Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites

Functional selectivity of Receptor Tyrosine Kinases regulates distinct cellular outputs

Functional selectivity refers to the activation of differential signalling and cellular outputs downstream of the same membrane-bound receptor when activated by two or more different ligands. Functional selectivity has been described and extensively studied for G-protein Coupled Receptors (GPCRs), leading to specific therapeutic options for dysregulated GPCRs functions. However, studies regarding the functional selectivity of Receptor Tyrosine Kinases (RTKs) remain sparse. Here, we will summarize recent data about RTK functional selectivity focusing on how the nature and the amount of RTK ligands and the crosstalk of RTKs with other membrane proteins regulate the specificity of RTK signalling. In addition, we will discuss how structural changes in RTKs upon ligand binding affects selective signalling pathways. Much remains to be known about the integration of different signals affecting RTK signalling specificity to orchestrate long-term cellular outcomes. Recent advancements in omics, specifically quantitative phosphoproteomics, and in systems biology methods to study, model and integrate different types of large-scale omics data have increased our ability to compare several signals affecting RTK functional selectivity in a global, system-wide fashion. We will discuss how such methods facilitate the exploration of important signalling hubs and enable data-driven predictions aiming at improving the efficacy of therapeutics for diseases like cancer, where redundant RTK signalling pathways often compromise treatment efficacy.

Recent advances in kinase signaling network profiling by mass spectrometry

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.

An integrative pan-cancer analysis reveals the carcinogenic effects of NCAPH in human cancer

BACKGROUND

Non-chromosomal structure maintenance protein condensin complex I subunit H (NCAPH) has been reported to play a regulatory role in a variety of cancers and is associated with tumor poor prognosis. This study aims to explore the potential role of NCAPH with a view to providing insights on pathologic mechanisms.

METHODS

The expression of NCAPH in different tumors was explored by The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx). The prognostic value of NCAPH was retrieved through GEPIA and Kaplan-Meier Plotter databases. Tumor Immunity Estimation Resource (TIMER) and Single-Sample Gene Set Enrichment Analysis (GSEA) to search for the association of NCAPH with tumor immune infiltration. The cBioPortal and PhosphoSite Plus databases showed NCAPH phosphorylation status in tumors. Gene set enrichment analysis (GSEA) was performed using bioinformatics.

RESULTS

Our findings revealed that NCAPH showed high expression levels in a wide range of tumor types, and was strongly correlated with the prognosis of patients. Moreover, a higher phosphorylation level at S59, S67, S76, S190, S222 and T38 site was discovered in head and neck squamous cell carcinoma (HNSC). NCAPH overexpression was positively correlated with the infiltration level of CD8+T cells and myeloid dendritic infiltration in breast cancer and thymoma.

CONCLUSIONS

The up-regulation of NCAPH was significantly correlated with the poor prognosis and immune infiltration in pan-cancer, and NCAPH could be served as a potential immunotherapeutic target for cancers.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.