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Convergence of ubiquitylation and phosphorylation signaling in rapamycin-treated yeast cells.

Iesmantavicius V, Weinert BT, Choudhary C - Mol. Cell Proteomics (2014)

Bottom Line: We found that proteome, phosphorylation, and ubiquitylation changes converged on the Rsp5-ubiquitin ligase, Rsp5 adaptor proteins, and Rsp5 targets.Furthermore, we found that permeases and transporters, which are often ubiquitylated by Rsp5, were biased for reduced ubiquitylation and reduced protein abundance.Collectively, these data reveal new insights into the global proteome dynamics in response to rapamycin treatment and provide a first detailed view of the co-regulation of phosphorylation- and ubiquitylation-dependent signaling networks by this compound.

View Article: PubMed Central - PubMed

Affiliation: From the ‡Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, 2200 Copenhagen, Denmark.

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Dynamics of the rapamycin-regulated phosphoproteome.A, identification of significantly regulated phosphorylation sites. The histogram shows the distribution of phosphorylation site SILAC ratios for 1h rapamycin/control (1h/ctrl) and the distribution of unmodified peptide SILAC ratios (red). The cutoff for regulated phosphorylation sites was determined based on two standard deviations from the median for unmodified peptides. Unregulated sites are shown in black, and regulated sites are shown in blue. The numbers of down-regulated and up-regulated phosphorylation sites is indicated. B, the bar chart shows the distribution of phosphorylation sites into seven clusters, where cluster zero represents unregulated sites. The clusters were generated through unsupervised clustering of SILAC ratios with the fuzzy c-means algorithm. C, six distinct temporal patterns were generated, and the match between the profile of the cluster and phosphorylation change is described by the membership value. D, the heatmap shows the clustering of GO terms associated with the temporal clusters from C. A more detailed description of the enriched GO terms is provided in supplemental Figs. S2H–S2M. E, sequence motifs for distinct clusters were generated using IceLogo and show the percent difference in amino acid frequency relative to unregulated sites at a p value cutoff of 0.05.
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Figure 3: Dynamics of the rapamycin-regulated phosphoproteome.A, identification of significantly regulated phosphorylation sites. The histogram shows the distribution of phosphorylation site SILAC ratios for 1h rapamycin/control (1h/ctrl) and the distribution of unmodified peptide SILAC ratios (red). The cutoff for regulated phosphorylation sites was determined based on two standard deviations from the median for unmodified peptides. Unregulated sites are shown in black, and regulated sites are shown in blue. The numbers of down-regulated and up-regulated phosphorylation sites is indicated. B, the bar chart shows the distribution of phosphorylation sites into seven clusters, where cluster zero represents unregulated sites. The clusters were generated through unsupervised clustering of SILAC ratios with the fuzzy c-means algorithm. C, six distinct temporal patterns were generated, and the match between the profile of the cluster and phosphorylation change is described by the membership value. D, the heatmap shows the clustering of GO terms associated with the temporal clusters from C. A more detailed description of the enriched GO terms is provided in supplemental Figs. S2H–S2M. E, sequence motifs for distinct clusters were generated using IceLogo and show the percent difference in amino acid frequency relative to unregulated sites at a p value cutoff of 0.05.

Mentions: We quantified 8961 high-confidence phosphorylation sites (referred to as class I sites with a localization probability > 0.75) in rapamycin-treated cells (Fig. 1B and supplemental Table S3); ∼86% of these sites were corrected for changes in protein abundance, providing a more accurate measure of phosphorylation changes at these positions. Phosphorylation changes were significantly correlated between experimental replicates (supplemental Fig. S2A). We quantified nearly four times as many phosphorylation sites as previously reported in the largest rapamycin-regulated phosphoproteome dataset (47), although we identified only 30% of the previously identified sites (supplemental Fig. S2B). The relatively low overlap between these two studies likely reflects the use of different yeast strains, time points, proteases (Lys-C versus trypsin), digestion strategies (in-gel versus in-solution), and phosphopeptide enrichment strategies (IMAC versus TiO2) in these studies, as well as the stochastic nature of phosphorylated peptide identification. Despite these differences, our data were significantly correlated (Spearman's correlation of 0.40, p value of 2.2e-16) with those of the previous study (supplemental Fig. S2C), providing additional confidence in the phosphorylation changes identified in our screen. The distribution of phosphorylation site ratios comparing rapamycin-treated cells to untreated cells was much broader than the distribution of unmodified peptides, suggesting extensive regulation of the phosphoproteome (Fig. 3A and supplemental Fig. S2D).


Convergence of ubiquitylation and phosphorylation signaling in rapamycin-treated yeast cells.

Iesmantavicius V, Weinert BT, Choudhary C - Mol. Cell Proteomics (2014)

Dynamics of the rapamycin-regulated phosphoproteome.A, identification of significantly regulated phosphorylation sites. The histogram shows the distribution of phosphorylation site SILAC ratios for 1h rapamycin/control (1h/ctrl) and the distribution of unmodified peptide SILAC ratios (red). The cutoff for regulated phosphorylation sites was determined based on two standard deviations from the median for unmodified peptides. Unregulated sites are shown in black, and regulated sites are shown in blue. The numbers of down-regulated and up-regulated phosphorylation sites is indicated. B, the bar chart shows the distribution of phosphorylation sites into seven clusters, where cluster zero represents unregulated sites. The clusters were generated through unsupervised clustering of SILAC ratios with the fuzzy c-means algorithm. C, six distinct temporal patterns were generated, and the match between the profile of the cluster and phosphorylation change is described by the membership value. D, the heatmap shows the clustering of GO terms associated with the temporal clusters from C. A more detailed description of the enriched GO terms is provided in supplemental Figs. S2H–S2M. E, sequence motifs for distinct clusters were generated using IceLogo and show the percent difference in amino acid frequency relative to unregulated sites at a p value cutoff of 0.05.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Figure 3: Dynamics of the rapamycin-regulated phosphoproteome.A, identification of significantly regulated phosphorylation sites. The histogram shows the distribution of phosphorylation site SILAC ratios for 1h rapamycin/control (1h/ctrl) and the distribution of unmodified peptide SILAC ratios (red). The cutoff for regulated phosphorylation sites was determined based on two standard deviations from the median for unmodified peptides. Unregulated sites are shown in black, and regulated sites are shown in blue. The numbers of down-regulated and up-regulated phosphorylation sites is indicated. B, the bar chart shows the distribution of phosphorylation sites into seven clusters, where cluster zero represents unregulated sites. The clusters were generated through unsupervised clustering of SILAC ratios with the fuzzy c-means algorithm. C, six distinct temporal patterns were generated, and the match between the profile of the cluster and phosphorylation change is described by the membership value. D, the heatmap shows the clustering of GO terms associated with the temporal clusters from C. A more detailed description of the enriched GO terms is provided in supplemental Figs. S2H–S2M. E, sequence motifs for distinct clusters were generated using IceLogo and show the percent difference in amino acid frequency relative to unregulated sites at a p value cutoff of 0.05.
Mentions: We quantified 8961 high-confidence phosphorylation sites (referred to as class I sites with a localization probability > 0.75) in rapamycin-treated cells (Fig. 1B and supplemental Table S3); ∼86% of these sites were corrected for changes in protein abundance, providing a more accurate measure of phosphorylation changes at these positions. Phosphorylation changes were significantly correlated between experimental replicates (supplemental Fig. S2A). We quantified nearly four times as many phosphorylation sites as previously reported in the largest rapamycin-regulated phosphoproteome dataset (47), although we identified only 30% of the previously identified sites (supplemental Fig. S2B). The relatively low overlap between these two studies likely reflects the use of different yeast strains, time points, proteases (Lys-C versus trypsin), digestion strategies (in-gel versus in-solution), and phosphopeptide enrichment strategies (IMAC versus TiO2) in these studies, as well as the stochastic nature of phosphorylated peptide identification. Despite these differences, our data were significantly correlated (Spearman's correlation of 0.40, p value of 2.2e-16) with those of the previous study (supplemental Fig. S2C), providing additional confidence in the phosphorylation changes identified in our screen. The distribution of phosphorylation site ratios comparing rapamycin-treated cells to untreated cells was much broader than the distribution of unmodified peptides, suggesting extensive regulation of the phosphoproteome (Fig. 3A and supplemental Fig. S2D).

Bottom Line: We found that proteome, phosphorylation, and ubiquitylation changes converged on the Rsp5-ubiquitin ligase, Rsp5 adaptor proteins, and Rsp5 targets.Furthermore, we found that permeases and transporters, which are often ubiquitylated by Rsp5, were biased for reduced ubiquitylation and reduced protein abundance.Collectively, these data reveal new insights into the global proteome dynamics in response to rapamycin treatment and provide a first detailed view of the co-regulation of phosphorylation- and ubiquitylation-dependent signaling networks by this compound.

View Article: PubMed Central - PubMed

Affiliation: From the ‡Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, 2200 Copenhagen, Denmark.

Show MeSH
Related in: MedlinePlus