Inference of kinase-signaling networks in human myeloid cell line models by Phosphoproteomics using kinase activity enrichment analysis (KAEA)

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

PD1 inhibits PKCθ-dependent phosphorylation of cytoskeleton-related proteins and immune synapse formation

ABSTRACT

The inhibitory surface receptor programmed cell death protein 1 (PD1) is a major target for antibody-based cancer immunotherapies. Nevertheless, a substantial number of patients fail to respond to the treatment or experience adverse effects. An improved understanding of intracellular pathways targeted by PD1 is thus needed to develop better predictive and prognostic biomarkers. Here, via unbiased phosphoproteome analysis of primary human T cells, we demonstrate that PD1 triggering inhibited the phosphorylation and physical association with protein kinase Cθ (PKCθ) of a variety of cytoskeleton-related proteins. PD1 blocked activation and recruitment of PKCθ to the forming immune synapse (IS) in a Src homology-2 domain-containing phosphatase-1/2 (SHP1/SHP2)-dependent manner. Consequently, PD1 engagement led to impaired synaptic phosphorylation of cytoskeleton-related proteins and formation of smaller IS. T-cell receptor induced phosphorylation of the PKCθ substrate and binding partner vimentin was long-lasting and it could be durably inhibited by PD1 triggering. Vimentin phosphorylation in intratumoral T cells also inversely correlated with the levels of the PD1 ligand, PDL1, in human lung carcinoma. Thus, PKCθ and its substrate vimentin represent important targets of PD1-mediated T-cell inhibition, and low levels of vimentin phosphorylation may serve as a biomarker for the activation of the PD1 pathway.

Inhibition of protein kinase C isozymes causes immune profile alteration and possibly decreased tumorigenesis in bladder cancer

Protein kinase C (PRKC) isozymes activate many signaling pathways and promote tumorigenesis, which can be confirmed by masking the kinase activity. In the present study, the kinase activity of PRKC ε and ζ isozymes was masked by siRNA in bladder cancer, and the consequent gene profile was evaluated. Here, we show that the commonly dysregulated genes affected by both the isozymes were the chemokines (CXCL8 & CXCL10), adhesion molecules (ICAM1, SPP1, MMP3, VEGFA) and mutated isoform of TP53. As these same genes were upregulated in bladder cancer patients, the activity of the kinase in downregulating them is confirmed. These genes are associated with regulating the tumor microenvironment, proliferation and differentiation of cancer cells and poor prognosis. The effect of kinase masking in downregulating these genes in bladder cancer indicates the benefits PRKC inhibitors may have in managing these patients.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.