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Background: Kinases are intracellular signalling mediators and key to sustaining the inflammatory process in rheumatoid arthritis (RA). Oral inhibitors of Janus Kinase family (JAKs) are widely used in RA, while inhibitors of other kinase families e.g. phosphoinositide 3-kinase (PI3K) are under development. Most current biomarker platforms quantify mRNA/protein levels, but give no direct information on whether proteins are active/inactive. Phosphoproteome analysis has the potential to measure specific enzyme activation status at tissue level., Methods: We validated the feasibility of phosphoproteome and total proteome analysis on 8 pre-treatment synovial biopsies from treatment-naive RA patients using label-free mass spectrometry, to identify active cell signalling pathways in synovial tissue which might explain failure to respond to RA therapeutics., Results: Differential expression analysis and functional enrichment revealed clear separation of phosphoproteome and proteome profiles between lymphoid and myeloid RA pathotypes. Abundance of specific phosphosites was associated with the degree of inflammatory state. The lymphoid pathotype was enriched with lymphoproliferative signalling phosphosites, including Mammalian Target Of Rapamycin (MTOR) signalling, whereas the myeloid pathotype was associated with Mitogen-Activated Protein Kinase (MAPK) and CDK mediated signalling. This analysis also highlighted novel kinases not previously linked to RA, such as Protein Kinase, DNA-Activated, Catalytic Subunit (PRKDC) in the myeloid pathotype. Several phosphosites correlated with clinical features, such as Disease-Activity-Score (DAS)-28, suggesting that phosphosite analysis has potential for identifying novel biomarkers at tissue-level of disease severity and prognosis., Conclusions: Specific phosphoproteome/proteome signatures delineate RA pathotypes and may have clinical utility for stratifying patients for personalised medicine in RA., (© 2024. The Author(s).)

Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .

Proteomics is a promising new tool for functional genomics. In addition to two-dimensional gel electrophoresis, other methods that are based on liquid chromatography and mass spectrometry are now available to study proteins. In this brief article, we review the strengths and limitations of the proteomic approaches currently available to the researcher, and we provide examples of how proteomics has been, and can in the future be, used to study the kidney.

Perineural invasion (PNI) is a common and characteristic feature of pancreatic ductal adenocarcinoma (PDAC) that is associated with poor prognosis, tumor recurrence, and generation of pain. However, the molecular alterations in cancer cells and nerves within PNI have not previously been comprehensively analyzed. Here, we describe our proteomic analysis of the molecular changes underlying neuro‐epithelial interactions in PNI using liquid chromatography–mass spectrometry (LC‐MS/MS) in microdissected PNI and non‐PNI cancer, as well as in invaded and noninvaded nerves from formalin‐fixed, paraffin‐embedded PDAC tissues. In addition, an in vitro model of PNI was developed using a co‐culture system comprising PDAC cell lines and PC12 cells as the neuronal element. The overall proteomic profiles of PNI and non‐PNI cancer appeared largely similar. In contrast, upon invasion by cancer cells, nerves demonstrated widespread plasticity with a pattern consistent with neuronal injury. The up‐regulation of SCG2 (secretogranin II) and neurosecretory protein VGF (nonacronymic) in invaded nerves in PDAC tissues was further validated using immunohistochemistry. The tested PDAC cell lines were found to be able to induce neuronal plasticity in PC12 cells in our in vitro established co‐culture model. Changes in expression levels of VGF, as well as of two additional proteins previously reported to be overexpressed in PNI, Nestin and Neuromodulin (GAP43), closely recapitulated our proteomic findings in PDAC tissues. Furthermore, induction of VGF, while not necessary for PC12 survival, mediated neurite extension induced by PDAC cell lines. In summary, here we report the proteomic alterations underlying PNI in PDAC and confirm that PDAC cells are able to induce neuronal plasticity. In addition, we describe a novel, simple, and easily adaptable co‐culture model for in vitro study of neuro‐epithelial interactions. [ABSTRACT FROM AUTHOR]