Diffuse reflectance absorbance spectroscopy taking in chemometrics (DRASTIC). A hyperspectral FT-IR-based approach to rapid screening for metabolite overproduction

Metabolomics for the design of new metabolic engineering strategies for improving aerobic succinic acid production in Escherichia coli

INTRODUCTION

Glycerol is a byproduct from the biodiesel industry that can be biotransformed by Escherichia coli to high added-value products such as succinate under aerobic conditions. The main genetic engineering strategies to achieve this aim involve the mutation of succinate dehydrogenase (sdhA) gene and also those responsible for acetate synthesis including acetate kinase, phosphate acetyl transferase and pyruvate oxidase encoded by ackA, pta and pox genes respectively in the ΔsdhAΔack-ptaΔpox (M4) mutant. Other genetic manipulations to rewire the metabolism toward succinate consist on the activation of the glyoxylate shunt or blockage the pentose phosphate pathway (PPP) by deletion of isocitrate lyase repressor (iclR) or gluconate dehydrogenase (gnd) genes on M4-ΔiclR and M4-Δgnd mutants respectively.

OBJECTIVE

To deeply understand the effect of the blocking of the pentose phosphate pathway (PPP) or the activation of the glyoxylate shunt, metabolite profiles were analyzed on M4-Δgnd, M4-ΔiclR and M4 mutants.

METHODS

Metabolomics was performed by FT-IR and GC-MS for metabolite fingerprinting and HPLC for quantification of succinate and glycerol.

RESULTS

Most of the 65 identified metabolites showed lower relative levels in the M4-ΔiclR and M4-Δgnd mutants than those of the M4. However, fructose 1,6-biphosphate, trehalose, isovaleric acid and mannitol relative concentrations were increased in M4-ΔiclR and M4-Δgnd mutants. To further improve succinate production, the synthesis of mannitol was suppressed by deletion of mannitol dehydrogenase (mtlD) on M4-ΔgndΔmtlD mutant that increase ~ 20% respect to M4-Δgnd.

CONCLUSION

Metabolomics can serve as a holistic tool to identify bottlenecks in metabolic pathways by a non-rational design. Genetic manipulation to release these restrictions could increase the production of succinate.

Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems

Although significant advances have been made in recent years, the structural elucidation of small molecules continues to remain a challenging issue for metabolite profiling. Many metabolomic studies feature unknown compounds; sometimes even in the list of features identified as "statistically significant" in the study. Such metabolic "dark matter" means that much of the potential information collected by metabolomics studies is lost. Accurate structure elucidation allows researchers to identify these compounds. This in turn, facilitates downstream metabolite pathway analysis, and a better understanding of the underlying biology of the system under investigation. This review covers a range of methods for the structural elucidation of individual compounds, including those based on gas and liquid chromatography hyphenated to mass spectrometry, single and multi-dimensional nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry and includes discussion of data standardization. Future perspectives in structure elucidation are also discussed; with a focus on the potential development of instruments and techniques, in both nuclear magnetic resonance spectroscopy and mass spectrometry that, may help solve some of the current issues that are hampering the complete identification of metabolite structure and function.

Variable selection in discriminant partial least-squares analysis

Variable selection enhances the understanding and interpretability of multivariate classification models. A new chemometric method based on the selection of the most important variables in discriminant partial least-squares (VS-DPLS) analysis is described. The suggested method is a simple extension of DPLS where a small number of elements in the weight vector w is retained for each factor. The optimal number of DPLS factors is determined by cross-validation. The new algorithm is applied to four different high-dimensional spectral data sets with excellent results. Spectral profiles from Fourier transform infrared spectroscopy and pyrolysis mass spectrometry are used. To investigate the uniqueness of the selected variables an iterative VS-DPLS procedure is performed. At each iteration, the previously found selected variables are removed to see if a new VS-DPLS classification model can be constructed using a different set of variables. In this manner, it is possible to determine regions rather than individual variables that are important for a successful classification.

Rapid reagentless quantification of alginate biosynthesis in Pseudomonas fluorescens bacteria mutants using FT-IR spectroscopy coupled to multivariate partial least squares regression

Alginate is an important medical and commercial product and currently is isolated from seaweeds. Certain microorganisms also produce alginate and these polymers have the potential to replace seaweed alginates in some applications, mainly because such production will allow much better and more reproducible control of critical qualitative polymer properties. The research conducted here presents the development of a new approach to this problem by analysing a transposon insertion mutant library constructed in an alginate-producing derivative of the Pseudomonas fluorescens strain SBW25. The procedure is based on the non-destructive and reagent-free method of Fourier transform infrared (FT-IR) spectroscopy which is used to generate a complex biochemical infrared fingerprint of the medium after bacterial growth. First, we investigate the potential differences caused by the growth media fructose and glycerol on the bacterial phenotype and alginate synthesis in 193 selected P. fluorescens mutants and show that clear phenotypic differences are observed in the infrared fingerprints. In order to quantify the level of the alginate we also report the construction and interpretation of multivariate partial least squares regression models which were able to quantify alginate levels successfully with typical normalized root-mean-square error in predictions of only approximately 14%. We have demonstrated that this high-throughput approach can be implemented in alginate screens and we believe that this FT-IR spectroscopic methodology, when combined with the most appropriate chemometrics, could easily be modified for the quantification of other valuable microbial products and play a valuable screening role for synthetic biology.

Fourier transform infrared spectroscopy as a metabolite fingerprinting tool for monitoring the phenotypic changes in complex bacterial communities capable of degrading phenol

The coking process produces great volumes of wastewater contaminated with pollutants such as cyanides, sulfides and phenolics. Chemical and physical remediation of this wastewater removes the majority of these pollutants; however, these processes do not remove phenol and thiocyanate. The removal of these compounds has been effected during bioremediation with activated sludge containing a complex microbial community. In this investigation we acquired activated sludge from an industrial bioreactor capable of degrading phenol. The sludge was incubated in our laboratory and monitored for its ability to degrade phenol over a 48 h period. Multiple samples were taken across the time-course and analysed by Fourier transform infrared (FT-IR) spectroscopy. FT-IR was used as a whole-organism fingerprinting approach to monitor biochemical changes in the bacterial cells during the degradation of phenol. We also investigated the ability of the activated sludge to degrade phenol following extended periods (2-131 days) of storage in the absence of phenol. A reduction was observed in the ability of the microbial community to degrade phenol and this was accompanied by a detectable biochemical change in the FT-IR fingerprint related to cellular phenotype of the microbial community. In the absence of phenol a decrease in thiocyanate vibrations was observed, reflecting the ability of these communities to degrade this substrate. Actively degrading communities showed an additional new band in their FT-IR spectra that could be attributed to phenol degradation products from the ortho- and meta-cleavage of the aromatic ring. This study demonstrates that FT-IR spectroscopy when combined with chemometric analysis is a very powerful high throughput screening approach for assessing the metabolic capability of complex microbial communities.

Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry

We sought to test the hypothesis that mutant bacterial strains could be discriminated from each other on the basis of the metabolites they secrete into the medium (their ‘metabolic footprint’), using two methods of ‘global’ metabolite analysis (FT–IR and direct injection electrospray mass spectrometry). The biological system used was based on a published study of Escherichia coli tryptophan mutants that had been analysed and discriminated by Yanofsky and colleagues using transcriptome analysis. Wild-type strains supplemented with tryptophan or analogues could be discriminated from controls using FT–IR of 24 h broths, as could each of the mutant strains in both minimal and supplemented media. Direct injection electrospray mass spectrometry with unit mass resolution could also be used to discriminate the strains from each other, and had the advantage that the discrimination required the use of just two or three masses in each case. These were determined via a genetic algorithm. Both methods are rapid, reagentless, reproducible and cheap, and might beneficially be extended to the analysis of gene knockout libraries.

Lipolytic activity of chromoblastomycosis agents measured by infrared spectroscopy and chemometric methods

The lipase activity of nine strains of six chromoblastomycosis agents (Fonsecaea pedrosoi, Phialophora verrucosa, Cladophialophora bantianum, Cladophialophora carrionii, Rhinocladiela aquaspersa and Exophiala jeanselmei) grown on solid medium was investigated using Fourier transform infrared spectroscopy and hierarchical clustering analysis. The data was quantified by p-nitrophenyl palmitate assay using partial least squares (PLS) regression. These methods allowed the correlation of six genera and species within the 1230-1650 and 2800+3000 cm(-1) spectral ranges among strains grown for 14 days from their respective lipolytic activity with RMSEV=0.048 and R2val=0.95 and ten latent variables. The lipolytic activity also was predicted using PLS models with 1230-1650 and 2800-3000 cm(-1) and 900-1450 cm(-1) spectral ranges for strains grown for 21 days. The separate analysis of F. pedrosoi strains yielded a prediction model for biomass at 21 days with RMSEV=0.065 and R2val=0.95 with eight latent variables using (1100-1300)+(1330-1460)+(1550-1650) cm(-1) spectral regions The best model obtained with the clustering of P. verrucosa, C. bantianum, C. carrionii, R. aquaspersa and E. jeanselmei strains was constructed with the same spectral ranges, but with RMSEV=0.074 and R2val=0.94 and ten latent variables. Infrared spectroscopy is suitable for the quantitation of extracellular lipase activity linked to the biomass of chromoblastomycosis agents.

Early and rapid detection of potato's fungal infection by Fourier transform infrared microscopy

Fungi are considered serious pathogens to many plants and can cause severe economic damage. Early detection of these pathogens is very important and might be critical for their control. The available methods for detection of fungi are time consuming and not always very specific. Fourier transform infrared (FT-IR) microscopy has proved to be a reliable and sensitive method for detection of molecular changes in cells. Fungi pathogens display typical infrared spectra that differ from the spectra of substrate material such as potato. In the present study we used FT-IR microscopy for early and rapid detection of the potato fungal pathogen Colletotrichum coccodes on the surface of potato tubers. Infected potatoes with this fungal pathogen and uninfected potatoes were examined and correctly classified as infected or not infected by FT-IR microscopy at very early stages of infection when no morphological signs of infection could be seen. Unique spectral biomarkers were found in naturally infected potatoes compared to disease-free control potatoes.

Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis

Liquid chromatography/mass spectrometry (LC/MS) followed by multivariate statistical analysis has been successfully applied to the plasma phospholipids metabolic profiling in type 2 diabetes mellitus (DM-2). Principal components analysis and partial least-squares discriminant analysis (PLS-DA) models were tested and compared in class separation between the DM2 and control. The application of an orthogonal signal correction filtered model highly improved the class distinction and predictive power of PLS-DA models. Additionally, unit variance scaling was also tested. With this methodology, it was possible not only to differentiate the DM2 from the control but also to discover and identify the potential biomarkers with LC/MS/MS. The proposed method shows that LC/MS combining with multivariate statistical analysis is a complement or an alternative to NMR for metabonomics applications.

Quantification of casein phosphorylation with conformational interpretation using Raman spectroscopy

Raman spectroscopy is emerging as a powerful method for obtaining both quantitative and qualitative information from biological samples. One very interesting area of research, for which the technique has rarely been used, is the detection, quantification and structural analysis of post-translational modifications (PTMs) on proteins. Since Raman spectra can be used to address both of these questions simultaneously, we have developed near infrared Raman spectroscopy with appropriate chemometric approaches (partial least squares regression) to quantify low concentration (4 microM) mixtures of phosphorylated and dephosphorylated bovine alpha(s)-casein. In addition, we have used these data in conjunction with Raman optical activity (ROA) spectra and NMR to assess the structural changes that occur upon phosphorylation.

Effect of PA-MSHA vaccine on plasma phospholipids metabolic profiling and the ratio of Th2/Th1 cells within immune organ of mouse IgA nephropathy

Phospholipids as a class of important constituents in the biomembranes have been paid increasing attention in many fields. IgA nephropathy is now generally known to be the most common form of primary glomerulonephritis in the world. However, phospholipids metabolism in IgA nephropathy was not clear. Until recently, there was no effective treatment available for patients with IgA nephropathy. In this paper, effect of PA-MSHA vaccine on plasma phospholipid metabolic profile of mouse IgA nephropathy was investigated using high performance liquid chromatography/mass spectrometry (HPLC/MS) and principal components analysis (PCA). Female Balb/c mice were divided into four groups: model group, control group, PA-MSHA treatment group and medicine control group (dipyridamole+common threewingnut root). The experimental IgA nephropathy model was established by the immunity combination method of oral BSA and injection of SEB. It was found that combination of LC/MS technology with PCA can be successfully applied to phospholipids profile analysis, clearly classify the model group and normal group, and PA-MSHA treatment group is closer to the normal control group than medicine control group. The result showed that Th(2)/Th(1) (=CD(4)(+)CD(30)(+)/CD(4)(+)CD(30)(-)) of the model group is 20.70+/-3.57, which is significantly higher than that of the control group (1.34+/-0.14) (P<0.001). The Th(2)/Th(1) ratio of the PA-MSHA treatment group and the medicine control group are lower than that of the model group (P<0.01). It is suggested that mouse IgA nephropathy has the phospholipids metabolic abnormality, PA-MSHA vaccine cannot only regulate the abnormal phospholipids metabolism mouse with the IgA nephropathy, but also correct the over unbalance of Th(2)/Th(1) proportion.

A bioinformatician's view of the metabolome

The study of a collection of metabolites as a whole (metabolome), as opposed to isolated small molecules, is a fast-growing field promising to take us one step further towards understanding cell biology, and relating the genetic capabilities of an organism to its observed phenotype. The new sciences of metabolomics and metabonomics can exploit a variety of existing experimental and computational methods, but they also require new technology that can deal with both the amount and the diversity of the data relating to the rich world of metabolites. More specifically, the collaboration between bioinformaticians and chemoinformaticians promises to advance our view of cognate molecules, by shedding light on their atomic structure and properties. Modelling of the interactions of metabolites with other entities in the cell, and eventually complete modelling of reaction pathways will be essential for analysis of the experimental data, and prediction of an organism's response to environmental challenges.

Improved dispersion of bacterial endospores for quantitative infrared sampling on gold coated porous alumina membranes

An improved method for qualitative and quantitative sampling of bacterial endospores using Fourier transform infrared (FT-IR) microscopy on gold-coated porous alumina membranes is presented. Bacillus subtilis endospores were filtered onto gold-coated alumina membranes serving as substrates. Studies in the mid-infrared (MIR) region revealed the characteristic bacterial absorption spectrum at low surface concentration, while scanning electron microscopy (SEM) images of the same samples provided precise calculation of the surface concentration of the bacterial endospores. Under the conditions of study, the average concentration of endospores was determined to be 1356 +/- 35 spores in a 100 x 100 mum(2) area, with a relative standard deviation of 0.0260. Examination of ten random spots on multiple substrates with FT-IR microscopy apertured to the same area gave an average relative standard deviation of 0.0482 in the signal strength of the amide A band at 3278 cm(-1). An extinction cross-section in reflection of sigma(ext) = (7.8 +/- 0.6) x 10(-9) cm(2)/endospore was calculated for the amide A band at the frequency of its peak absorbance, 3278 cm(-1). The absorption cross-section of the amide A band in reflection is estimated to be sigma(abs) approximately (2.10 +/- 0.12) x 10(-9) cm(2)/endospore.

Rapid monitoring of antibiotics using Raman and surface enhanced Raman spectroscopy

Comparatively few studies have explored the ability of Raman spectroscopy for the quantitative analysis of microbial secondary metabolites in fermentation broths. In this study we investigated the ability of Raman spectroscopy to differentiate between different penicillins and to quantify the level of penicillin in fermentation broths. However, the Raman signal is rather weak, therefore the Raman signal was enhanced using surface enhanced Raman spectroscopy (SERS) employing silver colloids. It was difficult by eye to differentiate between the five different penicillin molecules studied using Raman and SERS spectra, therefore the spectra were analysed by multivariate cluster analysis. Principal components analysis (PCA) clearly showed that SERS rather than the Raman spectra produced reproducible enough spectra to allow for the recovery of each of the different penicillins into their respective five groups. To highlight this further the first five principal components were used to construct a dendrogram using agglomerative clustering, and this again clearly showed that SERS can be used to identify which penicillin molecule was being analysed, despite their molecular similarities. With respect to the quantification of penicillin G it was shown that Raman spectroscopy could be used to quantify the amount of penicillin present in solution when relatively high levels of penicillin were analysed (>50 mM). By contrast, the SERS spectra showed reduced fluorescence, and improved signal to noise ratios from considerably lower concentrations of the antibiotic. This could prove to be advantageous in industry for monitoring low levels of penicillin in the early stages of antibiotic production. In addition, SERS may have advantages for quantifying low levels of high value, low yield, secondary metabolites in microbial processes.

Differentiation of Micromonospora isolates from a coastal sediment in Wales on the basis of Fourier transform infrared spectroscopy, 16S rRNA sequence analysis, and the amplified fragment length polymorphism technique

A number of actinomycetes isolates were recovered from coastal sediments in Aberystwyth (Wales, United Kingdom) with standard isolation techniques. Most of them were putatively assigned to the genera Streptomyces and Micromonospora on the basis of their morphological characteristics, and there appeared to be no difference whether the isolation media contained distilled water or seawater. A group of 20 Micromonospora isolates was selected to undergo further polyphasic taxonomic investigation. Three approaches were used to analyze the diversity of these isolates, 16S rDNA sequencing, fluorescent amplified fragment length polymorphism (AFLP), and Fourier transform infrared spectroscopy (FT-IR). The 16S rDNA sequence analysis confirmed that all of these isolates should be classified to the genus Micromonospora, and they were analyzed with a group of other Micromonospora 16S rDNA sequences available from the Ribosomal Database Project. The relationships of the 20 isolates were observed after hierarchical clustering, and almost identical clusters were obtained with these three techniques. This has obvious implications for high-throughput screening for novel actinomycetes because FT-IR spectroscopy, which is a rapid and reliable whole-organism fingerprinting method, can be applied as a very useful dereplication tool to indicate which environmental isolates have been cultured previously.

Spectral analysis and fingerprinting for biomedia characterisation

Classical culture media, as well as domestic and/or industrial wastewater treated by biological processes, have a complex composition. The on-line and/or in situ determination of some substances is possible, but expensive, as sample collection and pre-treatment are often necessary with strict rules of sterility. More global methods can be used to detect rapidly "accidents" such as the appearance of an undesirable by-product in a fermentation broth or of a toxic substance in wastewater. These methods combine a "hard" part, for sensing, and a "soft" part, for data treatment. Among potential "hard" candidates, spectroscopy can be the basis for non-invasive and non-destructive measuring systems. Some of them have been already tested in situ: ultra-violet-visible, infra-red (mid or near), fluorescence (mono-dimensional, two-dimensional or synchronous), dielectric, while others, more sophisticated, such as mass spectrometry, coupled or not to pyrolysis, nuclear magnetic resonance and Raman spectroscopy, have been proposed. All these methods provide spectra, i.e. large sets of data, from which meaningful information should be rapidly extracted, either for analysis or fingerprinting. The recourse to data-mining techniques (the "soft" part) such as principal components analysis, projection on latent structures or artificial neural networks, is a necessary step for that task. A review of techniques, mostly based on spectroscopy, with examples taken in the bioengineering field in general is proposed.

High-throughput metabolic fingerprinting of legume silage fermentations via Fourier transform infrared spectroscopy and chemometrics

Silage quality is typically assessed by the measurement of several individual parameters, including pH, lactic acid, acetic acid, bacterial numbers, and protein content. The objective of this study was to use a holistic metabolic fingerprinting approach, combining a high-throughput microtiter plate-based fermentation system with Fourier transform infrared (FT-IR) spectroscopy, to obtain a snapshot of the sample metabolome (typically low-molecular-weight compounds) at a given time. The aim was to study the dynamics of red clover or grass silage fermentations in response to various inoculants incorporating lactic acid bacteria (LAB). The hyperspectral multivariate datasets generated by FT-IR spectroscopy are difficult to interpret visually, so chemometrics methods were used to deconvolute the data. Two-phase principal component-discriminant function analysis allowed discrimination between herbage types and different LAB inoculants and modeling of fermentation dynamics over time. Further analysis of FT-IR spectra by the use of genetic algorithms to identify the underlying biochemical differences between treatments revealed that the amide I and amide II regions (wavenumbers of 1,550 to 1,750 cm(-1)) of the spectra were most frequently selected (reflecting changes in proteins and free amino acids) in comparisons between control and inoculant-treated fermentations. This corresponds to the known importance of rapid fermentation for the efficient conservation of forage proteins.

The rapid identification of Acinetobacter species using Fourier transform infrared spectroscopy

AIMS

Fourier transform infrared (FT-IR) was used to analyse a selection of Acinetobacter isolates in order to determine if this approach could discriminate readily between the known genomic species of this genus and environmental isolates from activated sludge.

METHODS AND RESULTS

FT-IR spectroscopy is a rapid whole-organism fingerprinting method, typically taking only 10 s per sample, and generates 'holistic' biochemical profiles (or 'fingerprints') from biological materials. The cluster analysis produced by FT-IR was compared with previous polyphasic taxonomic studies on these isolates and with 16S-23S rDNA intergenic spacer region (ISR) fingerprinting presented in this paper. FT-IR and 16S-23S rDNA ISR analyses together indicate that some of the Acinetobacter genomic species are particularly heterogeneous and poorly defined, making characterization of the unknown environmental isolates with the genomic species difficult.

CONCLUSIONS

Whilst the characterization of the isolates from activated sludge revealed by FT-IR and 16S-23S rDNA ISR were not directly comparable, the dendrogram produced from FT-IR data did correlate well with the outcomes of the other polyphasic taxonomic work.

SIGNIFICANCE AND IMPACT OF THE STUDY

We believe it would be advantageous to pursue this approach further and establish a comprehensive database of taxonomically well-defined Acinetobacter species to aid the identification of unknown strains. In this instance, FT-IR may provide the rapid identification method eagerly sought for the routine identification of Acinetobacter isolates from a wide range of environmental sources.

Development of a method for the determination of Fusarium fungi on corn using mid-infrared spectroscopy with attenuated total reflection and chemometrics

A novel method, which enables the determination of fungal infection with Fusarium graminearum on corn within minutes, is presented. The ground sample was sieved and the particle size fraction between >250 and 100 microm was used for mid-infrared/attenuated total reflection (ATR) measurements. The sample was pressed onto the ATR crystal, and reproducible pressure was applied. After the spectra were recorded, they were subjected to principle component analysis (PCA) and classified using cluster analysis. Observed changes in the spectra reflected changes in protein, carbohydrate, and lipid contents. Ergosterol (for the total fungal biomass) and the toxin deoxynivalenol (DON; a secondary metabolite) of Fusarium fungi served as reference parameters, because of their relevance for the examination of corn based food and feed. The repeatability was highly improved by sieving prior to recording the spectra, resulting in a better clustering in PCA score/score plots. The developed method enabled the separation of samples with a toxin content of as low as 310 microg/kg from noncontaminated (blank) samples. Investigated concentration ranges were 880-3600 microg/kg for ergosterol and 310-2596 microg/kg for DON. The percentage of correctly classified samples was up to 100% for individual samples compared with a number of blank samples.

Accelerated design of bioconversion processes using automated microscale processing techniques

Microscale processing techniques are rapidly emerging as a means to increase the speed of bioprocess design and reduce material requirements. Automation of these techniques can reduce labour intensity and enable a wider range of process variables to be examined. This article examines recent research on various individual microscale unit operations including microbial fermentation, bioconversion and product recovery techniques. It also explores the potential of automated whole process sequences operated in microwell formats. The power of the whole process approach is illustrated by reference to a particular bioconversion, namely the Baeyer-Villiger oxidation of bicyclo[3.2.0]hept-2-en-6-one for the production of optically pure lactones.

Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: application to gibberellic acid production

Two rapid vibrational spectroscopic approaches (diffuse reflectance-absorbance Fourier transform infrared [FT-IR] and dispersive Raman spectroscopy), and one mass spectrometric method based on in vacuo Curie-point pyrolysis (PyMS), were investigated in this study. A diverse range of unprocessed, industrial fed-batch fermentation broths containing the fungus Gibberella fujikuroi producing the natural product gibberellic acid, were analyzed directly without a priori chromatographic separation. Partial least squares regression (PLSR) and artificial neural networks (ANNs) were applied to all of the information-rich spectra obtained by each of the methods to obtain quantitative information on the gibberellic acid titer. These estimates were of good precision, and the typical root-mean-square error for predictions of concentrations in an independent test set was <10% over a very wide titer range from 0 to 4925 ppm. However, although PLSR and ANNs are very powerful techniques they are often described as "black box" methods because the information they use to construct the calibration model is largely inaccessible. Therefore, a variety of novel evolutionary computation-based methods, including genetic algorithms and genetic programming, were used to produce models that allowed the determination of those input variables that contributed most to the models formed, and to observe that these models were predominantly based on the concentration of gibberellic acid itself. This is the first time that these three modern analytical spectroscopies, in combination with advanced chemometric data analysis, have been compared for their ability to analyze a real commercial bioprocess. The results demonstrate unequivocally that all methods provide very rapid and accurate estimates of the progress of industrial fermentations, and indicate that, of the three methods studied, Raman spectroscopy is the ideal bioprocess monitoring method because it can be adapted for on-line analysis.

Metabonomics: a platform for studying drug toxicity and gene function

The later that a molecule or molecular class is lost from the drug development pipeline, the higher the financial cost. Minimizing attrition is therefore one of the most important aims of a pharmaceutical discovery programme. Novel technologies that increase the probability of making the right choice early save resources, and promote safety, efficacy and profitability. Metabonomics is a systems approach for studying in vivo metabolic profiles, which promises to provide information on drug toxicity, disease processes and gene function at several stages in the discovery-and-development process.

Recognition of environmental and genetic effects on barley phenolic fingerprints by neural networks

Through computational analysis of high-performance liquid chromatography (HPLC) traces we find correlations between secondary metabolites and growth conditions of six varieties of barley. Using artificial neural networks, it was possible to classify chromatograms for which the varieties were fertilized by nitrogen and treated by fungicide. For each variety of barley we could also differentiate it from the others. Surprisingly, all these classification tasks could be solved successfully by a simple network with no hidden units. When adding to the methodology pruning of the network weights, we were able to reduce the set of peaks in the chromatograms and obtain a necessary subset from which the growth conditions and differentiation may be decided. In some instances, more complex networks with hidden units could lead to a further reduction of the number of peaks used. In most cases, far more than half of the peaks are redundant. We find that it requires fewer information-rich peaks to perform the variety differentiation tasks than to recognize any of the growth conditions. Analysis of the network weights reveals correlations between weighted combinations of peaks.

A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations

A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are "silent, " that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing "metabolic snapshots," can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY-an abbreviation for functional analysis by co-responses in yeast.

FT-IR microspectroscopy for microbiological studies

In this article we present an infrared microspectroscopic investigation on Candida albicans microcolonies, taken as a model system for studies on other microorganisms. Excellent Fourier transform infrared (FT-IR) absorption spectra from 4000 to 850 cm(-1) have been collected in only 20 s from sampling areas of 100x100 microm(2) in microcolonies, which had been transferred from the agar plate onto zinc selenide (ZnSe) windows. When different regions within a single microcolony were investigated, absorption spectra with important differences in the carbohydrate absorption (from 1200 to 850 cm(-1)) were detected for the cells in the center and in the periphery of the colony. Results obtained on microcolonies grown on solid agar with increasing dextrose concentrations indicated that the observed spectral heterogeneity was related to differences in dextrose uptake, which was lower for the old cells in the center of the colony than for the metabolically active cells at the periphery. Although it is otherwise difficult to quantitatively evaluate the dextrose uptake in a microcolony, FT-IR absorption microspectroscopy offers a new and rapid method for the analysis of this process. The possibility of studying highly absorbing colonies by attenuated total reflection (ATR) by means of an ATR microscope germanium objective is also presented here for the first time. An evaluation of the contact area sampled by this technique is reported with a discussion of the spatial resolution, the quality and the potential of the ATR measurements.

Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.

Differentiation of brewing yeast strains by pyrolysis mass spectrometry and Fourier transform infrared spectroscopy

Two rapid spectroscopic approaches for whole-organism fingerprinting--pyrolysis mass spectrometry (PyMS) and Fourier transform infrared spectroscopy (FT-IR)--were used to analyse 22 production brewery Saccharomyces cerevisiae strains. Multivariate discriminant analysis of the spectral data was then performed to observe relationships between the 22 isolates. Upon visual inspection of the cluster analyses, similar differentiation of the strains was observed for both approaches. Moreover, these phenetic classifications were found to be very similar to those previously obtained using genotypic studies of the same brewing yeasts. Both spectroscopic techniques are rapid (typically 2 min for PyMS and 10 s for FT-IR) and were shown to be capable of the successful discrimination of both ale and lager yeasts. We believe that these whole-organism fingerprinting methods could find application in brewery quality control laboratories.

Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks

Three rapid spectroscopic approaches for whole-organism fingerprinting-pyrolysis mass spectrometry (PyMS), Fourier transform infra-red spectroscopy (FT-IR) and dispersive Raman microscopy--were used to analyse a group of 59 clinical bacterial isolates associated with urinary tract infection. Direct visual analysis of these spectra was not possible, highlighting the need to use methods to reduce the dimensionality of these hyperspectral data. The unsupervised methods of discriminant function and hierarchical cluster analyses were employed to group these organisms based on their spectral fingerprints, but none produced wholly satisfactory groupings which were characteristic for each of the five bacterial types. In contrast, for PyMS and FT-IR, the artificial neural network (ANN) approaches exploiting multi-layer perceptrons or radial basis functions could be trained with representative spectra of the five bacterial groups so that isolates from clinical bacteriuria in an independent unseen test set could be correctly identified. Comparable ANNs trained with Raman spectra correctly identified some 80% of the same test set. PyMS and FT-IR have often been exploited within microbial systematics, but these are believed to be the first published data showing the ability of dispersive Raman microscopy to discriminate clinically significant intact bacterial species. These results demonstrate that modern analytical spectroscopies of high intrinsic dimensionality can provide rapid accurate microbial characterization techniques, but only when combined with appropriate chemometrics.