Integrated in silico MS-based phosphoproteomics and network enrichment analysis of RASopathy proteins

Abstract

Background: RASopathies are a group of syndromes showing clinical overlap caused by mutations in genes affecting the RAS-MAPK pathway. Consequent disruption on cellular signaling leads and is driven by phosphoproteome remodeling. However, we still lack a comprehensive picture of the different key players and altered downstream effectors.

Methods: An in silico interactome of RASopathy proteins was generated using pathway enrichment analysis/STRING tool, including identification of main hub proteins. We also integrated phosphoproteomic and immunoblotting studies using previous published information on RASopathy proteins and their neighbors in the context of RASopathy syndromes. Data from Phosphosite database ( www.phosphosite.org ) was collected in order to obtain the potential phosphosites subjected to regulation in the 27 causative RASopathy proteins. We compiled a dataset of dysregulated phosphosites in RASopathies, searched for commonalities between syndromes in harmonized data, and analyzed the role of phosphorylation in the syndromes by the identification of key players between the causative RASopathy proteins and the associated interactome.

Results: In this study, we provide a curated data set of 27 causative RASopathy genes, identify up to 511 protein-protein associations using pathway enrichment analysis/STRING tool, and identify 12 nodes as main hub proteins. We found that a large group of proteins contain tyrosine residues and their biological processes include but are not limited to the nervous system. Harmonizing published RASopathy phosphoproteomic and immunoblotting studies we identified a total of 147 phosphosites with increased phosphorylation, whereas 47 have reduced phosphorylation. The PKB signaling pathway is the most represented among the dysregulated phosphoproteins within the RASopathy proteins and their neighbors, followed by phosphoproteins implicated in the regulation of cell proliferation and the MAPK pathway.

Conclusions: This work illustrates the complex network underlying the RASopathies and the potential of phosphoproteomics for dissecting the molecular mechanisms in these syndromes. A combined study of associated genes, their interactome and phosphorylation events in RASopathies, elucidates key players and mechanisms to direct future research, diagnosis and therapeutic windows.

Keywords: Interactomics; Mass spectrometry; Neurofibromatosis; Noonan syndrome; Phosphoproteomics; RASopathies; Rare diseases.

Fig. 1

RASopathy genes. Neurofibromatosis type I, Legius and Costello syndromes, intellectual disability and people with autism spectrum disorder (Autism), and capillary malformation-arteriovenous malformation syndrome (CM-AVM) have just one protein as the cause of the disease, whereas the rest of RASopathies may be triggered by several defective proteins. In more than 90% of cases juvenile myelomonocytic leukemia (JMML) (not illustrated in the figure) is driven by alterations in PTPN11, NRAS, CBL, KRAS and NF1 genes

Fig. 2

Association map of the 32 RASopathy proteins used in this study. Only 27 are shown since the RAS node includes HRAS, KRAS and NRAS, the RRAS node includes RRAS, RRAS2 and MRAS, and the MEK1/2 node includes MEK1 and MEK2. The thickness of the lines indicates the strength of data support according to STRING database. A Association map of the 32 RASopathy proteins used in this study. Corresponding proteins are shown in the figure. RAS includes six Ras proteins, whereas MEK1 and MEK2 are represented as MEK1/2. The thickness of the lines indicates the strength of data support according to STRING database. B Overlap of the interactome among the 32 RASopathy proteins. From left to right, protein names, number of direct partners, number of partners associated with other RASopathy proteins, and percentage of overlap. Last column indicates whether or not they interact with RAS/MAPK. Direct partners for each protein (N), were obtained from the interactome generated in this study including 27 RASopathy proteins and their neighbors (listed in Table S1)

Fig. 3

Analysis of the interactome and their implications in each syndrome and biological processes. A Comparison of the interactome of the different RASopathies. A total of 687 proteins were analyzed. The interactome of each RASopathy is intersected with the union of all the other RASopathies interactomes. B GO Biological processes overrepresentation test results using the 432 unique protein Ids of the interactomes on the PANTHER classification system

Fig. 4

Analysis of dysregulated phosphosites in RASopathy interactome proteins in NS, NSML. A Heatmap representing the log2 (fold change) of phosphosites in NS and NSML versus control. Clustering analysis of the dysregulated phosphosites identified in phosphoproteomics and within the RASopathy interactome in either NS or NSML. An asterisk is added to zero values representing reported non-altered phosphorylation levels, to distinguish them from those representing just lack of data. In case of duplicated quantitative values from different studies, ‘ symbol was added [91]. B GO Biological processes overrepresentation test results using the 33 unique UniProt IDs from human dysregulated phosphosites reported in the RASopathy interactome on PANTHER

Fig. 5

Identified phosphosites in RASopathy proteins and their cognate syndromes. The size of the bubbles represents the number of phosphosites for the corresponding protein, in the case of association to a cognate syndrome. Phosphosites for CBL, RASK, RASN, A2ML1 and PTN11 also overlap in JMML