Characterization and identification of actinomycetes by FT-IR spectroscopy

ELSEVIER

Journal

of Microbiological

Characterization

Methods

27 (1996)

157-163

Journal ofMicrobiological Methods

and identification of actinomycetes spectroscopy

Hubert Haag, Hans-Ulrich

Gremlich,

Reinhard Bergmann,

by FT-IR

Jean-Jacques

Sanglier”

Preclinical Research, Sandoz Pharma Ltd., Lichtstrasse 35, CH-4002 Basle, Switzerland Received

19 February

1996; revised

15 May 1996; accepted

28 June 1996

Abstract Pilot experiments were performed to analyze the potential of Fourier transformation infrared (FT-IR) spectroscopy for classification and identification of actinomycetes. The results indicate that the method allows a classification of

actinomycetes strains at a level comparable to that obtained by classical taxonomy, and that identification of strains is possible at a sub-species level. Keywords:

Infrared spectroscopy; Actinomycetes

1. Introduction A rapid characterization and identification of microbiological isolates are prerequisites for an effective industrial iscreening program. Detection of duplicates is neces,sary to reduce redundancy in screening assays and the selection of unusual strains can help in the discovery of novel compounds. Classical biochemical or serological methods in identification are often tedious and their capacity to differentiate is often not sufficient. As a result a variety of alternative approaches have been developed. Methods for a rapid identification of actinomycetes include the analysis of fatty acids composition by gas chromatography [l], pyrolysis of cells followed by mass spectrometry [2] or analysis of the DNA (31. Infrared-spectra of biological materials are com*Corresponding author. Tel: +41 61 3243594; fax: 3243279; email: [email protected]

0167-7012/96/$15.00 Copyright PII SO167-7012(96)00943-8

0

+41

61

posed of various absorption bands, originating by vibrational motions of the biomolecules. Infrared spectroscopy comprises several attractive features for bacterial identification. It can operate on whole, even living, cells; sample preparation needs only a small amount of biomaterial and is fast. By this technique, the total chemical complexity of microorganisms is analyzed and there is no reliance on specific marker molecules, e.g. quinones. The idea of using IR-spectra for bacterial identification dates back to the beginning of the 1950s [4], but no commercially available identification system had been developed at that time. The interest in IR spectroscopy was renewed when an increased sensitivity and accuracy was achieved using the Fourier transformation infrared (IT-IR) spectroscopy [5]. With the availability of dedicated software, efficient evaluation and cluster analysis for large series of data recently became possible. Using spectral libraries, this software also allows identification of strains. While the value of FT-IR for identification of

1996 Elsevier Science B.V. All rights reserved

158

H. Haag et al. I Journal of Microbiologicul

unicellular bacteria has been demonstrated for several taxa [6], no reports on the identification of filamentous microorganisms have been published so far. Actinomycetes are Gram-positive bacteria mostly producing a mycelium and which are important from a medical and industrial point of view; they represent the target organisms of this study. The goal of this project was to develop a standardized preparation procedure, to determine the influence of the culture conditions on the FT-IR spectra, to select the most appropriate spectral windows for efficient identification, and to test the validity of the system, using various Actinomycetes strains.

2. Materials

and methods

2. I. Strains Strains used as reference for the creation of a spectral library and for further studies are listed in Table 1. 2.2. Strain cultivation All strains were stored at -25°C on agar-slants protected by a solution of glycerol (lo%)-gelatin (1.25%) [w/v]. Strains were grown in a standard liquid medium (290) containing N-Z amine Type A (Sheffield Products, Norwich, UK) (0.25%), glucose (COST) (0.7%), malt extract liquid (Wander, Berne, CH) (0.5%), soluble starch (l%), yeast extract (BBL, Cockeysville, USA) (0.3%), and trace elements (calcium, sodium, iron, zinc, manganese, cobalt, copper, boron, iodine), on a rotary shaker (200 rev./min) at 27°C for 5 days. For the evaluation of the influence of the media composition, two further media were used; medium 282: glucose (l%), yeast extract (BBL) (0.4%), malt extract (Wander) (IS), mineral salts and vitamins (Becozyme ‘Roche’, m-inositol, folic acid, p-aminobenzoic acid), and medium 309: mannitol (2%) and soya flour (Nurupan, Diisseldorf, Germany) (2%). 2.3. Sample preparation For the preparation of samples, 30 ml of a liquid culture were washed two times with saline and

Methods 27 (1996) 157-163

Table I Reference

strains

Genus

Strain

Maduromycetes Actinomadura citrea Actinomadura ferruginea Microtetraspora glauca Microtetraspora salmonea Microtetruspora spiralis Microtetraspora viridis Streptosporangium album Streptosporangium corrugatum Streptosporangium longisporum Streptosporangium roseum Nocardioforms Amycolata autotrophica Amycolatopsis mediterranea Amycolatopsis orientalis Gordona rubropertincrus Gordona terrae Nocardia asteroides Nocardia coeliaca Nocardia farcinica Nocardia restricta Rhodococcus equi Rhodococcus erythropolis Saccharopolyspora erythraea Saccharopolyspora hirsuta Micromonosporaceae Actinoplanes auranticolor Actinoplanes brasiliensis Actinoplanes campanulatus Actinoplanes missouriensis Dactylosporangium vinaceum Micromonospora chalcea Micromonospora echinospora Micromonospora inyoensis Micromonospora purpurea Streptomycetaceae Streptomyces aureofaciens” Streptomyces coelicolor Streptomyces coelicolor” Streptomyces $lamentosusa Streptomyces griseus Streptomyces halstedii” Streptomyces hygroscopicus Streptomyces hygroscopicus Streptomyces hygroscopicus Streptomyces h_ygroscopicus” Streptomyces hygroscopicus” Streptomyces violaceus” Streptoverticillium griseocarneum Streptoverticillium reticuli “Strains not stored in the first reference

DSM 43461 DSM 43553 DSM 43311 DSM 43678 DSM 43555 DSM 43175 DSM 43023 ATCC 2933 1 DSM 43 180 DSM 43021 N 826 K 98 N 875 DSM 43248 DSM 43249 DSM 43132 DSM 43190 DSM 43231 DSM 43 199 DSM 43349 DSM 43066 DSM 40517 DSM 43463 ATCC 15330 DSM 43805 DSM 43148 DSM 43046 IF0 14181 ATCC 12452 ATCC 15837 NRRL 3292 NRRL 2972 DSM 40127 DSM 40233 ISP 5233 ISP 5022 ATCC 3302 1 ISP 5068 ATCC 21582 ATCC 21705 ATCC 31050 ATCC 14891 ATCC 29253 ISP 5082 ATCC 12628 ATCC 31159 database.

H. Haag et al. I Journal of Microbiological Methods 27 (1996) 157-163

finally resuspended in 10 ml saline. This suspension was homogenized with an ‘ultra turrax’ (Janke and Kunkel, Staufen i.B., Germany) for 1 min; 15-50 ~1 of this homogenous suspension were deposited on a sample wheel with 1.5 zinc selenide optical plates, thus allowing automatic measurement of up to 15 samples. After drying in vacua for 15 min, a biofilm was obtained.

2.5. Principal

component

1.59

analysis

Each spectrum was characterized by 831 absorbance values. Applying principal component analysis, these 831 absorbance values have been summarized by the three principal component coordinates which account for 90% of the total variance. 2.6. Gas chromatography

2.4. Sample measurement

and data processing

Spectra were recorded on an IFS 28/B FT-IR spectrometer (Bruker Analytische Messtechnik GmbH, Karlsruhe, Germany) between 4000 and 500 cm-’ with a resolution of 4 cm-‘. Data analysis was carried out using the OPUS@ 2.0 software for bacterial identificatilon (Bruker Analytische Messtechnik GmbH, Karlsruhe, Germany). IR-spectra were compared by calculating spectral distances between two or more spectra [6]. To minimize background noise, all digitized spectra were smoothed by derivation (1st) and normalized. For the construction of the database with the strains listed in Table 1, at least four independent preparations of each strain were measured and an average spectrum was calculated. The coefficient of similarity of various spectra wa,s determined by the spectral distance using Pearson’s product moment covariance coefficient. The construction of dendrograms was performed executing Ward’s linkage method [7]. For principal component analysis, digitized spectra were exported as ASCII data and analyzed with SASB statistical software. For identification, the spectral similarity was calculated by the spectral distances. The statistical significance of these data was analyzed in comparison with the variability of spectra recorded from successive preparations of the same strain. The spectral software generates values of similarity between 0 (for maximum identity) and 2 (for minimum identity). The quality of a reidentification was split into three levels: excellent, correct and no identification. The identification was judged as correct if the first hit reported corresponded to the target organism. The qualification excellent was awarded if in addition hit two and three were taxonomically-related organisms.

Analysis of fatty acids was done as described by O’Donnell et al. [8] using a Varian GC 3400 chromatograph.

3. Results 3.1. Development

of a reproducible

system

In contrast to unicellular microorganisms, sample preparation of Actinomycetes from agar plates gave poorly reproducible results. In order to obtain a homogenous biofilm, it was found necessary to use a homogenized cell suspension from a shaken liquid culture. The process should be improved for strains producing hard pellets; assays with ultrasonification of homogenized cultures are planned. Strain storage, cultivation and sample preparation had to be strictly standardized to achieve maximum reproducibility (results not shown). Besides the standardization of all growth parameters, the selection and combination of both appropriate and weighted spectral windows are essential for the spectroscopic comparison of strains. For the evaluation of spectral distances, spectra were divided into five different spectral windows each focusing on different cell components and each with a specific weighting factor. In order to find the best window combination, rates of correct reidentification were determined for each window. The combination of windows 1500-1200 cm-‘, 1200-900 cm-’ and 906-700 cm-’ with weighting factors 1, 1, and 2, respectively revealed a correct reidentification for 89% of the 39 strains analysed (Table 1) of the primary reference database (Table 2; reidentification rates generated by three independent experiments). This combination was selected as the standard configuration of the system. Reidentification prob-

H. Haag et al. I Journal

160 Table 2 Percentage

of correct reidentification

using different

Spectral windows

Excellent Correct Not identified

qf Microbiological

157-163

spectral windows

(cm-‘)

3000-2800

1800-1500

1500-1200

1200-900

900-700

Combination

6% 68% 26%

9% 69% 22%

30% 52% 18%

39% 37% 24%

39% 43% 18%

52% 37% 11%

lems occurred mostly with strains pellets in liquid culture.

producing

hard

3.2. Influence of culture conditions The influence of inoculation time, inoculation temperature and composition of the liquid medium on the IR-spectra of Streptomyces coelicolor strain ISP 5233 was analyzed estimating the relative spectral distances in relation to a reference spectrum of the same strain grown under standardized conditions. Spectra with minimum spectral distances to a reference spectrum were recorded from samples grown in the same batch of medium (Fig. 1). The variation of medium composition resulted in significant spectral distances indicating that the composition of the medium is the most important factor in terms of reproducibility, whereas the variation in incubation time and temperature influenced the IR-spectra only slightly.

3.3. IdentiJcation Actinomycetes

Methods 27 (1996)

and classijkation

Cluster analysis of the FT-IR results obtained with the reference strains was performed using ‘Ward’s algorithm’ [7]. The discrimination between different genus of actinomycetes obtained by analysis of FTIR spectra (Fig. 2) is similar to that obtained by classical taxonomy. The impact of FT-IR spectroscopy for the selection of strains in our routine screening program was tested using fresh isolates originating from a soil sample. Similar clustering was obtained using data

of

After constructing the primary reference database the discrimination capacity of FT-IR was evaluated (Table 3) using new preparations from four strains whose IR-spectrum were already stored in the database, as well as two strains unknown to the system. The strain is estimated as identified if a hit quality between 0 and 0.5 is reported. This graduation gave satisfactory results for the majority of strains (Table 3). Strains were identified on the species and biotype level as demonstrated for Streptomyces hygroscopicus ATCC 21705 and Streptomyces hygroscopicus ATCC 21582.

Fig. 1. Effect of different culture conditions on FT-IR analysis of coelicolor ISP 5233 evaluated by principal component analysis. 1: reference spectrum of the library; 2: medium 282, 4 days, 27°C; 3: medium 282, 8 days, 27°C; 4: medium 282, 6 days, 24°C; 5: medium 290, 8 days, 27°C; 6: medium 290, 8 days, 24°C; 7: medium 290, 6 days, 27°C; 8: medium 290,4 days. 24°C; 9: medium 290, 6 days, 24°C; 10: medium 290, 4 days, 27°C; 11: medium 309, 6 days, 27°C; 12: medium 309, 4 days, 27°C; 13: medium 309, 8 days, 27°C. Streptomyces

H. Haag

Table 3 Identification

of strains by a reference

et al. I Journal

of Microbiological

Methods

27 (1996)

161

database

Strain

1st and 2nd hit

Amycolatopsis

1.57-163

mediterranea

Amycolatopsis

K98

Hit quality mediteranea

0.2

orientalis

1.5

K98 Amycolatopsis

N875 Micromonospora

inyoensis

Micromonospora

NRRL 3292

inyoensis

0.4

echinospora

0.7

NRRL 3292 Micromonospora

ATCC 15837 Gordona

rubropertinctus

Gordona

DSM 43248

rubropertinctus

0.1

DSM 43248 Gordona

terrae

0.3

DSM 43249 Streptomyces

aureofaciers”

Streptosporangium

DSM 40127

album

1.2

DSM 43023 Streptomyces

griseus

1.4

hygroscopicus

0.2

hygroscopicus

0.8

hygroscopicus

0.06

halstedii

0.7

ATCC 33021 Streptomyces

hygroscopicus

Streptomyces

ATCC 14891”

ATCC 29253 Streptomyces

ATCC 31050 Streptomyces

hygroscopicus

Streptomyces

ATCC 21582

ATCC 21582 Streptomyces

ISP 5068 O
hit quality>l:

no identification.

from gas chromatographic

analysis of the lipids (Fig.

3).

4. Discussion The IT-IR technique turned out to be a rapid, reliable and cost effective method for the characterization of actinomycetes strains, requiring only a small amount of biomaterial. With the automated measurement, a high throughput of samples (60-90 analyses per day) is possible, as required in an industrial environment. The reliability and stability of the instrument is high, thus allowing the construction of a database. A correct identification rate of 89% demonstrates the reliability of the system and is similar to the values obtained for unicellular bacteria [9]. Thus, Ff-IR spectroscopy can compete with other already

established identification systems available on the market [lo]. The number of repetitions required is dependent on the quality level of the information: three for the construction of a database with reference strains, two to three for an identification, and one for a first analysis of a microbial population. The limit value for identification was determined empirically according to intraspecific variations. For maximum identification rates, intraspecific variations have to be determined for each species, using different isolates of one species when possible. Furthermore, the selection of an adequate combination of spectral windows improves the performance of the system significantly. Pilot experiments with reference strains indicated a discrimination capacity below the species level. Heterogenous species like Streptomyces hygroscopicus did not cluster in a uniform manner: the biochemical diversity of the species [l l] was reflected in spectral diversity as

162

H. Haag et al. I Journal of Microbiological Methods 27 (1996) 157-163

NO211 NO811 NO411 NO311 NO2411 A

NO2311 NO2111 NO2211 NO1611

I-

NOlGil NO2211 NOPlll II,

I,

I

I

I

I

I

NO811

II

Fig. 2. Dendrogram representing the relationships between various actinomycetes based on FT-IR data using Ward’s algorithm.

B

NO411 NO311 NO2411

well. This high sensitivity makes the system suitable for the elimination of identical isolates in pharmaceutical screenings. Construction of dendrograms revealed a good agreement with classical taxonomy and results obtained from fatty acid analysis. Therefore, FT-IR spectroscopy may also be useful for the detection of novel actinomycetes strains. The construction of a database with most of the correctly described actinomycete strains is a prerequisite for an efficient use of this method. This also requires an agreement on the culture conditions, mainly on the composition of the liquid medium. In summary, FT-IR spectroscopy provides a powerful tool for rapid characterization of actinomycetes. Its acceptance for microbiological screening would be greatly assisted by the availability of a commercial spectral database.

References cl] Dtucker, D.B. (1981) Microbiological Applications of Gas Chromatography. Cambridge University Press, Cambridge.

NO2311 NO211

Fig. 3. Dendrograms representing the relationship between new actinomycete isolates using Ward’s algorithm; (A) based on FT-IR data; (B) based on fatty acid data.

121Sanglier, J.-J., Whitehead,

D., Saddler, G.S., Ferguson, E.V and Goodfellow, M. (1992) Pyrolysis mass spectrometry as a method for the classification, identification and selection of actinomycetes. Gene 115, 235-242. t31 Anzai, Y., Okuda, T. and Watanabe, J. ( 1994) Application of the random amplified polymorphic DNA using polymerase chain reaction for efficient elimination of duplicate strains in microbiological screening. J. Antibiot. 47, 183-193. [41 Riddle, J.W., Kabler, P.W., Kenner, B.A., Bordner, R.H., Rookwood, S.W. and Stevenson, H.J.R. (1956) Bacterial identification by infrared spectrophotometry. I. Bacterial. 72, 593-603. [51 Gremlich, H.U. (1994) Infrared and Raman spectroscopy. In: Ullmann’s Encyclopedia of Industrial Chemistry,Vol. B5, pp. 429-469. PI Naumann, D., Fijala, V, Labischinski, H. and Giebrecht, P (1988) The rapid differentiation and identification of pathogenic bacteria using Fourier transform infrared spectroscopy and multivariate statistical analysis. J. Mol. Struct. 174, 165-170.

H. Haag et al. I Journal of Microbiological Methods 27 (1996) 1.57-163 [7] Ward, J.H. (1963) Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 237-244. [8] O’Donnell, A.G., Minnikin, D.E. and Goodfellow, M. (1985) Integrated lipid and cell wall analysis of actinomycetes. In: Chemical Methods in Bacterial Systematics (Eds. M. Goodfellow and D.E. Mlnnikin). Academic Press, London, pp. 131-144. [9] Curk, M.C., Peladan, F. and Hubert, J.C. (1994) Fourier transform infrared (IT-IR) spectroscopy for identifying factobacillus species. FEMS Microbial. Lett. 123. 241-248.

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Stager, S.E. and Davis, J.R. (1992) Automated systems for identification of microorganisms. Clin. Microbial. Rev. 5, 302-327. Labeda, D.P. and Lyons, A.J. (1991) The Streptomyces violaceusniger cluster is heterogenous in DNA relatedness among strains: emendation of the descriptions of S. violaceusniger and Streptomyces hygroscopicus. lnt. J. Syst. Bacterial. 39, 398-401.