Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

Phosphoproteomics data-driven signalling network inference: Does it work?

The advent of global phosphoproteome profiling has led to wide phosphosite coverage and therefore the opportunity to predict kinase-substrate associations from these datasets. However, the regulatory kinase is unknown for most substrates, due to biased and incomplete database annotations. In this study we compare the performance of six pairwise measures to predict kinase-substrate associations using a data driven approach on publicly available time resolved and perturbation mass spectrometry-based phosphoproteome data. First, we validated the performance of these measures using as a reference both a literature-based phosphosite-specific protein interaction network and a predicted kinase-substrate (KS) interactions set. The overall performance in predicting kinase-substrate associations using pairwise measures across both these reference sets was poor. To expand into the wider interactome space, we applied the approach on a network comprising pairs of substrates regulated by the same kinase (substrate-substrate associations) but found the performance to be equally poor. However, the addition of a sequence similarity filter for substrate-substrate associations led to a significant boost in performance. Our findings imply that the use of a filter to reduce the search space, such as a sequence similarity filter, can be used prior to the application of network inference methods to reduce noise and boost the signal. We also find that the current gold standard for reference sets is not adequate for evaluation as it is limited and context-agnostic. Therefore, there is a need for additional evaluation methods that have increased coverage and take into consideration the context-specific nature of kinase-substrate associations.

Integrating adipocyte insulin signaling and metabolism in the multi-omics era

Insulin stimulates glucose uptake into adipocytes via mTORC2/AKT signaling and GLUT4 translocation and directs glucose carbons into glycolysis, glycerol for TAG synthesis, and de novo lipogenesis. Adipocyte insulin resistance is an early indicator of type 2 diabetes in obesity, a worldwide health crisis. Thus, understanding the interplay between insulin signaling and central carbon metabolism pathways that maintains adipocyte function, blood glucose levels, and metabolic homeostasis is critical. While classically viewed through the lens of individual enzyme-substrate interactions, advances in mass spectrometry are beginning to illuminate adipocyte signaling and metabolic networks on an unprecedented scale, yet this is just the tip of the iceberg. Here, we review how 'omics approaches help to elucidate adipocyte insulin action in cellular time and space.

Bioinformatics toolbox for exploring protein phosphorylation network

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

An Integrative Analysis of Tumor Proteomic and Phosphoproteomic Profiles to Examine the Relationships Between Kinase Activity and Phosphorylation

Phosphorylation of proteins is a key way cells regulate function, both at the individual protein level and at the level of signaling pathways. Kinases are responsible for phosphorylation of substrates, generally on serine, threonine, or tyrosine residues. Though particular sequence patterns can be identified that dictate whether a residue will be phosphorylated by a specific kinase, these patterns are not highly predictive of phosphorylation. The availability of large scale proteomic and phosphoproteomic data sets generated using mass-spectrometry-based approaches provides an opportunity to study the important relationship between kinase activity, substrate specificity, and phosphorylation. In this study, we analyze relationships between protein abundance and phosphopeptide abundance across more than 150 tumor samples and show that phosphorylation at specific phosphosites is not well correlated with overall kinase abundance. However, individual kinases show a clear and statistically significant difference in correlation among known phosphosite targets for that kinase and randomly selected phosphosites. We further investigate relationships between phosphorylation of known activating or inhibitory sites on kinases and phosphorylation of their target phosphosites. Combined with motif-based analysis, this approach can predict novel kinase targets and show which subsets of a kinase's target repertoire are specifically active in one condition versus another.

AKTivation mechanisms

Akt1-3 (Akt) are a subset of the AGC protein Ser/Thr kinase family and play important roles in cell growth, metabolic regulation, cancer, and other diseases. We describe some of the roles of Akt in cell signaling and the biochemical and structural mechanisms of the regulation of Akt catalysis by the phospholipid PIP3 and by phosphorylation. Recent findings highlight a diverse set of strategies to control Akt catalytic activity to ensure its normal biological functions.

Visualization and Analysis of Epiproteome Dynamics

The epiproteome describes the set of all post-translational modifications (PTMs) made to the proteins comprising a cell or organism. The extent of the epiproteome is still largely unknown; however, advances in experimental techniques are beginning to produce a deluge of data, tracking dynamic changes to the epiproteome in response to cellular stimuli. These data have potential to revolutionize our understanding of biology and disease. This review covers a range of recent visualization methods and tools developed specifically for dynamic epiproteome data sets. These methods have been designed primarily for data sets on phosphorylation, as this the most studied PTM; however, most of these methods are also applicable to other types of PTMs. Unfortunately, the currently available methods are often inadequate for existing data sets; thus, realizing the potential buried in epiproteome data sets will require new, tailored bioinformatics methods that will help researchers analyze, visualize, and interactively explore these complex data sets.

Dynamic Metabolomics Reveals that Insulin Primes the Adipocyte for Glucose Metabolism

Insulin triggers an extensive signaling cascade to coordinate adipocyte glucose metabolism. It is considered that the major role of insulin is to provide anabolic substrates by activating GLUT4-dependent glucose uptake. However, insulin stimulates phosphorylation of many metabolic proteins. To examine the implications of this on glucose metabolism, we performed dynamic tracer metabolomics in cultured adipocytes treated with insulin. Temporal analysis of metabolite concentrations and tracer labeling revealed rapid and distinct changes in glucose metabolism, favoring specific glycolytic branch points and pyruvate anaplerosis. Integrating dynamic metabolomics and phosphoproteomics data revealed that insulin-dependent phosphorylation of anabolic enzymes occurred prior to substrate accumulation. Indeed, glycogen synthesis was activated independently of glucose supply. We refer to this phenomenon as metabolic priming, whereby insulin signaling creates a demand-driven system to "pull" glucose into specific anabolic pathways. This complements the supply-driven regulation of anabolism by substrate accumulation and highlights an additional role for insulin action in adipocyte glucose metabolism.

Phosphoproteomics reveals network rewiring to a pro-adhesion state in annexin-1-deficient mammary epithelial cells

Background

Annexin-1 (ANXA1) plays pivotal roles in regulating various physiological processes including inflammation, proliferation and apoptosis, and deregulation of ANXA1 functions has been associated with tumorigenesis and metastasis events in several types of cancer. Though ANXA1 levels correlate with breast cancer disease status and outcome, its distinct functional involvement in breast cancer initiation and progression remains unclear. We hypothesized that ANXA1-responsive kinase signaling alteration and associated phosphorylation signaling underlie early events in breast cancer initiation events and hence profiled ANXA1-dependent phosphorylation changes in mammary gland epithelial cells.

Methods

Quantitative phosphoproteomics analysis of mammary gland epithelial cells derived from ANXA1-heterozygous and ANXA1-deficient mice was carried out using stable isotope labeling with amino acids in cell culture (SILAC)-based mass spectrometry. Kinase and signaling changes underlying ANXA1 perturbations were derived by upstream kinase prediction and integrated network analysis of altered proteins and phosphoproteins.

Results

We identified a total of 8110 unique phosphorylation sites, of which 582 phosphorylation sites on 372 proteins had ANXA1-responsive changes. A majority of these phosphorylation changes occurred on proteins associated with cytoskeletal reorganization spanning the focal adhesion, stress fibers, and also the microtubule network proposing new roles for ANXA1 in regulating microtubule dynamics. Comparative analysis of regulated global proteome and phosphoproteome highlighted key differences in translational and post-translational effects of ANXA1, and suggested closely coordinated rewiring of the cell adhesion network. Kinase prediction analysis suggested activity modulation of calmodulin-dependent protein kinase II (CAMK2), P21-activated kinase (PAK), extracellular signal-regulated kinase (ERK), and IκB kinase (IKK) upon loss of ANXA1. Integrative analysis revealed regulation of the WNT and Hippo signaling pathways in ANXA1-deficient mammary epithelial cells, wherein there is downregulation of transcriptional effects of TEA domain family (TEAD) suggestive of ANXA1-responsive transcriptional rewiring.

Conclusions

The phosphoproteome landscape uncovered several novel perspectives for ANXA1 in mammary gland biology and highlighted its involvement in key signaling pathways modulating cell adhesion and migration that could contribute to breast cancer initiation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0924-4) contains supplementary material, which is available to authorized users.

mTORC1 Is a Major Regulatory Node in the FGF21 Signaling Network in Adipocytes

FGF21 improves the metabolic profile of obese animals through its actions on adipocytes. To elucidate the signaling network responsible for mediating these effects, we quantified dynamic changes in the adipocyte phosphoproteome following acute exposure to FGF21. FGF21 regulated a network of 821 phosphosites on 542 proteins. A major FGF21-regulated signaling node was mTORC1/S6K. In contrast to insulin, FGF21 activated mTORC1 via MAPK rather than through the canonical PI3K/AKT pathway. Activation of mTORC1/S6K by FGF21 was surprising because this is thought to contribute to deleterious metabolic effects such as obesity and insulin resistance. Rather, mTORC1 mediated many of the beneficial actions of FGF21 in vitro, including UCP1 and FGF21 induction, increased adiponectin secretion, and enhanced glucose uptake without any adverse effects on insulin action. This study provides a global view of FGF21 signaling and suggests that mTORC1 may act to facilitate FGF21-mediated health benefits in vivo.

Phosphoproteome Analysis Reveals Differential Mode of Action of Sorafenib in Wildtype and Mutated FLT3 Acute Myeloid Leukemia (AML) Cells

Constitutively activating internal tandem duplication (ITD) alterations of the receptor tyrosine kinase FLT3 (Fms-like tyrosine kinase 3) are common in acute myeloid leukemia (AML) and classifies FLT3 as an attractive therapeutic target. So far, applications of FLT3 small molecule inhibitors have been investigated primarily in FLT3-ITD⁺ patients. Only recently, a prolonged event-free survival has been observed in AML patients who were treated with the multikinase inhibitor sorafenib in addition to standard therapy. Here, we studied the sorafenib effect on proliferation in a panel of 13 FLT3-ITD⁻ and FLT3-ITD⁺ AML cell lines. Sorafenib IC50 values ranged from 0.001 to 5.6 μm, whereas FLT3-ITD⁺ cells (MOLM-13, MV4-11) were found to be more sensitive to sorafenib than FLT3-ITD⁻ cells. However, we identified two FLT3-ITD⁻ cell lines (MONO-MAC-1 and OCI-AML-2) which were also sorafenib sensitive. Phosphoproteome analyses revealed that the affected pathways differed in sorafenib sensitive FLT3-ITD⁻ and FLT3-ITD⁺ cells. In MV4-11 cells sorafenib suppressed mTOR signaling by direct inhibition of FLT3. In MONO-MAC-1 cells sorafenib inhibited the MEK/ERK pathway. These data suggest that the FLT3 status in AML patients might not be the only factor predicting response to treatment with sorafenib.