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The culturability of several actinobacteria is controlled by resuscitation-promoting factors (Rpfs). These are proteins containing a c. 70-residue domain that adopts a lysozyme-like fold. The invariant catalytic glutamate residue found in lysozyme and various bacterial lytic transglycosylases is also conserved in the Rpf proteins. Rpf from Micrococcus luteus, the founder member of this protein family, is indeed a muralytic enzyme, as revealed by its activity in zymograms containing M. luteus cell walls and its ability to (i) cause lysis of Escherichia coli when expressed and secreted into the periplasm; (ii) release fluorescent material from fluorescamine-labelled cell walls of M. luteus; and (iii) hydrolyse the artificial lysozyme substrate, 4-methylumbelliferyl-beta-D-N,N',N''-triacetylchitotrioside. Rpf activity was reduced but not completely abolished when the invariant glutamate residue was altered. Moreover, none of the other acidic residues in the Rpf domain was absolutely required for muralytic activity. Replacement of one or both of the cysteine residues that probably form a disulphide bridge within Rpf impaired but did not completely abolish muralytic activity. The muralytic activities of the Rpf mutants were correlated with their abilities to stimulate bacterial culturability and resuscitation, consistent with the view that the biological activity of Rpf results directly or indirectly from its ability to cleave bonds in bacterial peptidoglycan.

There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome “fingerprinting” to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.

The importance of metabolomic data in functional genomic investigations is increasingly becoming evident, as is its utility in novel biomarker discovery. We demonstrate a simple approach to the screening of metabolic information that we believe will be valuable in generating metabolomic data. Laser desorption ionisation mass spectrometry on porous silicon was effective in detecting 22 of 30 metabolites in a mixture in the negative-ion mode and 19 of 30 metabolites in the positive-ion mode, without the employment of any prior analyte separation steps. Overall, 26 of the 30 metabolites could be covered between the positive and negative-ion modes. Although the response for the metabolites at a given concentration differed, it was possible to generate direct quantitative information for a given analyte in the mixture. This technique was subsequently used to generate metabolic footprints from cell-free supernatants and, when combined with chemometric analysis, enabled us to discriminate haploid yeast single-gene deletants (mutants). In particular, the metabolic footprint of a deletion mutant in a gene encoding a transcriptional activator (Gln3p) showed increased levels of peaks, including one corresponding to glutamate, compared to the other mutants and the wild-type strain tested, enabling its discrimination based on metabolic information.

One element of classical systems analysis treats a system as a black or grey box, the inner structure and behaviour of which can be analysed and modelled by varying an internal or external condition, probing it from outside and studying the effect of the variation on the external observables. The result is an understanding of the inner make-up and workings of the system. The equivalent of this in biology is to observe what a cell or system excretes under controlled conditions - the 'metabolic footprint' or exometabolome - as this is readily and accurately measurable. Here, we review the principles, experimental approaches and scientific outcomes that have been obtained with this useful and convenient strategy.

In this paper, we generalize a previously-described model of the error-prone polymerase chain reaction (PCR) reaction to conditions of arbitrarily variable amplification efficiency and initial population size. Generalisation of the model to these conditions improves the correspondence to observed and expected behaviours of PCR, and restricts the extent to which the model may explore sequence space for a prescribed set of parameters. Error-prone PCR in realistic reaction conditions is predicted to be less effective at generating grossly divergent sequences than the original model. The estimate of mutation rate per cycle by sampling sequences from an in vitro PCR experiment is correspondingly affected by the choice of model and parameters.

Metabolomics, like other omics methods, produces huge datasets of biological variables, often accompanied by the necessary metadata. However, regardless of the form in which these are produced they are merely the ground substance for assisting us in answering biological questions. In this short tutorial review and position paper we seek to set out some of the elements of ‘‘best practice’’ in the optimal acquisition of such data, and in the means by which they may be turned into reliable knowledge. Many of these steps involve the solution of what amount to combinatorial optimization problems, and methods developed for these, especially those based on evolutionary computing, are proving valuable. This is done in terms of a ‘‘pipeline’’ that goes from the design of good experiments, through instrumental optimization, data storage and manipulation, the chemometric data processing methods in common use, and the necessary means of validation and cross-validation for giving conclusions that are credible and likely to be robust when applied in comparable circumstances to samples not used in their generation.

H.E. JOHNSON, R.J. MERRY, D.R. DAVIES, D.B. KELL, M.K. THEODOROU AND G.W. GRIFFITH. 2004. Aims: To determine the utility of vacuum-packed polythene bags as a convenient, flexible and cost-effective alternative to fixed volume glass vessels for lab-scale silage studies. Methods and Results: Using perennial ryegrass or red clover forage, similar fermentations (as assessed by pH measurement) occurred in glass tube and vacuum-packed silos over a 35-day period. As vacuum-packing devices allow modification of initial packing density, the effect of four different settings (initial packing densities of 0AE397, 0AE435, 0AE492 and 0AE534 g cm )3 ) on the silage fermentation over 16 days was examined. Significant differences in pH decline and lactate accumulation were observed at different vacuum settings. Gas accumulation was apparent within all bags and changes in bag volume with time was observed to vary according to initial packing density. Conclusions: Vacuum-packed silos do provide a realistic model system for lab-scale silage fermentations. Significance and Impact of the Study: Use of vacuum-packed silos holds potential for lab-scale evaluations of silage fermentations, allowing higher throughput of samples, more consistent packing as well as the possibility of investigating the effects of different initial packing densities and use of different wrapping materials.

Signaling by the transcription factor nuclear factor kappa B (NF-kappaB) involves its release from inhibitor kappa B (IkappaB) in the cytosol, followed by translocation into the nucleus. NF-kappaB regulation of IkappaBalpha transcription represents a delayed negative feedback loop that drives oscillations in NF-kappaB translocation. Single-cell time-lapse imaging and computational modeling of NF-kappaB (RelA) localization showed asynchronous oscillations following cell stimulation that decreased in frequency with increased IkappaBalpha transcription. Transcription of target genes depended on oscillation persistence, involving cycles of RelA phosphorylation and dephosphorylation. The functional consequences of NF-kappaB signaling may thus depend on number, period, and amplitude of oscillations.

Novel techniques for acquiring metabolomics data continue to emerge. Such data require proper storage in suitably configured databases, which then permit one to establish the size of microbial metabolomes (hundreds of major metabolites) and allow the nature, organisation and control of metabolic networks to be investigated. A variety of algorithms for metabolic network reconstruction coupled to suitable modelling algorithms are the ground substances for the development of metabolic network and systems biology. Even qualitative models of metabolic networks, when subject to stoichiometric constraints, can prove highly informative, and are the first step to the quantitative models, which alone can allow the true representation of complex biochemical systems.

We have exploited three methods for discriminating single-nucleotide polymorphisms (SNPs) by detecting the incorporation or otherwise of labeled dideoxy nucleotides at the end of a primer chain using single-molecule fluorescence detection methods. Good discrimination of incorporated vs free nucleotide may be obtained in a homogeneous assay (without washing steps) via confocal fluorescence correlation spectroscopy or by polarization anisotropy obtained from confocal fluorescence intensity distribution analysis. Moreover, the ratio of the fluorescence intensities on each polarization channel may be used directly to discriminate the nucleotides incorporated. Each measurement took just a few seconds and was done in microliter volumes with nanomolar concentrations of labeled nucleotides. Since the confocal volumes interrogated are approximately 1fL and the reaction volume could easily be lowered to nanoliters, the possibility of SNP analysis with attomoles of reagents opens up a route to very rapid and inexpensive SNP detection. The method was applied with success to the detections of SNPs that are known to occur in the BRCA1 and CFTR genes.

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.

Single and multiple resistance to antibacterial drugs currently in use is spreading, since they act against only a very small number of molecular targets; finding novel targets for anti-infectives is therefore of great importance. All protein sequences from three pathogens (Staphylococcus aureus, Mycobacterium tuberculosisandEscherichia coliO157:H7 EDL993) were assessed via comparative genomics methods for their suitability as antibacterial targets according to a number of criteria, including the essentiality of the protein, its level of sequence conservation, and its distribution in pathogens, bacteria and eukaryotes (especially humans). Each protein was scored and ranked based on weighted variants of these criteria in order to prioritize proteins as potential novel broad-spectrum targets for antibacterial drugs. A number of proteins proved to score highly in all three species and were robust to variations in the scoring system used. Sensitivity analysis indicated the quantitative contribution of each metric to the overall score. After further analysis of these targets, tRNA methyltransferase (trmD) and translation initiation factor IF-1 (infA) emerged as potential and novel antimicrobial targets very worthy of further investigation. The scoring strategy used might be of value in other areas of post-genomic drug discovery.

We do not really understand the continuing failure of Matsson and colleagues to recognise where the crucial arguments lie. The chief experimental technique of interest varies transporter expression as an independent variable, and measures the consequent effects on fluxes [1]. In a recent example of this, Superti-Furga and colleagues knocked out the gene encoding the SLC35F2 transporter, thereby abolishing ∼99.5% of the uptake of an anticancer drug called sepantronium bromide [2]. Thus any remaining transbilayer flux would be 0.5% at most, which we consider really is negligible, and even that 0.5% is almost certainly carried by other transporters (OCTs) previously considered to be the main transporters [2].

We have noted in previous work, in a variety of inflammatory diseases, where iron dysregulation occurs, a strong tendency for erythrocytes to lose their normal discoid shape and to adopt a skewed morphology (as judged by their axial ratios in the light microscope and by their ultrastructure in the SEM). Similarly, the polymerization of fibrinogen, as induced in vitro by added thrombin, leads not to the common ‘spaghetti-like’ structures but to dense matted deposits. Type 2 diabetes is a known inflammatory disease. In the present work, we found that the axial ratio of the erythrocytes of poorly controlled (as suggested by increased HbA1c levels) type 2 diabetics was significantly increased, and that their fibrin morphologies were again highly aberrant. As judged by scanning electron microscopy and in the atomic force microscope, these could be reversed, to some degree, by the addition of the iron chelators deferoxamine (DFO) or deferasirox (DFX). As well as their demonstrated diagnostic significance, these morphological indicators may have prognostic value.

The newly sequenced genome of Streptomyces coelicolor is estimated to encode 7825 theoretical proteins. We have mapped approximately 10% of the theoretical proteome experimentally using two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry. Products from 770 different genes were identified, and the types of proteins represented are discussed in terms of their annotated functional classes. An average of 1.2 proteins per gene was observed, indicating extensive post-translational regulation. Examples of modification by N-acetylation, adenylylation and proteolytic processing were characterized using mass spectrometry. Proteins from both primary and certain secondary metabolic pathways are strongly represented on the map, and a number of these enzymes were identified at more than one two-dimensional gel location. Post-translational modification mechanisms may therefore play a significant role in the regulation of these pathways. Unexpectedly, one of the enzymes for synthesis of the actinorhodin polyketide antibiotic appears to be located outside the cytoplasmic compartment, within the cell wall matrix. Of 20 gene clusters encoding enzymes characteristic of secondary metabolism, eight are represented on the proteome map, including three that specify the production of novel metabolites. This information will be valuable in the characterization of the new metabolites.

The effects of genes on phenotype are mediated by processes that are typically unknown but whose determination is desirable. The conversion from gene to phenotype is not a simple function of individual genes, but involves the complex interactions of many genes; it is what is known as a nonlinear mapping problem. A computational method called genetic programming allows the representation of candidate nonlinear mappings in several possible trees. To find the best model, the trees are 'evolved' by processes akin to mutation and recombination, and the trees that more closely represent the actual data are preferentially selected. The result is an improved tree of rules that represent the nonlinear mapping directly. In this way, the encoding of cellular and higher-order activities by genes is seen as directly analogous to computer programs. This analogy is of utility in biological genetics and in problems of genotype-phenotype mapping.

Mycobacterium tuberculosis and its close relative, Mycobacterium bovis (BCG) contain five genes whose predicted products resemble Rpf from Micrococcus luteus. Rpf is a secreted growth factor, active at picomolar concentrations, which is required for the growth of vegetative cells in minimal media at very low inoculum densities, as well as the resuscitation of dormant cells. We show here that the five cognate proteins from M. tuberculosis have very similar characteristics and properties to those of Rpf. They too stimulate bacterial growth at picomolar (and in some cases, subpicomolar) concentrations. Several lines of evidence indicate that they exert their activity from an extra-cytoplasmic location, suggesting that they are also involved in intercellular signalling. The five M. tuberculosis proteins show cross-species activity against M. luteus, Mycobacterium smegmatis and M. bovis (BCG). Actively growing cells of M. bovis (BCG) do not respond to these proteins, whereas bacteria exposed to a prolonged stationary phase do. Affinity-purified antibodies inhibit bacterial growth in vitro, suggesting that sequestration of these proteins at the cell surface might provide a means to limit or even prevent bacterial multiplication in vivo. The Rpf family of bacterial growth factors may therefore provide novel opportunities for preventing and controlling mycobacterial infections.

Micrococcus luteus secretes a small protein called Rpf, which has autocrine and paracrine signalling functions and is required for the resuscitation of dormant cells. Originally isolated from the supernatant of actively growing cultures, Rpf was also detected on the surface of actively growing bacteria. Most molecules may be sequestered non-productively at the cell surface, as a truncated form of the protein, encompassing only the 'Rpf domain' is fully active. The C-terminal LysM module, which probably mediates binding to the cell envelope, is not required for biological activity. Rpf was essential for growth of M. luteus. Washed cells, inoculated at low density into a minimal medium, could not grow in its absence. Moreover, the incorporation of anti-Rpf antibodies into the culture medium at the time of inoculation also prevented bacterial growth. We were unable to inactivate rpf using a disrupted form of the gene, in which most of the coding sequence was replaced with a selectable thiostrepton resistance marker. Gene disruption was possible in the presence of a second, functional, plasmid-located copy of rpf, but not in the presence of a rpf derivative whose protein product lacked the secretory signal sequence. As far as we are aware, Rpf is the first example of a truly secreted protein that is essential for bacterial growth. If the Rpf-like proteins elaborated by Mycobacterium tuberculosis and other mycobacteria prove similarly essential, interference with their proper functioning may offer novel opportunities for protecting against, and treating, tuberculosis and other mycobacterial disease.

We used parameter scanning to emulate changes to the limiting rate for steps in a fitted model of glucose-derepressed yeast glycolysis. Three flux-control regimes were observed, two of which were under the dominant control of hexose transport, in accordance with various experimental studies and other model predictions. A third control regime in which phosphofructokinase exerted dominant glycolytic flux control was also found, but it appeared to be physiologically unreachable by this model, and all realistically obtainable flux control regimes featured hexose transport as a step involving high flux control.

Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable “fingerprints.” Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10 7 bacteria·g −1 the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.

The ability of two different flow cytometers, the Microcyte (Optoflow) and the PAS-III (Partec), to differentiate sporangia of the late-blight pathogen Phytophthora infestans from other potential airborne particles was compared. With the PAS-III, light scatter and intrinsic fluorescence parameters could be used to differentiate sporangia from conidia of Alternaria or Botrytis spp., rust urediniospores, and pollen of grasses and plantain. Differentiation between P. infestans sporangia and powdery mildew conidia was not possible by these two methods but, when combined with analytical rules evolved by genetic programming methods, could be achieved after staining with the fluorescent brightener Calcofluor white M2R. The potential application of these techniques to the prediction of late-blight epiphytotics in the field is discussed. Current methods for predicting the occurrence of late-blight of potato (Phytophthora infestans) rely on climatic modeling to identify conditions conducive to pathogen reproduction and thus to disease spread (5). Such methods can provide considerable savings to growers, as well as environmental benefits. For United Kingdom potato production, calendar spraying is normal practice, with sprays commencing when the crop canopy has closed and continuing at 7- to 10-day intervals until crop desiccation or harvest. Use of forecasting models can reduce the number of required sprays by allowing a delay in the first fungicide application and/or an extension of the interval between subsequent sprays (e.g., references 25 and 28).

Kell, D. B. (2002). Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules. Molecular Biology Reports, 29, (1-2), 237-241.

We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their rank order varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies very greatly depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity. However, for a subset of such drugs, amounting to some 10 % of the set and a Tanimoto similarity of ~0.8 or greater, the similarity coefficient is relatively robust to the encoding used. This leads to a metric that, while arbitrary, suggests that a Tanimoto similarity of 0.75-0.8 or greater genuinely does imply a considerable structural similarity of two molecules in the drug-endogenite space. Although comparatively few (robustly similar to an endogenite, there is often at least one encoding with which they are genuinely similar (e.g. TS > 0.75). This is referred to as the Take Your Pick Improved Cheminformatic Analytical Likeness or TYPICAL encoding, and on this basis some 66 % of drugs are within a TS of 0.75 to an endogenite.We next explicitly recognise that natural evolution will have selected for the ability to transport dietary substances, including plant, animal and microbial ‘secondary’ metabolites, that are of benefit to the host. These should also be explored in terms of their closeness to marketed drugs. We thus compared the TS of marketed drugs with the contents of various databases of natural products. When this is done, we find that some 80 % of marketed drugs are within a TS of 0.7 to a natural product, even using just the MACCS encoding. For patterned and TYPICAL encodings, 80 % and 98 % of drugs are within a TS of 0.8 to (an endogenite or) an exogenous natural product. This implies strongly that it is these exogeneous (dietary and medicinal) natural products that are more to be seen as the ‘natural’ substrates of drug transporters (as is recognised, for instance, for the solute carrier SLC22A4 and ergothioneine). This novel analysis casts an entirely different light on the kinds of natural molecules that are to be seen as most like marketed drugs, and hence potential transporter substrates, and further suggests that a renewed exploitation of natural products as drug scaffolds would be amply rewarded.

We rehearse the processes of innovation and discovery in general terms, using as our main metaphor the biological concept of an evolutionary fitness landscape. Incremental and disruptive innovations are seen, respectively, as successful searches carried out locally or more widely. They may also be understood as reflecting evolution by mutation (incremental) versus recombination (disruptive). We also bring a platonic view, focusing on virtue and memory. We use ‘virtue’ as a measure of efforts, including the knowledge required to come up with disruptive and incremental innovations, and ‘memory’ as a measure of their lifespan, i.e. how long they are remembered. Fostering innovation, in the evolutionary metaphor, means providing the wherewithal to promote novelty, good objective functions that one is trying to optimize, and means to improve one's knowledge of, and ability to navigate, the landscape one is searching. Recombination necessarily implies multi- or inter-disciplinarity. These principles are generic to all kinds of creativity, novel ideas formation and the development of new products and technologies.

Because they mainly do not involve chemical changes, membrane transporters have been a Cinderella subject in the biotechnology of small molecule production, but this is a serious oversight. Influx transporters contribute significantly to the flux towards product, and efflux transporters ensure the accumulation of product in the much greater extracellular space of fermentors. Programmes for improving biotechnological processes might therefore give greater consideration to transporters than may have been commonplace. Strategies for identifying important transporters include expression profiling, genome-wide knockout studies, stress-based selection, and the use of inhibitors. In addition, modern methods of directed evolution and synthetic biology, especially those effecting changes in energy coupling, offer huge opportunities for increasing the flux towards extracellular product formation by transporter engineering.

Motivation: DNA sequencing instruments are enabling genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and interpret sequence data. Established methods for computational sequence analysis generally use nucleotide-level resolution of sequences, and while such approaches can be very accurate, increasingly ambitious and data-intensive analyses are rendering them impractical for applications such as genome and metagenome assembly. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction methods are routinely used to reduce the computational burden of analyses. We therefore seek to address the question of whether it is possible to improve the efficiency of sequence alignment by applying dimensionality reduction methods to numerically represented nucleotide sequences. Results: To explore the applicability of signal transformation and dimensionality reduction methods to sequence assembly, we implemented a short read aligner and evaluated its performance against simulated high diversity viral sequences alongside four existing aligners. Using our sequence transformation and feature selection approach, alignment time was reduced by up to 14-fold compared to uncompressed sequences and without reducing alignment accuracy. Despite using highly compressed sequence transformations, our implementation yielded alignments of similar overall accuracy to existing aligners, outperforming all other tools tested at high levels of sequence variation. Our approach was also applied to the de novo assembly of a simulated diverse viral population. Our results demonstrate that full sequence resolution is not a prerequisite of accurate sequence alignment and that analytical performance can be retained and even enhanced through appropriate dimensionality reduction of sequences.

For drug transport across (i.e., through) an intact biological membrane, two main routes are possible: drugs may cross (i) through the phospholipid bilayer portion of the membrane, and/or (ii) via proteinaceous pores or transporters. Perhaps surprisingly, there is in fact no direct scientific evidence that the first of these takes place at any significant rate because, in the experiments performed to date, it has neither been varied as an independent variable nor measured directly as a dependent variable. Using a standard hypothetico-deductive framework, I assess the intellectual and observable consequences of assuming that, for drugs, phospholipid bilayer diffusion is negligible - 'PBIN' - (i.e., may be neglected, relative to transporter-mediated transmembrane fluxes). Predictions and postdictions of the PBIN hypothesis are not refuted by available experimental evidence.

One approach to experimental science involves creating hypotheses, then testing them by varying one or more independent variables, and assessing the effects of this variation on the processes of interest. We use this strategy to compare the intellectual status and available evidence for two models or views of mechanisms of transmembrane drug transport into intact biological cells. One (BDII) asserts that lipoidal phospholipid Bilayer Diffusion Is Important, while a second (PBIN) proposes that in normal intact cells Phospholipid Bilayer diffusion Is Negligible (i.e., may be neglected quantitatively), because evolution selected against it, and with transmembrane drug transport being effected by genetically encoded proteinaceous carriers or pores, whose “natural” biological roles, and substrates are based in intermediary metabolism. Despite a recent review elsewhere, we can find no evidence able to support BDII as we can find no experiments in intact cells in which phospholipid bilayer diffusion was either varied independently or measured directly (although there are many papers where it was inferred by seeing a covariation of other dependent variables). By contrast, we find an abundance of evidence showing cases in which changes in the activities of named and genetically identified transporters led to measurable changes in the rate or extent of drug uptake. PBIN also has considerable predictive power, and accounts readily for the large differences in drug uptake between tissues, cells and species, in accounting for the metabolite-likeness of marketed drugs, in pharmacogenomics, and in providing a straightforward explanation for the late-stage appearance of toxicity and of lack of efficacy during drug discovery programmes despite macroscopically adequate pharmacokinetics. Consequently, the view that Phospholipid Bilayer diffusion Is Negligible (PBIN) provides a starting hypothesis for assessing cellular drug uptake that is much better supported by the available evidence, and is both more productive and more predictive.

Phenotyping of 1,200 ‘healthy’ adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography–mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the ‘normal’ relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/). Electronic supplementary material The online version of this article (doi:10.1007/s11306-014-0707-1) contains supplementary material, which is available to authorized users.

Kell, D. B., Mukamolova, G. V., Finan, C. L., Zhao, H., Goodacre, R., Kaprelyants, A. S., Young, M. (2004). Resuscitation of 'uncultured' microorganisms. In, Microbial Biodiversity and Bioprospecting, Bull, A.T. (ed). ISBN:9781555812676, pp. 100-108 Key Features: -Unique and comprehensive synthesis of biotechnology search and discovery in the post-genomic area. -Reveals the extraordinary scale of microbial diversity and how technologies enable it to be estimated, defined, and exploited. -Discusses the application of microbiology to a wide range of industrial sectors. -World leaders in their respective fields present state-of-the-art material.

Direct injection electrospray ionization mass spectrometry (ESI-MS) without prior analyte separation was investigated for the analysis of whole cell suspensions of bacteria. Thirty-six strains of aerobic endospore-forming bacteria, consisting of six Bacillus species and one Brevibacillus species, were studied. ESI was performed in the positive ion mode on the bacterial suspensions. Several peaks in the range of 250−1500 m/z were observed to contribute to variations in the spectral information among the species. Application of cluster analysis to the spectral data showed that this ESI-MS technique was capable of discriminating strains of the species B. subtilis. This investigation demonstrates the feasibility of measuring liquid samples with minimal sample preparation that can be useful for discrimination at the subspecies level. A change in the cone potential in the electrospray ion source was found to influence the spectral information of representative strains of all of the seven species tested. This ha...

The recovery of dilute populations of stationary phase cells of Escherichia coli was studied using an automatic growth analyser. The addition of 30% supernatant from 2-d-old stationary phase cells of the organism reproducibly shortened the apparent lag times by 22-57.5%, depending on the age of the inoculum. True lag times, as determined by colony counts, of stationary phase cells were reduced by supernatant addition by 41-62%. The growth-stimulating substance was characterized and partly purified from supernatants: the active material was shown to be dialysable, heat-stable, acid- and alkali-stable and protease-resistant. Extraction with ethyl acetate or ion-exchange resins was not successful, but the active material could be quantitatively extracted with ethanol after saturation with salt. It is concluded that the active substance is a small, non-proteinaceous, non-ionic organic molecule. Separation of extracts by HPLC indicated that the stimulatory substance is weakly hydrophobic and has retention times similar to those of uracil. So far, however, the exact chemical identity of the active substance has not been elucidated.

At present, the assignment of function to novel genes uncovered by the systematic genome-sequencing programmes is a problem. Many studies anticipate that this can be achieved by analysing patterns of gene expression via the transcriptome, proteome and metabolome. Thus, functional genomics is, in part, an exercise in pattern classification. Because many genes have known functional classes, the problem of predicting their functional class is a supervised learning problem. However, most pattern classification methods that have been applied to the problem have been unsupervised clustering methods. Consequently, the best classification tools have not always been used. Furthermore, the present functional classes are suboptimal and new unsupervised clustering methods are needed to improve them. Better-structured functional classes will facilitate the prediction of biochemically testable functions.

The deconvolution of biological dielectric spectra can be difficult enough with artefact-free spectra but is more problematic when machine baseline artefacts and electrode polarisation are present as well. In addition, these two sources of anomalies can be responsible for significant interference with dielectric biomass measurements made using one- or two-spot frequencies. The aim of this paper is to develop mathematical models of baseline artefacts and electrode polarisation which can be used to remove these anomalies from dielectric spectra in a way that can be easily implemented on-line and in real-time on the Biomass Monitor (BM). We show that both artefacts can be successfully removed in solutions of organic and inorganic ions; in animal cell and microbial culture media; and in yeast suspensions of varying biomass. The high quality of the compensations achieved were independent of whether gold and platinum electrodes were used; the electrode geometry; electrode fouling; current density; the type of BM; and of whether electrolytic cleaning pulses had been applied. In addition, the calibration experiments required could be done off-line using a simple aqueous KCl dilution series with the calibration constants being automatically calculated by a computer without the need for user intervention. The calibration values remained valid for a minimum of 3 months for the baseline model and indefinitely for the electrode polarisation one. Importantly, application of baseline correction prior to polarisation correction allowed the latter's application to the whole conductance range of the BM. These techniques are therefore exceptionally convenient to use under practical conditions.

Genetic programming, in conjunction with advanced analytical instruments, is a novel tool for the investigation of complex biological systems at the whole-tissue level. In this study, samples from tomato fruit grown hydroponically under both high- and low-salt conditions were analysed using Fourier-transform infrared spectroscopy (FTIR), with the aim of identifying spectral and biochemical features linked to salinity in the growth environment. FTIR spectra of whole tissue extracts are not amenable to direct visual analysis, so numerical modelling methods were used to generate models capable of classifying the samples based on their spectral characteristics. Genetic programming (GP) provided models with a better prediction accuracy to the conventional data modelling methods used, whilst being much easier to interpret in terms of the variables used. Examination of the GP-derived models showed that there were a small number of spectral regions that were consistently being used. In particular, the spectral region containing absorbances potentially due to a cyanide/nitrile functional group was identified as discriminatory. The explanatory power of the GP models enabled a chemical interpretation of the biochemical differences to be proposed. The combination of FTIR and GP is therefore a powerful and novel analytical tool that, in this study, improves our understanding of the biochemistry of salt tolerance in tomato plants.

Nonlinear dielectric spectroscopy (NLDS) was used to detect interaction of a pulsed magnetic field (PMF) with membrane protein dynamics in aggregating Dictyostelium discoideum amoebae. In the experiments reported here, a strong nonlinear dielectric response of Dictyostelium discoideum cells is shown, and a distinctive nonlinear dielectric response of cells previously exposed to PMF is shown. The method of NLDS is shown to be capable of monitoring and charting the dynamic frequency response of the cell to an electromagnetic field. Bioelectromagnetics 21:25‐33, 2000. fl 2000 Wiley-Liss, Inc.

Closed loop aptameric directed evolution, (CLADE) is a technique enabling simultaneous discovery, evolution, and optimization of aptamers. It was previously demonstrated using a fluorescent protein, and here we extend its applicability with the generation of surface-bound aptamers for targets containing no natural fluorescence. Starting from a random population, in four generations CLADE produced a new aptamer to thrombin with high specificity and affinity. The best aptameric sequence was void of the set of four guanine repeats typifying thrombin aptamers and, thus, highlights the benefits of evolution performed in an environment closely mimicking the final diagnostic application.

Thirty-six strains of aerobic endospore-forming bacteria confirmed by polyphasic taxonomic methods to belong to Bacillus amyloliquefaciens, Bacillus cereus, Bacillus licheniformis, Bacillus megaterium, Bacillus subtilis (including Bacillus niger and Bacillus globigii), Bacillus sphaericus, and Brevi laterosporus were grown axenically on nutrient agar, and vegetative and sporulated biomasses were analyzed by Curie-point pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier-transform infrared spectroscopy (FT-IR). Chemometric methods based on rule induction and genetic programming were used to determine the physiological state (vegetative cells or spores) correctly, and these methods produced mathematical rules which could be simply interpreted in biochemical terms. For PyMS it was found that m/z 105 was characteristic and is a pyridine ketonium ion (C6H3ON+) obtained from the pyrolysis of dipicolinic acid (pyridine-2,6-dicarboxylic acid; DPA), a substance found in spores but not in vegetative cells; this was confirmed using pyrolysis-gas chromatography/mass spectrometry. In addition, a pyridine ring vibration at 1447-1439 cm-1 from DPA was found to be highly characteristic of spores in FT-IR analysis. Thus, although the original data sets recorded hundreds of spectral variables from whole cells simultaneously, a simple biomarker can be used for the rapid and unequivocal detection of spores of these organisms.

We describe the ® rst application of dispersive Raman spectroscopy using a diode laser exciting at 780 nm and a charge-coupled device (CCD) detector to the noninvasive, on-line determination of the bio- transformation by yeast of glucose to ethanol. Software was devel- oped which automatically removed the effects of cosmic rays and other noise, normalized the spectra to an invariant peak, then re- moved the ``baseline'' arising from interference byuorescent im- purities, to obtain the ``true'' Raman spectra. Variable selection was automatically performed on the parameters of relevant Raman peaks (height, width, position of top and center, area and skewness), and a small subset used as the input to cross-validated models based on partial least-squares (PLS) regression. The multivariate calibra- tion models so formed were suf® ciently robust to be able to predict the concentration of glucose and ethanol in a completely different fermentation with a precision better than 5%. Dispersive Raman spectroscopy, when coupled with the appropriate chemometrics, is a very useful approach to the noninvasive, on-line determination of the progress of microbial fermentations. Index Headings: Raman spectroscopy; On-line monitoring; Chemo- metric methods; Bioinformatics; Fermentation; Variable selection.

Distortionless enhancement by polarization transfer (DEPT) pulse sequence was used to set up a quantitative high-resolution 13 C nuclear magnetic resonance (NMR) method to discriminate olive oils by cultivars and geographical origin. DEPT pulse sequence enhances the intensity of NMR signals from nuclei of low magnetogyric ratio. The nuclear spin polarization is transferred from spins with large Boltzmann population differences (usually protons) to nuclear species characterized by low Boltzmann factors, e.g., 13 C. The signal enhancement of 13 C spectra ensures the accuracy of resonance integration, which is a major task when the resonance intensities of different spectra must be compared. The resonances of triglyceride acyl chains C n:0 , C 18:1 , C 18:2 , and C 18:3 , were also assigned. Multivariate analysis was carried out on the 35 carbon signals obtained. By using variable reduction techniques, coupled with standard statistical methods-partial least squares and principal components analysis-it was largely possible to separate the samples according to their variety and region of origin. With one problem variety removed, 100% prediction of the three remaining varieties was achieved. Similarly, by using the three regions with greatest representation in the data, all but one of a test set of 34 samples were correctly predicted. Thus, the composition of olive oils from different cultivars and of different geographical origin were compared and successfully studied by multivariate analysis. These considerations in conjunction with the structural elucidations of triglyceride molecules demonstrated that 13 C NMR is among the most powerful techniques yet described for analysis of olive oils.

Cell pastes and supernatant Escherichia coli samples, taken from an industrial bioprocess overproducing recombinant alpha 2 IFN were analysed using pyrolysis mass spectrometry (PyMS) and diffuse reflectance-absorbance Fourier transform infrared spectroscopy (FT-IR). PyMS and FT-IR are physico-chemical methods which measure predominantly the bond strengths of molecules and the vibrations of bonds within functional groups, respectively. They therefore give quantitative information about the total biochemical composition of the bioprocess sample. The interpretation of these hyperspectral data, in terms of the quantity of alpha 2 IFN in the cell pastes and supernatant samples was possible only after the application of the 'supervised learning' methods of artificial neural networks (ANNs) and partial least squares (PLS) regression. Both PyMS and FT-IR are novel, rapid and economical methods for the screening and the quantitative analysis of complex biological bioprocess over producing recombinant proteins. Models established using either spectral data set had a similarly satisfactory predictive ability. This shows that whole-reaction mixture spectral methods, which measure all molecules simultaneously, do contain enough information to allow their quantification when the entire spectra are used as the inputs to methods based on supervised learning. Moreover, this is the first study where FT-IR in the mid-IR range has been used to quantify the expression of a heterologous protein directly from fermentation broths and the first study to compare the abilities of PyMS and FT-IR for the quantitative analyses of an industrial bioprocess.

We report the first application of electrospray ionisation mass spectrometry (ESI-MS) for the reproducible characterisation of strains of intact Gram-negative and Gram-positive bacteria. Electrospray ionisation was performed in both the positive and negative ion modes and the spectra obtained from Escherichia coli and Bacillus cereus were very information rich. Several of the observed negative mass ion fragments from E. coli could be assigned to specific fragmentation from bacterial phospholipids. Cluster analyses of these spectra showed that ESI-MS could be used to discriminate between these microorganisms to below species level. Therefore we conclude that ESI-MS constitutes a powerful approach to the characterisation and speciation of intact microorganisms. z 1999 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

By modelling the non-linear effects of membranous enzymes on an applied oscillating electromagnetic field using supervised multivariate analysis methods, Non-Linear Dielectric Spectroscopy (NLDS) has previously been shown to produce quantitative information that is indicative of the metabolic state of various organisms. The use of Genetic Programming (GP) for the multivariate analysis of NLDS data recorded from yeast fermentations is discussed, and GPs are compared with previous results using Partial Least Squares (PLS) and Artificial Neural Nets (NN). GP considerably outperforms these methods, both in terms of the precision of the predictions and their interpretability.

Background: When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles. Materials and Methods: We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification. Results: We show that while all cocktails and all methods gave good accuracy of predictions (G94%), careful selection of both the stains and the analysis method could improve this figure (to G99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model. Conclusions: Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis. Cytometry 35:162‐168, 1999. r 1999 Wiley-Liss, Inc.

A rheometrical study reveals that mayonnaise exhibits apparent wall slip in both small amplitude oscillatory shear flow and torsional flow. Whereas in the case of small amplitude oscillatory shear classical techniques are applicable for extracting bulk material properties, this is not the case for torsional flow. Progressively increasing the shear stress in torsional flow manifests another flow phenomenon that dominates the slip process. It is argued that “particles” within the mayonnaise migrate radially towards the center of the geometry. Spectroscopic techniques are employed to confirm that, following an experiment, material at the rim of the geometry differs from that towards the center. Experiments performed in a cone-and-plate geometry yield results that are qualitatively similar to those obtained in torsional flow tests thus indicating that the migration is not due solely to the radial shear-rate gradient that is present in torsional flow.

Electrode polarisation can interfere significantly with the measurement of the dielectric properties of biological systems, and in particular with the dielectric estimation of microbial biomass. We show that electrode polarisation can be modelled by a power law and that the parameters of this model can be simply modified to give reliable and intuitive measures of the magnitude ( L C p ) and rate of fall of polarisation with increasing frequency ( h f ). Importantly and surprisingly, although the magnitude of electrode polarisation depends strongly on the conductance of the medium and the material of the electrodes, above a certain value of medium conductance (of some 3–7 mS, conductivity=3.7–8.7 mS cm−1), the value of h f is a constant and independent of the conductance of the medium. It is also independent of the electrode metal in the probes, which probe was used, the ionic and other components in the medium, the electrode interface current density, the cell constant, the presence of fouling materials and whether or not any electrolytic cleaning pulses had been applied. This permits one simply to correct for the electrode polarisation present in dielectric spectra and which may sometimes be seen during dielectric biomass measurements.

This article describes the use of chemometric methods for prediction of biological parameters of cell suspensions on the basis of their light scattering profiles. Laser light is directed into a vial or flow cell containing media from the suspension. The intensity of the scattered light is recorded at 18 angles. Supervised learning methods are then used to calibrate a model relating the parameter of interest to the intensity values. Using such models opens up the possibility of estimating the biological properties of fermentor broths extremely rapidly (typically every 4 sec), and, using the flow cell, without user interaction. Our work has demonstrated the usefulness of this approach for estimation of yeast cell counts over a wide range of values (10(5)-10(9) cells mL-1), although it was less successful in predicting cell viability in such suspensions.