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Systematic analysis of protein pools, isoforms, and modifications affecting turnover and subcellular localization.

Ahmad Y, Boisvert FM, Lundberg E, Uhlen M, Lamond AI - Mol. Cell Proteomics (2011)

Bottom Line: Protein isoforms were also detected and their properties evaluated by fractionating cell extracts on one-dimensional SDS-PAGE prior to trypsin digestion and MS analysis and independently evaluating isotope ratio values for the same peptides isolated from different gel slices.The effect of protein phosphorylation on turnover rates was analyzed by comparing mean turnover values calculated for all peptides assigned to a protein, either including, or excluding, values for cognate phosphopeptides.Collectively, these experimental and analytical approaches provide a framework for expanding the functional annotation of the genome.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee DD1 5EH United Kingdom.

ABSTRACT
In higher eukaryotes many genes encode protein isoforms whose properties and biological roles are often poorly characterized. Here we describe systematic approaches for detection of either distinct isoforms, or separate pools of the same isoform, with differential biological properties. Using information from ion intensities we have estimated protein abundance levels and using rates of change in stable isotope labeling with amino acids in cell culture isotope ratios we measured turnover rates and subcellular distribution for the HeLa cell proteome. Protein isoforms were detected using three data analysis strategies that evaluate differences between stable isotope labeling with amino acids in cell culture isotope ratios for specific groups of peptides within the total set of peptides assigned to a protein. The candidate approach compares stable isotope labeling with amino acids in cell culture isotope ratios for predicted isoform-specific peptides, with ratio values for peptides shared by all the isoforms. The rule of thirds approach compares the mean isotope ratio values for all peptides in each of three equal segments along the linear length of the protein, assessing differences between segment values. The three in a row approach compares mean isotope ratio values for each sequential group of three adjacent peptides, assessing differences with the mean value for all peptides assigned to the protein. Protein isoforms were also detected and their properties evaluated by fractionating cell extracts on one-dimensional SDS-PAGE prior to trypsin digestion and MS analysis and independently evaluating isotope ratio values for the same peptides isolated from different gel slices. The effect of protein phosphorylation on turnover rates was analyzed by comparing mean turnover values calculated for all peptides assigned to a protein, either including, or excluding, values for cognate phosphopeptides. Collectively, these experimental and analytical approaches provide a framework for expanding the functional annotation of the genome.

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Correlation analysis of phosphopeptides with turnover. Graphs A, B, and C show the average protein turnover using nonphosphorylated peptides (x axis) against average protein turnover using both phosphorylated and nonphosphorylated peptides in each of the cytoplasmic, nuclear, and nucleolar compartments. Highlighted in blue are phosphorylated proteins that show a 1.5-fold change compared with the nonphosphorylated form of the protein. Comparison of graphs A (cytoplasm), B (nucleus), and C (nucleolus) show that the nucleolus has the greatest number of phosphorylated proteins compared with the cytoplasm and nucleus. The pie charts D, E, and F show the gene ontology analysis of the phosphorylate proteins that have a slower turnover in comparison with phosphorylated from and, similarly, pie charts G, H, and I show the gene ontology analysis of the phosphorylated proteins that have a faster turnover in comparison with the nonphosphorylated form.
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Figure 7: Correlation analysis of phosphopeptides with turnover. Graphs A, B, and C show the average protein turnover using nonphosphorylated peptides (x axis) against average protein turnover using both phosphorylated and nonphosphorylated peptides in each of the cytoplasmic, nuclear, and nucleolar compartments. Highlighted in blue are phosphorylated proteins that show a 1.5-fold change compared with the nonphosphorylated form of the protein. Comparison of graphs A (cytoplasm), B (nucleus), and C (nucleolus) show that the nucleolus has the greatest number of phosphorylated proteins compared with the cytoplasm and nucleus. The pie charts D, E, and F show the gene ontology analysis of the phosphorylate proteins that have a slower turnover in comparison with phosphorylated from and, similarly, pie charts G, H, and I show the gene ontology analysis of the phosphorylated proteins that have a faster turnover in comparison with the nonphosphorylated form.

Mentions: Finally, we investigated the potential relationship between post-translational modifications and the properties of subcellular localization, turnover and abundance we have measured for HeLa proteins using SILAC. In this study we analyzed the effect of phosphorylation on either serine, threonine or tyrosine residues on rates of protein turnover in each of the cytoplasmic, nuclear, and nucleolar compartments (Fig. 7) (see Supp. Table S7). Phosphopeptides were detected and quantitated for the HeLa protein localization and turnover SILAC data set using MaxQuant (see Experimental Procedures). Overall, 2444 phosphopeptides were detected and quantitated in this analysis, identifying phosphorylated residues in ∼46% of the HeLa proteins (supplemental Fig. S2). A comparison of protein abundance levels with the detection of phosphorylated peptides showed only a weak positive correlation. This indicates that the phosphopeptides studied are not reflecting the properties of only the most abundant proteins. The majority (53%) of phosphoproteins were identified with a single phosphorylated residue, although 23% had two phosphorylated peptides and 24% had three or more (supplemental Fig. S2).


Systematic analysis of protein pools, isoforms, and modifications affecting turnover and subcellular localization.

Ahmad Y, Boisvert FM, Lundberg E, Uhlen M, Lamond AI - Mol. Cell Proteomics (2011)

Correlation analysis of phosphopeptides with turnover. Graphs A, B, and C show the average protein turnover using nonphosphorylated peptides (x axis) against average protein turnover using both phosphorylated and nonphosphorylated peptides in each of the cytoplasmic, nuclear, and nucleolar compartments. Highlighted in blue are phosphorylated proteins that show a 1.5-fold change compared with the nonphosphorylated form of the protein. Comparison of graphs A (cytoplasm), B (nucleus), and C (nucleolus) show that the nucleolus has the greatest number of phosphorylated proteins compared with the cytoplasm and nucleus. The pie charts D, E, and F show the gene ontology analysis of the phosphorylate proteins that have a slower turnover in comparison with phosphorylated from and, similarly, pie charts G, H, and I show the gene ontology analysis of the phosphorylated proteins that have a faster turnover in comparison with the nonphosphorylated form.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3316725&req=5

Figure 7: Correlation analysis of phosphopeptides with turnover. Graphs A, B, and C show the average protein turnover using nonphosphorylated peptides (x axis) against average protein turnover using both phosphorylated and nonphosphorylated peptides in each of the cytoplasmic, nuclear, and nucleolar compartments. Highlighted in blue are phosphorylated proteins that show a 1.5-fold change compared with the nonphosphorylated form of the protein. Comparison of graphs A (cytoplasm), B (nucleus), and C (nucleolus) show that the nucleolus has the greatest number of phosphorylated proteins compared with the cytoplasm and nucleus. The pie charts D, E, and F show the gene ontology analysis of the phosphorylate proteins that have a slower turnover in comparison with phosphorylated from and, similarly, pie charts G, H, and I show the gene ontology analysis of the phosphorylated proteins that have a faster turnover in comparison with the nonphosphorylated form.
Mentions: Finally, we investigated the potential relationship between post-translational modifications and the properties of subcellular localization, turnover and abundance we have measured for HeLa proteins using SILAC. In this study we analyzed the effect of phosphorylation on either serine, threonine or tyrosine residues on rates of protein turnover in each of the cytoplasmic, nuclear, and nucleolar compartments (Fig. 7) (see Supp. Table S7). Phosphopeptides were detected and quantitated for the HeLa protein localization and turnover SILAC data set using MaxQuant (see Experimental Procedures). Overall, 2444 phosphopeptides were detected and quantitated in this analysis, identifying phosphorylated residues in ∼46% of the HeLa proteins (supplemental Fig. S2). A comparison of protein abundance levels with the detection of phosphorylated peptides showed only a weak positive correlation. This indicates that the phosphopeptides studied are not reflecting the properties of only the most abundant proteins. The majority (53%) of phosphoproteins were identified with a single phosphorylated residue, although 23% had two phosphorylated peptides and 24% had three or more (supplemental Fig. S2).

Bottom Line: Protein isoforms were also detected and their properties evaluated by fractionating cell extracts on one-dimensional SDS-PAGE prior to trypsin digestion and MS analysis and independently evaluating isotope ratio values for the same peptides isolated from different gel slices.The effect of protein phosphorylation on turnover rates was analyzed by comparing mean turnover values calculated for all peptides assigned to a protein, either including, or excluding, values for cognate phosphopeptides.Collectively, these experimental and analytical approaches provide a framework for expanding the functional annotation of the genome.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee DD1 5EH United Kingdom.

ABSTRACT
In higher eukaryotes many genes encode protein isoforms whose properties and biological roles are often poorly characterized. Here we describe systematic approaches for detection of either distinct isoforms, or separate pools of the same isoform, with differential biological properties. Using information from ion intensities we have estimated protein abundance levels and using rates of change in stable isotope labeling with amino acids in cell culture isotope ratios we measured turnover rates and subcellular distribution for the HeLa cell proteome. Protein isoforms were detected using three data analysis strategies that evaluate differences between stable isotope labeling with amino acids in cell culture isotope ratios for specific groups of peptides within the total set of peptides assigned to a protein. The candidate approach compares stable isotope labeling with amino acids in cell culture isotope ratios for predicted isoform-specific peptides, with ratio values for peptides shared by all the isoforms. The rule of thirds approach compares the mean isotope ratio values for all peptides in each of three equal segments along the linear length of the protein, assessing differences between segment values. The three in a row approach compares mean isotope ratio values for each sequential group of three adjacent peptides, assessing differences with the mean value for all peptides assigned to the protein. Protein isoforms were also detected and their properties evaluated by fractionating cell extracts on one-dimensional SDS-PAGE prior to trypsin digestion and MS analysis and independently evaluating isotope ratio values for the same peptides isolated from different gel slices. The effect of protein phosphorylation on turnover rates was analyzed by comparing mean turnover values calculated for all peptides assigned to a protein, either including, or excluding, values for cognate phosphopeptides. Collectively, these experimental and analytical approaches provide a framework for expanding the functional annotation of the genome.

Show MeSH
Related in: MedlinePlus