Kinome profiling

Kinase Inhibitors as Underexplored Antiviral Agents

Viral infections are a major health problem; therefore, there is an urgent need for novel therapeutic strategies. Antivirals used to target proteins encoded by the viral genome usually enhance drug resistance generated by the virus. A potential solution may be drugs acting at host-based targets since viruses are dependent on numerous cellular proteins and phosphorylation events that are crucial during their life cycle. Repurposing existing kinase inhibitors as antiviral agents would help in the cost and effectiveness of the process, but this strategy usually does not provide much improvement, and specific medicinal chemistry programs are needed in the field. Anyway, extensive use of FDA-approved kinase inhibitors has been quite useful in deciphering the role of host kinases in viral infection. The present perspective aims to review the state of the art of kinase inhibitors that target viral infections in different development stages.

OsteoBLAST: Computational Routine of Global Molecular Analysis Applied to Biomaterials Development

For bone purposes, surface modifications are a common trend in biomaterials research aiming to reduce the time necessary for osteointegration, culminating in faster recovery of patients. In this scenario, analysis of intracellular signaling pathways have emerged as an important and reliable strategy to predict biological responses from in vitro approaches. We have combined global analysis of intracellular protein phosphorylation, systems biology and bioinformatics into an early biomaterial analysis routine called OsteoBLAST. We employed the routine as follows: the PamChip tyrosine kinase assay was applied to mesenchymal stem cells grown on three distinct titanium surfaces: machined, dual acid-etched and nanoHA. Then, OsteoBLAST was able to identify the most reliable spots to further obtain the differential kinome profile and finally to allow a comparison among the different surfaces. Thereafter, NetworKIN, STRING, and Cytoscape were used to build and analyze a supramolecular protein-protein interaction network, and DAVID tools identified biological signatures in the differential kinome for each surface.

Human mesenchymal stromal cells in adhesion to cell-derived extracellular matrix and titanium: Comparative kinome profile analysis

The extracellular matrix (ECM) physically supports cells and influences stem cell behaviour, modulating kinase-mediated signalling cascades. Cell-derived ECMs have emerged in bone regeneration as they reproduce physiological tissue-architecture and ameliorate mesenchymal stromal cell (MSC) properties. Titanium scaffolds show good mechanical properties, facilitate cell adhesion, and have been routinely used for bone tissue engineering (BTE). We analyzed the kinomic signature of human MSCs in adhesion to an osteopromotive osteoblast-derived ECM, and compared it to MSCs on titanium. PamChip kinase-array analysis revealed 63 phosphorylated peptides on ECM and 59 on titanium, with MSCs on ECM exhibiting significantly higher kinase activity than on titanium. MSCs on the two substrates showed overlapping kinome profiles, with activation of similar signalling pathways (FAK, ERK, and PI3K signalling). Inhibition of PI3K signalling in cells significantly reduced adhesion to ECM and increased the number of nonadherent cells on both substrates. In summary, this study comprehensively characterized the kinase activity in MSCs on cell-derived ECM and titanium, highlighting the role of PI3K signalling in kinomic changes regulating osteoblast viability and adhesion. Kinome profile analysis represents a powerful tool to select pathways to better understand cell behaviour. Osteoblast-derived ECM could be further investigated as titanium scaffold-coating to improve BTE.

Technological advances for interrogating the human kinome

There is increasing appreciation among researchers and clinicians of the value of investigating biology and pathobiology at the level of cellular kinase (kinome) activity. Kinome analysis provides valuable opportunity to gain insights into complex biology (including disease pathology), identify biomarkers of critical phenotypes (including disease prognosis and evaluation of therapeutic efficacy), and identify targets for therapeutic intervention through kinase inhibitors. The growing interest in kinome analysis has fueled efforts to develop and optimize technologies that enable characterization of phosphorylation-mediated signaling events in a cost-effective, high-throughput manner. In this review, we highlight recent advances to the central technologies currently available for kinome profiling and offer our perspectives on the key challenges remaining to be addressed.

Gene expression profiles and signaling mechanisms in α2B-adrenoceptor-evoked proliferation of vascular smooth muscle cells

BACKGROUND

α2-adrenoceptors are important regulators of vascular tone and blood pressure. Regulation of cell proliferation is a less well investigated consequence of α2-adrenoceptor activation. We have previously shown that α2B-adrenoceptor activation stimulates proliferation of vascular smooth muscle cells (VSMCs). This may be important for blood vessel development and plasticity and for the pathology and therapeutics of cardiovascular disorders. The underlying cellular mechanisms have remained mostly unknown. This study explored pathways of regulation of gene expression and intracellular signaling related to α2B-adrenoceptor-evoked VSMC proliferation.

RESULTS

The cellular mechanisms and signaling pathways of α2B-adrenoceptor-evoked proliferation of VSMCs are complex and include redundancy. Functional enrichment analysis and pathway analysis identified differentially expressed genes associated with α2B-adrenoceptor-regulated VSMC proliferation. They included the upregulated genes Egr1, F3, Ptgs2 and Serpine1 and the downregulated genes Cx3cl1, Cav1, Rhoa, Nppb and Prrx1. The most highly upregulated gene, Lypd8, represents a novel finding in the VSMC context. Inhibitor library screening and kinase activity profiling were applied to identify kinases in the involved signaling pathways. Putative upstream kinases identified by two different screens included PKC, Raf-1, Src, the MAP kinases p38 and JNK and the receptor tyrosine kinases EGFR and HGF/HGFR. As a novel finding, the Src family kinase Lyn was also identified as a putative upstream kinase.

CONCLUSIONS

α2B-adrenoceptors may mediate their pro-proliferative effects in VSMCs by promoting the activity of bFGF and PDGF and the growth factor receptors EGFR, HGFR and VEGFR-1/2. The Src family kinase Lyn was also identified as a putative upstream kinase. Lyn is known to be expressed in VSMCs and has been identified as an important regulator of GPCR trafficking and GPCR effects on cell proliferation. Identified Ser/Thr kinases included several PKC isoforms and the β-adrenoceptor kinases 1 and 2. Cross-talk between the signaling mechanisms involved in α2B-adrenoceptor-evoked VSMC proliferation thus appears to involve PKC activation, subsequent changes in gene expression, transactivation of EGFR, and modulation of kinase activities and growth factor-mediated signaling. While many of the identified individual signals were relatively small in terms of effect size, many of them were validated by combining pathway analysis and our integrated screening approach.

Defining Substrate Specificities for Lipase and Phospholipase Candidates

Microorganisms produce a wide spectrum of (phospho)lipases that are secreted in order to make external substrates available for the organism. Alternatively, other (phospho)lipases may be physically associated with the producing organism causing a turnover of intrinsic lipids and frequently giving rise to a remodeling of the cellular membranes. Although potential (phospho)lipases can be predicted with a number of algorithms when the gene/protein sequence is available, experimental proof of the enzyme activities, substrate specificities, and potential physiological functions has frequently not been obtained. This manuscript describes the optimization of assay conditions for prospective (phospho)lipases with unknown substrate specificities and how to employ these optimized conditions in the search for the natural substrate of a respective (phospho)lipase. Using artificial chromogenic substrates, such as p-nitrophenyl derivatives, may help to detect a minor enzymatic activity for a predicted (phospho)lipase under standard conditions. Having encountered such a minor enzymatic activity, the distinct parameters of an enzyme assay can be varied in order to obtain a more efficient hydrolysis of the artificial substrate. After having determined the conditions under which an enzyme works well, a variety of potential natural substrates should be assayed for their degradation, a process that can be followed employing distinct chromatographic methods. The definition of substrate specificities for new enzymes, often provides hypotheses for a potential physiological role of these enzymes, which then can be tested experimentally. Following these guidelines, we were able to identify a phospholipase C (SMc00171) that degrades phosphatidylcholine to phosphocholine and diacylglycerol, in a crucial step for the remodeling of membranes in the bacterium Sinorhizobium meliloti upon phosphorus-limiting conditions of growth. For two predicted patatin-like phospholipases (SMc00930 and SMc01003) of the same organism, we could redefine their substrate specificities and clarify that SMc01003 is a diacylglycerol lipase.

A Synthetic Kinome Microarray Data Generator

Cellular pathways involve the phosphorylation and dephosphorylation of proteins. Peptide microarrays called kinome arrays facilitate the measurement of the phosphorylation activity of hundreds of proteins in a single experiment. Analyzing the data from kinome microarrays is a multi-step process. Typically, various techniques are possible for a particular step, and it is necessary to compare and evaluate them. Such evaluations require data for which correct analysis results are known. Unfortunately, such kinome data is not readily available in the community. Further, there are no established techniques for creating artificial kinome datasets with known results and with the same characteristics as real kinome datasets. In this paper, a methodology for generating synthetic kinome array data is proposed. The methodology relies on actual intensity measurements from kinome microarray experiments and preserves their subtle characteristics. The utility of the methodology is demonstrated by evaluating methods for eliminating heterogeneous variance in kinome microarray data. Phosphorylation intensities from kinome microarrays often exhibit such heterogeneous variance and its presence can negatively impact downstream statistical techniques that rely on homogeneity of variance. It is shown that using the output from the proposed synthetic data generator, it is possible to critically compare two variance stabilization methods.

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.