Literature DB >> 15895265

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

Mariët J van der Werf1, Renger H Jellema, Thomas Hankemeier.   

Abstract

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.

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Year:  2005        PMID: 15895265     DOI: 10.1007/s10295-005-0231-4

Source DB:  PubMed          Journal:  J Ind Microbiol Biotechnol        ISSN: 1367-5435            Impact factor:   3.346


  78 in total

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Review 9.  Microbial natural products as a source of antifungals.

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  31 in total

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6.  Central carbon metabolism in Mycobacterium tuberculosis: an unexpected frontier.

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7.  The undernourished neonatal mouse metabolome reveals evidence of liver and biliary dysfunction, inflammation, and oxidative stress.

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Review 8.  LC-MS-based metabolomics in drug metabolism.

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Journal:  Drug Metab Rev       Date:  2007       Impact factor: 4.518

Review 9.  Translational research in infectious disease: current paradigms and challenges ahead.

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Journal:  Microb Cell Fact       Date:  2012-03-08       Impact factor: 5.328

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