Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast

Interrogating endothelial barrier regulation by temporally resolved kinase network generation

Kinases are key players in endothelial barrier regulation, yet their temporal function and regulatory phosphosignaling networks are incompletely understood. We developed a novel methodology, Temporally REsolved KInase Network Generation (TREKING), which combines a 28-kinase inhibitor screen with machine learning and network reconstruction to build time-resolved, functional phosphosignaling networks. We demonstrated the utility of TREKING for identifying pathways mediating barrier integrity after activation by thrombin with or without TNF preconditioning in brain endothelial cells. TREKING predicted over 100 kinases involved in barrier regulation and discerned complex condition-specific pathways. For instance, the MAPK-activated protein kinase 2 (MAPKAPK2/MK2) had early barrier-weakening activity in both inflammatory conditions but late barrier-strengthening activity exclusively with thrombin alone. Using temporal Western blotting, we confirmed that MAPKAPK2/MK2 was differentially phosphorylated under the two inflammatory conditions. We further showed with lentivirus-mediated knockdown of MAPK14/p38α and drug targeting the MAPK14/p38α-MAPKAPK2/MK2 complex that a MAP3K20/ZAK-MAPK14/p38α axis controlled the late activation of MAPKAPK2/MK2 in the thrombin-alone condition. Beyond the MAPKAPK2/MK2 switch, TREKING predicts extensive interconnected networks that control endothelial barrier dynamics.

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.

The regulatory landscape of the yeast phosphoproteome

The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. Here, to gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 min of stress exposure. We identify shared and perturbation-specific stress response programs, uncover loss of phosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase-substrate networks, as we exemplify with target of rapamycin signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks.

Reducing Boolean networks with backward equivalence

BACKGROUND

Boolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis.

RESULTS

We present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance.

CONCLUSIONS

BBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.

Positive feedback induces switch between distributive and processive phosphorylation of Hog1

Cellular decision making often builds on ultrasensitive MAPK pathways. The phosphorylation mechanism of MAP kinase has so far been described as either distributive or processive, with distributive mechanisms generating ultrasensitivity in theoretical analyses. However, the in vivo mechanism of MAP kinase phosphorylation and its activation dynamics remain unclear. Here, we characterize the regulation of the MAP kinase Hog1 in Saccharomyces cerevisiae via topologically different ODE models, parameterized on multimodal activation data. Interestingly, our best fitting model switches between distributive and processive phosphorylation behavior regulated via a positive feedback loop composed of an affinity and a catalytic component targeting the MAP kinase-kinase Pbs2. Indeed, we show that Hog1 directly phosphorylates Pbs2 on serine 248 (S248), that cells expressing a non-phosphorylatable (S248A) or phosphomimetic (S248E) mutant show behavior that is consistent with simulations of disrupted or constitutively active affinity feedback and that Pbs2-S248E shows significantly increased affinity to Hog1 in vitro. Simulations further suggest that this mixed Hog1 activation mechanism is required for full sensitivity to stimuli and to ensure robustness to different perturbations.

A focus on yeast mating: From pheromone signaling to cell-cell fusion

Cells live in a chemical environment and are able to orient towards chemical cues. Unicellular haploid fungal cells communicate by secreting pheromones to reproduce sexually. In the yeast models Saccharomyces cerevisiae and Schizosaccharomyces pombe, pheromonal communication activates similar pathways composed of cognate G-protein-coupled receptors and downstream small GTPase Cdc42 and MAP kinase cascades. Local pheromone release and sensing, at a mobile surface polarity patch, underlie spatial gradient interpretation to form pairs between two cells of distinct mating types. Concentration of secretion at the point of cell-cell contact then leads to local cell wall digestion for cell fusion, forming a diploid zygote that prevents further fusion attempts. A number of asymmetries between mating types may promote efficiency of the system. In this review, we present our current knowledge of pheromone signaling in the two model yeasts, with an emphasis on how cells decode the pheromone signal spatially and ultimately fuse together. Though overall pathway architectures are similar in the two species, their large evolutionary distance allows to explore how conceptually similar solutions to a general biological problem can arise from divergent molecular components.

Emerging trends in developing biosensor techniques to undertake plant phosphoproteomic analysis

Protein modifications particularly phosphorylation is governed by a complex array of mechanisms to attain a functional conformation and regulate important biological processes in organisms during external environmental stimuli and hormone signaling. Phosphoproteomics is a promising field of proteomics for identification of proteins with phosphate groups and their impact on structure, function and localization of proteins. Techniques that allow quantitative detection of proteins and their post-translational modifications (PTMs) have immensely led to understand the structural and functional dynamics of proteins. Biosensor systems are a relatively new biotechnological approach that works on the principle of transforming the interactions of different biological samples viz proteins, enzymes, aptamers, nucleic acids and so on into the signals such as electrochemical, colorimetric, optical or magnetic which have been effectively useful in the detection and characterization of phosphoproteins. The focus of our review is to provide a comprehensive account of the critical role and utility of novel biosensors such as, fluorescence based, enrichment based, nanobody based biosensors, as promising technical intercessions to identify phosphoproteins and their influence on structural dynamics of proteins. Furthermore, by studying the innovative phosphoprotein biosensors we will be able to identify the aberrant phosphorylation patterns to precisely diagnose diseases.

Ultrastructural plasma membrane asymmetries in tension and curvature promote yeast cell fusion

Cell–cell fusion is central for sexual reproduction, and generally involves gametes of different shapes and sizes. In walled fission yeast Schizosaccharomyces pombe, the fusion of h+ and h− isogametes requires the fusion focus, an actin structure that concentrates glucanase-containing vesicles for cell wall digestion. Here, we present a quantitative correlative light and electron microscopy (CLEM) tomographic dataset of the fusion site, which reveals the fusion focus ultrastructure. Unexpectedly, gametes show marked asymmetries: a taut, convex plasma membrane of h− cells progressively protrudes into a more slack, wavy plasma membrane of h+ cells. Asymmetries are relaxed upon fusion, with observations of ramified fusion pores. h+ cells have a higher exo-/endocytosis ratio than h− cells, and local reduction in exocytosis strongly diminishes membrane waviness. Reciprocally, turgor pressure reduction specifically in h− cells impedes their protrusions into h+ cells and delays cell fusion. We hypothesize that asymmetric membrane conformations, due to differential turgor pressure and exocytosis/endocytosis ratios between mating types, favor cell–cell fusion.

Dynamic Transcriptomic and Phosphoproteomic Analysis During Cell Wall Stress in Aspergillus nidulans

The fungal cell-wall integrity signaling (CWIS) pathway regulates cellular response to environmental stress to enable wall repair and resumption of normal growth. This complex, interconnected, pathway has been only partially characterized in filamentous fungi. To better understand the dynamic cellular response to wall perturbation, a β-glucan synthase inhibitor (micafungin) was added to a growing A. nidulans shake-flask culture. From this flask, transcriptomic and phosphoproteomic data were acquired over 10 and 120 min, respectively. To differentiate statistically-significant dynamic behavior from noise, a multivariate adaptive regression splines (MARS) model was applied to both data sets. Over 1800 genes were dynamically expressed and over 700 phosphorylation sites had changing phosphorylation levels upon micafungin exposure. Twelve kinases had altered phosphorylation and phenotypic profiling of all non-essential kinase deletion mutants revealed putative connections between PrkA, Hk-8-4, and Stk19 and the CWIS pathway. Our collective data implicate actin regulation, endocytosis, and septum formation as critical cellular processes responding to activation of the CWIS pathway, and connections between CWIS and calcium, HOG, and SIN signaling pathways.

Crosstalk and spatiotemporal regulation between stress-induced MAP kinase pathways and pheromone signaling in budding yeast

Budding yeast, Saccharomyces cerevisiae, has been widely used as a model system to study cellular signaling in response to internal and external cues. Yeast was among the first organisms in which the architecture, feedback mechanisms and physiological responses of various MAP kinase signaling cascades were studied in detail. Although these MAP kinase pathways are activated by different signals and elicit diverse cellular responses, such as adaptation to stress and mating, they function as an interconnected signaling network, as they influence each other and, in some cases, even share components. Indeed, various stress signaling pathways interfere with pheromone signaling that triggers a distinct cellular differentiation program. However, the molecular mechanisms responsible for this crosstalk are still poorly understood. Here, we review the general topology of the yeast MAP kinase signaling network and highlight recent and new data revealing how conflicting intrinsic and extrinsic signals are interpreted to orchestrate appropriate cellular responses.

R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies

Recent developments in proteomics have enabled signaling studies where > 10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase-substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid-robotic phosphoproteomics (R2-P2), an end-to-end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry-ready phosphopeptides. R2-P2 is rapid, robust, versatile, and high-throughput. To showcase the method, we applied it, in combination with data-independent acquisition mass spectrometry, to study signaling dynamics in the mitogen-activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high-osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large-scale signaling studies involving hundreds of perturbations opening the door to systems-level studies aiming to capture signaling complexity.

Mass Spectrometry-Based Analysis of Time-Resolved Proteome Quantification

The aspect of time is essential in biological processes and thus it is important to be able to monitor signaling molecules through time. Proteins are key players in cellular signaling and they respond to many stimuli and change their expression in many time-dependent processes. Mass spectrometry (MS) is an important tool for studying proteins, including their posttranslational modifications and their interaction partners-both in qualitative and quantitative ways. In order to distinguish the different trends over time, proteins, modification sites, and interacting proteins must be compared between different time points, and therefore relative quantification is preferred. In this review, the progress and challenges for MS-based analysis of time-resolved proteome dynamics are discussed. Further, aspects on model systems, technologies, sampling frequencies, and presentation of the dynamic data are discussed.

SimPhospho: a software tool enabling confident phosphosite assignment

Motivation

Mass spectrometry combined with enrichment strategies for phosphorylated peptides has been successfully employed for two decades to identify sites of phosphorylation. However, unambiguous phosphosite assignment is considered challenging. Given that site-specific phosphorylation events function as different molecular switches, validation of phosphorylation sites is of utmost importance. In our earlier study we developed a method based on simulated phosphopeptide spectral libraries, which enables highly sensitive and accurate phosphosite assignments. To promote more widespread use of this method, we here introduce a software implementation with improved usability and performance.

Results

We present SimPhospho, a fast and user-friendly tool for accurate simulation of phosphopeptide tandem mass spectra. Simulated phosphopeptide spectral libraries are used to validate and supplement database search results, with a goal to improve reliable phosphoproteome identification and reporting. The presented program can be easily used together with the Trans-Proteomic Pipeline and integrated in a phosphoproteomics data analysis workflow.

Availability and implementation

SimPhospho is open source and it is available for Windows, Linux and Mac operating systems. The software and its user’s manual with detailed description of data analysis as well as test data can be found at https://sourceforge.net/projects/simphospho/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mechanical stress impairs pheromone signaling via Pkc1-mediated regulation of the MAPK scaffold Ste5

This study shows that Pkc1 inhibits yeast pheromone signaling upon intrinsic and extrinsic mechanical stress. Pkc1 phosphorylates the RING-H2 domains of the scaffolds Ste5 and Far1, thereby preventing their interaction with Gβγ at the plasma membrane. This crosstalk mechanism regulates polarized growth and cell–cell fusion during mating.

Cells continuously adapt cellular processes by integrating external and internal signals. In yeast, multiple stress signals regulate pheromone signaling to prevent mating under unfavorable conditions. However, the underlying crosstalk mechanisms remain poorly understood. Here, we show that mechanical stress activates Pkc1, which prevents lysis of pheromone-treated cells by inhibiting polarized growth. In vitro Pkc1 phosphorylates conserved residues within the RING-H2 domains of the scaffold proteins Far1 and Ste5, which are also phosphorylated in vivo. Interestingly, Pkc1 triggers dispersal of Ste5 from mating projections upon mechanically induced stress and during cell–cell fusion, leading to inhibition of the MAPK Fus3. Indeed, RING phosphorylation interferes with Ste5 membrane association by preventing binding to the receptor-linked Gβγ protein. Cells expressing nonphosphorylatable Ste5 undergo increased lysis upon mechanical stress and exhibit defects in cell–cell fusion during mating, which is exacerbated by simultaneous expression of nonphosphorylatable Far1. These results uncover a mechanical stress–triggered crosstalk mechanism modulating pheromone signaling, polarized growth, and cell–cell fusion during mating.

Novel interconnections of HOG signaling revealed by combined use of two proteomic software packages

Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis.

Aspergillus fumigatus High Osmolarity Glycerol Mitogen Activated Protein Kinases SakA and MpkC Physically Interact During Osmotic and Cell Wall Stresses

Aspergillusfumigatus, a saprophytic filamentous fungus, is a serious opportunistic pathogen of mammals and it is the primary causal agent of invasive aspergillosis (IA). Mitogen activated protein Kinases (MAPKs) are important components involved in diverse cellular processes in eukaryotes. A. fumigatus MpkC and SakA, the homologs of the Saccharomyces cerevisiae Hog1 are important to adaptations to oxidative and osmotic stresses, heat shock, cell wall damage, macrophage recognition, and full virulence. We performed protein pull-down experiments aiming to identify interaction partners of SakA and MpkC by mass spectrometry analysis. In presence of osmotic stress with sorbitol, 118, and 213 proteins were detected as possible protein interactors of SakA and MpkC, respectively. Under cell wall stress caused by congo red, 420 and 299 proteins were detected interacting with SakA and MpkC, respectively. Interestingly, a group of 78 and 256 proteins were common to both interactome analysis. Co-immunoprecipitation (Co-IP) experiments showed that SakA::GFP is physically associated with MpkC:3xHA upon osmotic and cell wall stresses. We also validated the association between SakA:GFP and the cell wall integrity MAPK MpkA:3xHA and the phosphatase PtcB:3xHA, under cell wall stress. We further characterized A. fumigatus PakA, the homolog of the S. cerevisiae sexual developmental serine/threonine kinase Ste20, as a component of the SakA/MpkC MAPK pathway. The ΔpakA strain is more sensitive to cell wall damaging agents as congo red, calcofluor white, and caspofungin. Together, our data supporting the hypothesis that SakA and MpkC are part of an osmotic and general signal pathways involved in regulation of the response to the cell wall damage, oxidative stress, drug resistance, and establishment of infection. This manuscript describes an important biological resource to understand SakA and MpkC protein interactions. Further investigation of the biological roles played by these protein interactors will provide more opportunities to understand and combat IA.

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.

REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES

Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

Fast Global Phosphoproteome Profiling of Jurkat T Cells by HIFU-TiO2-SCX-LC-MS/MS

We propose a new workflow for fast phosphoproteome profiling. The workflow is based on the use of accelerated in-solution trypsin digestion under an ultrasonic field provided by high-intensity focused ultrasound (HIFU) combined with an inverse strategy based on TiO₂ selective phosphopeptide enrichment, fractionation by strong cation exchange chromatography (SCX) and analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS) using a high-resolution mass spectrometer. The performance of the method was established for the global phosphoproteome analysis of unstimulated human Jurkat leukemia T cells (E6.1). Using this accelerated workflow, 15367 phosphorylation sites from 13029 different phosphopeptides belonging to 3163 different phosphoproteins were efficiently identified with high-throughput and reproducibility in less than 15 h. The functional analysis revealed significant phosphorylation-based networks that are implicated in immune function and tumor development pathways. The present strategy, HIFU-TiO₂-SCX-LC-MS/MS, is the fastest analytical method reported to date for generating large-scale phosphoproteomics data sets (<15 h).

Flux control through protein phosphorylation in yeast

Protein phosphorylation is one of the most important mechanisms regulating metabolism as it can directly modify metabolic enzymes by the addition of phosphate groups. Attributed to such a rapid and reversible mechanism, cells can adjust metabolism rapidly in response to temporal changes. The yeast Saccharomyces cerevisiae, a widely used cell factory and model organism, is reported to show frequent phosphorylation events in metabolism. Studying protein phosphorylation in S. cerevisiae allows for gaining new insight into the function of regulatory networks, which may enable improved metabolic engineering as well as identify mechanisms underlying human metabolic diseases. Here we collect functional phosphorylation events of 41 enzymes involved in yeast metabolism and demonstrate functional mechanisms and the application of this information in metabolic engineering. From a systems biology perspective, we describe the development of phosphoproteomics in yeast as well as approaches to analysing the phosphoproteomics data. Finally, we focus on integrated analyses with other omics data sets and genome-scale metabolic models. Despite the advances, future studies improving both experimental technologies and computational approaches are imperative to expand the current knowledge of protein phosphorylation in S. cerevisiae.

Identifying protein kinase-specific effectors of the osmostress response in yeast

The budding yeast Saccharomyces cerevisiae reacts to increased external osmolarity by modifying many cellular processes. Adaptive signaling relies primarily on the high-osmolarity glycerol (HOG) pathway, which is closely related to the mammalian p38 mitogen-activated protein kinase (MAPK) pathway in core architecture. To identify target proteins of the MAPK Hog1, we designed a mass spectrometry-based high-throughput experiment to measure the impact of Hog1 activation or inhibition on the Scerevisiae phosphoproteome. In addition, we analyzed how deletion of RCK2, which encodes a known effector protein kinase target of Hog1, modulated osmotic stress-induced phosphorylation. Our results not only provide an overview of the diversity of cellular functions that are directly and indirectly affected by the activity of the HOG pathway but also enabled an assessment of the Hog1-independent events that occur under osmotic stress conditions. We extended the number of putative Hog1 direct targets by analyzing the modulation of motifs consisting of serine or threonine followed by a proline (S/T-P motif) and subsequently validated these with an in vivo interaction assay. Rck2 appears to act as a central hub for many Hog1-mediated secondary phosphorylation events. This study clarifies many of the direct and indirect effects of HOG signaling and its stress-adaptive functions.

Proteomics insights into DNA damage response and translating this knowledge to clinical strategies

Genomic instability is a critical driver in the process of cancer formation. At the same time, inducing DNA damage by irradiation or genotoxic compounds constitutes a key therapeutic strategy to kill fast-dividing cancer cells. Sensing of DNA lesions initiates a complex set of signalling pathways, collectively known as the DNA damage response (DDR). Deciphering DDR signalling pathways with high-throughput technologies could provide insights into oncogenic transformation, metastasis formation and therapy responses, and could build a basis for better therapeutic interventions in cancer treatment. Mass spectrometry (MS)-based proteomics emerged as a method of choice for global studies of proteins and their posttranslational modifications (PTMs). MS-based studies of the DDR have aided in delineating DNA damage-induced signalling responses. Those studies identified changes in abundance, interactions and modification of proteins in the context of genotoxic stress. Here we review ground-breaking MS-based proteomics studies, which analysed changes in protein abundance, protein-protein and protein-DNA interactions, phosphorylation, acetylation, ubiquitylation, SUMOylation and Poly(ADP-ribose)ylation (PARylation) in the DDR. Finally, we provide an outlook on how proteomics studies of the DDR could aid clinical developments on multiple levels.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

Cells react to extracellular perturbations with complex and intertwined responses. Systematic identification of the regulatory mechanisms that control these responses is still a challenge and requires tailored analyses integrating different types of molecular data. Here we acquired time-resolved metabolomics measurements in yeast under salt and pheromone stimulation and developed a machine learning approach to explore regulatory associations between metabolism and signal transduction. Existing phosphoproteomics measurements under the same conditions and kinase-substrate regulatory interactions were used to in silico estimate the enzymatic activity of signalling kinases. Our approach identified informative associations between kinases and metabolic enzymes capable of predicting metabolic changes. We extended our analysis to two studies containing transcriptomics, phosphoproteomics and metabolomics measurements across a comprehensive panel of kinases/phosphatases knockouts and time-resolved perturbations to the nitrogen metabolism. Changes in activity of transcription factors, kinases and phosphatases were estimated in silico and these were capable of building predictive models to infer the metabolic adaptations of previously unseen conditions across different dynamic experiments. Time-resolved experiments were significantly more informative than genetic perturbations to infer metabolic adaptation. This difference may be due to the indirect nature of the associations and of general cellular states that can hinder the identification of causal relationships. This work provides a novel genome-scale integrative analysis to propose putative transcriptional and post-translational regulatory mechanisms of metabolic processes.

Phosphorylation is a broad regulatory mechanism with implications in nearly all processes of the cell. However, a global understanding of possible regulatory mechanisms remains elusive. In this study, we examined the potential regulatory role of kinases, phosphatases and transcription-factors in yeast metabolism across a variety of steady-state and dynamic conditions. The main novelty of our analysis was to infer putative regulatory interactions from in silico estimated activity of transcription-factors and kinases/phosphatases. This provided functional information about the proteins important for the experimental conditions at hand that had not been uncovered before. We showed that activity profiles are predictive features to estimate metabolite changes in dynamic experiments, while the same was not visible in steady-state conditions. We also showed that dynamic experiments could be used to recapitulate and provide novel TFs-metabolite and K/Ps-metabolite regulatory associations. We believe these findings illustrates the usefulness of this approach for future integrative studies interested in studying metabolic regulation.

New Genes Involved in Osmotic Stress Tolerance in Saccharomyces cerevisiae

Adaptation to changes in osmolarity is fundamental for the survival of living cells, and has implications in food and industrial biotechnology. It has been extensively studied in the yeast Saccharomyces cerevisiae, where the Hog1 stress activated protein kinase was discovered about 20 years ago. Hog1 is the core of the intracellular signaling pathway that governs the adaptive response to osmotic stress in this species. The main endpoint of this program is synthesis and intracellular retention of glycerol, as a compatible osmolyte. Despite many details of the signaling pathways and yeast responses to osmotic challenges have already been described, genome-wide approaches are contributing to refine our knowledge of yeast adaptation to hypertonic media. In this work, we used a quantitative fitness analysis approach in order to deepen our understanding of the interplay between yeast cells and the osmotic environment. Genetic requirements for proper growth under osmotic stress showed both common and specific features when hypertonic conditions were induced by either glucose or sorbitol. Tolerance to high-glucose content requires mitochondrial function, while defective protein targeting to peroxisome, GID-complex function (involved in negative regulation of gluconeogenesis), or chromatin dynamics, result in poor survival to sorbitol-induced osmotic stress. On the other side, the competitive disadvantage of yeast strains defective in the endomembrane system is relieved by hypertonic conditions. This finding points to the Golgi-endosome system as one of the main cell components negatively affected by hyperosmolarity. Most of the biological processes highlighted in this analysis had not been previously related to osmotic stress but are probably relevant in an ecological and evolutionary context.

Systems Level Analysis of the Yeast Osmo-Stat

Adaptation is an important property of living organisms enabling them to cope with environmental stress and maintaining homeostasis. Adaptation is mediated by signaling pathways responding to different stimuli. Those signaling pathways might communicate in order to orchestrate the cellular response to multiple simultaneous stimuli, a phenomenon called crosstalk. Here, we investigate possible mechanisms of crosstalk between the High Osmolarity Glycerol (HOG) and the Cell Wall Integrity (CWI) pathways in yeast, which mediate adaptation to hyper- and hypo-osmotic challenges, respectively. We combine ensemble modeling with experimental investigations to test in quantitative terms different hypotheses about the crosstalk of the HOG and the CWI pathways. Our analyses indicate that for the conditions studied i) the CWI pathway activation employs an adaptive mechanism with a variable volume-dependent threshold, in contrast to the HOG pathway, whose activation relies on a fixed volume-dependent threshold, ii) there is no or little direct crosstalk between the HOG and CWI pathways, and iii) its mainly the HOG alone mediating adaptation of cellular osmotic pressure for both hyper- as well as hypo-osmotic stress. Thus, by iteratively combining mathematical modeling with experimentation we achieved a better understanding of regulatory mechanisms of yeast osmo-homeostasis and formulated new hypotheses about osmo-sensing.

Recent findings and technological advances in phosphoproteomics for cells and tissues

Site-specific phosphorylation is a fast and reversible covalent post-translational modification that is tightly regulated in cells. The cellular machinery of enzymes that write, erase and read these modifications (kinases, phosphatases and phospho-binding proteins) is frequently deregulated in different diseases, including cancer. Large-scale studies of phosphoproteins – termed phosphoproteomics – strongly rely on the use of high-performance mass spectrometric instrumentation. This powerful technology has been applied to study a great number of phosphorylation-based phenotypes. Nevertheless, many technical and biological challenges have to be overcome to identify biologically relevant phosphorylation sites in cells and tissues. This review describes different technological strategies to identify and quantify phosphorylation sites with high accuracy, without significant loss of analysis speed and reproducibility in tissues and cells. Moreover, computational tools for analysis, integration and biological interpretation of phosphorylation events are discussed.

Environmental Interactions and Epistasis Are Revealed in the Proteomic Responses to Complex Stimuli

Ultimately, the genotype of a cell and its interaction with the environment determine the cell’s biochemical state. While the cell’s response to a single stimulus has been studied extensively, a conceptual framework to model the effect of multiple environmental stimuli applied concurrently is not as well developed. In this study, we developed the concepts of environmental interactions and epistasis to explain the responses of the S. cerevisiae proteome to simultaneous environmental stimuli. We hypothesize that, as an abstraction, environmental stimuli can be treated as analogous to genetic elements. This would allow modeling of the effects of multiple stimuli using the concepts and tools developed for studying gene interactions. Mirroring gene interactions, our results show that environmental interactions play a critical role in determining the state of the proteome. We show that individual and complex environmental stimuli behave similarly to genetic elements in regulating the cellular responses to stimuli, including the phenomena of dominance and suppression. Interestingly, we observed that the effect of a stimulus on a protein is dominant over other stimuli if the response to the stimulus involves the protein. Using publicly available transcriptomic data, we find that environmental interactions and epistasis regulate transcriptomic responses as well.