Parallel analysis of transcript and metabolic profiles: a new approach in systems biology

Abstract

The past few years in the medical and biological sciences have been characterized by the advent of systems biology. However, despite the well-known connectivity between the molecules described by transcriptomic, proteomic and metabolomic approaches, few studies have tried to correlate parameters across the various levels of systemic description. When comparing the discriminatory power of metabolic and RNA profiling to distinguish between different potato tuber systems, using the techniques described here suggests that metabolic profiling has a higher resolution than expression profiling. When applying pairwise transcript-metabolite correlation analyses, 571 of the 26,616 possible pairs showed significant correlation, most of which was novel and included several strong correlations to nutritionally important metabolites. We believe this approach to be of high potential value in the identification of candidate genes for modifying the metabolite content of biological systems.

Figure 1

Principal-component analysis of gene-expression and metabolite profiles of genetically and temporally distinct systems. (A) Gene-expression profiles. (B) Metabolite profiles. The distances between these populations were calculated as described in Roessner et al. (2001). The percentage of variance explained by each component is shown in parentheses. The transgenic lines INV2-30 (INV) and SP 29 (SP) are represented by black circles, and wild-type samples harvested 8, 9, 10, 13 and 14 weeks after transfer to the greenhouse are represented by coloured circles. Each data point represents an independent sample.

Figure 2

Correlation between metabolite and transcript levels of the analysed systems. All correlations between the relative levels of chemically defined metabolites and those of each selected clone were assessed. All correlations at p = 0.01 when assessed by Spearman's rank-order correlation are given in the supplementary information online. Several representative examples are given here (all are plotted using the logarithmic scale). r_s-values are given in parentheses. (A) Sucrose transporter versus sucrose (r_s = −0.52). (B) glutamate decarboxylase versus 4-aminobutric acid (r_s = 0.55). (C) Tryptophan synthase β-chain 1 versus tryptophan (r_s = 0.61). (D) Tryptophan synthase β-chain 1 versus tyrosine (r_s = 0.65). (E) Ornithine carbamoyltransferase versus serine (r_s = 0.53). (F) Ornithine carbamoyltransferase versus cysteine (r_s = 0.54). (G) Aminotransferase-like protein versus fructose-6-phosphate (r_s = 0.85). (H) Aminotransferase-like protein versus glucose-6-phosphate (r_s = 0.85). (I) Glutamate decarboxylase versus spermidine (r_s = 0.64). (J) Glutamate decarboxylase versus tyrosine (r_s = 0.63). (K) CONSTANS-like protein versus ascorbate (r_s = −0.72). (L) Succinyl-co-enzyme-A synthetase α-subunit versus tocopherol (r_s = −0.64). (M) Transcription factor WRKY6 versus lysine (r_s = 0.51). (N) S-adenosyl-L-methionine synthetase versus lysine (r_s = 0.6). (O) Ornithine carboxyltransferase versus lysine (r_s = 0.54). (P) Caffeoyl-co-enzyme-A O-methyltransferase versus lysine (r_s = −0.52).