PhosR enables processing and functional analysis of phosphoproteomic data

Abstract

SUMMARYMass spectrometry (MS)-based phosphoproteomics has revolutionised our ability to profile phosphorylation-based signalling in cells and tissues on a global scale. To infer the action of kinases and signalling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalisation using a novel set of ‘stably phosphorylated sites’, and in functional analysis for inferring active kinases and signalling pathways. In particular, we introduce a ‘signalome’ construction method for identifying a collection of signalling modules to summarise and visualise the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating novel biological knowledge from MS-based phosphoproteomic data.

Kidney failure alters parathyroid Pin1 phosphorylation and parathyroid hormone mRNA binding proteins leading to secondary hyperparathyroidism

“…(66), and the results displayed with ggplot2. A recently described R package, PhosR, was applied for differential phosphorylation analysis, for phosphosite-and gene-centered enrichment analyses of differentially phosphorylated sites and for kinase perturbation analysis (42). Finally, we searched for significant sequence motif alterations in phosphosites between Adenine and Control with Two Sample Logo approach, using a webserver interface (48).…”

“…However, we observed downregulation of the sodium-dependent phosphate transporter, Pit1, encoded by Slc20a1 (log2 fold change -1.17, p<0.01), and respectively reduced Pit1;Ser269 and Pit1;Ser271 phosphorylation. In this regard, possible increases in Mapk1/3 (Erk1/1) activity, proposed to transduce the downstream signaling of high phosphate levels sensed by unknown mechanisms (that may include Pit1 and the calcium sensing receptor, Casr) ( 44) is suggested by enrichment of a potential MAPK phosphorylation motif (45,46) PhosR prioritizes potential kinases that could be responsible for the phosphorylation change of phosphosites based on kinase recognition motif and phosphoproteome dynamics (42). Kinase activity scores that sum changes in phosphorylation of targets, for which the targeting kinase is prioritized with a probability score, pointed to increased activity of specific kinases in adenine (CKD) (Supplemental Fig 2B).…”

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

“…The muscle differentiation dataset generated from this study and six other public phosphoproteomics datasets generated from various cell types and tissues under experimental perturbations were used for model evaluation (table 1). Phoshoproteomics data from the C2C12 myoblast to myotube differentiation experiments were normalised and processed using PhosR package (35) for generating log2 fold change quantification at the four differentiation time points (Fig. 7A).…”

The cyclin dependent kinase inhibitor Roscovitine reverses diet- induced metabolic disruption in obese mice

“…To gain a greater understanding of processes mediating the beneficial effects of roscovitine and downstream signaling pathways affected by its treatment, we performed proteomic and phosphoproteomic profiling of eWAT from mice fed an HFD with or without We next computationally analyzed the identified phosphopeptides to predict the protein kinases targeted by roscovitine that are responsible for drug-induced changes in phosphorylation profiles we identified. To compare phosphorylation profiles between vehicle-and roscovitine-treated HFD mice, we applied the PhosR algorithm to differentially identified phosphosites 30 . The computational tool combines multi-step kinase-substrate scoring methods based on both annotated kinase recognition motifs and the dynamic phosphorylation profiles of specific sites.…”

“…Gene set enrichment analysis was performed using the fgsea R package using gene libraries generated by the Bader lab 60 . Upstream kinase prediction was analyzed using PhosR package 30 .…”