Proteome-Centre Rostock,
Joachim-Jungius-Str. 9,
18059 Rostock
Email: {Steffen.Moeller, Michael.Glocker, Hans-Jürgen.Thiesen}@med.uni.rostock.de
In order to reliably characterise aberrations in the transcriptome or proteome of disease tissues, a large number of independent samples must be analysed. While microarray studies and 2D-gels in conjunction with mass spectroscopy have proven most successful for an initial screening for disease-characteristic genes, the inherent costs of these methods usually prohibit their utilisation as a means for long term studies or population-wide screening.
The challenge is to find a subset of gene products that based upon the prior applied screening (by transcriptomics or proteomics), and in conjunction with knowledge from molecular pathways, one hopes to reliably distinguish between disease and non-disease states. This will be carried out by an extensive study, also incorporating additional measurements of gene expression rates in the samples. Other considerations to be taken into account are weaknesses of previously mentioned methods for gene products in low abundance and an increased dynamic range for quantification.
To study of RNA expression rates, the Proteome-Centre Rostock incorporates information from high-density array oligo nucleotide chips [1], data from Real-Time PCR [2], and "virtual" Northern blots. Information from RNAse-protection-assays or customised arrays can also be incorporated. Protein expression levels are derived from the normalised spot volumes on 2D gels is integrated with information from extensive Western-blot analyses [3].
In order to generate hypotheses, implicating a gene in a particular disease state, both the absolute expression levels and relative change in expression between control and disease tissue are compared. The optimal classifiers are those that distinguish between disease and non-disease states, are highly specific, and employ genes whose expression levels can be reliably measured. The genes should demonstrate a clear change in expression and these changes should be validated by different means, in many independent samples. The disease-related genes should be supported by good correlation-coefficients for both expression rates and fold-changes across the different means of measurement and should be investigated as potential drug targets.