INTRODUCTION

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Microbial extracts have been and continue to be a productive source of new biologically active molecules for drug discovery (2, 13). It is estimated that more than 30% of worldwide human pharmaceutical sales have compounds from natural sources as their origin (12). With advances in genomics and high-throughput screening (HTS) technology, many new therapeutic targets are accessible for identifying pharmaceutical agents. In addition to the historical practice of screening microbial fermentation extracts for antibiotic activity, extracts can now routinely be screened with a variety of new functional, receptor binding, enzyme inhibition, and protein-protein interaction assays. HTS formats used at many large pharmaceutical research organizations are, however, generally incompatible with complex fermentation extracts. The reasons for the incompatibility include nonspecific interference with assay systems, cost in dollars and time to identify and dereplicate active components from a complex mixture, and adverse physical properties for automated liquid-handling equipment. Therefore, additional investment in selection and preparation of fermentation extracts is one strategy to align natural product drug discovery with today's automated HTS assay systems. A typical approach to improve the compatibility of fermentation extracts with HTS was recently reported by Schmid et al., who described a multistage automated solid-phase extraction (SPE) system (12).

To make the best use of finite resources in natural product discovery organizations, it is important to identify collections of organisms that produce secondary metabolites, culture conditions that generally support secondary metabolite synthesis, and sample preparation processes that retain secondary metabolites while maintaining compatibility with HTS formats. It is well known that production of secondary metabolites by microorganisms is influenced by fermentation conditions. In a study of 29 Nodulisporium strains, Monaghan et al. found that synthesis of secondary metabolites was directly influenced by fermentation conditions and that there were differences of up to 400-fold in the concentrations of secondary metabolites between conditions (11). Additionally, Monaghan et al. found that rare metabolites were consistently found in extracts containing larger numbers of secondary metabolites. Yarbrough et al. reported that several different growth conditions were required to elicit secondary metabolite production in a set of 760 microorganisms (16).

Given that additional investment is required to make fermentation extracts compatible with modern HTS and that many fermentation extracts may contain insufficient secondary metabolites to warrant such investment, methods to characterize extracts from an industrial, high-throughput drug discovery perspective are needed. There are a number of previously developed methods which use direct chemical measurement to classify microorganisms (5-8, 14). Most previous studies focused on characterizing microorganisms by detecting the presence of known secondary metabolites. Frisvad et al. used high-performance liquid chromatography (HPLC) diode array detection and flow injection analysis together with electrospray ionization mass spectrometry (ES-MS) to detect secondary metabolites characteristic of fungal strains responsible for spoilage of stored cereals (7, 8, 14). Feistner employed HPLC-ES-MS to determine metabolic profiles of strains of the bacterial genus Erwinia (6). In our laboratories, Julian et al. developed an HPLC-ES-MS system with automatic data processing that compares crude fermentation extracts via an overall quantitative mixture similarity measure (10).

The objective of this study was to investigate a rapid ES-MS method to estimate the production of secondary metabolites in fermentation extracts. If throughput were not a consideration, an HPLC-evaporative light scattering detection (ELSD) analysis would provide a quasi-universal estimate of the number of secondary metabolites contained in an extract. ELSD is preferred due to the nonselective nature of the detection of compounds. Our goal was to identify a rapid ES-MS surrogate method for secondary metabolite production whose results correlate with the results of HPLC-ELSD analysis and provide some chemical information concerning the molecular and fragment ions of secondary metabolites. With a rapid estimator of secondary metabolite productivity, large collections of fermentation extracts could be characterized. Such a characterization would be a useful tool for identifying growth conditions that support synthesis of secondary metabolites. A tool for rapid characterization of extracts would also be a useful survey tool for identifying productive organisms for more detailed chemical analysis, purification, and biological screening.

	MATERIALS AND METHODS

Top Abstract Introduction Materials and Methods Results and Discussion References

Organisms and cultivation. Broth cultures of 43 actinomycetes (17) were started from cryogenic stocks in a vegetative medium that contained (per liter) 30 g of tryptic soy broth (Difco Laboratories, Detroit, Mich.), 3 g of yeast extract (Sigma Chemical Co., St. Louis, Mo.), 2 g of MgSO₄, 5 g of glucose, and 4 g of maltose. At the mid-log phase, 60 µl (~0.4% inoculum) of each vegetative culture was transferred into 12 ml of a complex growth medium containing (per liter) 10 g of glucose, 40 g of potato dextrin (Avedex, Keokuk, Iowa), 15 g of cane molasses (Cargill, Minneapolis, Minn.), 10 g of Hy-case amino (Sheffield Products, Norwich, N.Y.), 1 g of MgSO₄, and 2 g of CaCO₃. The fermentation vessel consisted of a rectangular Axid (Eli Lilly and Co., Indianapolis, Ind.) polypropylene bottle that was approximately 3.5 cm long by 4.25 cm wide by 6 cm high. The closure used for each small shake flask fermentation bottle consisted of a -irradiated, vented polypropylene cap lined with a gas-permeable membrane (Performance Systematix, Inc., Caledonia, Mich.). Submerged fermentations were incubated at 30°C for 7 days on an orbital shaker (stroke length, 2 in.) at 110 rpm. Fermentations were prepared for chemical analysis by solublization and extraction of secondary metabolites by the addition of an equal volume of absolute ethanol to the fermentation vessels. Fermentation vessels containing ethanol were agitated for 2 h on an orbital shaker (stroke length, 2 in.) at 200 rpm, and then the contents were allowed to settle for 16 h at 4°C. The ethanol extracts were then filtered through a 100-µm-pore-size polypropylene screen and transferred to 96-well plates for solid-phase extraction.

Extract bioassay. Gram-positive bactericidal bioassays were performed by applying 6-µl portions of ethanol-solublized fermentation broth to 0.25-in.-diameter sterile paper discs. The dried discs were placed on seeded agar assay plates containing Micrococcus luteus ATCC 9341 (Food and Drug Administration strain PCI1001) and were incubated for 2 days at 37°C.

SPE of actinomycete extracts. Nonpolar components were enriched from 0.7-ml samples of the actinomycete extracts by SPE on Empore octadecyl SD high-performance extraction disc plates (3M Company, St. Paul, Minn.). The SPE stationary phase was conditioned for use by sequential washing with 2 ml of distilled H₂O, with 2 ml of methanol, and finally with 2 ml of 1 mM ammonium acetate (pH 5.5) in distilled H₂O. The ethanol was removed from 0.7-ml samples under a vacuum (80 kPa) at 20°C for 16 h. The residual aqueous material was resuspended to a total volume of 0.5 ml with 1 mM ammonium acetate (pH 5.5) and loaded on the conditioned SPE stationary phase. The SPE stationary phase was washed with 5 ml of 1 mM ammonium acetate (pH 5.5), and the fraction analyzed, enriched for relatively nonpolar metabolites, was eluted with 0.7 ml of a 70% acetonitrile-30% (vol/vol) methanol solution which contained 6.5 mM ammonium acetate (pH 5.5). The secondary metabolite-containing fraction was transferred into microwell plates for chemical analyses.

Chemical analyses. Chemical analysis of SPE eluates was performed by (HPLC-ES-MS-ELSD) as described by Julian et al. and by direct-infusion ES-MS. For the HPLC-ES-MS-ELSD experiments, ethanol-solublized analytes (50 µl) were separated on a Novapak C₁₈ analytical column (3.9 by 150 mm) by using a 30-min linear gradient from 2% methanol-15 mM ammonium acetate to 95% methanol-15 mM ammonium acetate at a flow rate of 710 µl min¹. The column effluent was split between a Finnigan Navigator (160 µl min¹) equipped with a Finnigan electrospray source and a Sedex 55 evaporative light scattering detector (570 µl min¹; Sedere, Alfortville, France) in order to provide qualitative and quantitative data, respectively. The lower limit of detection for the evaporative light scattering detector was estimated to be approximately 2 µg/ml based on dilution of a standard mixture. UV and visible light absorption spectra (220 to 550 nm) were acquired with a Hewlett-Packard series 1100 photodiode array spectrophotometer by using column effluent prior to analysis by evaporative light scattering. The electrospray source was switched between positive ion mode and negative ion mode at 0.4-s intervals to acquire both positive and negative ion spectra. During data acquisition, the mass spectrometer probe voltage was maintained at 3.6 kV, the cone voltage was maintained at 35 V, the source temperature was kept at 180°C, and the drying gas flow rate was 500 liters · h¹. A standard solution consisting of 2-amino-6-chloropurine (50 µg/ml), caffeine (50 µg/ml), m-cresol purple (50 µg/ml), tylosin (25 µg/ml), and spinosyn A (50 µg/ml) was injected at the beginning of the analysis and then after every 10th experimental sample to assess instrument stability and performance.

1

	RESULTS AND DISCUSSION

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Analytical HPLC and collection of fractions of biologically active fermentation extracts are routinely done by natural product drug discovery groups to identify the active component(s) in a complex mixture (1). We coupled these familiar analytical techniques with automated algorithms to identify organisms that produced secondary metabolites under specific growth conditions. In this study we analyzed 43 actinomycete extracts by using a 30-min HPLC-ELSD method to identify the numbers and relative amounts of secondary metabolites present in ethanol extracts of fermentation broth. SPE of ethanol extracts on C₁₈ activated silica was employed to reduce the polar components, presumably including salts, cellular macromolecules, spent medium components, and other cellular debris present in the ethanol extracts. Despite some influence of solvent concentration and the molecular structure of an analyte, ELSD is generally considered a quasi-universal detector of the quantity of an analyte in a sample (3, 9). For the growth conditions considered here, we observed a broad range of apparent secondary metabolite production in organisms. Some extracts contained no detectable metabolites (Fig. 1d), while others contained many (Fig. 1f). In order to monitor detector sensitivity and column performance, a standard mixture consisting of five known compounds at known concentrations was injected between sets of 10 extracts. The mean natural log (log_e) of the area under the HPLC-ELSD chromatogram for the control injections was 11.0 with a coefficient of variation (COV) of 1.3%, indicating that sufficient reproducibility could be achieved in automated analyses of relatively complex samples. Because of the exponential nature of ELSD, a log transform was done in order to work in units that are linearly related to the log of the concentration of a solute (3). While the 30-min HPLC-ELSD method is generally effective for identifying the numbers and approximate concentrations of secondary metabolites present in fermentation extracts, the throughput is not sufficient for studies in which assessment of larger numbers of organisms is required. Additionally, the HPLC-ELSD method provides no information regarding the chemical composition of the metabolites present in an extract. For these reasons we investigated a higher-throughput ES-MS method that we believed had the potential to be a surrogate for the HPLC-ELSD method for estimating secondary metabolite production. In addition to serving as a rapid surrogate for the HPLC-ELSD method, the mass fragments from the rapid ES-MS method provided information about the chemical compositions of compounds in an extract.

View larger version (27K): [in this window] [in a new window]   FIG. 1.   Positive ion ES-MS spectra and HPLC-ELSD chromatograms for the actinomycete extract with the minimum positive ES-MS productivity index (1,607) (a and d), the actinomycete extract with the positive ES-MS productivity index nearest the median (2,277) (b and e), and the actinomycete extract with the maximum positive ES-MS productivity index (5,852) (c and f).

Analysis of direct-infusion ES-MS data and algorithm description. We found that simple summation of peak intensities in the ES-MS spectra determined for the extracts was only marginally effective at predicting the productivities of broths as benchmarked against the HPLC-ELSD analyses (data not shown). In response, we examined preprocessing steps to improve the prediction. Mass spectra are commonly binned by partitioning the m/z range into fixed, equal-size partitions and assigning each ion intensity to the appropriate partition or bin. The sum of all ion intensities that fall within each m/z bin is used as the intensity value for the bin. This simple binning scheme works well as long as the m/z values of ions do not lie near a bin boundary. For ions with m/z values near a bin boundary, small differences in the m/z values estimated by mass spectrometry can place the same ion from two samples into different bins, which results in errors when two binned spectra are compared. To address this problem, an overlapping binning scheme was developed in order to assign the intensity of an ion to two bins if the m/z value was within an overlap region centered at the bin boundary. The proportion of intensity assigned to each bin for an ion falling within an overlap region was scaled to be linearly proportional to the difference between the measured m/z value and the bin boundary. Thus, all examples presented in this paper are based on spectra that were preprocessed by binning the measured intensities into 5,800 m/z bins between 150 and 1,600 atomic mass units (amu) with a 0.2-amu bin overlap region forming a vector of fixed length for each extract analyzed. This transformation of raw data to vectors allowed spectral averaging and background subtraction in order to obtain an improved estimate of fermentation productivity.

View larger version (23K): [in this window] [in a new window]   FIG. 2.   Background- and medium-subtracted positive ion ES mass spectrum of a solid-phase actinomycete extract. The productivity index was computed by adding all of the ions above a series of ion count thresholds. In this example 12 thresholds are shown as horizontal dotted lines, resulting in a productivity index of 57.

System reproducibility. Mass spectrometer sensitivity was monitored throughout the analysis of experimental samples due to the potential for variable sensitivity caused by the plugging and coating of surfaces in the instruments. Detector sensitivity was monitored by bracketing sets of 10 extract injections with injections of the standard mixture. The background-corrected positive ion mass spectrum of the standard mixture (Fig. 3) had a mean positive ion productivity index of 3,490 with a COV of 7.5% (n = 12). The mean negative ion productivity index for the control injections was 59.6 with a COV of 19.7% (n = 12). The higher variability observed in the negative ion mode was attributed to the overall lower sensitivity of the mass spectrometer in the negative ion detection mode. For large studies conducted with multiple instruments or over long periods of time, the productivity indices for the bracketing controls may be used to scale the productivity indices of extracts.

View larger version (30K): [in this window] [in a new window]   FIG. 3.   Direct-infusion positive ES-MS of control mixture containing 2-amino-6-chloropurine (50 µg/ml), caffeine (50 µg/ml), m-cresol purple (50 µg/ml), tylosin (25 µg/ml), and spinosyn A (50 µg/ml). [M+H]+ ions and adduct ions are shown for each analyte in the control mixture.

Comparison of the rapid ES-MS and HPLC-ELSD methods. Examination of the positive ion mass spectrum and its associated HPLC-ELSD chromatogram for the extracts with minimum, median, and maximum positive ion productivity illustrated the visual correlation between the positive ion productivity and the HPLC-ELSD chromatogram (Fig. 1). To quantitatively assess this correlation, the natural log of the area under the HPLC-ELSD chromatogram was computed for each solid-phase extract and compared to positive and negative ion productivity indices via Pearson's correlation coefficient. Additionally, the positive and negative ion productivity indices were combined into a single composite productivity index by first linearly scaling the positive and negative ion productivity indices to be on the [0, 1] scale and then adding the two scaled indices. Scaling was required due to the large differences in productivity index values between the positive and negative modes. Pearson correlation coefficients for comparisons between the positive ion, negative ion, and combined productivity indices and the log_e HPLC-ELSD areas under the curve are shown in Table 1. A 90% confidence interval for each correlation coefficient was estimated by using the bias-corrected and accelerated (BC_a) method (10,000 bootstrap samples) described by Efron and Tibshirani (4). The bootstrap method is used as an alternative to Fisher's transformation of the correlation coefficient for estimating confidence intervals (mean ± 1.96 × standard error of the transformed data). Bootstrap confidence intervals are estimated by drawing many random samples (with replacement) from the original data, computing the correlation coefficient for each random sample, and then examining the distribution (histogram) of the correlation coefficients. Confidence intervals estimated with the bootstrap method are essentially identical to those estimated by using Fisher's transformation. The combined productivity index correlated best with the HPLC-ELSD measure of secondary metabolite productivity with a correlation coefficient (r_c) of 0.78 (Fig. 4). While the positive and negative ion productivity indices had correlation coefficients that were approximately equal (r₊ = 0.72 and r = 0.70, respectively), the length of the 90% confidence interval for the correlation coefficient was much narrower for positive ion data (0.24 for the positive ion data and 0.51 for the negative ion data). A bootstrap hypothesis testing procedure (4) using 10,000 bootstrap samples was used to test the one-sided hypotheses:


Based on these hypothesis tests, the combined productivity index appears to be better than the negative and positive ion productivity indices at the approximate achieved significance levels of 0.13 and 0.21, respectively (Table 2). The approximate significance levels were estimated by dividing the number of bootstrap samples for which the alternative hypothesis was false by the total number of bootstrap samples. There appeared to be no significant difference between the correlations of the positive and negative ion productivity indices with the HPLC-ELSD benchmark (approximate achieved significance level, 0.40). A direct-infusion ES-MS analysis of the crude extracts, bypassing the SPE step, was done in order to assess the correlation of a simplified sample preparation method with an HPLC-ELSD benchmark. The correlation coefficient for comparisons between the combined positive and negative ion productivity indices for the non-SPE approach and the HPLC-ELSD benchmark (r_c,nospe) was 0.68, a value somewhat lower than the correlation coefficient obtained when the SPE step preceded the ES-MS analysis. A one-sided test of the hypothesis H₀: r_c = r_c,nospe, H_A: r_c > r_c,nospe was rejected at the approximate achieved significance level of 0.08 with 10,000 bootstrap samples, indicating only marginal improvement with SPE.

View this table: [in this window] [in a new window]   TABLE 1.   Correlation coefficients and their 90% BCa bootstrap confidence intervals for comparisons between r+, r, and rc and loge HPLC-ELSD area under the curve

View larger version (20K): [in this window] [in a new window]   FIG. 4.   Area under the HPLC-ELSD chromatogram versus the combined positive and negative ion productivity index for 43 actinomycete SPE extracts. The correlation coefficient is 0.78 with a 90% BCa bootstrap confidence interval of [0.63, 0.91].