Elucidation of Signaling Pathways from Large-Scale Phosphoproteomic Data Using Protein Interaction Networks

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.

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.

A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks

Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of <12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify >46,000 phosphorylation sites on >6600 proteins, of which >4500 sites from 2110 proteins displayed a >2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at <3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a >2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.

Functional impact of protein-RNA variation in clinical cancer analyses

Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this manuscript we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

Turck C. Delineation of biomarkers and molecular pathways of residual effects of fluoxetine treatment in juvenile rhesus monkeys by proteomic profiling

Fluoxetine (Prozac™) is the only antidepressant approved by the US Food and Drug Administration (FDA) for the treatment of major depressive disorder (MDD) in children. Despite its considerable efficacy as a selective serotonin reuptake inhibitor, the possible long-term effects of fluoxetine on brain development in children are poorly understood. In the current study, we aimed to delineate molecular mechanisms and protein biomarkers in the brains of juvenile rhesus macaques (Macaca mulatta) one year after the discontinuation of fluoxetine treatment using proteomic and phosphoproteomic profiling. We identified several differences in protein expression and phosphorylation in the dorsolateral prefrontal cortex (DLPFC) and cingulate cortex (CC) that correlated with impulsivity in animals, suggesting that the GABAergic synapse pathway may be affected by fluoxetine treatment. Biomarkers in combination with the identified pathways contribute to a better understanding of the mechanisms underlying the chronic effects of fluoxetine after discontinuation in children.

A kinase inhibitor screen reveals MEK1/2 as a novel therapeutic target to antagonize IGF1R-mediated antiestrogen resistance in ERα-positive luminal breast cancer

Antiestrogen resistance of breast cancer has been related to enhanced growth factor receptor expression and activation. We have previously shown that ectopic expression and subsequent activation of the insulin-like growth factor-1 receptor (IGF1R) or the epidermal growth factor receptor (EGFR) in MCF7 or T47D breast cancer cells results in antiestrogen resistance. In order to identify novel therapeutic targets to prevent this antiestrogen resistance, we performed kinase inhibitor screens with 273 different inhibitors in MCF7 cells overexpressing IGF1R or EGFR. Kinase inhibitors that antagonized antiestrogen resistance but are not directly involved in IGF1R or EGFR signaling were prioritized for further analyses. Various ALK (anaplastic lymphoma receptor tyrosine kinase) inhibitors inhibited cell proliferation in IGF1R expressing cells under normal and antiestrogen resistance conditions by preventing IGF1R activation and subsequent downstream signaling; the ALK inhibitors did not affect EGFR signaling. On the other hand, MEK (mitogen-activated protein kinase kinase)1/2 inhibitors, including PD0325901, selumetinib, trametinib and TAK733, selectively antagonized IGF1R signaling-mediated antiestrogen resistance but did not affect cell proliferation under normal growth conditions. RNAseq analysis revealed that MEK inhibitors PD0325901 and selumetinib drastically altered cell cycle progression and cell migration networks under IGF1R signaling-mediated antiestrogen resistance. In a group of 219 patients with metastasized ER+ breast cancer, strong pMEK staining showed a significant correlation with no clinical benefit of first-line tamoxifen treatment. We propose a critical role for MEK activation in IGF1R signaling-mediated antiestrogen resistance and anticipate that dual-targeted therapy with a MEK inhibitor and antiestrogen could improve treatment outcome.

Cancer proteogenomics: current impact and future prospects

Genomic analyses in cancer have been enormously impactful, leading to the identification of driver mutations and development of targeted therapies. But the functions of the vast majority of somatic mutations and copy number variants in tumours remain unknown, and the causes of resistance to targeted therapies and methods to overcome them are poorly defined. Recent improvements in mass spectrometry-based proteomics now enable direct examination of the consequences of genomic aberrations, providing deep and quantitative characterization of tumour tissues. Integration of proteins and their post-translational modifications with genomic, epigenomic and transcriptomic data constitutes the new field of proteogenomics, and is already leading to new biological and diagnostic knowledge with the potential to improve our understanding of malignant transformation and therapeutic outcomes. In this Review we describe recent developments in proteogenomics and key findings from the proteogenomic analysis of a wide range of cancers. Considerations relevant to the selection and use of samples for proteogenomics and the current technologies used to generate, analyse and integrate proteomic with genomic data are described. Applications of proteogenomics in translational studies and immuno-oncology are rapidly emerging, and the prospect for their full integration into therapeutic trials and clinical care seems bright.

Perseus plugin "Metis" for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs

We introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity.

Hypothyroidism and Diabetes-Related Dementia: Focused on Neuronal Dysfunction, Insulin Resistance, and Dyslipidemia

The incidence of dementia is steadily increasing worldwide. The risk factors for dementia are diverse, and include genetic background, environmental factors, sex differences, and vascular abnormalities. Among the subtypes of dementia, diabetes-related dementia is emerging as a complex type of dementia related to metabolic imbalance, due to the increase in the number of patients with metabolic syndrome and dementia worldwide. Thyroid hormones are considered metabolic regulatory hormones and affect various diseases, such as liver failure, obesity, and dementia. Thyroid dysregulation affects various cellular mechanisms and is linked to multiple disease pathologies. In particular, hypothyroidism is considered a critical cause for various neurological problems-such as metabolic disease, depressive symptoms, and dementia-in the central nervous system. Recent studies have demonstrated the relationship between hypothyroidism and brain insulin resistance and dyslipidemia, leading to diabetes-related dementia. Therefore, we reviewed the relationship between hypothyroidism and diabetes-related dementia, with a focus on major features of diabetes-related dementia such as insulin resistance, neuronal dysfunction, and dyslipidemia.

Signalling dynamics in embryonic development

In multicellular organisms, cellular behaviour is tightly regulated to allow proper embryonic development and maintenance of adult tissue. A critical component in this control is the communication between cells via signalling pathways, as errors in intercellular communication can induce developmental defects or diseases such as cancer. It has become clear over the last years that signalling is not static but varies in activity over time. Feedback mechanisms present in every signalling pathway lead to diverse dynamic phenotypes, such as transient activation, signal ramping or oscillations, occurring in a cell type- and stage-dependent manner. In cells, such dynamics can exert various functions that allow organisms to develop in a robust and reproducible way. Here, we focus on Erk, Wnt and Notch signalling pathways, which are dynamic in several tissue types and organisms, including the periodic segmentation of vertebrate embryos, and are often dysregulated in cancer. We will discuss how biochemical processes influence their dynamics and how these impact on cellular behaviour within multicellular systems.

Using Multilayer Heterogeneous Networks to Infer Functions of Phosphorylated Sites

Mass spectrometry-based quantitative phosphoproteomics has become an essential approach in the study of cellular processes such as signaling. Commonly used methods to analyze phosphoproteomics datasets depend on generic, gene-centric annotations such as Gene Ontology terms, which do not account for the function of a protein in a particular phosphorylation state. Analysis of phosphoproteomics data is hampered by a lack of phosphorylated site-specific annotations. We propose a method that combines shotgun phosphoproteomics data, protein-protein interactions, and functional annotations into a heterogeneous multilayer network. Phosphorylation sites are associated to potential functions using a random walk on the heterogeneous network (RWHN) algorithm. We validated our approach against a model of the MAPK/ERK pathway and functional annotations from PhosphoSitePlus and were able to associate differentially regulated sites on the same proteins to their previously described specific functions. We further tested the algorithm on three previously published datasets and were able to reproduce their experimentally validated conclusions and to associate phosphorylation sites with known functions based on their regulatory patterns. Our approach provides a refinement of commonly used analysis methods and accurately predicts context-specific functions for sites with similar phosphorylation profiles.

Structural analysis of mammalian protein phosphorylation at a proteome level

Phosphorylation is an essential post-translational modification for almost all cellular processes. Several global phosphoproteomics analyses have revealed phosphorylation profiles under different conditions. Beyond identification of phospho-sites, protein structures add another layer of information about their functionality. In this study, we systematically characterize phospho-sites based on their 3D locations in the protein and establish a location map for phospho-sites. More than 250,000 phospho-sites have been analyzed, of which 8,686 sites match at least one structure and are stratified based on their respective 3D positions. Core phospho-sites possess two distinct groups based on their dynamicity. Dynamic core phosphorylations are significantly more functional compared with static ones. The dynamic core and the interface phospho-sites are the most functional among all 3D phosphorylation groups. Our analysis provides global characterization and stratification of phospho-sites from a structural perspective that can be utilized for predicting functional relevance and filtering out false positives in phosphoproteomic studies.

A deep analysis of the proteomic and phosphoproteomic alterations that occur in skeletal muscle after the onset of immobilization

KEY POINTS

A decrease in protein synthesis plays a major role in the loss of muscle mass that occurs in response to immobilization. In mice, immobilization leads to a rapid (within 6 h) and progressive decrease in the rate of protein synthesis and this effect is mediated by a decrease in translational efficiency. Deep proteomic and phosphoproteomic analyses of mouse skeletal muscles revealed that the rapid immobilization-induced decrease in protein synthesis cannot be explained by changes in the abundance or phosphorylation state of proteins that have been implicated in the regulation of translation.

ABSTRACT

The disuse of skeletal muscle, such as that which occurs during immobilization, can lead to the rapid loss of muscle mass, and a decrease in the rate of protein synthesis plays a major role in this process. Indeed, current dogma contends that the decrease in protein synthesis is mediated by changes in the activity of protein kinases (e.g. mTOR); however, the validity of this model has not been established. Therefore, to address this, we first subjected mice to 6, 24 or 72 h of unilateral immobilization and then used the SUnSET technique to measure changes in the relative rate of protein synthesis. The result of our initial experiments revealed that immobilization leads to a rapid (within 6 h) and progressive decrease in the rate of protein synthesis and that this effect is mediated by a decrease in translational efficiency. We then performed a deep mass spectrometry-based analysis to determine whether this effect could be explained by changes in the expression and/or phosphorylation state of proteins that regulate translation. From this analysis, we were able to quantify 4320 proteins and 15,020 unique phosphorylation sites, and surprisingly, the outcomes revealed that the rapid immobilization-induced decrease in protein synthesis could not be explained by changes in either the abundance, or phosphorylation state, of proteins. The results of our work not only challenge the current dogma in the field, but also provide an expansive resource of information for future studies that are aimed at defining how disuse leads to loss of muscle mass.

Measuring pathway database coverage of the phosphoproteome

Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data.

Neuroproteomics of the Synapse: Subcellular Quantification of Protein Networks and Signaling Dynamics

One of the most fascinating features of the brain is its ability to adapt to its surroundings. Synaptic plasticity, the dynamic mechanism of functional and structural alterations in synaptic strength, is essential for brain functioning and underlies a variety of processes such as learning and memory. Although the molecular mechanisms underlying such rapid plasticity are not fully understood, a consensus exists on the important role of proteins. The study of these neuronal proteins using neuroproteomics has increased rapidly in the last decades, and advancements in MS-based proteomics have broadened our understanding of neuroplasticity exponentially. In this review, we discuss the trends in MS-based neuroproteomics for the study of synaptic protein-protein interactions and protein signaling dynamics, with a focus on sample types, different labeling and enrichment approaches, and data analysis and interpretation. We highlight studies from the last 5 years, with a focus on synapse structure, composition, functioning, or signaling and finally discuss some recent developments that could further advance the field of neuroproteomics.

Denoising large-scale biological data using network filters

Background

Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.

Results

We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data.

Conclusions

Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

Supplementary Information

The online version supplementary material available at 10.1186/s12859-021-04075-x.

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.

PhosR enables processing and functional analysis of phosphoproteomic data

Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of "stably phosphorylated sites" and in functional analysis for inferring active kinases and signaling pathways. In particular, we introduce a "signalome" construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data.

Phosphoproteomics identifies microglial Siglec-F inflammatory response during neurodegeneration

Alzheimer's disease (AD) is characterized by the appearance of amyloid-β plaques, neurofibrillary tangles, and inflammation in brain regions involved in memory. Using mass spectrometry, we have quantified the phosphoproteome of the CK-p25, 5XFAD, and Tau P301S mouse models of neurodegeneration. We identified a shared response involving Siglec-F which was upregulated on a subset of reactive microglia. The human paralog Siglec-8 was also upregulated on microglia in AD. Siglec-F and Siglec-8 were upregulated following microglial activation with interferon gamma (IFNγ) in BV-2 cell line and human stem cell-derived microglia models. Siglec-F overexpression activates an endocytic and pyroptotic inflammatory response in BV-2 cells, dependent on its sialic acid substrates and immunoreceptor tyrosine-based inhibition motif (ITIM) phosphorylation sites. Related human Siglecs induced a similar response in BV-2 cells. Collectively, our results point to an important role for mouse Siglec-F and human Siglec-8 in regulating microglial activation during neurodegeneration.

Stage-specific protein-domain mutational profile of invasive ductal breast cancer

BACKGROUND

Understanding the mechanisms underlying the malignant progression of cancer cells is crucial for early diagnosis and therapeutic treatment for cancer. Mutational heterogeneity of breast cancer suggests that about a dozen of cancer genes consistently mutate, together with many other genes mutating occasionally, in patients.

METHODS

Using the whole-exome sequences and clinical information of 468 patients in the TCGA project data portal, we analyzed mutated protein domains and signaling pathway alterations in order to understand how infrequent mutations contribute aggregately to tumor progression in different stages.

RESULTS

Our findings suggest that while the spectrum of mutated domains was diverse, mutations were aggregated in Pkinase, Pkinase Tyr, Y-Phosphatase and Src-homology 2 domains, highlighting the genetic heterogeneity in activating the protein tyrosine kinase signaling pathways in invasive ductal breast cancer.

CONCLUSIONS

The study provides new clues to the functional role of infrequent mutations in protein domain regions in different stages for invasive ductal breast cancer, yielding biological insights into metastasis for invasive ductal breast cancer.

Tumour kinome re-wiring governs resistance to palbociclib in oestrogen receptor positive breast cancers, highlighting new therapeutic modalities

Combination of CDK4/6 inhibitors and endocrine therapy improves clinical outcome in advanced oestrogen receptor (ER)-positive breast cancer, however relapse is inevitable. Here, we show in model systems that other than loss of RB1 few gene-copy number (CN) alterations are associated with irreversible-resistance to endocrine therapy and subsequent secondary resistance to palbociclib. Resistance to palbociclib occurred as a result of tumour cell re-wiring leading to increased expression of EGFR, MAPK, CDK4, CDK2, CDK7, CCNE1 and CCNE2. Resistance altered the ER genome wide-binding pattern, leading to decreased expression of ‘classical’ oestrogen-regulated genes and was accompanied by reduced sensitivity to fulvestrant and tamoxifen. Persistent CDK4 blockade decreased phosphorylation of tuberous sclerosis complex 2 (TSC2) enhancing EGFR signalling, leading to the re-wiring of ER. Kinome-knockdown confirmed dependency on ERBB-signalling and G2/M–checkpoint proteins such as WEE1, together with the cell cycle master regulator, CDK7. Noteworthy, sensitivity to CDK7 inhibition was associated with loss of ER and RB1 CN. Overall, we show that resistance to CDK4/6 inhibitors is dependent on kinase re-wiring and the redeployment of signalling cascades previously associated with endocrine resistance and highlights new therapeutic networks that can be exploited upon relapse after CDK4/6 inhibition.

Sleep-wake cycles drive daily dynamics of synaptic phosphorylation

The circadian clock drives daily changes of physiology, including sleep-wake cycles, through regulation of transcription, protein abundance, and function. Circadian phosphorylation controls cellular processes in peripheral organs, but little is known about its role in brain function and synaptic activity. We applied advanced quantitative phosphoproteomics to mouse forebrain synaptoneurosomes isolated across 24 hours, accurately quantifying almost 8000 phosphopeptides. Half of the synaptic phosphoproteins, including numerous kinases, had large-amplitude rhythms peaking at rest-activity and activity-rest transitions. Bioinformatic analyses revealed global temporal control of synaptic function through phosphorylation, including synaptic transmission, cytoskeleton reorganization, and excitatory/inhibitory balance. Sleep deprivation abolished 98% of all phosphorylation cycles in synaptoneurosomes, indicating that sleep-wake cycles rather than circadian signals are main drivers of synaptic phosphorylation, responding to both sleep and wake pressures.

Universal Ready-to-Use Immunotherapeutic Approach for the Treatment of Cancer: Expanded and Activated Polyclonal γδ Memory T Cells

In the last years, important progresses have been registered in the treatment of patients suffering from oncological/haematological malignancies, but more still needs to be done to reduce toxicity and side effects, improve outcome and offer new strategies for relapsed or refractory disease. A remarkable part of these clinical benefits is due to advances in immunotherapy. Here, we investigate the generation of a novel, universal and ready-to-use immunotherapeutic product based on γδ-T lymphocytes. These cells are part of the innate immune system, exerting potent natural cytotoxicity against bacteria, viruses and tumours. This ability, coupled with their negligible alloreactivity, makes them attractive for adoptive immunotherapy approaches. To achieve a cell product suitable for clinical use, we developed a strategy capable to generate polyclonal γδ-T cells with predominant memory-Vδ1 phenotype in good manufacturing practice (GMP) procedures with the additional possibility of gene-modification to improve their anti-tumour activity. Irradiated, engineered artificial antigen-presenting cells (aAPCs) expressing CD86/41BBL/CD40L and the cytomegalovirus (CMV)-antigen-pp65 were used. The presence of CMV-pp65 and CD40L proved to be crucial for expansion of the memory-Vδ1 subpopulation. To allow clinical translation and guarantee patient safety, aAPCs were stably transduced with an inducible suicide gene. Expanded γδ-T cells showed high expression of activation and memory markers, without signs of exhaustion; they maintained polyclonality and potent anti-tumour activity both in vitro (against immortalised and primary blasts) and in in vivo studies without displaying alloreactivity signals. The molecular characterisation (phophoproteomic and gene-expression) of these cell products underlines their unique properties. These cells can further be armed with chimeric antigen receptors (CAR) to improve anti-tumour capacity and persistence. We demonstrate the feasibility of establishing an allogeneic third-party, off-the-shelf and ready-to-use, γδ-T-cell bank. These γδ-T cells may represent an attractive therapeutic option endowed with broad clinical applications, including treatment of viral infections in highly immunocompromised patients, treatment of aggressive malignancies refractory to conventional approaches, bridging therapy to more targeted immunotherapeutic approaches and, ultimately, an innovative platform for the development of off-the-shelf CAR-T-cell products.

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.

Personalization of prostate cancer therapy through phosphoproteomics

Castration-resistant prostate cancer (CRPC) remains incurable despite the approval of several new treatments. Identification of new biomarkers and therapeutic targets to enable personalization of CRPC therapy, with the aim of maximizing therapeutic responses and minimizing toxicity in patients, is urgently needed. Prostate cancer progression and therapeutic resistance are frequently driven by aberrantly activated kinase signalling pathways that are amenable to pharmacological inhibition. Personalized phosphoproteomics, which enables the analysis of signalling networks in individual tumours, is a promising approach to advance personalized therapy by discovering biomarkers of pathway activity and clinically actionable targets. Several technologies for global and targeted phosphoproteomic analysis exist, each with its own strengths and shortcomings. Global discovery phosphoproteomics is predominantly conducted using liquid chromatography-tandem mass spectrometry coupled with data-dependent or data-independent acquisition technologies. Multiplexed targeted phosphoproteomics can be divided into platforms based on mass spectrometry or antibodies, including selected or parallel reaction monitoring and triggered by offset, multiplexed, accurate mass, high-resolution, absolute quantification (known as TOMAHAQ) or forward-phase or reverse-phase protein arrays, respectively. Several obstacles still need to be overcome before the full potential of phosphoproteomics can be realized in routine clinical practice, but a future phosphoproteomics-centric trans-omic profiling approach should enable optimized personalized CRPC management through improved biomarkers and targeted treatments.

New SDC function prediction based on protein-protein interaction using bioinformatics tools

The precise roles for SDC have been complex to specify. Assigning and reanalyzing protein and peptide identification to novel protein functions is one of the most important challenges in postgenomic era. Here, we provide SDC molecular description to support, contextualize and reanalyze the corresponding protein-protein interaction (PPI). From SDC-1 data mining, we discuss the potential of bioinformatics tools to predict new biological rules of SDC. Using these methods, we have assembled new possibilities for SDC biology from PPI data, once, the understanding of biology complexity cannot be capture from one simple question.

A systematic approach to orient the human protein-protein interaction network

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

Conceptual Evolution of Cell Signaling

During the last 100 years, cell signaling has evolved into a common mechanism for most physiological processes across systems. Although the majority of cell signaling principles were initially derived from hormonal studies, its exponential growth has been supported by interdisciplinary inputs, e.g., from physics, chemistry, mathematics, statistics, and computational fields. As a result, cell signaling has grown out of scope for any general review. Here, we review how the messages are transferred from the first messenger (the ligand) to the receptor, and then decoded with the help of cascades of second messengers (kinases, phosphatases, GTPases, ions, and small molecules such as cAMP, cGMP, diacylglycerol, etc.). The message is thus relayed from the membrane to the nucleus where gene expression ns, subsequent translations, and protein targeting to the cell membrane and other organelles are triggered. Although there are limited numbers of intracellular messengers, the specificity of the response profiles to the ligands is generated by the involvement of a combination of selected intracellular signaling intermediates. Other crucial parameters in cell signaling are its directionality and distribution of signaling strengths in different pathways that may crosstalk to adjust the amplitude and quality of the final effector output. Finally, we have reflected upon its possible developments during the coming years.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

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 .

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.

Clinically Relevant Post-Translational Modification Analyses-Maturing Workflows and Bioinformatics Tools

Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra-many of which likely harbour PTMs-through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation.

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.

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

Cells sense and respond to stressful environments by utilizing complex signaling networks that integrate diverse signals to coordinate a multi-faceted physiological response. Much of this response is controlled by post-translational protein phosphorylation. Although many regulators that mediate changes in protein phosphorylation are known, how these regulators inter-connect in a single regulatory network that can transmit cellular signals is not known. It is also unclear how regulators that promote growth and regulators that activate the stress response interconnect to reorganize resource allocation during stress. Here, we developed an integrated experimental and computational workflow to infer the signaling network that regulates phosphorylation changes during osmotic stress in the budding yeast Saccharomyces cerevisiae. The workflow integrates data measuring protein phosphorylation changes in response to osmotic stress with known physical interactions between yeast proteins from large-scale datasets, along with other information about how regulators recognize their targets. The resulting network suggested new signaling connections between regulators and pathways, including those involved in regulating growth and defense, and predicted new regulators involved in stress defense. Our work highlights the power of using network inference to deliver new insight on how cells coordinate a diverse adaptive strategy to stress.

Identifying Novel Signaling Pathways: An Exercise Scientists Guide to Phosphoproteomics

We propose that phosphoproteomic-based studies will radically advance our knowledge about exercise-regulated signaling events. However, these studies use cutting-edge technologies that can be difficult for nonspecialists to understand. Hence, this review is intended to help nonspecialists 1) understand the fundamental technologies behind phosphoproteomic analysis and 2) use various bioinformatic tools that can be used to interrogate phosphoproteomic datasets.

The diabetic brain and cognition

The prevalence of both Alzheimer's disease (AD) and vascular dementia (VaD) is increasing with the aging of the population. Studies from the last several years have shown that people with diabetes have an increased risk for dementia and cognitive impairment. Therefore, the authors of this consensus review tried to elaborate on the role of diabetes, especially diabetes type 2 (T2DM) in both AD and VaD. Based on the clinical and experimental work of scientists from 18 countries participating in the International Congress on Vascular Disorders and on literature search using PUBMED, it can be concluded that T2DM is a risk factor for both, AD and VaD, based on a pathology of glucose utilization. This pathology is the consequence of a disturbance of insulin-related mechanisms leading to brain insulin resistance. Although the underlying pathological mechanisms for AD and VaD are different in many aspects, the contribution of T2DM and insulin resistant brain state (IRBS) to cerebrovascular disturbances in both disorders cannot be neglected. Therefore, early diagnosis of metabolic parameters including those relevant for T2DM is required. Moreover, it is possible that therapeutic options utilized today for diabetes treatment may also have an effect on the risk for dementia. T2DM/IRBS contribute to pathological processes in AD and VaD.