Empirical inference of circuitry and plasticity in a kinase signaling network

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.

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

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.

Community detection in empirical kinase networks identifies new potential members of signalling pathways

Phosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and cell growth. However, deriving knowledge of signalling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites as markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, AKT, MEK and ERK inhibitors were used to reconstruct individual kinase networks. We used modularity maximisation to detect communities in each network, and selected the community containing the main target of the inhibitor used to treat cells. These analyses returned communities that contained known canonical signalling components. Interestingly, in addition to canonical PI3K/AKT/mTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signalling. We drew similar insights from an external phosphoproteomics dataset from breast cancer cells treated with rapamycin and oestrogen. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signalling pathways.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

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.

KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients.

eEF2K Activity Determines Synergy to Cotreatment of Cancer Cells With PI3K and MEK Inhibitors

PI3K-mammalian target of rapamycin and MAPK/ERK kinase (MEK)/mitogen-activated protein kinase (MAPK) are the most frequently dysregulated signaling pathways in cancer. A problem that limits the success of therapies that target individual PI3K-MAPK members is that these pathways converge to regulate downstream functions and often compensate each other, leading to drug resistance and transient responses to therapy. In order to overcome resistance, therapies based on cotreatments with PI3K/AKT and MEK/MAPK inhibitors are now being investigated in clinical trials, but the mechanisms of sensitivity to cotreatment are not fully understood. Using LC-MS/MS-based phosphoproteomics, we found that eukaryotic elongation factor 2 kinase (eEF2K), a key convergence point downstream of MAPK and PI3K pathways, mediates synergism to cotreatment with trametinib plus pictilisib (which target MEK1/2 and PI3Kα/δ, respectively). Inhibition of eEF2K by siRNA or with a small molecule inhibitor reversed the antiproliferative effects of the cotreatment with PI3K plus MEK inhibitors in a cell model-specific manner. Systematic analysis in 12 acute myeloid leukemia cell lines revealed that eEF2K activity was increased in cells for which PI3K plus MEKi cotreatment is synergistic, while PKC potentially mediated resistance to such cotreatment. Together, our study uncovers eEF2K activity as a key mediator of responses to PI3Ki plus MEKi and as a potential biomarker to predict synergy to cotreatment in cancer cells.

Suppression of Endothelial Cell FAK Expression Reduces Pancreatic Ductal Adenocarcinoma Metastasis after Gemcitabine Treatment

Despite substantial advances in the treatment of solid cancers, resistance to therapy remains a major obstacle to prolonged progression-free survival. Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers, with a high level of liver metastasis. Primary PDAC is highly hypoxic, and metastases are resistant to first-line treatment, including gemcitabine. Recent studies have indicated that endothelial cell (EC) focal adhesion kinase (FAK) regulates DNA-damaging therapy-induced angiocrine factors and chemosensitivity in primary tumor models. Here, we show that inducible loss of EC-FAK in both orthotopic and spontaneous mouse models of PDAC is not sufficient to affect primary tumor growth but reduces liver and lung metastasis load and improves survival rates in gemcitabine-treated, but not untreated, mice. EC-FAK loss did not affect primary tumor angiogenesis, tumor blood vessel leakage, or early events in metastasis, including the numbers of circulating tumor cells, tumor cell homing, or metastatic seeding. Phosphoproteomics analysis showed a downregulation of the MAPK, RAF, and PAK signaling pathways in gemcitabine-treated FAK-depleted ECs compared with gemcitabine-treated wild-type ECs. Moreover, low levels of EC-FAK correlated with increased survival and reduced relapse in gemcitabine-treated patients with PDAC, supporting the clinical relevance of these findings. Altogether, we have identified a new role of EC-FAK in regulating PDAC metastasis upon gemcitabine treatment that impacts outcome.

SIGNIFICANCE

These findings establish the potential utility of combinatorial endothelial cell FAK targeting together with gemcitabine in future clinical applications to control metastasis in patients with pancreatic ductal adenocarcinoma.

Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data

Phosphoproteomic experiments routinely observe thousands of phosphorylation sites. To understand the intracellular signaling processes that generated this data, one or more causal protein kinases must be assigned to each phosphosite. However, limited knowledge of kinase specificity typically restricts assignments to a small subset of a kinome. Starting from a statistical model of a high-throughput, in vitro kinase-substrate assay, I have developed an approach to high-coverage, multi-label kinase-substrate assignment called IV-KAPhE (“In vivo-Kinase Assignment for Phosphorylation Evidence”). Tested on human data, IV-KAPhE outperforms other methods of similar scope. Such computational methods generally predict a densely connected kinase-substrate network, with most sites targeted by multiple kinases, pointing either to unaccounted-for biochemical constraints or significant cross-talk and signaling redundancy. I show that such predictions can potentially identify biased kinase-site misannotations within families of closely related kinase isozymes and they provide a robust basis for kinase activity analysis.

Proteins can pass around information inside cells about changes in the environment. This process, called intracellular signaling, helps to trigger appropriate cellular responses to environmental changes. One of the main ways information is passed to proteins is through chemical “tagging,” called phosphorylation, by enzymes called protein kinases. We can measure the phosphorylation state of practically all proteins in a cell at any moment. Starting from known cases of phosphorylation by a kinase, many computational methods have been developed to predict if the kinase might tag a certain spot on another protein or if an observed tag was attached by the kinase, with different models for each kinase. I have developed a new method that instead uses a single model to assign one or more kinases to each observed tag, built from the latest large-scale experimental data. This change in focus and unbiased training data allows my method to be significantly more accurate than past methods. I also explored useful applications for my method. For example, I used it to show that much of our knowledge about which kinase is responsible for each tag is probably inaccurately biased towards the commonly studied ones.

Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine

The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells.

Adipocytes disrupt the translational programme of acute lymphoblastic leukaemia to favour tumour survival and persistence

The specific niche adaptations that facilitate primary disease and Acute Lymphoblastic Leukaemia (ALL) survival after induction chemotherapy remain unclear. Here, we show that Bone Marrow (BM) adipocytes dynamically evolve during ALL pathogenesis and therapy, transitioning from cellular depletion in the primary leukaemia niche to a fully reconstituted state upon remission induction. Functionally, adipocyte niches elicit a fate switch in ALL cells towards slow-proliferation and cellular quiescence, highlighting the critical contribution of the adipocyte dynamic to disease establishment and chemotherapy resistance. Mechanistically, adipocyte niche interaction targets posttranscriptional networks and suppresses protein biosynthesis in ALL cells. Treatment with general control nonderepressible 2 inhibitor (GCN2ib) alleviates adipocyte-mediated translational repression and rescues ALL cell quiescence thereby significantly reducing the cytoprotective effect of adipocytes against chemotherapy and other extrinsic stressors. These data establish how adipocyte driven restrictions of the ALL proteome benefit ALL tumours, preventing their elimination, and suggest ways to manipulate adipocyte-mediated ALL resistance.

Inositol treatment inhibits medulloblastoma through suppression of epigenetic-driven metabolic adaptation

Deregulation of chromatin modifiers plays an essential role in the pathogenesis of medulloblastoma, the most common paediatric malignant brain tumour. Here, we identify a BMI1-dependent sensitivity to deregulation of inositol metabolism in a proportion of medulloblastoma. We demonstrate mTOR pathway activation and metabolic adaptation specifically in medulloblastoma of the molecular subgroup G4 characterised by a BMI1High;CHD7Low signature and show this can be counteracted by IP6 treatment. Finally, we demonstrate that IP6 synergises with cisplatin to enhance its cytotoxicity in vitro and extends survival in a pre-clinical BMI1High;CHD7Low xenograft model.

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

PHONEMeS: Efficient Modeling of Signaling Networks Derived from Large-Scale Mass Spectrometry Data

Post-translational modifications of proteins play an important role in the regulation of cellular processes. The mass spectrometry analysis of proteome modifications offers huge potential for the study of how protein inhibitors affect the phosphosignaling mechanisms inside the cells. We have recently proposed PHONEMeS, a method that uses high-content shotgun phosphoproteomic data to build logical network models of signal perturbation flow. However, in its original implementation, PHONEMeS was computationally demanding and was only used to model signaling in a perturbation context. We have reformulated PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one. We have also expanded the scenarios that can be analyzed. PHONEMeS can model data upon perturbation on not only a known target but also deregulated pathways upstream and downstream of any set of deregulated kinases. Finally, PHONEMeS can now analyze data sets with multiple time points, which helps us to obtain better insight into the dynamics of the propagation of signals. We illustrate the value of the new approach on various data sets of medical relevance, where we shed light on signaling mechanisms and drug modes of action.

CD44/HA signaling mediates acquired resistance to a PI3Kα inhibitor

Most luminal breast carcinomas (BrCas) bearing PIK3CA mutations initially respond to phosphoinositide-3-kinase (PI3K)-α inhibitors, but many eventually become resistant. The underlying mechanisms of this resistance remain obscure. In this work, we showed that a CD44^high state due to aberrant isoform splicing was acquired from adaptive resistance to a PI3Kα inhibitor (BLY719) in luminal BrCas. Notably, the expression of CD44 was positively correlated with estrogen receptor (ER) activity in PIK3CA-mutant breast cancers, and ER-dependent transcription upon PI3Kα pathway inhibition was in turn mediated by CD44. Furthermore, the interaction of CD44 with the ligand hyaluronan (HA) initiated the Src-ERK signaling cascade, which subsequently maintained AKT and mTOR activity in the presence of a PI3Kα inhibitor. Activation of this pathway was prevented by disruption of the CD44/HA interaction, which in turn restored sensitivity to BLY719. Our results revealed that an ER-CD44-HA signaling circuit that mediates robust compensatory activation of the Src-ERK signaling cascade may contribute to the development of acquired resistance to PI3Kα inhibitors. This study provides new insight into the mechanism of adaptive resistance to PI3Kα inhibition therapy.

Investigation of cancer drug resistance mechanisms by phosphoproteomics

Cancer cell mutations can be identified by genomic and transcriptomic techniques. However, they are not sufficient to understand the full complexity of cancer heterogeneity. Analyses of proteins expressed in cancers and their modification profiles show how these mutations could be translated at the functional level. Protein phosphorylation is a major post-translational modification critical for regulating several cellular functions. The covalent addition of phosphate groups to serine, threonine, and tyrosine is catalyzed by protein kinases. Over the past years, kinases were strongly associated with cancer, thus inhibition of protein kinases emanated as novel cancer treatment. However, cancers frequently develop drug resistance. Therefore, a better understanding of drug effects on tumors is urgently needed. In this perspective, phosphoproteomics arose as advanced tool to monitor cancer therapies and to discover novel drugs. This review highlights the role of phosphoproteomics in predicting sensitivity or resistance of cancers towards tyrosine kinase inhibitors and cytotoxic drugs. It also shows the importance of phosphoproteomics in identifying biomarkers that could be applied in clinical diagnostics to predict responses to drugs.

Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources

Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.

Reconstructing kinase network topologies from phosphoproteomics data reveals cancer-associated rewiring

Understanding how oncogenic mutations rewire regulatory-protein networks is important for rationalizing the mechanisms of oncogenesis and for individualizing anticancer treatments. We report a chemical phosphoproteomics method to elucidate the topology of kinase-signaling networks in mammalian cells. We identified >6,000 protein phosphorylation sites that can be used to infer >1,500 kinase-kinase interactions and devised algorithms that can reconstruct kinase network topologies from these phosphoproteomics data. Application of our methods to primary acute myeloid leukemia and breast cancer tumors quantified the relationship between kinase expression and activity, and enabled the identification of hitherto unknown kinase network topologies associated with drug-resistant phenotypes or specific genetic mutations. Using orthogonal methods we validated that PIK3CA wild-type cells adopt MAPK-dependent circuitries in breast cancer cells and that the kinase TTK is important in acute myeloid leukemia. Our phosphoproteomic signatures of network circuitry can identify kinase topologies associated with both phenotypes and genotypes of cancer cells.

Cancer associated fibroblast FAK regulates malignant cell metabolism

Emerging evidence suggests that cancer cell metabolism can be regulated by cancer-associated fibroblasts (CAFs), but the mechanisms are poorly defined. Here we show that CAFs regulate malignant cell metabolism through pathways under the control of FAK. In breast and pancreatic cancer patients we find that low FAK expression, specifically in the stromal compartment, predicts reduced overall survival. In mice, depletion of FAK in a subpopulation of CAFs regulates paracrine signals that increase malignant cell glycolysis and tumour growth. Proteomic and phosphoproteomic analysis in our mouse model identifies metabolic alterations which are reflected at the transcriptomic level in patients with low stromal FAK. Mechanistically we demonstrate that FAK-depletion in CAFs increases chemokine production, which via CCR1/CCR2 on cancer cells, activate protein kinase A, leading to enhanced malignant cell glycolysis. Our data uncover mechanisms whereby stromal fibroblasts regulate cancer cell metabolism independent of genetic mutations in cancer cells.

Cracking the context-specific PI3K signaling code

Specificity in signal transduction is determined by the ability of cells to "encode" and subsequently "decode" different environmental signals. Akin to computer software, this "signaling code" governs context-dependent execution of cellular programs through modulation of signaling dynamics and can be corrupted by disease-causing mutations. Class IA phosphoinositide 3-kinase (PI3K) signaling is critical for normal growth and development and is dysregulated in human disorders such as benign overgrowth syndromes, cancer, primary immune deficiency, and metabolic syndrome. Despite decades of PI3K research, understanding of context-dependent regulation of the PI3K pathway and of the underlying signaling code remains rudimentary. Here, we review current knowledge on context-specific PI3K signaling and how technological advances now make it possible to move from a qualitative to quantitative understanding of this pathway. Insight into how cellular PI3K signaling is encoded or decoded may open new avenues for rational pharmacological targeting of PI3K-associated diseases. The principles of PI3K context-dependent signal encoding and decoding described here are likely applicable to most, if not all, major cell signaling pathways.

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.

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.

A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis

Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org .

LncRNAs-directed PTEN enzymatic switch governs epithelial-mesenchymal transition

Despite the structural conservation of PTEN with dual-specificity phosphatases, there have been no reports regarding the regulatory mechanisms that underlie this potential dual-phosphatase activity. Here, we report that K27-linked polyubiquitination of PTEN at lysines 66 and 80 switches its phosphoinositide/protein tyrosine phosphatase activity to protein serine/threonine phosphatase activity. Mechanistically, high glucose, TGF-β, CTGF, SHH, and IL-6 induce the expression of a long non-coding RNA, GAEA (Glucose Aroused for EMT Activation), which associates with an RNA-binding E3 ligase, MEX3C, and enhances its enzymatic activity, leading to the K27-linked polyubiquitination of PTEN. The MEX3C-catalyzed PTENK27-polyUb activates its protein serine/threonine phosphatase activity and inhibits its phosphatidylinositol/protein tyrosine phosphatase activity. With this altered enzymatic activity, PTENK27-polyUb dephosphorylates the phosphoserine/threonine residues of TWIST1, SNAI1, and YAP1, leading to accumulation of these master regulators of EMT. Animals with genetic inhibition of PTENK27-polyUb, by a single nucleotide mutation generated using CRISPR/Cas9 (PtenK80R/K80R), exhibit inhibition of EMT markers during mammary gland morphogenesis in pregnancy/lactation and during cutaneous wound healing processes. Our findings illustrate an unexpected paradigm in which the lncRNA-dependent switch in PTEN protein serine/threonine phosphatase activity is important for physiological homeostasis and disease development.

Combining Phosphoproteomics Datasets and Literature Information to Reveal the Functional Connections in a Cell Phosphorylation Network

Protein phosphorylation modulates many biological processes. However, the characterization of the complex regulatory circuits underlying cell response to external and internal stimuli is still limited by our inability to describe the phosphorylation network on a global scale. Modern MS-based phosphoproteomics allows monitoring tens of thousands of phosphorylation sites in multiple conditions, making the approach ideal to explore signaling pathways mediated by phosphorylation. Here, we review recent advances in phosphoproteomics and discuss some of the computational approaches developed to facilitate extraction of signaling information from these datasets. Finally, this review focuses on approaches that integrate prior literature information with unbiased phosphoproteomics experiments.

Illuminating the dark phosphoproteome

Protein phosphorylation is a major regulator of protein function and biological outcomes. This was first recognized through functional biochemical experiments, and in the past decade, major technological advances in mass spectrometry have enabled the study of protein phosphorylation on a global scale. This rapidly growing field of phosphoproteomics has revealed that more than 100,000 distinct phosphorylation events occur in human cells, which likely affect the function of every protein. Phosphoproteomics has improved the understanding of the function of even the most well-characterized protein kinases by revealing new downstream substrates and biology. However, current biochemical and bioinformatic approaches have only identified kinases for less than 5% of the phosphoproteome, and functional assignments of phosphosites are almost negligible. Notably, our understanding of the relationship between kinases and their substrates follows a power law distribution, with almost 90% of phosphorylation sites currently assigned to the top 20% of kinases. In addition, more than 150 kinases do not have a single known substrate. Despite a small group of kinases dominating biomedical research, the number of substrates assigned to a kinase does not correlate with disease relevance as determined by pathogenic human mutation prevalence and mouse model phenotypes. Improving our understanding of the substrates targeted by all kinases and functionally annotating the phosphoproteome will be broadly beneficial. Advances in phosphoproteomics technologies, combined with functional screening approaches, should make it feasible to illuminate the connectivity and functionality of the entire phosphoproteome, providing enormous opportunities for discovering new biology, therapeutic targets, and possibly diagnostics.

Systemic analysis of tyrosine kinase signaling reveals a common adaptive response program in a HER2-positive breast cancer

Drug-induced compensatory signaling and subsequent rewiring of the signaling pathways that support cell proliferation and survival promote the development of acquired drug resistance in tumors. Here, we sought to analyze the adaptive kinase response in cancer cells after distinct treatment with agents targeting human epidermal growth factor receptor 2 (HER2), specifically those that induce either only temporary cell cycle arrest or, alternatively, apoptosis in HER2-overexpressing cancers. We compared trastuzumab, ARRY380, the combination thereof, and a biparatopic, HER2-targeted designed ankyrin repeat protein (DARPin; specifically, 6L1G) and quantified the phosphoproteome by isobaric tagging using tandem mass tag liquid chromatography/tandem mass spectrometry (TMT LC-MS/MS). We found a specific signature of persistently phosphorylated tyrosine peptides after the nonapoptotic treatments, which we used to distinguish between different treatment-induced cancer cell fates. Next, we analyzed the activation of serine/threonine and tyrosine kinases after treatment using a bait peptide chip array and predicted the corresponding active kinases. Through a combined system-wide analysis, we identified a common adaptive kinase response program that involved the activation of focal adhesion kinase 1 (FAK1), protein kinase C-δ (PRKCD), and Ephrin (EPH) family receptors. These findings reveal potential targets to prevent adaptive resistance to HER2-targeted therapies.

Oxidative Stress in Cells with Extra Centrosomes Drives Non-Cell-Autonomous Invasion

Centrosomal abnormalities, in particular centrosome amplification, are recurrent features of human tumors. Enforced centrosome amplification in vivo plays a role in tumor initiation and progression. However, centrosome amplification occurs only in a subset of cancer cells, and thus, partly due to this heterogeneity, the contribution of centrosome amplification to tumors is unknown. Here, we show that supernumerary centrosomes induce a paracrine-signaling axis via the secretion of proteins, including interleukin-8 (IL-8), which leads to non-cell-autonomous invasion in 3D mammary organoids and zebrafish models. This extra centrosomes-associated secretory phenotype (ECASP) promotes invasion of human mammary cells via HER2 signaling activation. Further, we demonstrate that centrosome amplification induces an early oxidative stress response via increased NOX-generated reactive oxygen species (ROS), which in turn mediates secretion of pro-invasive factors. The discovery that cells with extra centrosomes can manipulate the surrounding cells highlights unexpected and far-reaching consequences of these abnormalities in cancer.

Reconstructing phosphorylation signalling networks from quantitative phosphoproteomic data

Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.

Role for ERK1/2-dependent activation of FCHSD2 in cancer cell-selective regulation of clathrin-mediated endocytosis

Clathrin-mediated endocytosis (CME) regulates the uptake of cell-surface receptors as well as their downstream signaling activities. We recently reported that signaling can reciprocally regulate CME in cancer cells and that this crosstalk can contribute to cancer progression. To further explore the nature and extent of the crosstalk between signaling and CME in cancer cell biology, we analyzed a panel of oncogenic signaling kinase inhibitors for their effects on CME across a panel of normal and cancerous cells. Inhibition of several kinases selectively affected CME in cancer cells, including inhibition of ERK1/2, which selectively inhibited CME by decreasing the rate of clathrin-coated pit (CCP) initiation. We identified an ERK1/2 substrate, the FCH/F-BAR and SH3 domain-containing protein FCHSD2, as being essential for the ERK1/2-dependent effects on CME and CCP initiation. Our data suggest that ERK1/2 phosphorylation activates FCHSD2 and regulates EGF receptor (EGFR) endocytic trafficking as well as downstream signaling activities. Loss of FCHSD2 activity in nonsmall cell lung cancer (NSCLC) cells leads to increased cell-surface expression and altered signaling downstream of EGFR, resulting in enhanced cell proliferation and migration. The expression level of FCHSD2 is positively correlated with higher NSCLC patient survival rates, suggesting that FCHSD2 can negatively affect cancer progression. These findings provide insight into the mechanisms and consequences of the reciprocal regulation of signaling and CME in cancer cells.

Benchmarking substrate-based kinase activity inference using phosphoproteomic data

Motivation

Phosphoproteomic experiments are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies.

Results

We used curated phosphoproteomic experiments and a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur to benchmark and compare different kinase activity inference strategies: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model. We also tested weighted variants of the Z-test and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence (in vivo, in vitro or in silico) supporting these influence the predictions.

Conclusions

Most models performed well with the Z-test and the GSEA performing best as determined by the area under the ROC curve (Mean AUC = 0.722). Weighting kinase targets by the kinase target sequence preference improves the results marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions.

Availability and Implementation

The KSEA implementation is available in https://github.com/ evocellnet/ksea. Additional data is available in http://phosfate.com

Supplementary information

Supplementary data are available at Bioinformatics online.

PHLDA1 Mediates Drug Resistance in Receptor Tyrosine Kinase-Driven Cancer

Development of resistance causes failure of drugs targeting receptor tyrosine kinase (RTK) networks and represents a critical challenge for precision medicine. Here, we show that PHLDA1 downregulation is critical to acquisition and maintenance of drug resistance in RTK-driven cancer. Using fibroblast growth factor receptor (FGFR) inhibition in endometrial cancer cells, we identify an Akt-driven compensatory mechanism underpinned by downregulation of PHLDA1. We demonstrate broad clinical relevance of our findings, showing that PHLDA1 downregulation also occurs in response to RTK-targeted therapy in breast and renal cancer patients, as well as following trastuzumab treatment in HER2+ breast cancer cells. Crucially, knockdown of PHLDA1 alone was sufficient to confer de novo resistance to RTK inhibitors and induction of PHLDA1 expression re-sensitized drug-resistant cancer cells to targeted therapies, identifying PHLDA1 as a biomarker for drug response and highlighting the potential of PHLDA1 reactivation as a means of circumventing drug resistance.

Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells

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/ .

Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability

Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks.

Protein kinases that phosphorylate splicing factors: Roles in cancer development, progression and possible therapeutic options

Disturbed alternative splicing is a common feature of human tumors. Splicing factors that control alternative splicing are phosphorylated by multiple kinases, including these that specifically add phosphoryl groups to serine-arginine rich proteins (e.g. SR-protein kinases, cdc2-like kinases, topoisomerase 1), and protein kinases that govern key cellular signaling pathways (i.e. AKT). Phosphorylation of splicing factors regulates their subcellular localization and interactions with target transcripts and protein partners, and thus significantly contributes the final result of splicing reactions. In this review we aim to summarize the current knowledge on the role of splicing kinases in cancer. Published studies and recently released data of The Cancer Genome Atlas demonstrate that expressions and activities of splicing kinases are commonly disturbed in cancers. Aberrant functioning of splicing kinases results in changed alternative splicing of tumor suppressors (e.g. p53) and regulators of cell signaling (e.g. MAPKs), apoptosis (e.g. MCL), and angiogenesis (VEGF). Splicing kinases act in complicated regulatory networks in which they mutually affect each other's activity to provide tight control of cellular signaling. Dysregulation of these regulatory networks contributes to oncogenic transformation, uncontrolled proliferation, enhanced migration and invasion. Furthermore, the activities of splicing kinases significantly contribute to cellular responses to genotoxic stress. In conclusion, published data provide strong evidence that splicing kinases emerge as important regulators of key processes governing malignant transformation, progression, and response to therapeutic treatments, suggesting their potential as clinically relevant targets.

Phosphoproteomic comparison of Pik3ca and Pten signalling identifies the nucleotidase NT5C as a novel AKT substrate

To identify novel effectors and processes regulated by PI3K pathway activation, we performed an unbiased phosphoproteomic screen comparing two common events of PI3K deregulation in cancer: oncogenic Pik3ca mutation (Pik3caH1047R) and deletion of Pten. Using mouse embryonic fibroblast (MEF) models that generate inducible, low-level pathway activation as observed in cancer, we quantified 7566 unique phosphopeptides from 3279 proteins. A number of proteins were found to be differentially-regulated by Pik3caH1047R and Pten loss, suggesting unique roles for these two events in processes such as vesicular trafficking, DNA damage repair and RNA splicing. We also identified novel PI3K effectors that were commonly-regulated, including putative AKT substrates. Validation of one of these hits, confirmed NT5C (5',3'-Nucleotidase, Cytosolic) as a novel AKT substrate, with an unexpected role in actin cytoskeleton regulation via an interaction with the ARP2/3 complex. This study has produced a comprehensive data resource and identified a new link between PI3K pathway activation and actin regulation.

Exploiting receptor tyrosine kinase co-activation for cancer therapy

Studies over the past decade have shown that many cancers have evolved receptor tyrosine kinase (RTK) co-activation as a mechanism to drive tumour progression and limit the lethal effects of therapy. This review summarises the general principles of RTK co-activation and discusses approaches to exploit this phenomenon in cancer therapy and drug discovery. Computational strategies to predict kinase co-dependencies by integrating drug screening data and kinase inhibitor selectivity profiles will also be described. We offer a perspective on the implications of RTK co-activation on tumour heterogeneity and cancer evolution and conclude by surveying emerging computational and experimental approaches that will provide insights into RTK co-activation biology and deliver new developments in effective cancer therapies.

Label-Free Phosphoproteomic Approach for Kinase Signaling Analysis

Phosphoproteomics is a powerful platform for the unbiased profiling of kinase-driven signaling pathways. Quantitation of phosphorylation can be performed by means of either labeling or label-free mass spectrometry (MS) methods. Because of their simplicity and universality, label-free methodology is gaining acceptance and popularity in molecular biology research. Analytical workflows for label-free quantification of phosphorylation, however, need to overcome several hurdles for the technique to be accurate and precise. These include the use of biochemical extraction procedures that efficiently and reproducibly isolate phosphopeptides from complex peptide matrices and an analytical strategy that can cope with missing MS/MS phosphopeptide spectra in a subset of the samples being compared. Testing the accuracy of the developed workflows is an essential prerequisite in the analysis of small molecules by MS, and this is achieved by constructing calibration curves to demonstrate linearity of quantification for each analyte. This level of analytical rigor is rarely shown in large-scale quantification of proteins using either label-based or label-free techniques. In this chapter we show an approach to test linearity of quantification of each phosphopeptide quantified by liquid chromatography (LC)-MS without the need to synthesize standards or label proteins. We further describe the appropriate sample handling techniques required for the reproducible recovery of phosphopeptides and explore the essential algorithmic features that enable the handling of missing MS/MS spectra and thus make label-free data suitable for such analyses. The combined technology described in this chapter expands the applicability of phosphoproteomics to questions not previously tractable with other methodologies.

Oxidative stress downstream of mTORC1 but not AKT causes a proliferative defect in cancer cells resistant to PI3K inhibition

Compounds targeting phosphatidylinositol-3-kinase/mammalian target of rapamycin (PI3K/mTOR) signaling are being investigated in multiple clinical settings, but drug resistance may reduce their benefit. Compound rechallenge after drug holidays can overcome such resistance, yet little is known about the impact of drug holidays on cell biochemistry. We found that PI3K inhibitor (PI3Ki)-resistant cells cultured in the absence of PI3Ki developed a proliferative defect, increased oxygen consumption and accumulated reactive oxygen species (ROS), leading to lactate production through hypoxia-inducible factor-1α. This metabolic imbalance was reversed by mammalian target of rapamycin complex 1 (mTORC1) inhibitors. Interestingly, neither AKT nor c-MYC was involved in mediating the metabolic phenotype, despite the latter contributing to resistant cells' proliferation. These data suggest that an AKT-independent PI3K/mTORC1 axis operates in these cells. The excessive ROS hampered cell division, and the metabolic phenotype made resistant cells more sensitive to hydrogen peroxide and nutrient starvation. Thus, the proliferative defect of PI3Ki-resistant cells during drug holidays is caused by defective metabolic adaptation to chronic PI3K/mTOR pathway inhibition. This metabolic imbalance may open the therapeutic window for challenge with metabolic drugs during drug holidays.

An atlas of human kinase regulation

The coordinated regulation of protein kinases is a rapid mechanism that integrates diverse cues and swiftly determines appropriate cellular responses. However, our understanding of cellular decision‐making has been limited by the small number of simultaneously monitored phospho‐regulatory events. Here, we have estimated changes in activity in 215 human kinases in 399 conditions derived from a large compilation of phosphopeptide quantifications. This atlas identifies commonly regulated kinases as those that are central in the signaling network and defines the logic relationships between kinase pairs. Co‐regulation along the conditions predicts kinase–complex and kinase–substrate associations. Additionally, the kinase regulation profile acts as a molecular fingerprint to identify related and opposing signaling states. Using this atlas, we identified essential mediators of stem cell differentiation, modulators of Salmonella infection, and new targets of AKT1. This provides a global view of human phosphorylation‐based signaling and the necessary context to better understand kinase‐driven decision‐making.

Stabilization of the metaphase spindle by Cdc14 is required for recombinational DNA repair

Cells are constantly threatened by multiple sources of genotoxic stress that cause DNA damage. To maintain genome integrity, cells have developed a coordinated signalling network called DNA damage response (DDR). While multiple kinases have been thoroughly studied during DDR activation, the role of protein dephosphorylation in the damage response remains elusive. Here, we show that the phosphatase Cdc14 is essential to fulfil recombinational DNA repair in budding yeast. After DNA double‐strand break (DSB) generation, Cdc14 is transiently released from the nucleolus and activated. In this state, Cdc14 targets the spindle pole body (SPB) component Spc110 to counterbalance its phosphorylation by cyclin‐dependent kinase (Cdk). Alterations in the Cdk/Cdc14‐dependent phosphorylation status of Spc110, or its inactivation during the induction of a DNA lesion, generate abnormal oscillatory SPB movements that disrupt DSB‐SPB interactions. Remarkably, these defects impair DNA repair by homologous recombination indicating that SPB integrity is essential during the repair process. Together, these results show that Cdc14 promotes spindle stability and DSB‐SPB tethering during DNA repair, and imply that metaphase spindle maintenance is a critical feature of the repair process.

Disulfide-activated protein kinase G Iα regulates cardiac diastolic relaxation and fine-tunes the Frank-Starling response

The Frank–Starling mechanism allows the amount of blood entering the heart from the veins to be precisely matched with the amount pumped out to the arterial circulation. As the heart fills with blood during diastole, the myocardium is stretched and oxidants are produced. Here we show that protein kinase G Iα (PKGIα) is oxidant-activated during stretch and this form of the kinase selectively phosphorylates cardiac phospholamban Ser16—a site important for diastolic relaxation. We find that hearts of Cys42Ser PKGIα knock-in (KI) mice, which are resistant to PKGIα oxidation, have diastolic dysfunction and a diminished ability to couple ventricular filling with cardiac output on a beat-to-beat basis. Intracellular calcium dynamics of ventricular myocytes isolated from KI hearts are altered in a manner consistent with impaired relaxation and contractile function. We conclude that oxidation of PKGIα during myocardial stretch is crucial for diastolic relaxation and fine-tunes the Frank–Starling response.

The stroke volume of the heart increases in response to an increase in the blood volume filling the heart. Here the authors reveal that this coordinated process is mediated in part by oxidative activation of the protein kinase G Iα, which phosphorylates phospholamban to enhance diastolic relaxation in mice.