Major remodelling of the murine stem cell kinome following differentiation in the hematopoietic compartment

Kinome-wide analysis of the effect of statins in colorectal cancer

BACKGROUND

Epidemiological studies and meta-analyses show an association between statin use and a reduced incidence of colorectal cancer (CRC). We have shown that statins act on CRC through bone morphogenetic protein (BMP) signalling, but the exact cellular targets and underlying mechanism of statin action remain elusive. In this study, we set out to assess the influence of statins on global cancer cell signalling by performing an array-based kinase assay using immobilised kinase substrates spanning the entire human kinome.

METHODS

CRC cells with or without Lovastatin treatment were used for kinome analysis. Findings on kinome arrays were further confirmed by immunoblotting with activity-specific antibodies. Experiments in different CRC cell lines using immunoblotting, siRNA-mediated knockdown and treatment with specific BMP inhibitor Noggin were performed. The relevance of in vitro findings was confirmed in xenografts and in CRC patients treated with Simvastatin.

RESULTS

Kinome analysis can distinguish between non-specific, toxic effects caused by 10 µM of Lovastatin and specific effects on cell signalling caused by 2 µM Lovastatin. Statins induce upregulation of PTEN activity leading to downregulation of the PI3K/Akt/mTOR signalling. Treatment of cells with the specific BMP inhibitor Noggin as well as PTEN knockdown and transfection of cells with a constitutively active form of AKT abolishes the effect of Lovastatin on mTOR phosphorylation. Experiments in xenografts and in patients treated with Simvastatin confirm statin-mediated BMP pathway activation, activation of PTEN and downregulation of mTOR signalling.

CONCLUSIONS

Statins induce BMP-specific activation of PTEN and inhibition of PI3K/Akt/mTOR signalling in CRC.

Modulation of Human Peripheral Blood Mononuclear Cell Signaling by Medicinal Cannabinoids

Medical marijuana is increasingly prescribed as an analgesic for a growing number of indications, amongst which terminal cancer and multiple sclerosis. However, the mechanistic aspects and properties of cannabis remain remarkably poorly characterized. In this study we aimed to investigate the immune-cell modulatory properties of medical cannabis. Healthy volunteers were asked to ingest medical cannabis, and kinome profiling was used to generate comprehensive descriptions of the cannabis challenge on inflammatory signal transduction in the peripheral blood of these volunteers. Results were related to both short term and long term effects in patients experimentally treated with a medical marijuana preparation for suffering from abdominal pain as a result of chronic pancreatitis or other causes. The results reveal an immunosuppressive effect of cannabinoid preparations via deactivation of signaling through the pro-inflammatory p38 MAP kinase and mTOR pathways and a concomitant deactivation of the pro-mitogenic ERK pathway. However, long term cannabis exposure in two patients resulted in reversal of this effect. While these data provide a powerful mechanistic rationale for the clinical use of medical marijuana in inflammatory and oncological disease, caution may be advised with sustained use of such preparations.

Systems medicine approaches for peptide array-based protein kinase profiling: progress and prospects

INTRODUCTION

Pharmacological manipulation of signalling pathways is becoming an increasingly important avenue for the rational clinical management of disease but is hampered by a lack of technologies that allow the generation of comprehensive descriptions of cellular signalling.

AREAS COVERED

Herein, the authors discuss the potential of peptide array-based kinome profiling for evaluating cellular signalling in the context of drug discovery. Expert commentary: Genomic and proteomic approaches have been of significant value to our elucidation of the molecular mechanisms that govern physiology. However, an equally, if not more important goal, is to define those proteins that participate in signalling pathways that ultimately control cell fate, especially kinases. Traditional genetic and biochemical approaches can certainly provide answers here, but for technical and practical reasons, are typically pursued one gene or pathway at a time. A more comprehensive approach is one in which peptide arrays of kinase-specific substrates are incubated with cell lysates and (33)P-ATP generating comprehensive descriptions, or where arrays are interrogated with phosphospecific antibodies. Both approaches allow analysis of cellular signalling without a priori assumptions to possibly influenced pathways.

Improved intra-array and interarray normalization of peptide microarray phosphorylation for phosphorylome and kinome profiling by rational selection of relevant spots

Massive parallel analysis using array technology has become the mainstay for analysis of genomes and transcriptomes. Analogously, the predominance of phosphorylation as a regulator of cellular metabolism has fostered the development of peptide arrays of kinase consensus substrates that allow the charting of cellular phosphorylation events (often called kinome profiling). However, whereas the bioinformatical framework for expression array analysis is well-developed, no advanced analysis tools are yet available for kinome profiling. Especially intra-array and interarray normalization of peptide array phosphorylation remain problematic, due to the absence of “housekeeping” kinases and the obvious fallacy of the assumption that different experimental conditions should exhibit equal amounts of kinase activity. Here we describe the development of analysis tools that reliably quantify phosphorylation of peptide arrays and that allow normalization of the signals obtained. We provide a method for intraslide gradient correction and spot quality control. We describe a novel interarray normalization procedure, named repetitive signal enhancement, RSE, which provides a mathematical approach to limit the false negative results occuring with the use of other normalization procedures. Using in silico and biological experiments we show that employing such protocols yields superior insight into cellular physiology as compared to classical analysis tools for kinome profiling.

Protein Kinase C Enzymes in the Hematopoietic and Immune Systems

The protein kinase C (PKC) family, discovered in the late 1970s, is composed of at least 10 serine/threonine kinases, divided into three groups based on their molecular architecture and cofactor requirements. PKC enzymes have been conserved throughout evolution and are expressed in virtually all cell types; they represent critical signal transducers regulating cell activation, differentiation, proliferation, death, and effector functions. PKC family members play important roles in a diverse array of hematopoietic and immune responses. This review covers the discovery and history of this enzyme family, discusses the roles of PKC enzymes in the development and effector functions of major hematopoietic and immune cell types, and points out gaps in our knowledge, which should ignite interest and further exploration, ultimately leading to better understanding of this enzyme family and, above all, its role in the many facets of the immune system.

PIIKA 2: an expanded, web-based platform for analysis of kinome microarray data

Kinome microarrays are comprised of peptides that act as phosphorylation targets for protein kinases. This platform is growing in popularity due to its ability to measure phosphorylation-mediated cellular signaling in a high-throughput manner. While software for analyzing data from DNA microarrays has also been used for kinome arrays, differences between the two technologies and associated biologies previously led us to develop Platform for Intelligent, Integrated Kinome Analysis (PIIKA), a software tool customized for the analysis of data from kinome arrays. Here, we report the development of PIIKA 2, a significantly improved version with new features and improvements in the areas of clustering, statistical analysis, and data visualization. Among other additions to the original PIIKA, PIIKA 2 now allows the user to: evaluate statistically how well groups of samples cluster together; identify sets of peptides that have consistent phosphorylation patterns among groups of samples; perform hierarchical clustering analysis with bootstrapping; view false negative probabilities and positive and negative predictive values for t-tests between pairs of samples; easily assess experimental reproducibility; and visualize the data using volcano plots, scatterplots, and interactive three-dimensional principal component analyses. Also new in PIIKA 2 is a web-based interface, which allows users unfamiliar with command-line tools to easily provide input and download the results. Collectively, the additions and improvements described here enhance both the breadth and depth of analyses available, simplify the user interface, and make the software an even more valuable tool for the analysis of kinome microarray data. Both the web-based and stand-alone versions of PIIKA 2 can be accessed via http://saphire.usask.ca.

Kinome profiling

The use of arrays in genomics has led to a fast and reliable way to screen the transcriptome of an organism. It can be automated and analysis tools have become available and hence the technique has become widely used within the past few years. Signal-transduction routes rely mainly on the phosphorylation status of already available proteins; therefore kinases are central players in signal-transduction routes. The array technology can now also be used for the analysis of the kinome. To enable array analysis, consensus peptides for kinases are spot on a solid support. After incubation with cell lysates and in the presence of radioactive ATP, radioactive peptides can be visualized and the kinases that are active in the cells can be determined. The present paper reviews comprehensively the different kinome array platforms available and results obtained hitherto using such platforms. It will appear that this technology does not disappoint its high expectations and is especially powerful because of its species independence. Nevertheless, improvements are still possible and I shall also sketch future possible directions.