KEGG pathway Proteins with
detected P-sites Proteins with
regulated P-sites p-value Insulin signaling
pathway 52 19 0.0002
MAPK signaling
pathway 74 21 0.004
mTOR signaling
pathway 22 9 0.005
ErbB signaling
pathway 40 13 0.007
Axon guidance 34 10 0.04
Prostate cancer 21 7 0.04
Nonsmall cell lung
cancer 17 6 0.04
53 Analysis of Phosphoproteomics Data
ribosomal protein S6 (UniProt id P62753). Whereas five of them, all between amino acid position 235 and 242, are downregulated, two of them, at 244 and 246, are upregulated. Furthermore, KEGG, like other publicly available pathway resources, cover the pathways not in all details. In case of the mTOR pathway, we therefore draw the essential parts of the pathway based on the current literature (e.g., (32) using Inkscape http://www.inkscape.
org, see Fig. 6). The identified phosphorylation sites can then be added to the protein nodes as small ellipses labeled by the posi-tion and colored by regulaposi-tion factor. This allows capturing all details of the regulated pathway and supports the understanding of the mode of action of the substance. Mapping of the regulated phosphorylation sites to signal transduction pathways reveals that sorafenib treatment leads to severe downregulation of the MAP kinase pathway in PC3 cells. In addition, several other pathways are deregulated. In particular, the mTOR pathway is significantly affected by sorafenib in PC3 cells. Obviously, these hypotheses have to be validated with independent technologies that confirm the downstream effect on transcription or translation.
Fig. 5. KEGG MAPK signaling pathway (31). Proteins with detected phosphorylation sites are colored dark gray, if they are downregulated after the treatment with sorafenib, and light gray if they are not regulated at all. See the online version of this chapter for a colored figure.
54 Schaab
The analysis of global phosphoproteomes is a relatively new field within bioinformatics. In the last few years, technical advances have led to a steady increase in the number of detectable phos-phorylation sites. It has recently become possible to detect and quantify 6,600 sites (8) or even 16,000 sites (5) in a single experi-ment. The processing of phosphoproteomics raw data requires software that combines standard search engines, such as Mascot and Sequest, with specialized algorithms for the identification of phosphorylated peptides, the localization of the phosphorylation sites, and their quantification. Examples of such software are MSQuant, SuperHirn, and MaxQuant.
We have seen that many of the methods that have been devel-oped for gene expression analysis can also be applied to the down-stream analysis of phosphoproteomics data. Additional methods that take the particular nature of the data into account have been developed, e.g., the enrichment of kinase motifs in the set of dif-ferential phosphorylated sites.
Unlike genetic mutational analysis or gene expression analysis that measure surrogates only, phosphoproteome analysis directly measures the signaling activity in the cell. Therefore, phosphop-roteome analysis will be a valuable tool whenever effects on
4. Conclusion
Fig. 6. mTOR pathway with identified phosphorylation sites. Sites, that are downregulated after the treatment with sorafenib, depicted as ellipses, upregulated sites as rectangles. See the online version of this chapter for a colored figure.
55 Analysis of Phosphoproteomics Data
cellular signaling activity are studied. For example, such an analysis may reveal the mode of action of drugs that inhibit certain kinases.
Or, more visionary, such an analysis may discover biomarker sig-natures that allow to predict the optimal targeted therapy for a patient (personalized medicine, see (6)).
1. The peptide ion counts depend not only on the peptide concentration but on a number of additional parameters, such as the ionization efficiency, the elution behavior in the nanoLC, and the enrichment efficiency. These parameters differ for different peptides. Thus, two different peptides with identical concentration in the sample may have very different ion counts in the MS. On the other hand, these parameters do not differ for chemically identical peptides of different iso-tope composition. Thus, labeling methods, such as SILAC or iTRAQTM, allow the relative quantification of a peptide in dif-ferent samples, whereas absolute quantification is impossible in principle (see however Note 2). The situation is analogous to the situation with microarray-based gene expression data, where due to the differences in the hybridization efficiency only comparisons between samples rather than between fea-tures are possible.
2. If defined amounts of synthetically produced, isotopically labeled peptides are spiked into the samples, absolute quanti-fication of the corresponding natural peptides is possible (33).
3. If only a certain set of phosphopeptides is to be analyzed, one can use so-called targeted approaches to improve the cover-age of this set. This includes the use of inclusion lists (34) or MRM-based methods (35).
4. Depending on the quality of the MS/MS spectra, it is not always possible to assign the phosphorylation to a specific amino acid. MaxQuant calculates the localization probability that the given amino acid is indeed the one that is phospho-rylated. It often makes sense to restrict oneself to phosphory-lation sites that are identified and localized with high confidence. Therefore, so-called class I sites are defined as the ones that have a localization probability of at least 75% and a score difference of at least 5 (8).
5. A very different approach has been taken by Zhou et al. (30) who proposed a “global rank test” for microarray data. Here, the sites are ranked by ratios within each replicate. Sites that are consistently ranked top or bottom T are identified as
5. Notes
56 Schaab
differentially phosphorylated sites. The parameter T is fixed by an appropriate FDR that is estimated parametrically or based on permutations. A nice feature of this test procedure is that the FDR actually decreases with the number of tested sites. Standard FDR procedures show the opposite behavior.
6. There are a number of databases containing signal transduc-tion pathways, including KEGG (http://www.genome.jp/
kegg/pathway.html), BioCarta (http://www.biocarta.com/), and PANTHER (http://www.pantherdb.org/pathway/). By identifying pathways in which differentially phosphorylated proteins are overrepresented, one can expect that the corre-sponding biological processes differentially respond to the tested conditions.
7. Many motifs are known and the above approach can be used to identify motifs for which differential phosphorylation sites are overrepresented. Another approach is to de novo identify motifs from all differentially phosphorylated sites (36).
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Part II
Databases
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