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Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

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Author: Werf, M.J. van der · Pieterse, B. · Luijk, N. van · Schuren, F. · Werff van der - Vat, B. van der · Overkamp, K. · Jellema, R.H.
Type:article
Date:2006
Institution: TNO Kwaliteit van Leven
Source:Microbiology, 1, 152, 257-272
Identifier: 239072
doi: doi:10.1099/mic.0.28278-0
Keywords: Biology · Biotechnology · bacterial RNA · cytochrome · fructose · gluconic acid · glucose · glucose dehydrogenase · iron · quinone derivative · succinic acid · article · bacterial gene · bacterium culture · carbohydrate metabolism · carbon source · cluster analysis · controlled study · discriminant analysis · down regulation · fermentation medium · fructose metabolism · gene identification · genetic transcription · glucose metabolism · intermethod comparison · metabolic regulation · microarray analysis · molecular cloning · multivariate analysis · nonhuman · plots and curves · principal component analysis · priority journal · Pseudomonas putida · regulatory mechanism · respiratory chain · RNA analysis · RNA isolation · transcriptomics · upregulation · Carbohydrate Metabolism · Culture Media · Genes, Bacterial · Multivariate Analysis · Oligonucleotide Array Sequence Analysis · Pseudomonas putida · Bacteria (microorganisms) · Pseudomonas putida

Abstract

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.