Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic RAS and CIP2A signaling

Abstract

Hyperactivated RAS drives progression of many human malignancies. However, oncogenic activity of RAS is dependent on simultaneous inactivation of protein phosphatase 2A (PP2A) activity. Although PP2A is known to regulate some of the RAS effector pathways, it has not been systematically assessed how these proteins functionally interact. Here we have analyzed phosphoproteomes regulated by either RAS or PP2A, by phosphopeptide enrichment followed by mass-spectrometry-based label-free quantification. To allow data normalization in situations where depletion of RAS or PP2A inhibitor CIP2A causes a large uni-directional change in the phosphopeptide abundance, we developed a novel normalization strategy, named pairwise normalization. This normalization is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of the pairwise normalization was verified by various independent methods. Additionally, we demonstrate how the selected normalization method influences the downstream analyses and interpretation of pathway activities. Consequently, bioinformatics analysis of RAS and CIP2A regulated phosphoproteomes revealed a significant overlap in their functional pathways. This is most likely biologically meaningful as we observed a synergistic survival effect between CIP2A and RAS expression as well as KRAS activating mutations in TCGA pan-cancer data set, and synergistic relationship between CIP2A and KRAS depletion in colony growth assays.

Temporal proteomic analysis of HIV infection reveals remodelling of the host phosphoproteome by lentiviral Vif variants

“…Proteins and phosphopeptides downregulated (red) or upregulated (green) with a fold change > 2 and q value < 0.01 are highlighted. ( B ) Comparison of changes in phosphopeptide abundance between WT and ΔVif-infected CEM-T4 T cells with previously published data for okadaic acid-treated HeLa cells (Kauko et al, 2015). Lines show linear correlation with associated 95% confidence areas, r 2 values and p values of a non-zero correlation.…”

“…Furthermore, as predicted for antagonism of a phosphatase, almost all changes represented increases in phosphopeptide abundance, indicating increased protein phosphorylation (with a total of 238 peptides from 192 proteins showing abundance changes of >2 fold with a q value of <0.01). To confirm that the observed changes resulted from PP2A antagonism, we compared our Vif-dependent changes in protein phosphorylation with published alterations to the phosphoproteome of HeLa cells following treatment with the PP2A inhibitor okadaic acid (Kauko et al, 2015). Despite the different cell types and treatments, a highly significant correlation was found between our observed Vif-dependent changes in HIV-infected cells and the published changes resulting from okadaic acid treatment (Figure 6B and Figure 6—figure supplement 1D).…”

“…Kinases represented in the dataset by a single target phosphosite were excluded. ( D – E ) Comparison of phosphoproteomic dataset with previously published data for ( D ) okadaic acid-treated (Kauko et al, 2015) and ( E ) kinase inhibitor-treated (Kettenbach et al, 2011) HeLa cells. Each row shows a different pairwise comparison: top row, HIV WT versus mock; middle row, HIV ΔVif vs mock; bottom row, HIV WT vs HIV ΔVif.…”

Phosphoproteomic comparison of Pik3ca and Pten signalling identifies the nucleotidase NT5C as a novel AKT substrate

“…This high level of replication was selected to facilitate reproducible detection of small quantitative changes. Following a TiO 2 enrichment for phosphopeptides, samples were analysed by LC-MS/MS and peptides were identified and quantified as previously described1821. Strong quantitative reproducibility was achieved as measured by Pearson Correlation Coefficient which averaged r = 0.83 for experimental replicates and r = 0.87 for biological replicates (Supplementary Table S1).…”

“…Label-free mass spectrometry is an alternative to label-based methods such as iTRAQ and SILAC, and has the benefit of allowing analyses of large numbers of samples as well as more consistent quantification of low abundance peptides2122.…”