Rubricas de actividades propuestas para la evaluación de competencias

As outlined in Chapter 1, the cell constantly receives signals from its surroundings to which it has to respond appropriately. Growth factors, for example, are essential signals as they are capable of stimulating cellular differentiation and cellular proliferation and regulate a variety of cellular processes (Hunter, 2000; Pawson and Nash, 2003). In our study we used integrated phosphoproteomic technology combining phosphopeptide enrichment, high-accuracy identification, and stable isotope labelling by amino acids in cell culture (SILAC) (Ong et al.,

56

2002) to quantify changes in phosphopeptide levels and to investigate the global in-vivo phosphoproteome and its temporal dynamics upon growth-factor stimulation. The epidermal growth factor (EGF) acts by binding to the EGF receptor (EGFR) on the cell surface and stimulating its intrinsic protein-tyrosine kinase activity initiating a signal transduction cascade. This results in a number of biochemical changes ranging from cell proliferation to the increased expression of certain genes including the EGFR.

The application of triple-encoding SILAC for monitoring activation profiles, SCX and TiO2 chromatography for phosphopeptide enrichment (Gruhler et al., 2005; Larsen et al., 2005), and high-accuracy mass spectrometric characterization allows the investigation of the phosphoproteome in considerable depth. The approach is completely generic for identification of phosphorylation events.

Serum-starved HeLa cells labelled with L-arginine and L-lysine, L-arginine-U-13C614N4 and L-lysine-2H4, or L-arginine-U-13C6-15N4 and L-lysine-U-13C6-15N2 were treated with EGF for 0 min, 5 min, and 10 min. A second, identically labelled set of HeLa cells was treated with EGF for 1 min, 5 min, and 20 min. Then cells were combined, lysed and enzymatically digested. After the strong-cation exchange chromatography of digests, TiO2 enrichment of phosphopeptides was performed (Figure 4.18).

Next, MS2 and MS3 spectra were merged into a single peak-list file and searched against the human IPI database. To establish a cutoff score threshold for a false-positive rate of less than one percent, we performed a MASCOT search against a concatenated target/decoy database (Elias et al., 2005) consisting of a combined forward and reverse version of the IPI human database including known nonhuman contaminants such as porcine trypsin. All spectra and all sequence assignments made by MASCOT (Perkins et al., 1999) were imported into MSQuant. The assignments of individual phosphosphorylation sites were automatically scored using the algorithm implemented in the PHOSIDA upload process (Chapter 3). The identified phosphorylation sites along with additional information including matching kinase motifs and structural constraints were then uploaded to the PHOSIDA database as described in Chapter 4.2.1. In addition, transformed profiles reflecting phosphorylation dynamics upon EGF stimulation were clustered as described in Chapter 4.5.2. We classified the derived clusters into ‘increasing’, ‘decreasing’ and ‘not changing’ and uploaded the clustering assignments to PHOSIDA.

Figure 4.18: Quantitative and Time-Resolved Phosphoproteomics using SILAC

This quantitative, phosphosite-specific approach to detect phosphorylation dynamics upon EGF stimulus on the basis of SILAC-labelling yielded the identification of 6600 phosphorylation sites from 2244 proteins (Olsen et al., 2006).

We grouped potential phosphorylation sites into three categories depending on their PTM localization score and motifs. In the category with highest confidence in localization (class I), the given site had a localization probability for the phospho-group of at least 0.75. In class II, the localization probability is between 0.25 and 0.75, but these sites also had to match a known kinase motif. Class III sites had the same localization probabilities as class II but did not match any of the kinase motifs. According to this categorization, we determined 5674 class I sites, 2256 class II sites, and 1818 class III sites on mainly single phosphorylated peptides (Figure 4.19). In PHOSIDA, identified phosphorylation sites of a given protein of interest, which do not satisfy the class I criteria, are indicated in brackets (Figure 4.11). We determined the distribution between individually identified sites to be 4901 pS, 670 pT, and 103 pY class I sites (Figure 4.19). Thus, our data set suggests that the distribution of pS, pT, and pY is 86.4%, 11.8%, and 1.8%, respectively.

58

The proportion of detected phosphoserines and phosphothreonines is in concordance with the one observed in previous studies (Hunter, 2000). However, the percentage of determined phosphotyrosines is much higher (1.8%) than reported previsoulsy (0.05%).

Figure 4.19: (A) Distribution of single, doubly, triply, quadruply and higher phosphorylated peptides. (B) Distribution of phosphorylation sites by amino acid

To determine the novelty of our dataset, we compared it with all annotated human phosphosites in the SwissProt database that were based on experimental data (3262 sites in version 48.0) and also included four previous phosphoproteomes in our analysis.

We found that more than 90% of our sites were novel with respect to SwissProt. In total, 691 (37%) out of 1890 phosphorylation sites from the four previous studies that could be mapped to IPI version 3.13 (Chapter 4.4) were also found in our study. PHOSIDA lists all sites determined from the other large-scale studies or annotated in SwissProt (accessible via the corresponding ‘sites from other sources’ button (Figure 4.20)). As discussed in Chapters 4.2.1.3 and 4.4, all SwissProt entries were mapped to the IPI database via BLAST, in order to ensure accurate comparisons.

Figure 4.20: (A) Overlapping phosphorylation sites between our set and SwissProt (top) and the large scale datasets by Gygi and co-workers, Aebersold and co-workers, Stover et al., and Amanchy et al. (bottom); (B) PHOSIDA: Illustration of sites determined by other mass spectrometric approaches

In addition, we investigated the phosphorylation dynamics upon EGF stimulus: EGF signalling begins with activation of the EGF receptor and extends to a cascade of downstream kinases and other effector proteins. We derived four clusters with upregulated phosphopeptides and two with downregulated ones (Chapter 4.5.2). Cluster A, for example, embraced phosphorylation sites that can be classified as signal initiators involved in membrane-proximal signalling events and are enriched in phosphotyrosines. The resulting temporal cluster profiles are illustrated in Figure 4.21. As highlighted in Chapter 4.5.2, the online interface of the PHOSIDA database shows the corresponding clustering of each identified phosphopeptide.

Notably, around 77% of phosphorylated proteins contained at least two peptides that were detected to show different phosphorylation dynamics upon EGF stimulation on the basis of our clustering approach. This suggests that phosphoproteins serve as signal integrators. Interestingly, transcriptional regulators made up a large class of regulated proteins. We identified 26 phosphosphorylated transcription factors, with 33 novel phosphorylation sites showing diverse phosphorylation dynamics.

60

Figure 4.21: Clustering of dynamic phosphorylation profiles.

The y axis is log10 transformed and normalized. Each member (temporal profile) is color coded according to its

membership value ranging from close membership (magenta) to distant membership (green) (Olsen et al., 2006).