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Microbial metabolomics: Replacing trial-and-error by the unbiased selection and ranking of targets

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Author: Werf, M.J. van der · Jellema, R.H. · Hankemeier, T.
Institution: TNO Kwaliteit van Leven
Source:Journal of Industrial Microbiology and Biotechnology, 6, 32, 234-252
Identifier: 238532
doi: doi:10.1007/s10295-005-0231-4
Keywords: Nutrition · Biotechnology · Bioprocess optimization · Biostatistics · Holistic analysis · Metabolic engineering · Metabolomics · Multivariate statistics · Quenching · Strain improvement · accuracy · algorithm · analytic method · article · bioinformatics · bioprocess · biostatistics · data base · DNA modification · extraction · information processing · interstitial fluid · metabolic engineering · metabolite · metabolomics · microorganism · molecular weight · nonhuman · observer variation · reproducibility · separation technique · statistical analysis · strain difference · validation process · Bacteria · Computational Biology · Energy Metabolism · Genomics · Industrial Microbiology · Proteomics


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.