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Messenger RNA levels change on a minutes scale due to both degradation and de novo transcription. Consequently, alterations in the transcript profiles that are not representative for the condition of interest are easily introduced during sample harvesting and work-up. In order to avoid these unwanted changes we have validated a - 45°C methanol-based quenching method for obtaining reliable and reproducible 'snapshot' samples of Lactobacillus plantarum cells for transcriptome analyses. Transcript profiles of cells harvested with the quenching method were compared with transcript profiles of cells that were harvested according to two different commonly applied protocols. Significant differences between the transcript profiles of cells harvested by the different methods from the same steady-state culture were observed. In total, 42 genes or operons were identified from which the transcript levels were altered when the cells were not immediately quenched upon harvesting. Among these, several have previously been associated with cold-shock response. Furthermore, the reproducibility of transcript profiles improved, as indicated by the fact that the variation in the data sets obtained from the quenched cells was smaller than in the data sets obtained from the cells that were harvested under non-quenched conditions. © 2005 Elsevier B.V. All rights reserved.

The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. RNA isolated from these samples was analysed in duplicate on an anonymous clone-based array to avoid bias during data analysis. The relevant transcripts were identified by analysing the loadings of the principal components (PC) and discriminants (D) in PCA and PCDA, respectively. Even more specifically, the relevant transcripts for a specific phenotype could also be ranked from the loadings under an angle (biplot) obtained after PCDA analysis. The leads identified in this way were compared with those identified using the commonly applied fold-difference and hierarchical clustering approaches. The different data analysis methods gave different results. The methods used were complementary and together resulted in a comprehensive picture of the processes important for the different carbon sources studied. For the more subtle, regulatory processes in a cell, the PCDA approach seemed to be the most effective. Except for glucose and gluconate dehydrogenase, all genes involved in the degradation of glucose, gluconate and fructose were identified. Moreover, the transcriptomics approach resulted in potential new insights into the physiology of the degradation of these carbon sources. Indications of iron limitation were observed with cells grown on glucose, gluconate or succinate but not with fructose-grown cells. Moreover, several cytochrome- or quinone-associated genes seemed to be specifically up- or downregulated, indicating that the composition of the electron-transport chain in P. putida S12 might change significantly in fructose-grown cells compared to glucose-, gluconate- or succinate-grown cells. © 2006 SGM.

Microbial production strains are currently improved using a combination of random and targeted approaches. In the case of a targeted approach, potential bottlenecks, feed-back inhibition, and side-routes are removed, and other processes of interest are targeted by overexpressing or knocking-out the gene(s) of interest. To date, the selection of these targets has been based at its best on expert knowledge, but to a large extent also on 'educated guesses' and 'gut feeling'. Therefore, time and thus money is wasted on targets that later prove to be irrelevant or only result in a very minor improvement. Moreover, in current approaches, biological processes that are not known to be involved in the formation of a specific product are overlooked and it is impossible to rank the relative importance of the different targets postulated. Metabolomics, a technology that involves the non-targeted, holistic analysis of the changes in the complete set of metabolites in the cell in response to environmental or cellular changes, in combination with multivariate data analysis (MVDA) tools like principal component discriminant analysis and partial least squares, allow the replacement of current empirical approaches by a scientific approach towards the selection and ranking of targets. In this review, we describe the technological challenges in setting up the novel metabolomics technology and the principle of MVDA algorithms in analyzing biomolecular data sets. In addition to strain improvement, the combined metabolomics and MVDA approach can also be applied to growth medium optimization, predicting the effect of quality differences of different batches of complex media on productivity, the identification of bioactives in complex mixtures, the characterization of mutant strains, the exploration of the production potential of strains, the assignment of functions to orphan genes, the identification of metabolite-dependent regulatory interactions, and many more microbiological issues. © Society for Industrial Microbiology 2005.