Rapid quantitative analysis of binary mixtures of Escherichia coli strains using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks

Pyrolysis mass spectrometry (PyMS) and multivariate calibration were used to show the high degree of relatedness between Escherichia coli HB101 and E. coli UB5201. Next, binary mixtures of these two phenotypically closely related E. coli strains were prepared and subjected to PyMS. Fully interconnected feedforward artificial neural networks (ANNs) were used to analyse the pyrolysis mass spectra to obtain quantitative information representative of the level of E. coli UB5201 in E. coli HB101. The ANNs exploited were trained using the standard back propagation algorithm, and the nodes used sigmoidal squashing functions. Accurate quantitative information was obtained for mixtures with >3% E. coli UB5201 in E. coli HB101. To remove noise from the pyrolysis mass spectra and so lower the limit of detection, the spectra were reduced using principal components analysis (PCA) and the first 13 principal components used to train ANNs. These PCA-ANNs allowed accurate estimates at levels as low as 1% E. coli UB5201 in E. coli HB101 to be predicted. In terms of bacterial numbers, it was shown that the limit of detection for PyMS in conjunction with ANNs was 3 × 10⁴ E. coli UB5201 cells in 1·6 × 10⁷ E. coli HB101 cells. It may be concluded that PyMS with ANNs provides a powerful and rapid method for the quantification of mixtures of closely related bacterial strains.     

Rapid authentication of animal cell lines using pyrolysis mass spectrometry and auto-associative artificial neural networks

Summary Pyrolysis mass spectrometry (PyMS) was used to produce biochemical fingerprints from replicate frozen cell cultures of mouse macrophage hybridoma 2C11-12, human leukaemia K562, baby hamster kidney BHK 21/C13, and mouse tumour BW-O, and a fresh culture of Chinese hamster ovary CHO cells. The dimensionality of these data was reduced by the unsupervised feature extraction pattern recognition technique of auto-associative neural networks. The clusters observed were compared with the groups obtained from the more conventional statistical approaches of hierarchical cluster analysis. It was observed that frozen and fresh cell line cultures gave very different pyrolysis mass spectra. When only the frozen animal cells were analysed by PyMS, auto-associative artificial neural networks (ANNs) were employed to discriminate between them successfully. Furthermore, very similar classifications were observed when the same spectral data were analysed using hierarchical cluster analysis. We demonstrate that this approach can detect the contamination of cell lines with low numbers of bacteria and fungi; this approach could plausibly be extended for the rapid detection of mycoplasma infection in animal cell lines. The major advantages that PyMS offers over more conventional methods used to type cell lines and to screen for microbial infection, such as DNA fingerprinting, are its speed, sensitivity and the ability to analyse hundreds of samples per day. We conclude that the combination of PyMS and ANNs can provide a rapid and accurate discriminatory technique for the authentication of animal cell line cultures.     

Rapid screening for metabolite overproduction in fermentor broths, using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks

Binary mixtures of model systems consisting of the antibiotic ampicillin with either Escherichia coli or Staphylococcus auresu were subjected to pyrolysis mass spectrometry (PyMS). To deconvolute the pyrolysis mass spectra, so as to obtain quantitative information on the concentration of ampicilin in the mixtures, partial least squares regression (PLS), principal components regression (PCR), and fully interconnected feedforward artificial neural networks (ANNs) were studied. In the latter case, the weights were modified using the standard backpropagation algorithm, and the nodes used a sigmoidal squsahing funciton. It was found that each of the methods could be used to provide calibration models which gave excellent predictions for the concentrations of ampicillin in samples on which they had not been trained. Furthermore, ANNs trained to predict the amount of ampicilin in E. coli were able to generalise so as to predict the concentration of ampicillin in a S. aureus background, illustrating the robustness of ANNs to rather substantial variations in the biological background. The PyMS of the complex mixture of ampicilin in bacteria could not be expressed simply in terms of additive combinations of the spectra describing the pure components of the mixtures and their relative concentrations. Intermolecular reactions took place in the pyrolysate, leading to a lack of superposition of the spectral components and to a dependence of the normalized mass spectrum on sample size. Samples from fermentations of a single organism in a complex production medium were also analyzed quantitatively for a drug of commercial interest. The drug could also be quantified in a variety of mutant-producing strains cultivated in the same medium. The combination of PyMS and ANNs constitutes a novel, rapid, and convenient method for exploitation in strain improvement screening programs. (c) 1994 John Wiley & Sons, Inc.     

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry.

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.     

Flow infusion electrospray ionisation mass spectrometry for high throughput,non-targeted metabolite fingerprinting: a review

Producing a comprehensive overview of the chemical content of biologically-derived material is a major challenge. Apart from ensuring adequate metabolome coverage and issues of instrument dynamic range, mass resolution and sensitivity, there are major technical difficulties associated with data pre-processing and signal identification when attempting large scale, high-throughput experimentation. To address these factors direct infusion or flow infusion electrospray mass spectrometry has been finding utility as a high throughput metabolite fingerprinting tool. With little sample pre-treatment, no chromatography and instrument cycle times of less than 5 min it is feasible to analyse more than 1,000 samples per week. Data pre-processing is limited to aligning extracted mass spectra and mass-intensity matrices are generally ready in a working day for a month’s worth of data mining and hypothesis generation. ESI-MS fingerprinting has remained rather qualitative by nature and as such ion suppression does not generally compromise data information content as originally suggested when the methodology was first introduced. This review will describe how the quality of data has improved through use of nano-flow infusion and mass-windowing approaches, particularly when using high resolution instruments. The increasingly wider availability of robust high accurate mass instruments actually promotes ESI-MS from a merely fingerprinting tool to the ranks of metabolite profiling and combined with MS/MS capabilities of hybrid instruments improved structural information is available concurrently. We summarise current applications in a wide range of fields where ESI-MS fingerprinting has proved to be an excellent tool for “first pass” metabolome analysis of complex biological samples. The final part of the review describes a typical workflow with reference to recently published data to emphasise key aspects of overall experimental design.     

Investigating alginate production and carbon utilization in Pseudomonas fluorescens SBW25 using mass spectrometry-based metabolic profiling

Metabolic profiling of Pseudomonas fluorescens SBW25 and various mutants derived thereof was performed to explore how the bacterium adapt to changes in carbon source and upon induction of alginate synthesis. The experiments were performed at steady-state conditions in nitrogen-limited chemostats using either fructose or glycerol as carbon source. Carbon source consumption was up-regulated in the alginate producing mutant with inactivated anti-sigma factor MucA. The mucA- mutants (also non-alginate producing mucA- control strains) had a higher dry weight yield on carbon source implying a change in carbon and energy metabolism due to the inactivation of the anti-sigma factor MucA. Both LC–MS/MS and GC–MS methods were used for quantitative metabolic profiling, and major reorganization of primary metabolite pools in both an alginate producing and a carbon source dependent manner was observed. Generally, larger changes were observed among the phosphorylated glycolytic metabolites, the pentose phosphate pathway metabolites and the nucleotide pool than among amino acids and citric acid cycle compounds. The most significant observation at the metabolite level was the significantly reduced energy charge of the mucA- mutants (both alginate producing and non-producing control strains) compared to the wild type strain. This reduction was caused more by a strong increase in the AMP pool than changes in the ATP and ADP pools. The alginate-producing mucA- mutant had a slightly increased GTP pool, while the GDP and GMP pools were strongly increased compared to non-producing mucA- strains and to the wild type. Thus, whilst changes in the adenosine phosphate nucleotide pool are attributed to the mucA inactivation, adjustments in the guanosine phosphate nucleotide pool are consequences of the GTP-dependent alginate production induced by the mucA inactivation. This metabolic profiling study provides new insight into carbon and energy metabolism of the alginate producer P. fluorescens.     

High-throughput metabolic screening of microalgae genetic variation in response to nutrient limitation

Metabolomics - Microalgae produce metabolites that could be useful for applications in food, biofuel or fine chemical production. The identification and development of suitable strains require...     

Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait

Introduction

Past studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM).

Objectives

Here we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible.

Methods

Four NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography–time of flight-mass spectrometry (GC–TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.

Results

Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices.

Conclusion

The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

     

Proposed minimum reporting standards for chemical analysis

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at or . Further, community input related to this document can also be provided via this electronic forum. The contents of this paper do not necessarily reflect any position of the Government or the opinion of the Food and Drug Administration Sponsor: Metabolomics Society http://www.metabolomicssociety.org/ Reference: http://msi-workgroups.sourceforge.net/bio-metadata/reporting/pbc/ http://msi-workgroups.sourceforge.net/chemical-analysis/ Version: Revision: 5.1 Date: 09 January, 2007     

Investigating plant-plant interference by metabolic fingerprinting

New analytical developments in post-genomic technologies are being introduced to the field of plant ecology. FT-IR fingerprinting coupled with chemometrics via cluster analysis is proposed as a tool for correlating global metabolic changes with abiotic or biotic perturbation and/or interactions. The current study concentrates on detecting chemical responses by inter-species competition between a monocotyledon Brachypodium distachyion and a dicotyledon Arabidopsis thaliana. Growth analysis of 42 days old plants showed differences in both species under competition. Clear changes in the FT-IR metabolic fingerprints of B. distachyion in competition with A. thaliana were observed, whilst there were no apparent chemical differences in the A. thaliana plant tissues. This study demonstrates the power of this approach in detecting changes in the global metabolic profiles of plants in response to biotic interactions, and we believe FT-IR is appropriate for rapid screening (10 s per sample) prior to targeted metabolite analyses.