PHOXTRACK-a tool for interpreting comprehensive datasets of post-translational modifications of proteins

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.

PI3K/mTOR is a therapeutically targetable genetic dependency in diffuse intrinsic pontine glioma

Diffuse midline glioma (DMG), including tumors diagnosed in the brainstem (diffuse intrinsic pontine glioma; DIPG), are uniformly fatal brain tumors that lack effective treatment. Analysis of CRISPR/Cas9 loss-of-function gene deletion screens identified PIK3CA and MTOR as targetable molecular dependencies across patient derived models of DIPG, highlighting the therapeutic potential of the blood-brain barrier-penetrant PI3K/Akt/mTOR inhibitor, paxalisib. At the human-equivalent maximum tolerated dose, mice treated with paxalisib experienced systemic glucose feedback and increased insulin levels commensurate with patients using PI3K inhibitors. To exploit genetic dependence and overcome resistance while maintaining compliance and therapeutic benefit, we combined paxalisib with the antihyperglycemic drug metformin. Metformin restored glucose homeostasis and decreased phosphorylation of the insulin receptor in vivo, a common mechanism of PI3K-inhibitor resistance, extending survival of orthotopic models. DIPG models treated with paxalisib increased calcium-activated PKC signaling. The brain penetrant PKC inhibitor enzastaurin, in combination with paxalisib, synergistically extended the survival of multiple orthotopic patient-derived and immunocompetent syngeneic allograft models; benefits potentiated in combination with metformin and standard-of-care radiotherapy. Therapeutic adaptation was assessed using spatial transcriptomics and ATAC-Seq, identifying changes in myelination and tumor immune microenvironment crosstalk. Collectively, this study has identified what we believe to be a clinically relevant DIPG therapeutic combinational strategy.

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 Provides Novel Insights into the Response of Primary Acute Lymphoblastic Leukemia Cells to Microtubule Depolymerization in G1 Phase of the Cell Cycle

Microtubule targeting agents (MTAs) have been used for the treatment of cancer for many decades and are among the most successful chemotherapeutic agents. However, their application and effectiveness are limited because of toxicity and resistance as well as a lack of knowledge of molecular mechanisms downstream of microtubule inhibition. Insights into key pathways that link microtubule disruption to cell death is critical for optimal use of these drugs, for defining biomarkers useful in patient stratification, and for informed design of drug combinations. Although MTAs characteristically induce death in mitosis, microtubule destabilizing agents such as vincristine also induce death directly in G1 phase in primary acute lymphoblastic leukemia (ALL) cells. Because many signaling pathways regulating cell survival and death involve changes in protein expression and phosphorylation, we undertook a comprehensive quantitative proteomic study of G1 phase ALL cells treated with vincristine. The results revealed distinct alterations associated with c-Jun N-terminal kinase signaling, anti-proliferative signaling, the DNA damage response, and cytoskeletal remodeling. Signals specifically associated with cell death were identified by pre-treatment with the CDK4/6 inhibitor palbociclib, which caused G1 arrest and precluded death induction. These results provide insights into signaling mechanisms regulating cellular responses to microtubule inhibition and provide a foundation for a better understanding of the clinical mechanisms of MTAs and for the design of novel drug combinations. The mass spectrometry proteomics data have been deposited to the PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) via the PRIDE partner repository with the data set identifier PXD027190 and 10.6019/PXD027190.

Heat shock protein 27 activity is linked to endothelial barrier recovery after proinflammatory GPCR-induced disruption

Vascular inflammation causes endothelial barrier disruption and tissue edema. Several inflammatory mediators act through G protein–coupled receptors (GPCRs), including protease-activated receptor-1 (PAR1), to elicit inflammatory responses. The activation of PAR1 by its ligand thrombin stimulates proinflammatory, p38 mitogen-activated protein kinase (MAPK) signaling that promotes endothelial barrier disruption. Through mass spectrometry phosphoproteomics, we identified heat shock protein 27 (HSP27), which exists as a large oligomer that binds to actin, as a promising candidate for the p38-mediated regulation of barrier integrity. Depletion of HSP27 by siRNA enhanced endothelial cell barrier permeability and slowed recovery after thrombin stimulation. We further showed that two effector kinases of p38 MAPK, MAPKAPK2 (MK2) and MAPKAPK3 (MK3), differentially phosphorylated HSP27 at Ser15, Ser78, and Ser82. Whereas inhibition of thrombin-stimulated p38 activation blocked HSP27 phosphorylation at all three sites, inhibition of MK2 reduced the phosphorylation of only Ser15 and Ser78. Inhibition of both MK2 and MK3 was necessary to attenuate Ser82 phosphorylation. Thrombin-stimulated p38-MK2-MK3 signaling induced HSP27 oligomer disassembly. However, a phosphorylation-deficient mutant of HSP27 exhibited defective oligomer disassembly and altered the dynamics of barrier recovery after thrombin stimulation. Moreover, blocking HSP27 oligomer reassembly with the small-molecule inhibitor J2 enhanced endothelial barrier permeability in vitro and vascular leakage in vivo in response to PAR1 activation. These studies reveal the distinct regulation of HSP27 phosphorylation and function induced by the GPCR-stimulated p38-MK2-MK3 signaling axis that controls the dynamics of endothelial barrier recovery in vitro and vascular leakage in vivo.

Phosphomatics: interactive interrogation of substrate-kinase networks in global phosphoproteomics datasets

MOTIVATION

Mass spectrometry-based phosphoproteomics can routinely identify and quantify thousands of phosphorylated peptides from a single experiment. However interrogating possible upstream kinases and identifying key literature for phosphorylation sites is laborious and time-consuming.

RESULTS

Here, we present Phosphomatics-a publicly available web resource for interrogating phosphoproteomics data. Phosphomatics allows researchers to upload phosphoproteomics data and interrogate possible relationships from a substrate-, kinase- or pathway-centric viewpoint.

AVAILABILITY AND IMPLEMENTATION

Phosphomatics is freely available via the internet at: https://phosphomatics.com.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

Triple negative breast cancer (TNBC) is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with non-tumorigenic epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method that had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFβ signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling. We observed overexpression of PTEN, which antagonizes the PI3K/AKT/mTOR pathway, and MYC, which downregulates the same pathway in the HCC1937 cells relative to the MDA-MB-231 cells. The PI3K/AKT/mTOR and Wnt/beta-catenin pathways are both downregulated in HCC1937 cells relative to MDA-MB-231 cells, which likely explains the divergent sensitivities of these cell lines to inhibitors of downstream signaling pathways. The DNA methylation and RNAseq data is freely available via GEO GSE171958 and the proteomics data is available via the ProteomeXchange PXD025238.

Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression

Lung cancer in East Asia is characterized by a high percentage of never-smokers, early onset and predominant EGFR mutations. To illuminate the molecular phenotype of this demographically distinct disease, we performed a deep comprehensive proteogenomic study on a prospectively collected cohort in Taiwan, representing early stage, predominantly female, non-smoking lung adenocarcinoma. Integrated genomic, proteomic, and phosphoproteomic analysis delineated the demographically distinct molecular attributes and hallmarks of tumor progression. Mutational signature analysis revealed age- and gender-related mutagenesis mechanisms, characterized by high prevalence of APOBEC mutational signature in younger females and over-representation of environmental carcinogen-like mutational signatures in older females. A proteomics-informed classification distinguished the clinical characteristics of early stage patients with EGFR mutations. Furthermore, integrated protein network analysis revealed the cellular remodeling underpinning clinical trajectories and nominated candidate biomarkers for patient stratification and therapeutic intervention. This multi-omic molecular architecture may help develop strategies for management of early stage never-smoker lung adenocarcinoma.

Computational Analysis of Phosphoproteomics Data in Multi-Omics Cancer Studies

Multiple types of molecular data for the same set of clinical samples are increasingly available and may be analyzed jointly in an integrative analysis to maximize comprehensive biological insight. This analysis is important as separate analyses of individual omics data types usually do not fully explain disease phenotypes. An increasing number of studies have now been focusing on multi-omics data integration, yet not many studies have included phosphoproteomics data, an important layer for understanding signaling pathways. Multi-omics integration methods with phosphoproteomics data are reviewed in the context of cancer research as well as multi-omics methods papers that would be promising to apply to phosphoproteomics data. Analysis of individual data types is still the major approach even in large cohort proteogenomics studies. Hence, a section is dedicated on possible integrative methods for multi-omics and phosphoproteomics data. In summary, this review provides the readers with both currently used integrative methods previously applied to phosphoproteomics and multi-omics data integration and other algorithms for multi-omics data integration promising for future application to phosphoproteomics data.

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.

Trio Haploinsufficiency Causes Neurodevelopmental Disease-Associated Deficits

Heterozygous coding mutations in TRIO are associated with neurodevelopmental disorders, including autism, schizophrenia, bipolar disorder, and epilepsy, and impair TRIO's biochemical activities. To model mutant alleles, we ablated one or both Trio alleles from excitatory neurons in the cortex and hippocampus of mice. Trio haploinsufficiency increases anxiety and impairs social preference and motor coordination. Trio loss reduces forebrain size and dendritic arborization but increases dendritic spine densities. Cortical synapses in Trio haploinsufficient mice are small, exhibit pre- and postsynaptic deficits, and cannot undergo long-term potentiation. Similar phenotypes are observed in Trio knockout mice. Overall, Trio haploinsufficiency causes severe disease-relevant deficits in behavior and neuronal structure and function. Interestingly, phosphodiesterase 4A5 (PDE4A5) levels are reduced and protein kinase A (PKA) signaling is increased when TRIO levels are reduced. Elevation of PDE4A5 and drug-based attenuation of PKA signaling rescue Trio haploinsufficiency-related dendritic spine defects, suggesting an avenue for therapeutic intervention for TRIO-related neurodevelopmental disorders.

CIGB-300 anticancer peptide regulates the protein kinase CK2-dependent phosphoproteome

Casein-kinase CK2 is a Ser/Thr protein kinase that fosters cell survival and proliferation of malignant cells. The CK2 holoenzyme, formed by the association of two catalytic alpha/alpha' (CK2α/CK2α') and two regulatory beta subunits (CK2β), phosphorylates diverse intracellular proteins partaking in key cellular processes. A handful of such CK2 substrates have been identified as targets for the substrate-binding anticancer peptide CIGB-300. However, since CK2β also contains a CK2 phosphorylation consensus motif, this peptide may also directly impinge on CK2 enzymatic activity, thus globally modifying the CK2-dependent phosphoproteome. To address such a possibility, firstly, we evaluated the potential interaction of CIGB-300 with CK2 subunits, both in cell-free assays and cellular lysates, as well as its effect on CK2 enzymatic activity. Then, we performed a phosphoproteomic survey focusing on early inhibitory events triggered by CIGB-300 and identified those CK2 substrates significantly inhibited along with disturbed cellular processes. Altogether, we provided here the first evidence for a direct impairment of CK2 enzymatic activity by CIGB-300. Of note, both CK2-mediated inhibitory mechanisms of this anticancer peptide (i.e., substrate- and enzyme-binding mechanism) may run in parallel in tumor cells and help to explain the different anti-neoplastic effects exerted by CIGB-300 in preclinical cancer models.

Elucidating essential kinases of endothelin signalling by logic modelling of phosphoproteomics data

Endothelins (EDN) are peptide hormones that activate a GPCR signalling system and contribute to several diseases, including hypertension and cancer. Current knowledge about EDN signalling is fragmentary, and no systems level understanding is available. We investigated phosphoproteomic changes caused by endothelin B receptor (ENDRB) activation in the melanoma cell lines UACC257 and A2058 and built an integrated model of EDNRB signalling from the phosphoproteomics data. More than 5,000 unique phosphopeptides were quantified. EDN induced quantitative changes in more than 800 phosphopeptides, which were all strictly dependent on EDNRB. Activated kinases were identified based on high confidence EDN target sites and validated by Western blot. The data were combined with prior knowledge to construct the first comprehensive logic model of EDN signalling. Among the kinases predicted by the signalling model, AKT, JNK, PKC and AMP could be functionally linked to EDN‐induced cell migration. The model contributes to the system‐level understanding of the mechanisms underlying the pleiotropic effects of EDN signalling and supports the rational selection of kinase inhibitors for combination treatments with EDN receptor antagonists.

Clinical potential of mass spectrometry-based proteogenomics

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

A Curated Resource for Phosphosite-specific Signature Analysis

Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTMSignature Enrichment Analysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0.

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.

Phosphoproteomic Analysis Reveals the Importance of Kinase Regulation During Orbivirus Infection

Bluetongue virus (BTV) causes infections in wild and domesticated ruminants with high morbidity and mortality and is responsible for significant economic losses in both developing and developed countries. BTV serves as a model for the study of other members of the Orbivirus genus. Previously, the importance of casein kinase 2 for BTV replication was demonstrated. To identify intracellular signaling pathways and novel host-cell kinases involved during BTV infection, the phosphoproteome of BTV infected cells was analyzed. Over 1000 phosphosites were identified using mass spectrometry, which were then used to determine the corresponding kinases involved during BTV infection. This analysis yielded protein kinase A (PKA) as a novel kinase activated during BTV infection. Subsequently, the importance of PKA for BTV infection was validated using a PKA inhibitor and activator. Our data confirmed that PKA was essential for efficient viral growth. Further, we showed that PKA is also required for infection of equid cells by African horse sickness virus, another member of the Orbivirus genus. Thus, despite their preference in specific host species, orbiviruses may utilize the same host signaling pathways during their replication.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Harnessing the power of proteomics for identification of oncogenic, druggable signalling pathways in cancer

INTRODUCTION

Genomic and transcriptomic profiling of tumours has revolutionised our understanding of cancer. However, the majority of tumours possess multiple mutations, and determining which oncogene, or even which pathway, to target is difficult. Proteomics is emerging as a powerful approach to identify the functionally important pathways driving these cancers, and how they can be targeted therapeutically. Areas covered: The authors provide a technical overview of mass spectrometry based approaches for proteomic profiling, and review the current and emerging strategies available for the identification of dysregulated networks, pathways, and drug targets in cancer cells, with a key focus on the ability to profile cancer kinomes. The potential applications of mass spectrometry in the clinic are also highlighted. Expert opinion: The addition of proteomic information to genomic platforms - 'proteogenomics' - is providing unparalleled insight in cancer cell biology. Application of improved mass spectrometry technology and methodology, in particular the ability to analyse post-translational modifications (the PTMome), is providing a more complete picture of the dysregulated networks in cancer, and uncovering novel therapeutic targets. While the application of proteomics to discovery research will continue to rise, improved workflow standardisation and reproducibility is required before mass spectrometry can enter routine clinical use.