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Rapid and Quantitative Analysis of Metabolites in Fermentor Broths

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Rapid and Quantitative Analysis of Metabolites in Fermentor Broths ANALmcA CHIMICA ACTG ELSEVIER Analytica Chimica Acta 313 (1995) 25-43 Rapid and quantitative analysis of metabolites in fermentor broths using pyrolysis mass spectrometry with supervised learning: application to the screening of Penicillium chysogenum fermentations for the overproduction of penicillins * Royston Goodacre ap* , Sally Trew b, Carys Wrigley-Jones b,l, Gunter Saunders ‘, Mark J. Neal a, Neil Porter b, Douglas B. Kell a a Institute of Biological Sciences, University of Wales, Aberystwyth, Dyfed SY.23 3DA, UK b Xenova Ltd., 545 Ipswich Road, Slough, Berkshire SLI 4EQ, UK ’ Department of Biological and Health ScienceA, Universi@ of Westminster, II5 New Cavendish Street, London WlM &IS, UK Received 6 December 1994; revised 14 March 1995; accepted 15 March 1995 Abstract The combination of pyrolysis mass spectrometry (PyMS) and artificial neural networks (ANNs) can be used to quantify levels of penicillins in strains of Penicillium chrysogenum and ampicillin in spiked samples of Escherichia coli. Four P. chrysogenum strains (NRRL 1951, Wis Q176, Pl, and P2) were grown in submerged culture to produce penicillins, and fermentation samples were taken aseptically and subjected to PyMS. To deconvolute the pyrolysis mass spectra so as to obtain quantitative information on the titre of penicillins, fully-interconnected feedforward artificial neural networks (ANNs) were studied; the weights were modified using the standard back-propagation algorithm, and the nodes used a sigmoidal squashing function. In addition the multivariate linear regression techniques of partial least squares regression (PLS), principal components regression (PCR) and multiple linear regression (MLR) were applied. The ANNs could be trained to give excellent estimates for the penicillin titre, not only from the spectra that had been used to train the ANN but more importantly from previously unseen pyrolysis mass spectra. All the linear regression methods failed to give accurate predictions, because of the very variable biological backgrounds (the four different strains) in which penicillin was produced and also of the inability of models using linear regression accurately to map non-linearities. Comparisons of squashing functions on the output nodes of identical 150-8-l neural networks revealed that networks employing linear functions gave more accurate estimates of ampicillin in E. coli near the edges of the concentration range than did those using sigmoidal functions. It was also shown that these neural networks could be successfully used to extrapolate beyond the concentration range on which they had been trained. PyMS with the multivariate clustering technique of principal components analysis was able to differentiate between four strains of P. chrysogenum studied, and was also able to detect phenotypic differences at * Invited paper on Analytical Biotechnology. l Corresponding author. 1 Present address: Department of Biological and Health Sciences, University of Westminster, 115 New Cavendish Street, London WlM 8Js, UK. 0003-2670/95/$09.50 0 1995 Elsevier Science B.V. All rights reserved SSDI 0003-2670(95)00170-O 26 R. Goodacre et al. /Analytica Chimica Acta 313 (1995) 25-43 five, seven, nine or 11 days growth. A crude sampling procedure consisting of homogenised agar plugs proved applicable for rapid analysis of a large number of samples. Keywords: Mass spectrometry; Artificial neural networks; Fermentor broths; Regression analysis; Chemometrics; Biotechnology; Pyrolysis mass spectrometry 1. Introduction identification of a variety of microbial and biotech- nological systems over a number of years 126-341 Within medicine and biotechnology there is a and, because of its high discriminatory ability [35], continuing need to find new pharmaceuticals, and represents a powerful fingerprinting technique, which hence for the development of rapid and efficient is applicable to any organic material. methods for the screening of large numbers of mi- Our own aims have been to extend the PyMS crobial cultures for the production of biologically technique for the quantitative analysis of the chemi- active metabolites (e.g., see [l-9]). Such metabolites cal constituents of microbial and other samples [36]. can provide new structural templates for drug design To this end, we have sought to apply fully-intercon- and development through chemical synthesis. nected feedforward artificial neural networks (ANNs) Screening for such metabolites usually involves the (see [37-491 for introductory surveys), and the mul- modulation of a particular biochemical step impli- tivariate linear regression techniques of partial least cated in a particular disease process. Very often squares regression (PLS) and principal components metabolites showing activity during screening are regression (PCR) (see [50-601 for first-rate texts) to produced only in very small amounts by the organ- the deconvolution and interpretation of pyrolysis ism, and therefore increasing the titre of the metabo- mass spectra. Thus, we have been able to follow the lite is essential in order to provide sufficient material production of indole in a number of strains of Es- for chemical characterization and further biological cherichia coli grown on media incorporating various evaluation. Metabolite titres can be significantly im- amounts of tryptophan [61], to estimate the amount proved through the generation and isolation of over- of casamino acids in mixtures with glycogen [62,63], producing mutants derived from the original wild- to deconvolute the pyrolysis mass spectra of com- type organism [5,10-241. Over-producing mutants plex biochemical and microbiological mixtures [64], arise typically at frequencies around lop4 [25], and and for the analysis of recombinant cytochrome b, therefore tens of thousands of cultures need to be expression in E. coli [65]. screened in the search for an improved, overproduc- With regard to discrimination of materials from ing strain. their pyrolysis mass spectra we have also exploited Pyrolysis mass spectrometry (PyMS) is a rapid, ANNs for the rapid and accurate assessment of the automated, instrument-based technique which per- presence of lower-grade seed oils as adulterants in mits the acquisition of spectroscopic data from 300 extra virgin olive oils [66,67], and for the identifica- or more samples per working day. The method typi- tion of strains of Propionibacterium spp. [68,69]. cally involves the thermal degradation of complex Chun et al. [70] and Freeman et al. [71] have also material in a vacuum by Curie-point pyrolysis; this used the combination of PyMS and ANNs for the causes molecules to cleave at their weakest points to differentiation of strains of Streptomyces and my- produce smaller, volatile fragments called pyrolysate cobacteria respectively. 1261. A mass spectrometer can then be used to sepa- Industry exploits the biosynthetic capabilities of rate the components of the pyrolysate on the basis of microorganisms to produce pharmaceuticals and other their mass-to-charge ratio (m/z) to produce a pyrol- products through fermentation. It is imperative there- ysis mass spectrum, which can then be used as a fore that the concentration of the product (the deter- chemical signature (fingerprint) of the complex ma- minand) is assessed accurately so as to optimise terial analysed. control of the fermentation process. Whilst on-line PyMS has been applied to the characterisation and tandem mass spectrometry (MS/MS) has been used R. Goodacre et al. /Analytica Chimica Acta 313 (1995) 25-43 27 to analyse fermentation broth extracts for flavones [5]. This represents a substantial increase from 1.2 1721, the majority of mass spectrometry (MS) appli- mg 1-l to some 50 g 1-l and well illustrates the cations during fermentations have been for the analy- value and power of strain selection (although the sis of gases and volatiles produced over the reactor efficiency of glucose conversion to penicillin is still 173,741, or by employing a membrane inlet probe for less than 10% [91]). volatile compounds dissolved in the broths [75-801. The first noteworthy improvement in yield came Although, Hansen et al. [81], using membrane-inlet in 1943 with the isolation of P. chrysogenum strain MS, have shown that compounds of relatiuely low NRRL 1951; this organism was chosen because it volatility may be monitored in fermentors, it is obvi- was better suited to submerged production than ous, that more worthwhile information could be Fleming’s isolate. Strain NRRL 1951 was subse- gained by measuring the non-volatile components of quently subjected to mutagenesis using X-ray and fermentation broths. Indeed, Heinzle et al. [82] were ultraviolet radiation treatment to produce Wis Q176, able to characterise the states of fermentations using the original strain in the famous Wisconsin culture off-line PyMS, and this technique was extended to line [92]. Strain Q176 has been used as the starting on-line analysis [83]. These authors, however, were strain for many improvement programs, one of which not very satisfied with their system and although by Panlabs Inc. produced a number of high yielding they have continued to use mass spectrometry for the derivatives including Pl and P2 [93]. The wild type analysis of volatiles produced during fermentation P. chrysogenum NRRL 1951, and its higher yielding [74,84] the analysis of non-volatiles by PyMS seems derivatives Q176, Pl and P2 were used in this study; to have been less than fully exploited. in terms of their genealogy Pl and P2 are distantly More
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