Metabolomics and transcriptomics

Toghe T, Nishiyama Y, Hirai MY, Yano M, Nakajima J, Awazuhara M, Inoue E, Takahashi H, Goodenowe DB, Kitayama M, Noji M, Yamazaki M, Saito K. 2005. Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J42 218-235. 

Kristensen, C., Morant, M., Olsen, C.E., Ekstrom, C.T., Galbraith, D.W., Mailer, B.L., and Bak, S. (2005) Metabolic engineering of dhurrin in transgenic Arabidopsis plants with marginal inadvertent effects on the metabolome and transcriptome. PNAS -Proc. Natl. Acad. Sci, 102,1779-84. 

Figure 1 shows a timeline of a PubMed search for papers sharing the terms metabolomics and transcriptomics from 2000 until 2012. The amount has increased steadily and may increase further because both technologies can be applied today on a routine basis. 

FIGURE 1 Timeline for PubMed search using the keywords metabolomics and transcriptomics. Amount of publications using a combination of both technologies was steadily increasing since 2000. Both types of analysis can be used in routine analysis therefore, in the near future a vast amount of combined efforts can be expected. 

After preprocessing the different data types, they are ready for data fusion. Two different types of data fusion between metabolome and transcriptome data can be distinguished. Low-level fusion combines raw data of both data types to produce new raw data. In contrast to this high-level fusion, results from independent data analysis are merged for combined interpretation. The latter is the case for often-used tools such as overrepresentation or enrichment analysis. 

If correlation analysis is used with the raw data, ideally both data types should have similar ranges and distributions. If data is directly linearly correlated, this can be neglected, but is rarely the case for metabolome and transcriptome data. Changes in gene expression may not alter metabolite pools significantly. Therefore, data have to be normalized in an appropriate way and correlation methods other than linear correlation have to be used (e.g., Spearman s rank-order correlation or Kendall rank correlation should be preferred over Pearson correlation). 

FIGURE 5 (A) Workflow for metabolomics and transcriptomic data. Results from both data types are mapped together on metabolic pathways obtained from... 

B., Schomburg, D., Kramer, R., and Burkovski, A. (2009) A combination of metabolome and transcriptome analyses reveals new targets of the Corynebacterium glutamicum nitrogen regulator AmtR. /. Biotechnol, 140 (1-2), 68 -74. 

Tohge T et al (2005) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant 142 218-235... 

Nakamura, Y, Kitayama, M., Suzuki, H., Sakurai, N., Shibata, D., Tokuhisa, J., Reichelt, M., Gershenzon, J., Papenbrock, J., and Saito, K. 2005. Elucidation of gene-to-gene and metabolite-to-gene networks in arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem 280 25590-25595. 

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