typical selectiveisolation plates supportingthe growth of streptosporangiaare shown in Figure 5, page 130. It is evident from the information in Table 19 that Microbispora, Microtetraspora and Streptosporangiumstrains are widely distributedin the soils examinedthough they were not isolatedfrom the compositesoil samplecollected from aroundMount Bromo in Indonesiaor from the sampleof Brazilian rainforest soil, that is, from the soils with bulk pH valuesof 3.8. The highestactinomycete counts were consistentlyrecorded from the soil samplesthat were subjectto the lessextreme pretreatment regimes, namely when 10-1dilutions wereheated in the presenceof the germicide sodium dodecyl sulphate(0.05%, w/v) or the spore germinant yeast extract (6%, w/v). The highest counts were recorded for the composite 8 sample(soils 579-581) that was pretreatedwith yeast extract (6%, w/v) for 20 minutes at 400C prior to dilution and plating out onto HV agar supplementedwith actidione (50mgA)and nalidixic acid (30mgA)and incubated for 4 weeksat 300C. In nearly all cases,streptosporangia were isolatedfrom dried soil samples pretreatedat 1200Cfor an hour and from 10-1dilutions of soil treatedwith yeast extractat 400Cfor 20 minutes. In general,the highercounts were recordedusing 122 '4q E V) 0 ýc cq ON C14 tr) -4 ý_w = (14 C4 x 00 ý2 en ZO cq ggm cq C14 12 Ici -ts cn crý I-i 0 $.« ýo ao Q +1 0 .a tz C\ 06 .I C14 Iq 00 1.0llý p aH C9 1 0, 12 0 9 *t ýý 8 IV-, C14 Cd -> 12C ýi Gn ý4,ý "0 ,ý I t eq "0 r. -ý fl. -S -B C 29 -:s - -9 0 4r) u t 0 0 9 4 , cn 0 ý- 40. 'm' I 123 rA .Z r_ 1- 116 . -4 cn K 3 ce .: 9 Z,2 ýu JD 0-9 cu a 1ýTu 00 en 10 lýx9e Q 1t cn 1uq= Gn .ý Cw :=U0 -ý ýc ÜE-0 r_ 0E0 Ci. ýc; 0, ci 0 5 E L, i yo "" uz>::i xul -c 124 q en 6 N- In C4+1 C4 C,4 en M m x 00 0 rA 00 C5 vi Wý 0 M 65 X R L c C cn C> O\ 1-4 vt x 0 cý F Z = rý 10 Co %:w CAPA m -21 ce -40 r- 00j 'g 4r) 2cd .ä CI.) 4)CO 0 0 CI.) z9 s -ý LZ .;5 A c2. CDW 0 .x 1--, 0.0 1: 1, u m 0. 06 I Q ý-11 45 Gn r- 125 v cý rq Ge 0 +i r9 8 n r= x le M rn 0 CA .-3 x CD CD 0 "3 Z M Ci to CD : x c; ON u 4225C2D ýo 99 "i CD 2e ke) V) Gn '8 V') 0 = ce . ce =5 0 >, ce0 LU CA u0 A A-20 v 0 A 42 .0=4 Ln .-90,0 8 0 00 Gn L- 2 0= E9=iz M c2. -2 0ý Gn .. -4 0 im JD LZ 122Ow um L lý R ce 0 =; , .Z E- -5 126 tn C4+1 M \JD wl en kn C2ý C14 CIO 60 r, 4 Ci In C'i ZO '6 lIcý ei Ci cý "e oý (n Cyý týIn cý 00 9 124 01 - C' x 4) ýAF.' cl) ll- m 4) 0 cl) C,, . E iz liý 0 CL. 0 I (,i ý- 1-111-cc; -ý 127 Cf) C73 -H C4) Cf) C1400 C14 ON CS Ci aývý It tn CR*H i ý -. 4 Oý mOý I'? Cý6 m 9 -Tr IQ CO) 0 -H '4R 0 0 cu cw CO) tt en A 4 tý t? O N kn 0 ýo eq ý r- r- Cw -4 0 1.4 . W) 1 Is 0 - ý co . W-,a ý11 wl, -r, 15 L) 0 C.) W) C.) 0 06d COD 0 Oý C- 0 0 = 4) 0 I-i 0 v 0 tei 5ý 04 "6 w r-: 06 (4 128 aý CD v NT00 W) cn It rA C) O\ c ti ýo lKt C) N to (4 Cý %jý +1 - en x wiV) (9 ý 1 9 cn I er 0 vl 00 w41 00CA 00 ý', i 0 0 00 =2- 0 0 C/, Gn 00 CN F.. 4 r- -= en CA .0 Gn0 0 um a, 0 F- 0 129 Figure 5 Streptosporangia growing on humic acid vitamins agar plates supplementedwith actidione (50mgA) and nalidixic acid (30mgA) and incubated for 4 weeksat 300C. Representativecolonies of streptosporangiaare indicatedby an asterisk. v4. * I the latter procedure. The highestcount, 7.94 ± 1.19 x 104colony forming units per gram dry weight soil, was recordedfrom a sampleof compositesoil 8 which had been the subject of the pretreatmentregime involving yeast extract prior to plating out onto HV agarand incubation at 300Cfor 4 weeks. In this instance,the streptosporangiaaccounted for almost40% of the sporoactinomycetesgrowing on the isolation plates. However, in most cases,streptosporangia accounted for less than 5% of the actinomycetesgrowing on isolation plates(Table 19, pages 123- 129). Indeed,the vast majority of the coloniesgrowing on isolation plateswere assignedto the "catch-all" group, namely the "other actinomycetes". It was subsequentlyshown that most of the organismsbelonging to this group were streptomycetes. The highest numbers of microbisporaeand microtetrasporaewere also recorded from soil suspensionstreated with yeast extract and heated for 20 minutes at 400C. It is also evident (Table 19, pages 123 to 129) that heat pretreated soil subsequentlytreated with phenol (1.5%, w/v) at 300C for 30 minutesfavoured the growth of microbisporaeas opposedto microtetrasporaeand streptosporangia.Nevertheless, the highestcounts of microbisporae,6.18 ± 0.76 x 104 colony forming units per gram dry weight soil, were recorded from suspensionsof composite soil 8 treated with yeast extract (6%, w/v) for 20 minutes at 400C. The highest counts of microtetrasporae,2.38 ± 1.52 x 104 colony forming units per gram dry weight soil, were also recorded from suspensionsof compositesoil 8 treatedwith yeast extract(6%, w/v) at 400Cfor 20 minutes. 2. SELECTION AND PUREFICATION Presumptivestreptosporangial colonies growing on humic acid vitamins (HV) agarsupplemented with actidione(50mg/1) and nalidixic acid (30mg/1)were 131 examinedfor the presenceof sporevesicles (sporangia) using a Nikon Optiphot binocular light microscope fitted with a long distance objective (X400 magnification). One hundred and fifty-three presumptively identified streptosporangiawere picked from the isolation plates using sterile tooth-picks and inoculatedonto HV agarplates which were incubatedfor two weeksat 300C. The resultantcultures were examinedfor purity both by eye and using the Nikon Optiphot binocular light microscope(X400 magnification) and single colonies usedto inoculatefurther HV plateswhich were also incubatedfor two weeksat 300C. This procedurewas repeateduntil all of the isolateswere in pure culture. The sourcesand proceduresused to isolate all of the test strainsare given in Table 20, pages 133 to 136. In subsequentstudies, 136 of the presumptive streptosporangiawere examinedfor the presenceof isomersof diaminopimelic acid in whole-organismhydrolysates, screened against diagnostic tests included in a computer-assistedprocedure designed for the identification of streptosporangia and examinedusing a battery of rapid enzymetests to determinetheir enzymatic profiles. 3. CHARACTERISATION a. Morphological Studies All of the isolates presumptivelyidentified as streptosporangiaproduced spore vesicles (Figures 6a to 6f, pages 137 to 139). Scanning electron micrographsof threeof the test strains(HJ 047, HJ 084 andW 094) are shown in Figure 7, pages140 to 141. b. Diandnopimelic Acid Analysis of Soil Isolates The LL- and meso-diaminopimelicacid standards were clearly separatedon the cellulose TLC plates. All of the presumptive streptosporangiacontained 132 Cd 4) ýfdl ZC C5 "a "tý oo 00 tn "I W) V2 00 Wi t4 -sl . .-tl -5- ru 10) 78 78 -it A 8 ") -.rz C) C14 u (:ý 't C,cl 9 Cý4 o 0 Cý ff)- 9 't *t. t 0 S,A, 0"c to'I) *' *' *' coý ý *- *- ('4 C9 ýo 00 cl en .1999--- C) (=ý 0C C) 0'(S 8 C-1) 8 C) 0 C) 133 ca Z I 1. "0 I 0 I 00 (Y)1 z CS CV) I It r- ke) 19 "o I Ii I=A9t -e Iz 78 78 '8 481 19 00 Cf) 00 C) f r- 00 ct 00 Ell * * 00 * o--- § 00 ** * Cý In NO r- O- (n 00 (f) 00 00 -- 00 00 00 s -- 00) 8H88SSCO C) . 134 9 LLIed F- ow cn ti r1o > ý 2 ýo C\ fn 00 z "1 00 vi Gn 9= e_ CD 0. u (A 42 Z 40. gg b 1ý5 ý. g Cf) (A 9 9 r4 -ýg -> -S 9 Z I 15 I 'o Cv. la 78 78 7g A9 ii gl 00 Cf) * * rz * * * ..o 00 -' Cf) * I ** N ('4 n ph en -- I -- 135 ý4 >b N "Ci I 10 Ici I I lz N Iz N ek 9 10 40. A ý51 Ici "0 a w I . 40 ý20 tn 00 c02 I 't tr) 136 Figure 6a Sporevesicles of isolate HJ 14 growing on HV agar after 4 weeksat 3W (X4(X)magnification) lij mci 4 "cckýs at 01) 'ýpolc \C.SICICs ()I I.Solilic ý1:) 301 (X4(X) magnification) it ILI -ý I 4 137 Figure Oc SPOreVesicles Of isolate I-IJ 48 growing on [IV agar after 4 weeks at 3(YI(X4(X) magnification) LI * I Figure 6d Spore vesicles of isolate IIJ o9 poýýiw, on IIV agai alt,: i -1 ýýccksat 3W (X4(X) magnification) I lib I 138 Figure 6e Spore vesicles of isolate HJ 99 growing on HV agar after 4 weeks at 301(X4(X) magnification) Figurc W ý)polc I IJ I I ýCslclesot lNolkitc ý0 -,-Ilkm lqo oll Ilitcl at 3011(X4(H) ma!,nific: il ion) I W .0t, t 139 isolates HJ 047 Figure 7a Morphology of spore vesiclesof Streptosporangium (I 1,4KX) isolates HJ 084 Figure 7b Morphology of spore vesiclesof Streptosporangium (683KX) 140 Figure 7c Morphology of spore vesicles of Streptosporangium isolates HJ 094 (6,57KX) Figure 7d Morphology of spore vesicles of Streptosporangium isolates HJ 094 (I 1,6KX) 141 meso-diaminopimelic acid, and all of the 46 unknown actinomycetes LL- diaminopimelic acid(Figure 8, page 143). c. Condusions It is evident from the chemical and morphological studies that strains presumptivelyidentified as streptosporangiacan be classified in this genus with considerableconfidence. Similarly, most of the unknown actinomyceteshave chemical and morphological propertiesconsistent with their classificationin the genusStreptomyces (Williams et al., 1989). B. NUNWRICAL IDENTIFICATION OF STREPTOSPORANGIA 1. PRACTICAL EVALUATION OF THE FREQUENCY MATRIX All seventyrepresentatives of the twelve major streptosporangialclusters circumscribed in the numerical phenetic survey of Whitham (1988) were unambiguouslyassigned to their parent cluster with high identification scores (Table 21, Pages144 to 156). In all casesthe identification scoresof strains assigned to their parent cluster were much better than the two next best alternatives. The ten centrotype strains, namely TW 166 (cluster 1), TW 292 (cluster 2), TW 116(cluster 4), TW 141(cluster 6), TW 226 (cluster 7), TW 213 (cluster8), TW 005 (cluster9), TW 002 (cluster 10), TW 126(cluster 11)and TW 182 (cluster 12) had high Willcox probabilities(> 0.9999 in all but one instance), taxonomic distancessmaller than the 95% taxonomic radius and high Gaussian distance probability values (range 51.2-100, apart from strain TW 005 with a value of 1.98). Similarly, all thirty representativesof cluster I showed high Willcox probabilities (range 0.9844-0.9999),taxonomic distancessmaller than the 95% taxonomic radius and high Gaussiandistance probability values (range 7.561- 142 Figure 8 Identificationof diaminopimelicacid isomersby one dimensionalthin layer chromatographyof whole-organismhydrolysates of test strainsusing the solventsystem methanol: water: ION HCI:pyridine = 80:26.25: 3.75: 10, v/v. PlateA Plate B tracks(from left to right) tracks(from left to right) I. a, c-diaminopimelicacid 1. a, c-diaminopimelicacid 2. HJ 104 2. HJ 109 3. A 001 3. A 005 4. HJ 105 4. A 007 5. A 003 5. A 009 6. cc,F--diaminopimelic acid 6., a, F,-diaminopimelic acid 7. HJ 106 7. HJ Ill 8. HJ 107 8. HJ 112 9. A 004 9. A 010 10. HJ 108 10. A 011 11. 11. a, e-diaminopimelicacid - a, P--diaminopimelicacid Plate C Plate D tracks (from left to right) tracks (from left to right) 1. a, c-diaminopimelicacid 1. a, E-diarninopimelicacid . 2. HJ 034 2. HJ 143 3. HJ 035 3. HJ 144 4. HJ 036 4. HJ 146 5. HJ 037 5. HJ 147 6. HJ 038 6. HJ 148 7. HJ 039 7. HJ 149 8. HJ 040 8. HJ 150 9. HJ 041 9. HJ 151 10. HJ 042 10. HJ 152 11. oc,P--diaminopimelic acid 11. t oc,e-diaminopimelic acid i Plate A Plate B 00* qf 0 40 q* lp AkikAft II Plate C Plate D 40 0 q# *9v9 qp,o I LL-DAP in DAP MAW' 0000000" 000 OH- LL-DAP, LL-diaminopimelic acid; meso-DAP,meso-diaminopimelic acid; DAP, 2,6-diamino-3-hydroxypimelicacid. 143 2 .=m ,AA uu22 et 1-1 00 ON en N, W') Cf) aý en a, t- § th ýo 04§§ S'sen § P0C ,q: 6 en 0ýeq ci c; wý r- V) r- oý V) r- 0) o\ vý r- g 9 ýg g1110 -) l> en en M e4-1 rn 1- l'r, c-n ei ei ti 11 lýI lýI d ý (=; d (Z; cý (Z; (Z 0o c> 0 c5 c; c; cý ci (: (=; mA cq eq N 1ý en ON A (ONV') M 00 m n "t I It kl) AqA cl 0: Cli en ei -1: 5 0 C) C), 00 060 coo 06(:: 4-a Ae, (=,4 (n v ,::ý =; e::ý (Z; cý c=; (:ý (=ýc> 000 0 W') - C'4 til - C14"T ý eq tn - C,4 I", ý C14W) -N (A I oi,e C.:,tm,g. geC., e nee 0.- §5ý e ý, zKEIIE . - riNK k. K k. 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CD 0 00 eq CD N 00 en eq (Y) cl) I 6 C; 6 (6 o tn Cý ej 'I SS- 00 0ý SS- 81 00 kr) 00 Cý ýý VI '41; "Rt 000c; ý'--0 ? tN 1 11 C) 0 C) CD C5ci ýo0000 99 MM (D C> E z IC r- e r- ýc - I to 0.0 Mw --:i a 21.a21 -Q j 1 F CIO)z eke 00 Q( ý 1 »1 0 -13 Iz 155 f 0a N. 0 rq (9 - rq rq ý Gn - vi eý vi \O r4 CD ý N en C5 Cý C) 0Q0 W) en WI) en :1tf) k1l) W) 199 M 0ý . H C5 - Iz Z .a .ýto .bo-Z .00-Z --Z 00 00 14 112 - lk lz, I X ZLI a 00 * * I L) * 156 98.815), apart from strain TW 224 which had a high Willcox probability score (0.9999) but a taxonomic distance larger than 95% taxonomic radius and a low Gaussiandistance probability. The raw data obtainedfor all of the test strainsare given in AppendixC. Given the high identification scoresobtained with the markerstrains it was decided in the first instance that stringent criteria should be set for a positive identification of both known andunknown strains, namely: 0) a Willcox probability scoreof 0.9700or above. 00 a taxonomic distance score below the 95% taxonomic radius score. (M) high Gaussian probability scores signifying that there was not any significant overlap between the cluster strains were assigned to and the immediate next best alternatives. (iv) the best identification scores to be much better than the two next best alternatives Sixty-five of the seventymarker strainsfulfilled thesecut-off criteria for a positive identification. Three of the four exceptions,namely strains TW 224 (cluster 1), TW 165 (cluster 1), TW 169 (cluster 6) and TW 005 (cluster 9) had Willcox probabilities above 0.9999 but showed taxonomic distance values somewhatabove the 95% taxonomic radius scores. The remaining organism, strain TW 129 showed similar identification scoresbut in this case the Willcox probability wasrelatively low at 0.9615. 2. IDENTIFICATION OF UNKNOWN STREPTOSPORANGIA Twelve out of the one hundred and thirty-six unknown streptosporangia were identified to known clusters using the stringent cut-off criteria mentioned above. Ten of the strainswere identified to cluster I (Streptosporangiumsp. ) and 157 two to cluster 2 (Streptosporangiumsp.; Table 22, pages 159 to 177). The raw datarecorded for all of thesestrains are given in AppendixD. A further nineteenorganisms were identified to known clustersusing less stringentcut-off criteria, namely strainsHJ 005, HJ 0 10, HJ 0 11, HJ 0 12, M 020, HJ 021, HJ 032, HJ 036, HJ 053, HJ 055, HJ 056, FIJ 068, HJ 087, HJ 092, HJ 096, HJ 097, HJ 113, HJ 126 and FU 129. 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I r. z I i"fl i >14 3u j fln C4) .0Co 4 E- 9ý .,d9z zS 175 CD (D ,It 9" r- t- CIACý tn C' ý2 tn ýo " \0 W) VI) ýc C14 \0 ý2 ,, I ýý 00C, 000 I coo - r- (4) 00 00 00 00 r- e. ) 00 eq 00 1- W) 00 W) 00 0 V) W) kn 00 0 r- C14 Oll ýo - (14 ýn 00 r- r- r- 0) 00 1- en 00 00 00 V') 't It) Vý In In Wi 60 660000a CD 00 CD 11000 CC) C) rA 2 c) '. ' v) ý vi vý vý Ilý n vý Ilý 1j; " -! r,ý N 1: `t (D 0 CD 0 COO 0 0 CD 0 cý cý (D CD q C-4 C'4 z dý a 00 00 '00 60 CIO Do GO 60 00 00 c 00 I Ip§§ýI§%%5 U cc %'1, 1 1, '1 's 's 's 1-11. QQ is -1Olt 11 ET Id z Lý Cýi (r) e',- e;s AAiý's AAA ý;s 44A g g2 1A .0 - CA t4 C14 Clq41 NO r- In in ýt 176 2p 2z4 :§ 9e= = e: .m (ý - i cý Cý E2 o 'U u .2 . . -S jr. JE, m cý c::ý c=; 4ý ýA tu C2W"Ci =M Z, 15ým CD 00 "CJ e § cz ýv. E .9 N- v) -0 E 10 I 0 I I 4.) ,mý 10 @ . 1:: ýg8 I 0 Ii IC 177 C. PYROLYSIS MASS SPECTROMETRY The pyrolysis mass spectral data support the taxonomic integrity of streptosporangial clusters I and 2 (Figure 9, page 179). It is also evident that cluster I accommodates more variation than cluster 2 (Figures 9 and 10, pages 179 and 180) and that representatives of clusters I and 2 had little in common with the type strains of validly described species of Streptosporangium (Figures II and 12, pages 181 and 182), apart from Streptosporangium roseum TW 005 which was loosely associated with representatives of these clusters. It is also evident that the type strain of Streptosporangium viridogriseum subspecies viridogriseum has little in common with bona fide members of the genus Streptosporangium. Six of the twelve isolates identified to cluster I using the stringent cut-off criteria wererecovered in the group correspondingto this cluster. It is evenmore encouraging,however, that four of the ten organismsidentified to cluster 1 in the computer-assistedidentification using less stringentcriteria, namely strainsa02O, a036, a055 and a056, were closely associatedwith the representativesof this taxon. Similarly, organismsidentified to cluster2 using the less stringentcriteria, namely strains FU 021,1U 126 and HJ 129, were associated with the representativesof this taxon. The unidentified strains were assignedto two groups (Figures 13 and 14, pages 183 and 184). The group comprising strains c009, cl3l andc149 was sharply separatedfrom all of the remaining test strains (Figure 14,page, 184). 178 6 A100 A A121 4 A 82691 Al 17 A cl) B366 Br 218281 C', > A167A A AA166* C', B30jBf7 OB292o A140 C.) B273 C OB274 0 ALA109 C BJ5 Bl% AA124 0 C', A14& A AA225 0 1 8113 B272 AA118 A245 C A, A 0 A142A A101 0 B8 AA256 (I) AA169 A235 A, A369 A131 -4 A179 A127 -6 -15 -10 -5 05 10 First Canonical Variate Figure 9 Ordination plot along the first two canonical variate axes showing the mean position of the representatives of streptosporangial clusters I and 2 (see Table 16, pages 105 to 108; Whitham, 1988; Whitham et al., 1993). The first two axes accounted for 76% of the variation between strains. .&Representatives of cluster I-n Representativesof cluster 2; * Centrotypestrains. 179 A225 AM A109 A256 A112 AM AIN - A369 - A159 cluster I - A115a A167 AM A1661 Al17 A121 A235 AIN A131 B269 B366 -8281 -08 B272 9274 8287 cluster 2 B308 B303 B213 B2921 0113 B399 B275 60 70 80 90 100 PercentageSimilarity Figure 10 Dendrogram representing the relationships found between representative Streptosporangium strains from clusters I and 2 (see Table 16, pages 105 to 108, Whitham, 1988; Whitharn et al., 1993). The dendrogram is based on similarity values derived from Mahalanobis distances with clustering achieved using the unweighted pair group method with arithmetic averages algorithm. *, Centrotype strains. 180 20 15 0 S vindogriseumsubsp. id)ldognseum 10 Co 5 A101 S vioh2ceochromogenes 0A (Z A221 S.roseum A145a S. vulgare A245 AAA124 '0 AA viridialbum A142A A166* A179 A B287 S non&astaticum A A118 B113 m S albidum B274 40 A109 B303 S ftagile B275 B272 S B273 corrugatum B399 B292* B308 s. amefk&ogenes S pseudovulgare -10 - 5 10 15 -20 -15 -10 -5 0 First Canonical Variate Figure II Ordination plot along the first two canonicalvariate axes showing the mean position of the representativesof streptosporangialclusters 1 and 2 (Whithan-41988; Whitharn et al., 1993) and type strains of validly described Streptosporangiumspecies (see Table 16, pages105 to 108). The first two axes accounted67% of the variationbetween strains. Ah.Representatives ofcluster I, = Representativesof cluster 2;'K, Centroqpe strains and -mop Type strains. 181 -AI18 - A245 A225 A145a A124-cluster I A1661 A109 B28? -8271 - B292x - BM 02 - -cluster 2 - B2?3 B399 B113 B303 B30B-j T005S roseum TORS corrugatum T006Sa Ibiduin TOM Spseudovulgare Tool S amethystogenes TOD?S vulgare T022S nondiastaticum TOD9Sfragile D13849S violaceocliromogenes D13801 S viridialbum T021 S viridogrisewn subsp.viridogrisewn 48 64 80 96 100 PercentageSimilarity Figure 12 Dendrogram representing the relationships found between representatives of streptosporangial clusters I and 2 (Whitham, 1988; Whitham et al., 1993) and the type strains of validly described Streptosporangium species (see Table 16, pages 105-108). ne dendrogram is based on similarity values derived from Mahalanobis distances with clustering achieved using the unweighted pair group method with arithmetic average algorithm. S, Streptosporangilan; *, Centrotype strains. 182 A109A A Al 01 42 a56 AM AA al 12 Al 45a 5 Al 79 A A A a123 A118 Al 24 A AA A225 a9n A a107 a2O A245 Al 66* (1) AA a98 AAA co AA L- al 25 m a55 a36 B275m B113 B287m f2*303 IB292* B271 C.) I c b126 b9i b129IB399 0 0664 5 0 B273 B308 B272 C) 0 0 (2) C/) cl 49 c93 0 cl 4 cl 31 -10 C9 0 -15 L 0 246 -10 -8 -6 -4 -2 First CanonicalVariate Figure 13 Ordination plot along the first two canonical variate axes showing the mean 1 2 (wNtham, 1988; position of representatives of streptosporangial clusters and I 2 together WNtham et aL, 1993) and isolates identified to clusters and with unidentified for 71% the strains (see Table 16, pages 105 to 108). The first two axes accounted of variation between strains. 2; A Strainsidentified to cluster I; A Representativesof cluster I, m Representativesof cluster * Centrotype I Strainsidentified to cluster2-,, Unidentified strains; strains. 183 6112 A215 aJ23 al12 2125 AD clusterI AS Vo d107 A115a AIM AJOI AIM A124j 9287- B113 8303 0272 b126 cluster2 b123 9273 9215 92921 b091 b021 CON C033 d61 uriidentified C009 C119 isolates C131 48 64 80 96 100 PercentageSimilarity Figure 14 Dendrogram representing the relationships found between representatives of streptosporangial clusters I and 2 (Whitham, 1988; Whitham el at., 1993) and isolates identified to clusters I and 2 together with unidentified strains (see Table 16, pages 105 to 108). The dendrogram. is based on similarity values derived from Mahalanobis distances with clustering achieved using the unweighted pair group method with arithmetic averagesalgorithm. A, representatives of cluster 1; B, representatives of cluster 2; a, representative isolates identified to cluster 1; b, representative isolates identified to cluster 2; c, representative unidentified isolates; *, centrotype strains. 184 D. 5S REBOSOMAL RNA SEQUENCING The 5S ribosomalRNA of all nine test strainsconsisted of 120nucleotides with the samesequences being found in someof the loop regionsof the secondary structure,namely regions bLc, cLb', b'Ld (Figure 15, page 186). The secondary structure models that were obtained are exemplified by the model for Streptosporangiumvulgare TW 007 (Figure 16,page 187). The 5S rRNA sequencesof the strains were aligned by juxtaposing the defined secondarystructures which were then divided into fifteen regions(Figure 15, page 186). Streptosporangiumstrain HJ 090 (cluster 1) had the same5S rRNA nucleotidesequence as the centrotypestrain (TW 292) of streptosporangial cluster 2 (Whitham, 1988; Table 23, page 188). In contrast,Streptosporangium albidum TW 006 and Streptosporangiumviridogriseum subspecies viridogriseum TW 021 showed low homology values with the remaining test strains. It is evident from the phylogenetic tree (Figure 17, page 189) that these latter organismsform a distinct evolutionaryline. Strain HJ 011, which was identified to cluster I in the computer-assistedidentification exercise based on the less stringentidentification criteria, showeda low similarity with the other strains. 185 uu uuuuu u u << -t << -tc <<< -0 5ý ju 10 ttot ***** uu -ýt g 00 uuuuuUUUU ot 00 00 60 (30 Oo (X 00 (10 00 sss 6gps k. 6. tz tz 6.1.. K. &Z 55$. -4 2ý to uuI uuuuuuuu4.4 186 bLc AAC AA UAC BGccuA UGGCGAAGGG CCGG u aLb A **** cLcl U Au cuucuc GGCC c AUUCGGCGGU A B' GA C' Cc GAA AAG ACAGGCCGCCGC CAG A' UG uu GCc Ib GC AG d'Lal UA b'Ld GU AG GG AU GA G*C G*U U*G D, G*C D U*A G*C G*C C*G AG GG dLd, Figure 16 Secondary structure model of the 5S rRNA of Streptosporangium vulgare TW 007. *, Base pairing. 187 00 00 r- 00 oo cý V-i vl 00 00 oo kr) cý,00 0',r- 00r- 2 CD (vý 2 00 \C V) kt) 35 r- co 9 rqv oo r- oo 00 O, t c9 8-4 cý c> (ZN \ 8 (D r-ý ON r- c\ cq C, ýo - lZ t- V) V-) V) r- vi 00 V i. rý - CD CD ci %j k e 4 cý *I:: re te re Go2 ZM 2 M 2 2 2 c14 _e 211 lýI VD tnk iz b- ý cýA * . f9 en 6 cý- E- CA vý ,c; r.: C. 188 "0 I CL CL z Iz 0 ýj a a Q, Cý f;. $ s 2'. R- 4k . ý-- 0 3n !9 . ce COD CA 00 :a 66 00 a Z, cl >, C45 0. *,;a 4 189 E. RAPID ENZYME TESTS Inclusion of the seventeenduplicated strains in the fluorogenic enzyme tests enabledexperimental test error to calculated(Table 24, pages 191 to 193). The averageprobability of an erroneoustest result (p) calculatedfrom the pooled variance (Si2) for all of the strains was 0.29%. The percentagepositive frequenciesfor each of the conjugatedfluorogenic substratesfor all of the test strains is given in Table 24, pages 191 to 193. Twenty-two 7-amino-4- methy1cournarinand seven4-methylumbelliferone substrates were deleted from the final data matrix as they were not of any differential value. The final data matrix, therefore,contained information on 142 strains and 42 tests. The raw enzymaticdata for all of the test strainsis given in AppendixD. Very little taxonomic structurewas obtainedwhen the information in the final databasewas examined using the Dp, Sj and Ssm coefficients and the UPGMA algorithm. This somewhatdisappointing result can be attributedto the small numberof unit charactersinvolved. It was, however,encouraging that some of the test strains can be distinguishedby their capacity to cleave particular conjugatedsubstrates (Table 25, pages194 to 196). 190 Table 24 Test error calculatedfrom comparisonof the rapid enzymetest data from the seventeenduplicated cultures together with the percentagepositive valuesfor all of the test strains Substrate Agreement Test % Of between Variance Strains Duplicated (Si2) Positive+ Strains(%) A. 7-amino-4-methylcoumarins(7AMC) Endopeptidasesubstrates Boc-iso-L-Leucine-L-glutaniine-glycine-L-argiwne-HCI-100 0.000 100 7AMC* Boc-L-Leucine-glycine-L-arginine-7AMC* 100 0.000 100 Boc-L-Valine-L4eucine-L-lysine-7AMC* 100 0.000 100 Boe-L-Valine-L-proline-L-arginine-HCI-7AMC* 100 0.000 100 Bz-L-Valine-glycine-L-arginine-HCI-7AMC* 100 0.000 100 Glutaryl-glycine-glycine-L-phenylalanine-7AMC 100 0.000 98 Suceinyl-L-WaWne-L-alanine-L-phenylalanine-7AMC*100 0.000 100 Succinyl-glycine-L-proline-7AMC 100 0.000 99 Succinyl-L-leucine-L-leucine-L-valine-L-tyrosine-7AMC*100 0.000 100 Succinyl-L4eucine-L-tyrosine-7AMC* 100 0.000 100 Z-L-Arginine-L-arginine-7AMC 100 0.000 98 Z-Glycine-glycine-L-leucine-7AMC* 100 0.000 100 Z-Glycine-L-proline-7AMC* 100 0.000 100 Exopeptidasesubstrates D-Alanine-TFA-7AMC* 100 0.000 100 L-Alanine-7AMC* 100 0.000 100 P-A]aWne-TFA-7AMC 100 0.000 99 L-Arginine-7AMC* 100 0.000 100 L-Arginine-L-argiriine-3HCI-7AMC* 100 0.000 100 L-Asparagine-TIFA-7AMC 100 0.000 96 Asparate-7AMC 100 0.000 52 L-Cysteine(Bzl)-7AMC* 100 0.000 100 L-Glutamine-HCI-7AMC* 100 0.000 100 Glycine-HBr-7AMC 100 0.000 91 191 Table 24 continued Substrate Agreement Test % Of between Variance Strains Duplicated (Si2) Positive+ Strains(%) Glycine-L-proline-HBr-7AMC 100 0.000 99 L-Histidine-7AMC 100 0.000 99 iso-L-Leucine-TFA-7AMC 100 0.000 94 L-Leucine-7AMC* 100 0.000 100 L-Methionine-7AMC* 100 0.000 100 L-Proline-HBr-7AMC* 100 0.000 100 L-Pyroglutamate-7AMC 100 0.000 76 L-Serine-HCI-7AMC 100 0.000 97 L-Tyrosine-7AMC* 100 0.000 100 L-Valine-7AMC 100 0.000 99 Other peptidasesubstrates L-Alanine-L-phenylalanine-L-lysine-2TFA-7AMC* 100 0.000 100 L-Lysine-L-alariine-7AMC* 100 0.000 100 B. 4-Methylumbebeffiferones(4MU) Glycosides 4MU-2-Acetamido-4,6-o-benzylidene-2-deoxy-o-D- 100 0.000 97 glucopyranoside 4MU-2-Acetamido-2-deoxy-o-D-galactopyranoside 93.72 0.059 83 4MU-2-Acetamido-2-deoxy-o-D-glucopyranoside 100 0.000 98 4MU-N-Acetyl-o-D-galactosamine 100 0.000 95 4MU-N-Acetyl-o-D-glucosaniine too 0.000 89 4MU-0-D-Cellobiopyranoside 100 0.000 99 4MU-a-L-Fucopyranoside 100 0.000 95 4MU-0-D-Fucoside 100 0.000 99 4MU-0-L-Fucoside 100 0.000 99 4MU-a-D-Galactoside 100 0.000 94 4MU-0-D-Galactoside 100 0.000 99 4MU-a-D-Glucoside* 100 0.000 100 4MU-0-D-Glucoside* 100 0.000 100 4MU-(x-D-Glucuronide 96.97 0.029 82 192 Table 24 continued Substrate Agreement Test % Of between Variance Strains Duplicated (Si2) Positive+ Strains(%) 4MU-0-D-Maltoside 96.97 0.029 96 4MU-a-D-Mannopyranoside 100 0.000 97 4MU-0-D-Mannopyranoside 100 0.000 57 4MU-0-D-Ribofuranoside 100 0.000 62 4MU-2,3,5-Trio-o-benzyl-a-L-arabinofuranoside 100 0.000 99 4MU-0-D-Xylopyr-anoside 100 0.000 98 4MU-0-D-Xyloside 100 0.000 99 Inorganic esters Bis-(4MU)-Phosphate 100 0.000 96 4MU-Phosphate* 100 0.000 100 4MU-Pyrophosphate 100 0.000 99 4MU-Sulphate 100 0.000 94 Organk esters 4MU-Eicosanoate* 100 0.000 100 4MU-Elaidate* 100 0.000 100 4MU-Heptanoate* 100 0.000 100 4MU-Laurate 100 0.000 97 4MU-Lignocerate 96.97 0.029 94 4MU-Myristate 96.97 0.029 77 4MU-Octadecanoate 100 0.000 74 4MU-Palmitate 100 0.000 97 4MU-Pentadecanoate 100 0.000 60 4MU-Protectedacetate* 100 0.029 100 4MU-Stearate 100 0.000 98 Datafor duplicatedstrains not included. Testsfor which all of test strainsgave positive results. Abbreviations: Boc, tert-butyloxycarbonyl;Bz, benzoyl; Bzl, benzyl; HBr, hydrogen bromide; HCI, hydrochloride;TFA, trifluoroacetate;Z, benzyloxycarbonyl. 193 (woidjaS - . S.,SUavlpu! va,?, - - - *viods.iguol viods.iqouz7d - - - - K "0 210JOW * - - *vqlvoaAlu viodsvi, - 22 - - - -ci 2 . vonvI2 viodsmiajoi-m - vosof viodsutmonm - - - c> ,Z. > - - - , vo,voi viodslqojmN 7!N - - - - . rouagouicuynvuodnqoj. - - - - (jjr&L) . -ds umlSuviockoidany (,,, - - .- - IA,LL) . *-di; twq*mvjodvOldO-IJS ... ( :ZZ zs GOD $2 ds - - - - (9 11Rd) **, umiSmuods'01410-11S ds - - - - > (Z6ZNj) *., umjSstv.'Od9OJdO-I)S 00 - - .- (99, &D ***ds t&MStmodvOfdaJJS - - twq8vviodsoida-my - - - - e u umquatodsoidaijS - - - .ffl ý. *umasu$op.u? A -dsqns umaiwSOPWA - 3vtuodsojdaqs, - - - *asumoy -dsqnsumosmSOP914 um! - - - - *Umosoj wqSuviodsoidaus 0 - 0 ,oL ajvsjnAopnard umloumodsoidatis - - - umquwadvoidatis - ., um.ýjjv, sMpuou ;. »u9 - - swf sumod-VO14as - - 8. :Z r- * I! wiq - - * 0 *, Mlvinjjo,, umquviodrosdaws ", r- = - - - ý12uvjodsmda-oS - *$aua$o,s4,, ujv W, - - juvjodsoida-tyg - - *,, ql, w, ý, - - - - . ump?qlv umqumodrojdaIS c) I-. 2:ý 3 (L) N . - fr N . <> lý21 V) c9 Q -5 mbZCA v2 I 194 CD CD CD CIL E- 195 -Iý0 00 Q ef) 10'. (4 i30 Of.) Cý t4 ýo 1ý3 00 . 4 111 r. (020, '5" -9Z, - 4.) .0 196 DISCUSSION A. SELECTIVE ISOLATION There is compelling evidenceto show that the discovery of previously unknown bioactive compoundsoccurs when rare and novel microorganisms, notably actinomycetes,are examinedusing new and existing screeningsystems (Nolan and Cross, 1988; Okami and Hotta, 1988). Members of the order Actinomycetaleshave been the most widely exploitedgroup of microorganismsin terms of biotechnologicalapplications (Cross, 1982; Goodfellow and ODonnell, 1989;Labeda and Shearer,1991). Antibiotics, enzymesand enzymeinhibitors of commercialimportance have been produced by large-scalecultivation of members of this taxon for the past 50 years with new productsbeing discovered,patented andmarketed every year. There is, therefore,a strongincentive to usenatural, in addition to genetically engineered,microorganisms in pharmacologicalscreening programmesdesigned to discover replacementantibiotics and biopharmaceutics neededto meetgrowing consumerdemand for naturalproducts (Bull et al., 1992). The numericalpredominance of streptomycetesin soils explains why the majority of secondarymetabolites from actinomycetesdiscovered and developed in the 1950s and 1960s were from these organisms. The capacity of streptomycetesto produce new natural products remains unsurpassedthough membersof other actinomycetegenera are becomingincreasingly important as a sourceof novel products (Okami and Hotta, 1988; Goodfellow and O'Donnell, 1989; Labedaand Shearer,1991). It is, therefore,important that new isolation proceduresare developed to ensure a steady supply of novel and unconunon actinomycetesfor both high and low throughputscreens. It is difficult to know which kinds of actinomycetesshould be selectedfor screeningsince commercially important products such as antibioticsand enzymes 197 are producedby membersof taxonomicallydiverse genera. It can be useful to know whether thereis any relationshipbetween the classof compoundsought and specific taxa. Actinoplanes strains, for instance,are known to be a sourceof polyether ionophoreantibiotics, Actinomadurastrains produce depsipeptides and Micromonosporastrains aminoglycoside, ansamacrolide and macrolideantibiotics (Labedaand Shearer,1991). It may, however,be muchmore productive,in terms of the discovery of novel compounds,to screenactinomycete groups that have receivedrelatively little attention. Membersof the family Streptosporangiaceae, including the type genus,Streptosporangium, can be cited to exemplify this point. Little is known about the occurrence, numbers, kinds or activities of such organismsin naturalhabitats (Goodfellow, 1991). There are several reasonswhy the extent of actinomycetediversity in natural environmentsis underestimated.These include difficulties in achievinga representativesample of actinomycetesfrom heterogeneoussubstrates such as soil (Hopkins et al., 1991), absenceof acceptedcriteria for delineatingspecies and genera(Goodfellow and O'Donnell, 1993),and a lack of objective and effective selectiveisolation procedures(Williams et al., 1984a;Goodfellow and O'Donnell, 1989; Bull et al., 1992). However, given developments in actinomycete systematicsit is now possible to develop reliable strategiesfor the selective isolation, classification and identification of membersof specific taxa and to recogniseand characterise novel organisms. It is important that taxonomyreflects the extent of the natural diversity of actinomycetesin natural habitats. In practice,the numberof speciesin a genusis still markedly influenced by the aims of the taxonomist,the extent to which the taxon has been studied, the criteria used to define the species,and the easeby which strains can be brought into pure culture (Williams et al., 1984a; Goodfellow and O'Donnell, 1989,1993). The fact that there are over 150 198 described taxospeciesof Streptomyces(Williams et al., 1989) but only 15 validly described speciesof Streptosporangiummay merely be due to the current pre- eminenceof the former in actinomycetebiology. One of the primary aims of the presentinvestigation was to further unravel the extent of variation encompassedby the genus Streptosporangiumby evaluating the taxonomic databasesgenerated by Whitham (1988) for the classification and identification of these organisms. In particular, Curie-point pyrolysis mass spectrometrywas used to evaluate the taxonomic integrity of representativenumerically circumscribedclusters of streptosporangiaand to check the identity of strains examinedusing the numerical identification procedure. Experimentswere also carried out to determinethe value of fluorogenic enzyme tests in the rapid circumscription of streptosporangiaisolated from natural habitats. The selectivity of isolation proceduresfor actinomycetesis influenced by factors that include the nature of pretreatmentregimes and the selectivity of isolation media and incubation conditions. Innumerableprocedures have been recommendedfor the selective isolation of specific actinomycetegenera from natural habitats(Cross, 1982;Williams and Wellington, 1982;Nolan and Cross, 1988) but little attempt has been made to determinetheir effectiveness. The importanceof evaluating how effective selective isolation proceduresare was underlined in the present investigation when it was demonstratedthat the vast majority of the actinomycetes from pretreated soil growing on HV agar supplementedwith actidione(50mg/1) and nalidixic acid (30mg/1)and incubated for 4 weeksat 300Cwere streptomycetes. In the presentinvestigation streptosporangia were isolatedfrom ten of the twelve composite soil samplesusing selective isolation procedures(Nonomura and Ohara,1969a; Nonomura, 1984; Hayakawa and Nonomura, 1987a; Nonomura 199 and Hayakawa, 1988) that involved the application of various pretreatment regimesprior to plating soil dilutions onto HV agarsupplemented with actidione and nalidixic acid. Streptosporangialcolonies were recognisedby their capacity to produce spore vesicles on an abundantaerial mycelium. The numbers of streptosporangiafell within the range3.8 ± 2.2 x 103to 7.9 ± 1.19 x 104colony forming units per gram dry weight soil, but there was no obvious correlation betweenthe counts and the pH, moisturecontent or organic mattercontent of the composite soil samples. Counts within the recordedrange are similar to those reported by Nonomura and his colleaguesfor Japanesesoils (Nonomura and Ohara, 1960,1969a, b; Hayakawa and Nonomura, 1987a, b; Nonomura and Hayakawa,1988; Hayakawa et al., 1991). The failure to isolate streptosporangia from the acidic Mount Bromo (Indonesia)and Brazilian rainforest soils suggests that theseorganisms cannot cope with low pH regimes. Slightly acidic, humic rich gardensoils are consideredto offer favourablehabitats for streptosporangia (Nonomura,1984; Nonomura and Hayakawa, 1988). The highest actinomycetecounts, including those for streptosporangia, were consistentlyfound with the compositesoil samplesthat were subjectto the less extremepretreatment regimes, namely the heatpretreatment of 10-1dilutions of air dried soil in the presenceof either sodiumdodecyl sulphateor yeastextract. The highestactinomycete counts were recorded for compositesoil 8, that is, post- harvestedGinseng soil, that was pretreatedwith yeast extract for 20 minutes at 400Cprior to dilution and plating onto HV agar supplementedwith actidioneand nalidixic acid and incubation at 300C for 4 weeks. Previous studieshave also shown that there is a markeddecrease in streptosporangialnumbers when dried soil is heatedat 1200Cfor an hour (Nonomuraand Ohara, 1969a,b; Whitham, 1988). The resultsof the presentstudy show that it is no longer necessaryto use 200 such drastic pretreatmentregimes for the selective isolation of streptosporangia andrelated organisms from environmentalsamples. The highest numbers of microbisporaeand microtetrasporaewere also observedon isolation platesseeded with soil suspensionscontaining yeast extract and heatedfor 20 minutesat 400C. It was also evident that heat pretreatedsoil (1200Cfor an hour) treated with phenol at 300C for half an hour favoured the growth of microbisporaeas opposedto microtetrasporaeand streptosporangia; similar resultswere reportedby Nonomuraand Hayakawa(1988). Nevertheless, the highest counts of microbisporae,6.18 ± 0.76 x 104colony forming units per gram dry weight soil, were obtained with suspensionsof composite soil 8 pretreatedwith yeast extract. It is evident from the presentstudy that this latter procedureis the most effective one of those studiedfor isolating microbisporae, microtetrasporaeand streptosporangia from soil. It is evident both from this and earlier investigations (Couch, 1955a; Nonomura.and Ohara, 1960;Hayakawa and Nonomura,1987a, b; Nonomuraand Hayakawa, 1988; Hayakawa et al., 1991; Whitharn et al., 1993) that streptosporangiaare more common and widely distributed in soil than was suggestedby early studies(Van Brummelenand Went, 1957; Potekhina,1965). Nevertheless,it is evident from the present study that the vast majority of actinomycetesgrowing on HV isolation plates, irrespectiveof the pretreatment regime, are almost invariably streptomycetes.Clearly more effective procedures are neededfor the selectiveisolation of streptosporangiafrom natural habitats. it is possiblethat treatingdried soil with a solution of benzethoniurnchloride prior to preparinga dilution seriesand plating onto HV agar will reducethe numberof streptomycetesand therebyfoster the recoveryof streptosporangia(Hayakawa et al., 1991). 201 B. CLASSIFICATION The family Streptosporangiaceaeforms a distinct phyletic line (Stackebrandtand Schleifer,1984; Stackebrandt, 1986; Kudo et al., 1993;Ochi et al., 1993) that encompassessix generadefined primarily by a few judiciously chosenmorphological and chemicalproperties. Stackebrandtet al, (1993) found that while streptosporangiashare a number of chemical and morphological featuresthey fell into two groupsbased on the discontinuousdistribution of some chemicalmarkers known to be a value in actinomycetesystematics (Goodfellow, 1989; Suzuki et al., 1993). AN of their test strainswere known to have a wall chemotype III (Lechevalier and Lechevalier, 1970), a fatty acid pattern 3c (Kroppenstedt, 1985), 2-hydroxy fatty acids (Kroppenstedtand Goodfellow, 1991) andDNA rich in guanineplus cytosine(Nonomura, 1989). The majority of species, including Streptosporangium roseum, the type species, had a phospholipid pattern type IV (Lechevalier et al., 1977) and predominant menaquinonesof the MK-9 (112)and MK-9 (11,VIH-H4), MK-9 and /or MK-9 (H,6) type whereas the type strains of Streptosporangium albidum, Streptosporangiumviridogriseum subspecieskofuense and Streptosporangium viridogriseum subspeciesviridogriseum had a phospholipidpattern type H and principal menaquinonesof the MK-9 (11,111-114)type. The division of the genus into two groups is supportedby scanningelectron microscopy studieson the morphology of spores and spore vesicles (Nonomura, 1989), electrophoretic mobility of ribosomalprotein AT-L30 (Ochi and Miyadoh, 1992) and the results of 16SrDNA (Kernmerlinget al., 1993)and 5S rRNA sequencinganalyses (Kudo et al., 1993). The 5S rRNA sequencingand Curie-point pyrolysis mass spectrometric analysescarried out in the presentinvestigation provided further evidenceof the heterogeneityof the genus Streptosporangium. Bacterial 5S rRNAs can be 202 assignedto three groups based on differences in their primary and secondary structures(Hori and Osawa,1986; Park et al., 1987a,b, 1991,1993). Thosefrom Gram-positivebacteria with a low genomicG+C content (< 55 mol %) usually have 116 nucleotides,five base-pairsand a U-U mismatch in the D-D' helix. Ribosomal RNAs from actinomycetesand Gram-negativebacteria have around 120 nucleotidesand eight base-pairsin the D-D' region. It was clear from the primary and secondarystructures of the 5S rRNAs that all nine test strainshad sequencesbelonging to the 120 nucleotide group typical of actinomycetes (Simoncsits,1980; Dekio et al., 1984;Dams et al., 1987; Park et al., 1987a,b, 1991,1993). The phylogenetictree generatedfrom the 5S rRNA sequencedata showed that representativesof the five validly describedspecies of Streptosporangium formed two phyletic lines, one containing the type strainsof Streptosporangium amethystogenes,Streptosporangium pseudovulgare and Streptosporangium vulgare and the other the type strains of Streptosporangiumalbidum and Streptosporangiumviridogriseum subspeciesviridogriseum. These findings confirm and extendthe earlier 5S rRNA sequencingstudies of Kudo et al. (1993). They also provide further evidencethat small rRNA sequencingstudies can be used to establishfine evolutionary relationshipsbetween prokaryotes (Hori and Osawa,1986; Van den Eyndeet al., 1990),including actinomycetes(Dekio et al., 1984;Park et al., 1987a,b, 1991,1993). The type strain of Streptosporangiumviridogriseum was found as an outlier when representativestrains of Streptosporangiumwere analysed by pyrolysis massspectrometry. Nine of the remainingten type strainswere assigned to two broad groups, one containing Streptosporangium albidum, Streptosporangium amethystogenes, Streptosporangium corrugatum, Streptosporangium nondiastaticum, Streptosporangium pseudovulgare and 203 Streptosporangium vulgare, and the second Streptosporangium fragile, Streptosporangium violaceochromogenes and Streptosporangium viridialbum. The recovery of the type strain of Streptosporangium albidum in the first group is puzzling as it is clear from other studies that this organism is closely related to Streptosporangium viridogriseum (Mertz and Yao, 1990; Ochi and Miyadoh, 1992; Stackebrandt et al., 1993). The remaining organism, the type strain of Streptosporangium roseum, formed a single membered cluster. These data provide further evidence that pyrolysis mass spectrometry can be used as a quick and effective way of evaluating the taxonomic integrity of actinomycete taxa (Hindmarch et al., 1990; Sanglier et al., 1992). The clustering of Streptosporangium species according to the chemotaxonomic and morphological features outlined above is in excellent agreementwith the phylogeneticanalysis of representativesof the two clusters (Kernmerling et al., 1993). 16S rDNA analysis indicated a close similarity between Streptosporangiumroseum, Streptosporangiumnondiastaticum and Streptosporangiumpseudovulgare, Streptosporangiumcorrugatum was also associatedwith this group. In contrast, the type strain of Streptosporangium viridogriseumsubspecies viridogriseum showed a closer similarity to membersof the family Pseudonocardiaceaethan to otherStreptosporangium species. It is evident from the presentand earlier studies(Nonornura, 1989; Ochi andMiyadoh, 1992;Kernmerling et al., 1993;Kudo et al., 1993;Stackebrandt et al., 1993; Whitharn et al., 1993) that Streptosporangium albidum and Streptosporangium viridogriseum cannot be retained within the genus Streptosporangium.Differences in the primary structureof 5S and 16SrRNA, as well as in chemotaxonomic:properties, between members of the two streptosporangialgroups are greater than those found to separategenera of the family Streptosporangiaceae. In light of these findings the genus 204 Streptosporanglumshould be restricted to Streptosporangiumroseum and its relatives,namely Streptosporangiumalbum, Streptosporangiumamethystogenes, Streptosporangiumcarneum, Streptosporangium corrugatum, Streptosporangium fragile, Streptosporangium longisporum, Streptosporangiumnondiastaticum, Streptosporangium pseudovulgare, Streptosporangium violaceochromogenes, Streptosporangiumviridialbum and Streptosporangiumvulgare. It is, however, possible that additional studieswill supportthe exclusionof Streptosporangium corrugatum from the genusas substantialdifferences exist betweenthis organism and Streptosporangium roseum in the primary structure of 16S rRNA (Kemmerling et al., 1993; Stackebrandtet al., 1993) and in the amino acid sequenceof the AT-1,30protein (Ochi andMiyadoh, 1992). It is clear from both phenotypicand genotypic data that Streptosporangium viridogriseumshould be classifiedin the family Pseudonocardiaceae,adjacent to but distinct from Saccharothrixaustraliensis (Kemmerling et al., 1993;Kudo et al., 1993; Stackebrandtet al., 1993;Whitham et al., 1993). Streptosporangium albidum and Streptosporangiumviridogriseum form a distinct taxon but can be separatedon the basisof chemicaland DNA relatednessdata (Stackebrandtet al., 1993; Whithain et al., 1993). Consequently,there is a wealth of evidenceto support the proposal that Streptosporangiumalbidum and Streptosporangium viridogriseumbe classifiedin the genusKutzneria as proposedby Stackebrandtet al. (1993). The proposalfor the genusKutzneria leavesthe genusStreptosporangium as a relatively homogeneoustaxon with characteristicgenotypic and phenotypic properties. The exclusionof the two speciesfrom the genusStreptosporangium togetherwith additionalinformation on bonafide membersof the taxonjustify an emendationof this genus. 205 Emendeddescription of StreptosporangiumCouch 1955,148AL Strep.to. sporan'gi. um. Gr.adj. streptostwisted; Gr. n. spora a seed;Gr. n. angeion a vessel;M. L. neut.n. Streptosporangiumspore coiled within a sporangium. The descriptionis basedon the characteristicsgiven by Nonomura(1989), Stackebrandtet al. (1993) and Whitham et al. (1993) togetherwith information from the presentstudy. Aerobic, Gram-positive,non-acid fast actinomycetesthat form a stable branchedmycelium. Globosespore vesicles, up to 10 pm in diameter,are formed on aerial hyphae. Sporangiophoresare produced by septation of a coiled, unbranchedhypha within the sporevesicle; they are oval, sphericalor rod shaped (0.2-1.3 x 0.2-1.5 pm) and non-motile. Some strains require B vitamins for growth. All are mesophilic and chemoorganotrophicwith an oxidative type of metabolism. Streptosporangiadegrade casein and gelatin, produce hydrogen sulphide, use cellobiose as a sole carbon sourceand grow in the presenceof crystal violet (0.001%, w/v). They also cleave a range of conjugatedsubstrates that include Boc-iso-L-leucine-L-glutamine-glycine-L-arginine-HCI-7AMC, Boc-L-leucine- glycine-L-arginine-7AMC, Boc-L-valine-L-leucine-L-lysine-7AMC, Boc-L- vahne-L-proline-L-arginine-HCI-7AMC, Bz-L-valine-glycine-L-arginine-HCI- 7AMC, succinyl-L-alanine-L-alanine-L-phenylalanine-7AMC,succinyl-L-leucine- L-leucine-L-valine-L-tyrosine-7AMC, succinyl-L-leucine-L-tyrosine-7AMC,Z- glycine-glycine-L-leucine-7AMC, Z-glycine-L-proline-7AMC (endopeptidases); D-alanine-TFA-7AMC, L-alanine-7AMC, L-arginine-7AMC, L-arginine-L- arginine-3HCI-7AMC, L-cysteine(Bzl)-7AMC, L-glutamine-HCI-7AMC, L- leucine-7AMC, L-methionine-7AMC, L-proline-HBr-7AMC, L-tyrosine-7AMC (exopeptidases); L-alanine-L-phenylalanine-L-lysine-2TFA-7AMC,L-lysine-L- alanine-7AMC (other peptidases); 4MU-0-D-fucopyranoside, 4MU- 206 galactopyranoside,4MU-cc-D-glucopyranoside, 4MU-P-D-glucopyranoside, 4MU- a-D-glucoside, 4MU-P-D-glucoside (glycosides); 4MU-phosphate (inorganic ester); 4MU-eicosanoate,4MU-elaidate, 4MU-heptanoateand 4MU-protected acetate(organic esters). Cell walls contain N-acetylatedmuramic acid, meso-diaminopimelicacid but no characteristicsugars. Whole-organismhydrolysates contain madurose. Major phospholipids include diphosphatidylglycerol, hydroxy- phosphatidylethanolamine,phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol,phosphatidylinositol mannosides and a n-acetylglucosamine containingphospholipid but no phosphatidylcholine.Predominant menaquinones are MK-9 (112)and MK-9 (11,V111-144), MK-9 and/ or MK-9 (H6). Complex mixturesof straightand branched chain fatty acidsare formed. Themol % G+C of theDNA is 69-71(Tm). Theprimary habitat is soil. Thetype species is Streptosporangiumroseum Couch 1955,15 1 AL. All twelveof thevalidly described species of Streptosporangiummentioned earlier have propertiesconsistent with their assignmentto this revisedtaxon. Numericaland chemical data also support the taxonomic integrity of thesespecies (Mertzand Yao, 1990;Stackebrandt et al., 1993;Whitharn et al., 1993). The speciesconcept remains a difficult themein prokaryoticsystematics (Goodfellow and O'Donnell, 1993). Early definitions of actinomycetespecies were often based on monothetic groups described on the basis of a few morphological,pigmentation and biochemical features. This conceptof speciation is clearly flawed as strainswhich vary in key characterswill be misclassified. It is, however,now good practiceto distinguisha taxospecies,a group of strainsthat sharea high proportionof propertiesfrom a genomicspecies, a group of organisms which share high DNA relatednessvalues. It can be anticipated that the application of polyphasic taxonomy, that is, the use of a comprehensiveset of 207 phenetic and genomic data for the circumscriptionof specieswill lead to better classifications and hence to more reliable methods for the identification of prokaryotic species(Goodfellow and O'Donnell, 1993;O'Donnell et al., 1993). The emendedgenus Streptosporangiumis well circumscribedgiven an impressiveset of data derived from extensivechemical, molecularand numerical taxonomic studies (Ochi and Miyadoh, 1992; Kernmerling et al., 1993; Stackebrandtet al., 1993;Whitham et al., 1993). In contrast,the circumscription and definition of Streptosporangiumspecies is still weak with the majority of speciesregarded as taxospecies(Whitham et al., 1993).However, the extensive numerical taxonomic survey of streptosporangiacarried out by Whitham.and his colleagueswas nevermeant to be an endin itself but as a meansto an end. It is widely acknowledged that relationships expressedin numerical taxonomiescan be distortedby factors suchas test and strain selection,test error andby the geneticinstability of the organismsunder examination (Goodfellow and O'Donnell, 1993). It is important, therefore, that numerical taxonomies are checkedin light of independenttaxonomic features derived from the applicationof other modem taxonomictechniques, that is, by adoptingthe polyphasicapproach to classification(Colwell, 1970; Murray et al., 1990). In the presentstudy, the pyrolysis massspectral data supportthe taxonomic integrity of streptosporangial clustersI and 2 as defined by Whitham et al. (1993). Thesedata, taken together with thosefrom an earlier study on streptomycetes(Sanglier et al., 1992)indicate that pyrolysis mass spectrometrycan be used as a rapid and reliable way of determiningthe taxonomicstatus of numericallydefined clusters. C. IDENTIFICATION Good classificationis a prerequisiteof accurateidentification. This means that the quality of a frequencymatrix is only as good as the classificationfrom 208 which it was derived. It is necessaryin practiceto haveat least as many testsas taxa in frequencymatrices (Sneath and Chater, 1978;Priest and Williams, 1993). The frequency matrix generatedby Whitharn (1988) fulfils this latter criterion. This matrix, which is basedon twelve numericallydefined clustersand twenty-six diagnostictests, was shown to be theoretically sound. In addition, it was used togetherwith the MATIDEN program (Sneath,1979a) in the identification of a small number of unknown streptosporangiafrom soil (Whitham, 1988). One of the primary aims of the presentinvestigation was to extend these preliminary studies. It has repeatedly been stressedthat experience is needed to interpret identification scores based on Willcox probabilities, taxonomic distancesand standard errors of taxonomic distances (Lapage et al., 1973; Sneath, 1978; Williams et al., 1983b; Priest and Williams, 1993). Thus, use of Willcox probabilitiesalone can lead to false positive resultsfor unknownstrains when data for the unknowntaxon/ taxa are not included in the frequencymatrix (Willcox et al., 1973;Priest and Williams, 1993). Suchanomalous results can be attributedto the normalisation process in the calculation of this coefficient. Values for taxonomic distanceand its standarderror are not deceptivein this way (Sneath, 1979a). In the presentstudy, the application of the 95% taxonomic radius and Gaussianprobability coefficients proved useful in the definition of acceptable identification scores. The need to evaluatefrequency matrices using strainsthat were not usedin their constructionhas also been emphasised(Sneath and Sokal, 1973;Williams et al., 1983b;Langham et al., 1989;Priest andWilliams, 1993). The percentageof unknownstrains identified partly reflects the choice of cut-off points and the state of the taxonomyof the organismsunder study. Consequently,the criteria chosen for a successfulidentification tend to be somewhatarbitrary (Williams et al., 209 1985a;Priest and Williams, 1993). As statedearlier, the widely adoptedcriteria for the identification of streptomycetesare Willcox probabilities greater than 0.850, low scores for taxonomic distance and its standard error, all scores significantly betterthan thosefor the next best alternatives,and a small numberof charactersof the unknowncited as being atypical of thoseof the cluster to which it has been assigned. More stringent criteria have been recommendedfor the identification of aerobic endospore-formingbacilli (Priest and Alexander, 1988), ofcoryneform" bacteria (Hill et al., 1978),mycobacteria (Wayne et al., 1980) and Gram-negativebacteria (Lapage et al., 1973;Dawson and Sneath,1985; Homes, 1986a,b). The MATIDEN program (Sneath, 1979a) and the frequency matrix of Williams et al. (1983b) have been used to identify unknown neutrophilic streptomycetesfrom several distinct natural habitats and to evaluate media formulated for the selective isolation of uncommon and rare streptomycetes (Vickers et al., 1984;Williams et al., 1984a;Goodfellow and O'Donnell, 1989). Thus, 81% of neutrophilic streptomycetesfrom soil (Williams et al., 1983b; Langham.et al., 1989),44% from freshwaterhabitats (Stanton, 1984),70% from marine sediments (Goodfellow and Haynes, 1984) and 30% of alkalitolerant streptomyeeteshave been identified using these procedures(Saddler, 1988). Correspondingstudies have been carried out on bacteriaother than streptomycetes. Probabilistic identification of Grain-negativeisolates has been especially effective. Of more than nine hundred strains of fermentative Gram-negative bacteria and over six hundred strains of non-fermenters,98% (Holmes et al., 1986a) and 92% (Hohnes et al., 1986b) respectivelywere identified using the Willcox probability coefficient at > 0.999. Similarly, of nearly two hundredand fifty vibrios isolatedfrom freshwaterhabitats, most (72% at a Willcox probability > 0.999 or 79% at Willcox probability > 0.990) were identified using a 50-test 210 matrix (Dawsonand Sneath,1985). Resultsfrom similar studieson Gram-positive bacteria have not been so encouraging. Hill et al. (1978) only identified half of nearly three hundredstrains of "coryneform" bacteriausing a Willcox probability of > 0.999. Similarly, mycobacteria(47% of 298 strainsat > 0.99; Wayne et al., 1980) and aerobic, endospore-formingbacilli (70% of 58 strainsat > 0.95; Priest and Alexander,1988) have proved difficult to identify. Two related explanations can be offered to account for the low identification valuescited above. First, the natureof the unidentified isolates. It seemsprobable that almost all of theseisolates are representativesof undescribed speciesthat were not included in the frequencymatrix. It is, of course,possible that some will be atypical strains of establishedspecies but theseare probablya minority. Generasuch as Bacillus and Streptomyceshave been overclassifiedin the past but are now underclassifiedwith many new speciesawaiting description (Sanglieret al., 1992;Labeda and Lyons, 1991a, b; Atalan, 1993;Priest, 1993). A secondreason is that some taxospecies,such as Bacillus megaterium (Hungerand Claus,198 1), Bacilluspolymyxa (Nakamura,1987a), Bacillus subtilis (Nakamura, 1987b, 1989), Streptomycescyaneus (Labeda and Lyons, 1991a), Streptomyceshygroscopicus and Streptomycesviolaceusniger (Labeda and Lyons, 1991b) are species-groups.Probabilistic identification to such taxa will almost invariably result in low scoresgiven the variation encompassedby the taxon and overlap with neighbouring taxa. Thus, poor identification is a function of inadequateclassification as mentionedearlier. In contrast,Gram-negative genera of medical interest have been extensively studied with relatively few species awaiting description. Such species are homogeneousand in most cases comprehensivephenotypic descriptions have been supportedby DNA homology studies. 211 In the present investigation, the frequency matrix recommendedby Whithain (1988) was comprehensivelyevaluated using seventyrepresentatives of the twelve major Streptosporangiumclusters defined in the initial numerical taxonomic survey (Whitharn et al., 1993). The identification scoresobtained for these organisms were critically examined and cut-off points set for the identification of both marker strains and unknown isolates derived from the selectiveisolation studies. Sixty-five of the seventymarker strainswere assigned to their parent clusters using stringent identification criteria, namely Willcox probability scores of 0.9700 or above, taxonomic distance scores below the corresponding95% taxonomic radius scores, high Gaussianprobability scores signifying that there was no significant overlap betweenthe cluster strains were assignedto with the bestidentification scoresbeing much better than the two next best alternatives. These cut-off criteria for a positive identification of streptosporangiaare similar to thoserecommended by Whitham(1988). The final logical step in the practicalevaluation of a frequencymatrix is to isolate new strains and attempt to identify them. In the present investigation, twelve of the one hundredand thirty-six unknownstreptosporangia, isolated from a range of compositesoils using severalisolation procedures,were identified to known clustersusing the stringentcut-off criteria mentionedearlier. Ten of the isolateswere identified to cluster I (Streptosporangiumsp. ) and two to cluster 2 (Streptosporangiumsp. ). An additional twelve strainswere assignedto cluster I and sevento cluster 2 using less stringent cut-off points. The remaining one hundredand five strains,that is, 77% of the isolateswere not identified. Surprisingly little attention has been given to validating identifications derived from probabilistic methods. In most cases identifications based on Willcox probabilities, either alone or in combination with other identification coefficients, havebeen acceptedat face value. However, as mentionedearlier, 212 Willcox probability scorescan sometimesbe erroneouslyhigh for an unknown strain, when data for the appropriatetaxon are not in the identification matrix. It is, therefore, important to obtain some information on the validity of the identifications. In the investigationscarried out by Holmes et al. (1986a, b) identifications were validated using conventional identification schemes. This approachis sound for well classified taxa, such as constituentsof the family Enterobacteriaceae,but is not practicable in the case of actinomycetesand aerobic,endospore-forming bacilli which are underclassified. In an elegantstudy, DNA reassociationwas usedas an absolutemeasure of relatednessby which to assessphenetic identifications of unknown, aerobic endospore-formingbacilli (Priest and Williams, 1993). Representativeisolates identified to various Bacillus species,namely Bacillus amylolyticus, Bacillus circulans, Bacillus glucanolyticus, Bacillus lautus, Bacillus pabuli and Bacillus validus, werecompared against labelled DNA from referencestrains. In all cases, the test strains showed more than 70% sequencesimilarity to DNA from the appropriate referencestrain and less than 30% similarity to DNA from other referencestrains. Thus, the phenotypicidentifications were thoroughlyvalidated. Other taxonomicmethods, such as whole-organismprotein electrophoresis,which arecongruous with DNA similarity, could be usedin placeof DNA reassociation. In the presentstudy, fourteenof the thirty-one isolatesidentified to either clusters I or 2, and five unidentified streptosporangia,were compared with representativesof the two clustersusing Curie-pointpyrolysis massspectrometry. All six of the representativeisolates identified to cluster I using the stringentcut- off criteria groupedwith the representativestrains of this cluster. In addition, the four representativeisolates assignedto cluster I using the less stringentcut-off criteria were alsorecovered with the cluster I strains. Similarly, the identifications of the four isolates assignedto cluster 2 were validatedby the PyMS data. In 213 contrast,the five unidentified isolateswere assignedto two groups,one of which was sharply separatedfrom clusters I and 2. Thesepreliminary results are most encouragingas they suggestthat Curie-point pyrolysis mass spectrometrycan be used to validate phenotypic identifications and to set cut-off points for positive identification of strains in computer-assistedidentification systems. It can also be concluded that the ability to analyse small amountsof bacterial growth with minimal samplepreparation to obtain,in minutes,fingerprint datathat can be used to validate phenotypicidentifications is unparalleledby other taxonomicmethods, including molecularfingerprinting techniques. Rapid and reproducible tests are neededto distinguish between validly describedspecies of Streptosporangiumand to classify novel isolatesprior to the examinationof representativestrains using more exactingtaxonomic methods that cannotreadily be usedto classify large numbersof environmentalisolates. In the present investigation, known and unidentified streptosporangiawere screened against seventy-onefluorogenic enzymetests using an automatedprocedure that had been successfully used to classify representativesof closely related fast growing speciesof mycobactelia(Hamid et al., 1993)and to assignstreptomycetes taken from selective isolation plates to three putatively novel species(Atalan, 1993). The low test error of 0.29% recordedfor the streptosporangiacompared favourablywith the correspondingfigures from the earlier studies(p 5.8%, Hamid et al., 1993;p 4.9%, Atalan, 1993). The results of the presentstudy are in good agreementwith those from previous investigations as they show that rapid enzyme tests based on the fluorophores 7-amino-4-methylcoumafin(7AMC) and 4-methylumbelliferone (4MU) provided data of value for the classification and identification of actinomycetes(Goodfellow et al., 1987b,c, 1988,1990b, 1991;O'Donnell et al., 1993; Whitham et al., 1993). It has already been pointed out that someof the 214 rapid enzymetests provide data that can be usedto strengthenthe definition of the genus Streptosporangium,others have potential for the circumscription of streptosporangialspecies. It was, however, disappointingthat only forty-two of the seventy-onetests, that is, 59%, gave data of differential value; consequently there was insufficient information in the databaseto assign the unidentified isolates to artificial groups. Clearly additional enzymatic tests are neededfor artificial grouping of streptosporangiaisolated from natural habitats. Possible areasof developmenthave been considered in recentreview articles(Manafi et al., 1991;Goodfellow andJames, 1993; James, 1993). D. FUTURE STUDIES The results of the present study show that the emended genus Streptosporangiumis a well defined taxon. It is evident from the numerical phenetic data that the revised genus is grossly underspeciatedand that minimal standardsare needed to delineate both existing and putatively novel species. Additional comparativetaxonomic studiesneed to be carried out on well chosen representativestrains to determinethe most appropriatemethods likely to be of value. A number of powerful chemical and molecular techniques,such as ribotyping and quantitative analysesof cellular fatty acids and whole-organism proteins,can be expectedto yield high quality datafor improvedclassification. In addition, representativesof existing and presumptivelynew taxospeciesshould be the subjectof DNA relatednessstudies. The application of suitable chemical and molecular methods on representativesof the genusStreptosporangium should help unravel the diversity encompassingby this taxon. This is turn will help in the developmentof improved methodsfor the identification of unknownstreptosporangia and therebyfacilitate the taxonomic approach to selective isolation of specific fractions of the 215 strePtosporangialcommunity from natural habitats. In the first instance,selective media for this purposeshould be formulated using information in the taxonomic databasegenerated by Whitham (1988). The current procedures used to determine the number and types of strePtosPorangiain soil invariably involve plating out tenfold dilutions of pretreated environmental samples onto selective media. The results of such experimentsare influenced by the propertiesof the environmentalsamples, the effect of extraction and recoveryprocedures, competition on isolation plates and difficulties in identifying isolates. It seemslikely, however, that more effective representative sampling of streptosporangiawould be achieved using the dispersionand differential centrifugationtechnique introduced by Hopkins et al. (1991). This techniquehas beenshown to be threeto twelve times moreeffective in extracting streptomycetepropagules from a range of soil samples than the conventionalreciprocal shakingprocedure (Atalan, 1993). Theseresults suggest that actinomycete-soilinteractions may be major limitations to quantitatively samplingand that the use of sodium cholate,Tris buffer and mild ultrasonication are effective in breaking down such interactions. It may also be possible to directly identify streptosporangiaon isolation plates by examining small samples of colonial growth using pyrolysis massspectrometry and analysingthe resultant datausing neuralnetworks. The results of the fluorogenic enzymetests, althoughdisappointing, were sufficiently encouragingto suggest that streptosporangiamight show species specificpatterns. However,additional studies on representativestrains and further conjugatedsubstrates are neededto prove the point. Fortunately,there is still scopefor the design and synthesisof additional 7AMC and 4MU derivativesin order to extend the range of enzymatic activities detectable(Goodfellow and James, 1993; James, 1993). Thus, the short chain estersof 4MU could be of 216 considerable taxonomic value but their use is currently limited by stability problems. Recently, a protected 4MU-acetate was synthesisedby James and found to be more stablethan 4MU-acetate. Its value in actinomycetesystematics has also been demonstrated. Additional protected esters, including ones for butyrateand propionate,are being synthesised.The rangeof peptidasesubstrates that can be obtainedis even greaterwith 7AMC derivativesavailable for most of the known aminopeptidasesand many endopeptidases. The fluorescencegenerated by 7AMC and 4MU is intense and usually easily detected. Fluorescence in the blue region has, however, certain disadvantages,particular where organismsproduce pigments which fluoresce. Such endogenousfluorescence is generally in this blue region. Alternative fluorophoresare availablewhich fluorescein other regionsof the visible spectrum (James, 1993). These include 6-aminoquinoline,resorufin and trifluoroethyl cournarins. Thesefluoresce in yellow, orange-redand greenregions, respectively. The numberof substratesbased on such fluorophoresis currently limited but it is possible that improved methods of synthesisand greater demandmay lead to greateravailability. Another potentially valuable areaof developmentcould be the searchfor taxonomically useful enzymatic activities that are not normally included in identification protocols. An exampleof one suchactivity has beendemonstrated using the compoundanthranilonitrile (Whitehead, 1989). This non-fluorescent moleculeis convertedto the highly fluorescentamide or to the acid by the activity of nitrile hydratasesand/ or nitrilases (Figure 18, page 218). Within the family Pseudonocardiaceae,Amycolata strains were separatedfrom Amycolatopsis, Pseudonocardiaand Saccharopolysporastrains by their capacity to form strong blue fluorescencedue to the conversionof anthranilonitrileto the amide or acid. Similarly, the conversionof Haloxon (3-chloro-7-hydroxy-4-methylcoumarin-bis- 217 x 'r ('4 13 (ý 1 Co 6'(-.4 I (-.4 z .0 C) Cd, 0 r. 1 - -7 V>1 I II I z U ' 0 218 [2-chloroethyl]-phosphate) to 3-chloro-7-hydroxy-4-methylumbelliferonecauses an intense sky-blue fluorescenceunder ultra-violet light. Most representativesof the family Pseudonocardiaceaeare characterisedby their ability to degrade Haloxon, exceptionsinclude Amycolata autotrophica,Amycolatopsis rugosa and Saccharomonosporaviridis (Whitehead,1989). The application of the strategy outlined above should help highlight minimal standardsfor the delineationof Streptosporangiumspecies. It will not be possibleto effectively apply the taxonomicapproach to selectiveisolation in any comprehensive way until the subgeneric classification of the genus Streptosporangiumis clarified. In the meantime representativesof the genus should be isolated from natural habitats using the more "gende" isolation procedures highlighted in the present investigation and examined in pharmacologicalscreening programmes. 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Similarity in the DNA of various morphologically distinct actinomycetes.Journal of Bacteriology89,444-453. ZAKRZEWSKA-CZERWINSKA, J., MORDARSKI, M. AND GOODFELLOW, A (1988). DNA basecomposition and homologyvalues in the classificationof someRhodococcus species. Journal of GeneralMicrobiology 134,2807-2813. ZEMANY, P.D. (1952). Identification of complex organic materials by mass spectrometry analysis of their pyrolysis products. Analytical Chemistry 24,1709- 1713. T. P. (1987). A MANG, Y., LIU, W., FENG, Y. -X. AND WANG, simple and rapid solid-phaseRNA sequencingmethod. Annals of Biochemistry 163,513- 516. 285 APPENDIX A TAXONPROGRAM The TAXON computerprogram was written in UCSD Pascalfor the Apple He computersby Dr. A. C. Ward of the Departmentof Microbiology, University of Newcastleupon Tyne. The program has been transferredto IBM PC, written in standardPascal for the Propascompiler running under MSDOS. The program allows for: (a) a simple data entry system for binary data (+/-) derived from numerical taxonomicstudies, (b) pre-processingof data, (c) on-line transferof data from IBM PC/AT microcomputersto the Northumbria Universities Multiple Access Computer (NUMAC) AMDAHL 5860 in a form suitable for direct analysis using the CLUSTAN suite of programs (Wishart, 1978), (d) analysisof datafor clustersof strainsdefined by the numericalanalysis and (e) identification of unknown strains to clustersof strains defined in numerical analyses. (a) Entry of Data Initially, a screeneditor is employed to generatea text file containing information on organism names (up to five alphanumeric characters), test names (up to three alphanumeric characters ) and organisms and test groupings. The definition of groups of organisms and tests is necessarydue to the limited size of the screen; the largest matrix that can be displayed on the screen is 70 tests by 18 organisms. 286 The program is completely menu driven and incorporates full error checking. Upon running the TAXON program, the text file is read in and an empty data matrix of the appropriatesize createdand held in random access memory (RAM). Using the organismand test groups defined in the text file a screensized "window" of part of the data matrix is displayed. The contentsof eachwindow can be setto contain positive,negative or blank valuesdepending on the natureof the results. Thus, if the resultsare predominantlypositive then the window can be completely filled with positive or negativevalues, respectively. The minority resultscan then be enteredwhere appropriate saving the overall time required for data entry. Data are entereddirectly as single key strokes(+, - or space). After all the resultsfor a "window" havebeen entered, the dataare saved to RAM and another "window" displayed. The data matrix can be savedonto hard disc once the whole data matrix has been filled or during data entry. The TAXON program can handle a data matrix containing up to 512 unit characters for 512 organisms. A facility exists for the addition of further organismsand/or tests to an establisheddatabase. Thus, a new matrix file is createdwith additionalorganisms and/ortest namesand organism/test groups. This amendedtext file is readin by the TAXON programand superimposedon the existing datamatrix. The latter is expandedaccordingly. Organismsand test namescan be deletedfrom the matrix using a similar procedure. Once the matrix hasbeen altered,it may be storedin harddisc with a new,or the existing file name. (b) Pre-Processingof Data Raw datacan be examinedto determinethe overall percentagedistribution of positive charactersand the reproducibility of individual testsprior to numerical analysis. Percentagepositive resultsfor eachcharacter can be determinedso that 287 any test that is positive or negativefor all of the organismswithin the matrix can be identified. In addition,by enteringthe namesof duplicatedstrains an output is obtained giving information on the individual test variancesand percentage agreementbetween duplicated strains, and also the averageprobability of an erroneousresult, the test error as definedby Sneathand Johnston (1972). Testsconsidered to havelittle if any differential value or which showpoor reproducibility can be removedfrom a data matrix by the creation of a new text file from which the appropriatetests have been deleted. The resultscorresponding to the missing testsare automaticallydiscarded from the matrix when the new text file is readby TAXON. The reduceddata matrix is then savedon harddisc. (C) Transfer of Data CLUSTAN requires databasesto exist in a pre-determinedformat. The first 8 columnsmay contain a label, 9 and 10 are left blank. Columns II to 80 are available for data in a binary format (1/0). Data are written on multiple lines using the same format when more than 70 tests are used. A facility within TAXON allows the conversionof +/- resultsof a data matrix into the 1/0 format which are then savedto a text file. A secondtext file containingthe namesof the in organismsin the datasetcan be generatedto contain the organism names a format suitablefor readingby CLUSTAN procedureLABELS. Finally, a batch- file is generatedcontaining the information required for the execution of the numericalanalysis on CLUSTAN. The formationof the batchfile is facilitated by a simple question and answer procedure where many of the parametersare commonly availableas defaultoptions. This is also savedto a text file. The three text files are then transferred to the mainframe computer to run a better CLUSTANjob. 288 (d) Analysis of Defined Clusters The clustersof test strainsdefined during the numerical analysiscan be superimposedonto the data matrix by a simple modification of the organism groups in the text file. Post-clusteranalysis data processingfacilities for a given clusterinclude: (i) determinationof averagesimilarity, or dissimilarity of each strain to the other strainsin the sairnecluster, (H) calculationof meaninter-cluster similarity or dissimilarity, (iii) designationof centrotypestrain, (iv) calculationof percentageof strainsin the clusterwhich are positive for each clusterfor eachtest (v) calculation of the observedsimilarities, or dissimilarities, betweenpairs of duplicated, or defined groups of strainsexpressed by either the Dp, Sj or Ssm coefficientsand (vi) analysisof percentagepositive data for clustersusing procedurestaken from the DIACHAR program(Sneath, 1980a) The inclusion of the DIACHAR procedurewithin the TAXON program allows the selection of characterseither for an identification matrix or for the design of isolation media selectivefor the recoveryof representativesof chosen clusters,or groups of clusters, from natural habitats. The organismand the test groupingin post-analysistext files may be usedto define the size of datamatrices to be examinedusing DIACHAR. The output lists severalproperties for each clusterexamined: (i) the differencevalues and corresponding diagnostic scores for eachtest, (H) the sum of diagnosticscores for testsin eachof the testsgroupings examined and (M) a total sumof scoresfor all test setsexamined. 289 The output is listed in descendingorder of difference values. Thus, tests are chosenthat haveboth a high difference scoreand for which a high percentage of strainsin a cluster are positive. The percentagepositive valuesof the selected testscan be either displayedon the screenor printed out. The data for clustersin which all the strainsare negativefor oneor more of the chosentests are removed from the data matrix. The reduceddata matrix is then subjectedto a second analysiswhen more testsare selectedto distinguishbetween the targetcluster and remainingclusters. The data matrix may be reducedin this way until no further tests are highlighted that were scorednegative for all membersof any of the remaining clusters. Using this facility, a minimum number of characterscan be chosenwhich enableeach and every cluster to be distinguishedfrom the others. (e) Identiflcation of Unknown Organisms Unknown organismscan be examinedfor the appropriatediagnostic tests once an identification matrix has been constructed. The unknown organisms can be added to the TAXON data matrix by the creation of a new text file which contains the names of the organisms. This new file is read in the TAXON program and the binary data for unidentified strains entered into a data matrix as described previously. Identification scores are then calculated for each of the unknown strains to every one of the clusters defined in the data matrix, using the Procedure IDENTIFY which is based on the MATIDEN program (Sneath, 1980a, b). The identification coefficients calculated include: (i) Willcox probability, 00 taxonomic distance of each known strain to the centroid of every cluster, (M) the 95% taxonomic radius of each cluster and (iv) the Gaussian distance probability, that is, the percentage probability of a member of the cluster to which the unknown strain is being identified lying further 290 away from the cluster centroid that the unknownorganism itself. The derivation andvalues required for a good identification aredescribed in detail on page3 1. The procedure COMPARE, also included in the TAXON program, calculates the same identification scores for centrotype strains, hypothetical median organisms,and outer most memberof eachcluster; these valuescan be usedto measurethe degreeof confidencethat can be placed in the identification of unknownorganisms to definedclusters. The identification of large numbersof strainscan be performedin batch and the resultseither displayedon the screenor printed out. 291 APPENDIX B MEDIA AND REAGENTS AV MEDIUM (Nonomuraand Ohara,1969a) Basal medium:L-arginine, 0.3 g; glucose, 1.0 g; glycerol, 1.0 g; K2HP04,0.3 g; MgS04.7H20,0.2 g; NaCl, 0.3 g; agar, 15 g; distilled water, 1.0 litre. To this basal medium add 1.0 ml per litre of a trace salt solution (i) and adjustto pH 6.8. Autoclaveat 1200Cfor 15 minutes. Following autoclaving,add 1.0 in] per litre of a filter sterilisedB-vitamins solution(ii) just prior to pouring the medium. (I) Trace salt solution (Nonomura.and Ohara, 1969a) CuS04.5H20,0.1 g; ZnS04.71120,0.1 g; MnS04.7H20,0-1 g; distilled water, 100MI. (H) B-vitamins solution (Nonomuraand Ohara,1969a) Thiamine(aneurine) hydrochloride, 50.0 mg; riboflavin, 50.0mg; niacin (nicotinic acid), 50 mg; pyridoxine hydrochloride, 50.Omg; inositol, 50.0 mg; calcium pantothenate,50.0 mg; para-aminobenzoic acid, 50.0 mg; biotin, 25.0 mg; distilled water, 100MI. MODIFEED BENNETT'S MEDIUM (Agrawal, unpublisheddata) Lab lemco, 10.0g; peptone,2.0 g; yeastextract, 2.0 g-,tryptose, 2.0 g; CaC03,100 mg; starch, 100 mg; D-glucose, 10.0 g; agar, 15.0 g; COC12,trace; ferric ammoniumcitrate, trace; distilled water, I litre andadjust to pH 7.0. Autoclaveat 1200Cfor 15minutes. CARBON SOURCE UTILISATION (Shirling andGottlieb, 1966) ISP 9 medium(modified from Pridhamand Gottlieb, 1948) Basal mineral salts agar: (NH004,2.64 g; KH2PO, 2.38 g; K2HPO, 4.68 g; M9S04.7H20,1.00 g. To this basal medium add 1.0 ml per litre of a Pridharn 292 andGottlieb trace salts solution (ii) and adjustto pH 6.8. Autoclaveat 1200Cfor 15 minutes. Following autoclaving,add eachof the Tyndallisedcarbon sources (i) just prior to pouring the medium. (i) Sterile carbonsources D-glucoseused as positive control; solutionsof eachcarbon sourcesterilised by Tyndallisation. (H) Pridham andGottlieb trace salts CuSO4.5H20,0.64 g; FeS04.71120,0.11g; Mncl2.4H20,0.79 g; ZnS04.7H20, 0.15 g; distilled water, 100ml. DEGRADATION TESTS AV agar was supplementedwith keratin (5 g/l, Aldrich) prior to autoclavingat 1200Cfor 15 minutes. DNA DEGRADATION Bacto-DNasetest agar(Difco, 0632-01),42.0 g; distilled water, 1.0 litre, adjustto pH 7.3. Autoclavedat 1200Cfor 15minutes. HV MEDIUM (Nonomura, 1984; Hayakawa and Nonomura, 1987a) Basal medium: humic acid, 1.0 g; CaC03,0.02 g; FeS04.7H20,0-01 g; KCI, 1.7 1.0 litre 9; MgS04.7HO, 0.05 g; Na2HP04,0.5 g; agar, 18 g; distilled water, and adjust to pH 6.8. Autoclave at 1200C for 15 minutes. Following autoclaving, add 1.0 ml per litre of a filter sterilised B-vitarnins solution (i) just prior to pouring the medium. (i) B-vitamins solution (Nonomura and Ohara, 1969a) Thiamine (aneurine) hydrochloride, 50.0 mg; riboflavin, 50.0 mg; niacin (nicotinic acid), 50 mg; pyridoxine hydrochloride, 50.0 mg; inositol, 50.0 mg; calcium 293 pantothenate,50.0 mg; para-aminobenzoicacid, 50.0 mg; biotin, 25.0 mg; distilledwater, 100 ml. OATMEAL AGAR (Koster, 1959) Oatmeal,20.0 g; agar, 18.0 g; trace salts solution, 1.0 ml (i); distilled water, 1.0 litre. Steamthe oatmealin 1.0 litre of distilled water for one hour, filter and make up the volume to 1.0 litre with more distilled water. Add trace salts solution and agar,adjust to pH 7.0 andautoclave at 1200Cfor 15 minutes. (i) Trace salts solution FeS043H20,0.1 g; Mncl2.4H20,0.1 g; ZnS04.7H20,0.1 g; distilled water, 100MI. UREA DEGRADATION (Gordon, 1966) Basal broth: KH2PO4,9.1 g; Na2HP04 (anhydrous),9.5 g; yeast extract, 0.1 g; phenol red, 0.01 g; distalledwater, 1.0 litre and adjustto pH 6.8. Add 10 ml of a filter sterilised solution (150 g/1) of urea to 75 ml of sterilised.basal broth and dispenseinto steriletubes (2.0 ml). 294 APPENDIX C Table 26 Practicalevaluation of the Streptosporangiumfrequency matrix: Results of the diagnostictests* Tests GMTSTBPTAAKSDUACCMCGNNSFFR StrainsA TUCPVEAEEETNRMPRFANEETUUF LLR42T3212RDAE231225464566 TWO01 ...... TWO02 ------TWO05 ...... TWO07 ...... TW101 ------TW104 ++++++++++ TW106 +++++++++ TW115 ++++++++++++++ TWI16 ++++++++++ TW1 17 ------TW121 ------TW126 ++++++++++ TW127 +++++++ TW128 ------TW129 ++++++ TW13 6++++++++ TW141 ++++++++ TW143 -++++++++++ TW144 -++++++++ T145A ------TW148 -++++++++++ TW153 ------TW155 -+++++++++++ TW159 -+++++++++++++ TW161 -++++++++++ TW163 -++++++++++++++ TW165 ------TW166 +++++++++ TW168 ------TW169 ------TW170 ...... TW179 ------TWI82 ++++++++++++ TW194 ...... TW209 ...... TW213 ------TW218 ++++++ TW220 ------TW222 ------TW224 ------TW226 ------TW227 ...... TW232 ------TW23 5------TW245 ------TW251 +++++++++++ TW253 ++++++++++ TW254 -+++++++ TW256 ------TW263 ------TW266 ++++++ 295 Table 26 continued Tests GMTSTBPTAAKSDUACCMCGNNSFFR StrainsA TUCPVEAEEETNRMPRFANEETUUF LLR42T3212RDAE231225464566 TW269 +-+ TW270 +-+ TW271 +-+ TW274 4- - j. TW276 + + TW282 + TW286 TW292 + TW303 + TW320 + ++ ...... TW353 ------TW354 ...... TW355 ý-ý---ýýI--IýI-ýI--- +--+ TW366 + ...... ---+- -- III TW375 -+-- TW393 +++ +4 TW541 +++ ------TW547 ++------ * Testsrecommended by Whitham (1988). IW, Representativesrepresenting the twelve major Streptosporangiumclusters (see Table 13, pages86 to 90; Whidiam, 1988; Whitham et al., 1993). Key for the diagnostic tests: GAL, Galactoseas a sole carbon sourcr; MTL, Mannitol as a sole carbon source; TUR, Turanoseas a sole carbon source; SC4, Growth in the presenceof sodium chloride; TP2, Growth at 370C; BVT, Growth in the absenceof B-vitamins; PE3, Growth in the presenceof phenyl ethanol; TAZ Growth in the pzrsence of thallous acetate;AEJ, Aerial mycebum colour-pink; AE2, Aerial mycelium colour-white; KER, Degradation of keratin; STD, Degradation of starch; DNA, Degradation of DNA; URE, Urease production; AM2, Resistanceto amoxicillin (25Ng/ml); CP3, Resistanceto cePhaloridine (5Ng/ml); CRI, Resistanceto cepbradine(5004glml); N92, Resistanceto cefoxitin (250lig/mi); CA2, Resistanceto clavulanic acid (250gg/ml); GN5, Resistanceto gentamycin sulphate(5gg/ml); NE4, Resistance to neomycin sulphate(25gg/ml); NE6, Resistance to neomycin sulphate(O.5Ag/ml); ST4, Resistanceto streptomycinsulphate(25Aglml); FU4, Resistanceto fusidic acid (5gg/ml); FU6, Resistanceto fusidic acid (0.5ggln-A);RF6, Resistanceto rifampicin (0.5gg/ml). 296 APPENDIX D Table 27 Identification of the Streptosporangiumisolates: Results of the diagnostictests* Tests GMTSTBPTAAKSDUACCMCGNNSFFR StrainsA TUCPVEAEEETNRMPRFANEETUUF LLR42T3212RDAE231225464566 HJ001 ...... HJ002 ...... HJ005 +++++++ HJ006 +++++++++++++++ HJ008 ++++++ HJ009 ++++++++++++ HJ010 +++++++++++ HJ011 +++++++++ HJ012 +++++++++++ HJ013 ...... HJ014 ...... HJ015 ...... HJ016 +++++++++++ HJ017 ...... HJ019 ------HJ020 ...... HJ021 ...... HJ022 ...... HJ023 ...... HJ024 +++++++++ HJ025 +++++++++ HJ026 ++++++++ HJ027 ...... HJ028 ...... HJ029 +++++++++++ HJ030 ...... HJ031 ...... HJ032 ++++++++++++ HJ033 ...... HJ034 ...... HJ035 ...... HJ036 ++++++++++++ HJ037 ...... HJ038 ...... HJ039 +++++++++ HJ040 ...... HJ041 ...... HJ042 ...... HJ043 ...... HJ044 ------HJ045 ------HJ046 +++++++++++++ HJ047 ----+++++++ HJ048 ...... HJ049 ...... HJ050 -++++++++++++ HJ051 ++++++++++++++ HJ052 +++++++++++++ HJ053 ...... HJ054 ------HJ055 +++++++++++ 297 Table 27 continued Tests GMTSTBPTAAKSDUACCMCGNNSFFR StrainsA TUCPVEAEEETNRMPRFANEETUUF LLR42T3212RDAE231225464566 HJ056 +++++++++++ HJ057 ------HJ058 -++++++++++ HJ059 ------HJ060 +++++++++++ HJ061 +++++++++++++ HJ062 +++++++++++ HJ063 +++++++++++++++ HJ064 ...... HJ065 ++++++++++++ HJ066 ...... HJ067 ...... HJ068 ...... HJ069 ...... HJ070 ...... HJ071 +++++++++++++++ HJ072 ++++++++++++ HJ073 -++++++ HJ074 -+++++++ HJ075 -++++++ HJ07 6...... HJ077 ...... HJ078 ++++++++++++++ HJ079 ...... HJ080 ++++ HJ081 ...... HJ082 ...... HJ083 ++++++ HJ084 ...... HJ085 +++++++++++++++ HJ086 ...... HJ087 ++++++++++++ HJ090 ------HJ091 ++++++++++++ HJ092 ++++++++++++ HJ093 ...... HJ094 +++++++++++ HJ096 +++++++++++++ HJ097 +++++++++++++++ HJ098 ...... HJ099 +++++++++++ HJ100 ...... HJ101 ------HJ102 ...... HJ103 +++++++++++ HJ104 ...... HJ105 ++++++++++++ HJ106 ...... HJ107 ------HJ108 ------HJ109 ------ 298 Table 27 continued Tests GMTSTBPTAAKSDUACCMCGNNSFFR StrainsA TUCPVEAEEETNRMPRFANEETUUF LLR42T3212RDAE231225464566 HJ111 ...... HJ112 ------HJ113 ------HJ114 ++++++++++++ HJ115 +++++++++++ HJ116 ------HJ117 ------HJ118 ...... HJ122 ...... HJ123 ++++++++ HJ124 +++++++++++ HJ125 ------HJ126 ...... HJ128 +++++++++++++++ HJ129 ...... HJ130 ...... HJ131 ...... HJ132 ...... HJ133 +++++++++ HJ135 ...... HJ138 ...... HJ139 ...... HJ140 ...... HJ141 ------HJ143 ------HJ144 ...... HJ146 ------HJ147 ------HJ148 ------HJ149 ++++++++++ HJ150 ------HJ151 ++++++++++++ HJ152 ------HJ153 ++++++++++++++ * Testsrecommended by Whitham(1988). HJ, PresumptiveStreptosporangiumisolates (see Table 12, pages 82 to 83). Key for the diagnostictests: GAL, Galactoseas a solecarbon source; MTL, Mannitol as a solecarbon TP2, source;TM Turanoseas a solecarbon source; SC4, Growth in the presenceof sodiumchloride; Growthat 370C;BVT, Growthin the absenceof B-vitamins;PE3, Growth in the presenceof phenyl AE2, ethanol;TA2, Growth in the presenceof thallousacetate; AEL Aerial mycelituncolour-pink; Aerial mYceliumCO]Our-white; KER, Degradationof keratin; STD, Degradationof starch;DNA, Degradationof DNA; URE, Ureaseproduction; AM2, Resistanceto amoxicillin (250jig/ml); CP3, Resistanceto cephaloridine(50gg/ml); CRI, Resistanceto cephradine(500gg/ml); MEF2, Resistance to cefoxitin (250gg/ml);CA2, Resistanceto clavulanicacid (250gglml);GN5, Resistanceto gentamycin sulphate(5gg/ml);NE4, Resistanceto neomycinsulphate(25gg/ml); NE6, Resistanceto neomycin sulphate(O-5gg/ml);ST4, Resistance to streptomycinsulphate(25gg/ml); FU4, Resistance to fusidicacid (5gghnl);FU6, Resistance to fusidicacid (0.5gg/n-d); RF6, Resistance to rifampicin(0.5gg/ml). 299 APPENDIX E Table28 Dataobtained from the fluorogenic enzyme tests for the 159test strains includingthe seventeenduplicated strains Tests XXXXXXXXXXXXXXXXXXXXOONNNNNNNNNNNNNIIIIOOOOOOOOOOOGGGGGGGGGGGGGGGGGGGGG StrainsOO000001111112222222000000111111122000000000011111000000111111111222222 12345680124570124567344678035678902123424578903457123458012345679012456 KHOOl ...... KH002 ...... KH005 ...... KH006 ...... KH008 ...... KH009 ...... DH009 ...... KHO10 ...... KHOll ...... KHO12 ...... KHO13 ...... KHO14 ...... KHO15 ...... RHO16 ...... DHO16 ...... KHO17 ...... KHO19 ...... KH020 ...... KH021 ...... DH021 ...... KH022 ...... KH023 ...... KH024 ...... KH025 ...... KH026 ...... KH027 ...... KH028 ...... DH028 ...... KH029 ...... KH030 ...... KH031 ...... DH031 ...... KH032 ...... KH033 ...... KH034 ...... KH035 ...... DH035 ...... KH036 ...... KH037 ...... KH038 ...... KH039 ...... KH040 ...... KH041 ...... DH041 ...... KH042 ...... KH043 ...... KH044 ...... KH045 ...... KH046 ...... KH047 ...... KH048 ...... KH049 ...... KH051 ...... KH052 ...... DH052 ...... KH053 ...... KH054 ...... 300 Table 28 continued Tests XXXXXXXXXXXXXXXXXXXXOONNNNNNNNNNNNNIIIIOOOOOOOOOOOGGGGGGGGGGGGGGGGGGGGG StrainsOO000001111112222222000000111111122000000000011111000000111111111222222 12345680124570124567344678035678902123424578903457123458012345679012456 KH055 ...... KHO56 ...... KHO57 ...... KH058 ...... KH059 ...... KH060 ...... KH061 ...... KH062 ...... KH063 ...... DH063 ...... KH064 ...... KHO65 ...... KH066 ...... KH067 ...... KH068 ...... KH069 ...... KH070 ...... KH071 ...... KH072 ...... KH073 ...... KH074 ...... KH075 ...... KH076 ...... KH077 ...... DH077 ...... KH078 ...... KH079 ...... KHO80 ...... KHO81 ...... KHO82 ...... KHO83 ...... KH084 ...... KHO85 ...... DH085 ...... KH086 ...... KH087 ...... KH090 ...... KH091 ...... KH092 ...... KH093 ...... KH094 ...... DH094 ...... KH096 ...... KH097 ...... KH098 ...... KH099 ...... DH099 ...... KH100 ...... KH101 ...... KH102 ...... KH103 ...... KH104 ...... KH105 ...... KH106 ...... DH106 ...... KH107 ...... KH108 ...... KH109 ...... KH111 ...... KH112 ...... 301 Table 28 continued Tests XXXXXXXXXXXXXXXXXXXXOONNNNNNNNNNNNNIIIIOOOOOOOOOOOGGGGGGGGGGGGGGGGGGGGG StrainsOO000001111112222222000000111111122000000000011111000000111111111222222 12345680124570124567344678035678902123424578903457123458012345679012456 KH113 ...... KH114 ...... KH115 ...... KH116 ...... KH117 ...... KH118 ...... KH122 ...... KH123 ...... KH124 ...... KH125 ...... KH126 ...... KH128 ...... KH129 ...... DH129 ...... KH130 ...... KH131 ...... KH132 ...... KH133 ...... KH135 ...... KH137 ...... KH138 ...... KH139 ...... KH140 ...... KH141 ...... KH143 ...... KH144 ...... KFI146 ...... KH147 ...... KH148 ...... KH149 ...... KH150 ...... KH151 ...... KH152 ...... KH153 ...... KTO01 ...... DTOOI ...... KTO02 ...... KTO04 ...... KTO05 ...... KTO06 ...... DT006 ...... KTO07 ...... KTO08 ...... KTO09 ...... KTO10 ...... KT020 ...... KT021 ...... KT022 ...... KT023 ...... KT029 ...... KT030 ...... KT032 ...... KT038 ...... KT041 ...... KT166 ...... KT292 ...... KT116 ...... KT141 ...... KT213 ...... 302 DH and DT, duplicated strains; HJ, Streptosporangiumisolates; TW, markcr strains and centrotypc strains of the streptosporangiaclusters (See Table 17,pages 114 to 1] 6) N04 Boc-L-Leucine-glycine-L-arginine-7AMC GOI 4MU-2-Acetamido-4,6-o-benzylidene-2- deoxy-p-D-glucopymnoside N06 Boc-L-Vabne-L-leucine-L-lysine-7AMC G02 4MU-2-Acetwnido-2-deoxy-p-D- galactopyranoside N07 Boc-L-Valine-L-prohne-L-arginine-7AMC G03 4MU-2-Acrtwnido-2-deoxy-p-D- glucopyranoside N08 Boc-iso-L-Leucine-L-glutamine-glycine-L- G04 4MU-N-Acetyl-p-D-galactosan-tine argkiine-HCI-7AMC NIO Bz-L-Valine-glycine-L-arginine-HCI-7AMC G05 4MU-N-Acetyl-p-D-glucosainine N13 Glutaryl-Glycine-glycine-L-phenylalanine- G08 4MU-0-D-Cellobiopyranoside 7AMC N15 Succinyl-Glycine-L-proline-7AMC G10 4MU-a-L-Fucopyranoside N16 Suceinyl-L-Leucine-L-tyrosine-7AMC Gll 4MU-P-D-Fucoside N17 Succinyl-L-alanine-L-alanine-L- G12 4MU-0-L-Fucoside phenylatanine-7AMC N18 Succinyl-L-L4eucine-L-leucine-L-valine-L- G13 4MU-(%-D-Galactoside tyrosine-7AMC N19 Z-L-Arginine-L-arginine-7AMC G14 4MU-0-D-Galactoside N20 Z-Glycine-L-proline-7AMC G15 4MU-a-D-Glucoside N22 Z-L-Glycine-glycine-L-leucine-7AMC G16 4MU-P-D-Glucoside 003 L-Lysine-L-alanine-7AMC G17 4MU-a-D-Glucuronide 004 L-Alanine-L-phenyhilmine-L-lysine-7AMC G19 4MU-0-D-Maltoside X01 L-Alanine-7AMC G20 4MU-a-D-Mannopyranoside X02 P-Alanine-7AMC G21 4MU-0-D-Mannopyranoside X03 D-Alanine-7AMC G22 4MU-0-D-Ribofuranoside X04 L-Arginine-7AMC G24 4MU-2,3,5-Trio-o-benzyl-a-L- arabinofuranoside X05 Asparate-7AMC G25 4MU-0-D-Xyloside X06 L-Asparagine-7AMC G26 4MU-0-D-Xylopyranoside X08 L-Cysteine(Bzl)-7MAC 002 4MU-Phosphate XIO L-Glutamine-HCI-7AMC 004 4MU-Pyrophosphate XII L-Glycine-HBr-7AMC 005 4MU-Sulphate X12 L-Histidine-7AMC 007 bis-(4MU)-phosphate X14 iso-Leucine-7AMC 008 4MU-(protected)Acetate X15 L-Lzucine-7AMC 009 4MU-Eicosanoate X17 L-Methionine-7AMC DIO 4MU-Elaidate X20 L-Proline-HBr-7AMC D13 4MU-Heptanoate X21 L-Pyroglutamate-7AMC D14 4MU-Laurate X22 L-Serine-HCI-7AMC D15 4MU-Lignocerate X24 L-Tyrosine-7AMC )17 4MU-Myristate X25 L-Valine-7AMC [01 4MU-Palmitate X26 L-Glycine-L-proline-HBr-7AMC '02 4MU-Pentadecanoate X27 L-Arginine-L-arginine-3HCI-7AMC 03 4MU-Stearate .04 4MU-Octadecanoate , 303